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  • A general algorithm for distributed treatments of multiple brain metastases MEDICAL PHYSICS Chen, M., Wardak, Z., Stojadinovic, S., Gu, X., Lu, W. 2021: 1832–38

    Abstract

    Stereotactic radiosurgery (SRS) has become a primary treatment for multiple brain metastases (BM) but may require distribution of BMs over several sessions to make delivery time and radiation toxicity manageable. Contrasting to equal fraction dose in conventional fractionation, distributed SRS delivers full dose to a subset of BMs in each session while avoiding adjacent BMs in the same session to reduce toxicity from overlapping radiation. However, current clinical treatment planning for distributed SRS relies on manual BM assignment, which can be tedious and error prone. This work describes a novel approach to automate the distribution of BM in the Gamma Knife (GK) clinical workflow.We represent each BM as an electrostatic field of the same polarity that exerts repulsive forces on other BMs in the same session. This representation naturally leads to separation of close BMs into different sessions to lower the potential energy. Indeed, the BM distribution problem can be formulated as minimization of the total potential energy from all treatment sessions subject to delivery time constraints in mixed-integer quadratic programming (MIQP). We retrospectively studied eight clinical GK cases of multiple BM and compared the automated MIQP solution with clinically used BM distribution to demonstrate the efficacy of the proposed approach.With the problem size equal to the number of BMs times the number of sessions, this MIQP can be solved in a minute on a personal workstation. The MIQP solution effectively separated BMs for a given number of treatment sessions and evened out the delivery time distribution among sessions. Compared to the clinically used manual BM distributions in paired t-test for a similar range of delivery time variation, the automated BM distributions had lower energy objectives (range of decrease: [11% 89%]; median: 25%; P = . 073 ), more uniformly distributed treatment volumes (range of decrease for the normalized standard deviation of volume distribution: [0.02 0.95]; median: 0.16; P = . 013 ), more scattered BMs in each treatment session (range of increase for the mean minimum BM distance: [0 14] mm; median: 6 mm; P = . 008 ), and lower overall V 12 (range of decrease: [0.0 1.6] cc; median: 0.2 cc; P = . 052 ). Moreover, without distribution, that is, with all BMs treated in the same session, V 12 was substantially larger compared to both manual and automated BM distributions; the increase ranged from 0.1 to 16.6 cc with a median of 1.3 cc.The proposed approach models the clinical practice and provides an efficient solution for optimal selection of BM subsets for distributed SRS. Further evaluations are underway to establish this approach as a tool for improving clinical workflow and to facilitate systematic study on the benefits of distributed SRS treatments.

    View details for DOI 10.1002/mp.14722

    View details for Web of Science ID 000620296400001

    View details for PubMedID 33449357

    View details for PubMedCentralID PMC8058245

  • Cosmetic Outcomes of a Phase 1 Dose Escalation Study of 5-Fraction Stereotactic Partial Breast Irradiation for Early Stage Breast Cancer. International journal of radiation oncology, biology, physics Rahimi, A., Morgan, H. E., Kim, D. W., Zhang, Y., Leitch, M., Wooldridge, R., Goudreau, S., Haley, B., Rao, R., Rivers, A., Spangler, A. E., Jones, R. T., Stevenson, S., Staley, J., Albuquerque, K., Ahn, C., Neufeld, S., Alluri, P. G., Ding, C., Garwood, D., Seiler, S., Zhao, B., Gu, X., Timmerman, R. 2021

    Abstract

    PURPOSE: Our purpose was to evaluate cosmetic changes after 5-fraction adjuvant stereotactic partial breast irradiation (S-PBI).METHODS AND MATERIALS: Seventy-five women with in situ or invasive breast cancer stage 0, I, or II, with tumor size ≤3 cm, were enrolled after lumpectomy in a phase 1 dose escalation trial of S-PBI into cohorts receiving 30, 32.5, 35, 37.5, or 40 Gy in 5 fractions. Before S-PBI, 3 to 4 gold fiducial markers were placed in the lumpectomy cavity for tracking with the Synchrony respiratory tracking system. S-PBI was delivered with a CyberKnife robotic radiosurgery system. Patients and physicians evaluated global cosmesis using the Harvard Breast Cosmesis Scale. Eight independent panelists evaluated digital photography for global cosmesis and 10 subdomains at baseline and follow-up. McNemar tests were used to evaluate change in cosmesis, graded as excellent/good or fair/poor, from baseline to year 3. Wilcoxon signed rank tests were used to evaluate change in subdomains. Cohen's kappa (kappa) statistic was used to estimate interobserver agreement (IOA) between raters, and Fleiss' kappa was used to estimate IOA between panelists.RESULTS: Median cosmetic follow-up was 5, 5, 5, 4, and 3 years for the 30, 32.5, 35, 37.5, and 40 Gy cohorts. Most patients reported excellent/good cosmesis at both baseline (86.3%) and year 3 (89.8%). No dose cohort had significantly worsened cosmesis by year 3 on McNemar analysis. No cosmetic subdomain had significant worsening by year 3. IOA was fair for patient-physician (kappa = 0.300, P < .001), patient-panel (kappa = 0.295, P < .001), physician-panel (kappa = 0.256, P < .001), and individual panelists (Fleiss kappa = 0.327, P < .001).CONCLUSIONS: Dose escalation of S-PBI from 30 to 40 Gy in 5 fractions for early stage breast cancer was not associated with a detectable change in cosmesis by year 3. S-PBI is a promising modality for treatment of early stage breast cancer.

    View details for DOI 10.1016/j.ijrobp.2021.01.015

    View details for PubMedID 33476737

  • Deep learning-based medical image segmentation with limited labels PHYSICS IN MEDICINE AND BIOLOGY Chi, W., Ma, L., Wu, J., Chen, M., Lu, W., Gu, X. 2020; 65 (23)

    Abstract

    Deep learning (DL) based auto-segmentation has the potential for accurate organ delineation in radiotherapy applications but requires large amounts of clean labeled data to train a robust model. However, annotating medical images is extremely time-consuming and requires clinical expertise, especially for segmentation that demands voxel-wise labels. On the other hand, medical images without annotations are abundant and highly accessible. To alleviate the influence of the limited number of clean labels, we propose a weakly-supervised DL training approach using deformable image registration (DIR)-based annotations, leveraging the abundance of unlabeled data. We generate pseudo-contours by utilizing DIR to propagate atlas contours onto abundant unlabeled images and train a robust DL-based segmentation model. With 10 labeled TCIA dataset and 50 unlabeled CT scans from our institution, our model achieved Dice similarity coefficient of 87.9%, 73.4%, 73.4%, 63.2% and 61.0% on mandible, left & right parotid glands and left & right submandibular glands of TCIA test set and competitive performance on our institutional clinical dataset and a third party (PDDCA) dataset. Experimental results demonstrated the proposed method outperformed traditional multi-atlas DIR methods and fully-supervised limited data training and is promising for DL-based medical image segmentation application with limited annotated data.

    View details for DOI 10.1088/1361-6560/abc363

    View details for Web of Science ID 000618027800001

    View details for PubMedID 33086205

    View details for PubMedCentralID PMC8058113

  • Deep learning-based inverse mapping for fluence map prediction PHYSICS IN MEDICINE AND BIOLOGY Ma, L., Chen, M., Gu, X., Lu, W. 2020; 65 (23)

    Abstract

    We developed a fluence map prediction method that directly generates fluence maps for a given desired dose distribution without optimization for volumetric modulated arc therapy (VMAT) planning. The prediction consists of two steps. First, projections of the desired dose are calculated and then inversely mapped to fluence maps in the phantom geometry by a deep neural network (DNN). Second, a plan scaling technique is applied to scale fluence maps from phantom to patient geometry. We evaluated the performance of the proposed fluence map prediction method for 102 head and neck (H&N) and 14 prostate cancer VMAT plans by comparing the patient doses calculated from the predicted fluence maps with the given desired dose distributions. The mean dose differences were 1.42%±0.37%, 1.53%±0.44% and 1.25%±0.44% for the planning target volume (PTV), the region from the PTV boundary to the 50% isodose line, and the region from the 50% to the 20% isodose line, respectively. The gamma passing rate was 98.06%±2.64% with the 3 mm/3% criterion. The prediction time for a single VMAT plan was less than one second. In conclusion, we developed an inverse mapping-based method that predicts fluence maps for desired dose distributions with high accuracy. Our method is effectively an optimization-free inverse planning approach, which was orders of magnitude faster than fluence map optimization (FMO). Combining the proposed method with leaf sequencing has the potential to dramatically speed up VMAT treatment planning.

    View details for DOI 10.1088/1361-6560/abc12c

    View details for Web of Science ID 000617566700001

    View details for PubMedID 33053515

    View details for PubMedCentralID PMC8044255

  • Risk Factors for Fat Necrosis After Stereotactic Partial Breast Irradiation for Early-Stage Breast Cancer in a Phase 1 Clinical Trial INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Rahimi, A., Zhang, Y., Kim, D. W., Morgan, H., Hossain, F., Leitch, M., Wooldridge, R., Seiler, S., Goudreau, S., Haley, B., Rao, R., Rivers, A., Spangler, A., Ahn, C., Stevenson, S., Staley, J., Albuquerque, K., Ding, C., Gu, X., Zhao, B., Timmerman, R. 2020; 108 (3): 697–706

    Abstract

    This study reports predictive dosimetric and physiologic factors for fat necrosis after stereotactic-partial breast irradiation (S-PBI).Seventy-five patients with ductal carcinoma-in situ or invasive nonlobular epithelial histologies stage 0, I, or II, with tumor size <3 cm were enrolled in a dose-escalation, phase I S-PBI trial between January 2011 and July 2015. Fat necrosis was evaluated clinically at each follow-up. Treatment data were extracted from the Multiplan Treatment Planning System (Cyberknife, Accuray). Univariate and stepwise logistic regression analyses were conducted to identify factors associated with palpable fat necrosis.With a median follow-up of 61 months (range: 4.3-99.5 months), 11 patients experienced palpable fat necrosis, 5 cases of which were painful. The median time to development of fat necrosis was 12.7 months (range, 3-42 months). On univariate analyses, higher V32.5-47.5 Gy (P < .05) and larger breast volume (P < .01) were predictive of any fat necrosis; higher V35-50 Gy (P < .05), receiving 2 treatments on consecutive days (P = .02), and higher Dmax (P = .01) were predictive of painful fat necrosis. On multivariate analyses, breast volume larger than 1063 cm3 remained a predictive factor for any fat necrosis; receiving 2 treatments on consecutive days and higher V45 Gy were predictive of painful fat necrosis. Breast laterality, planning target volume (PTV), race, body mass index, diabetic status, and tobacco or drug use were not significantly associated with fat necrosis on univariate analysis.Early-stage breast cancer patients treated with breast conserving surgery and S-PBI in our study had a fat necrosis rate comparable to other accelerated partial breast irradiation modalities, but S-PBI is less invasive. To reduce risk of painful fat necrosis, we recommend not delivering fractions on consecutive days; limiting V42.5 < 50 cm3, V45 < 20 cm3, V47.5 < 1 cm3, Dmax ≤ 48 Gy and PTV < 100 cm3 when feasible; and counseling patients about the increased risk for fat necrosis when constraints are not met and for those with breast volume >1000 cm3.

    View details for DOI 10.1016/j.ijrobp.2020.05.025

    View details for Web of Science ID 000574862100026

    View details for PubMedID 32464155

  • Robustness study of noisy annotation in deep learning based medical image segmentation PHYSICS IN MEDICINE AND BIOLOGY Yu, S., Chen, M., Zhang, E., Wu, J., Yu, H., Yang, Z., Ma, L., Gu, X., Lu, W. 2020; 65 (17): 175007

    Abstract

    Partly due to the use of exhaustive-annotated data, deep networks have achieved impressive performance on medical image segmentation. Medical imaging data paired with noisy annotation are, however, ubiquitous, but little is known about the effect of noisy annotation on deep learning based medical image segmentation. We studied the effect of noisy annotation in the context of mandible segmentation from CT images. First, 202 images of head and neck cancer patients were collected from our clinical database, where the organs-at-risk were annotated by one of twelve planning dosimetrists. The mandibles were roughly annotated as the planning avoiding structure. Then, mandible labels were checked and corrected by a head and neck specialist to get the reference standard. At last, by varying the ratios of noisy labels in the training set, deep networks were trained and tested for mandible segmentation. The trained models were further tested on other two public datasets. Experimental results indicated that the network trained with noisy labels had worse segmentation than that trained with reference standard, and in general, fewer noisy labels led to better performance. When using 20% or less noisy cases for training, no significant difference was found on the segmentation results between the models trained by noisy or reference annotation. Cross-dataset validation results verified that the models trained with noisy data achieved competitive performance to that trained with reference standard. This study suggests that the involved network is robust to noisy annotation to some extent in mandible segmentation from CT images. It also highlights the importance of labeling quality in deep learning. In the future work, extra attention should be paid to how to utilize a small number of reference standard samples to improve the performance of deep learning with noisy annotation.

    View details for DOI 10.1088/1361-6560/ab99e5

    View details for Web of Science ID 000565759600001

    View details for PubMedID 32503027

    View details for PubMedCentralID PMC7567130

  • BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis PHYSICS IN MEDICINE AND BIOLOGY Zhang, E., Seiler, S., Chen, M., Lu, W., Gu, X. 2020; 65 (12): 125005

    Abstract

    We propose a novel BIRADS-SSDL network that integrates clinically-approved breast lesion characteristics (BIRADS features) into task-oriented semi-supervised deep learning (SSDL) for accurate diagnosis of ultrasound (US) images with a small training dataset. Breast US images are converted to BIRADS-oriented feature maps (BFMs) using a distance-transformation coupled with a Gaussian filter. Then, the converted BFMs are used as the input of an SSDL network, which performs unsupervised stacked convolutional auto-encoder (SCAE) image reconstruction guided by lesion classification. This integrated multi-task learning allows SCAE to extract image features with the constraints from the lesion classification task, while the lesion classification is achieved by utilizing the SCAE encoder features with a convolutional network. We trained the BIRADS-SSDL network with an alternative learning strategy by balancing the reconstruction error and classification label prediction error. To demonstrate the effectiveness of our approach, we evaluated it using two breast US image datasets. We compared the performance of the BIRADS-SSDL network with conventional SCAE and SSDL methods that use the original images as inputs, as well as with an SCAE that use BFMs as inputs. The experimental results on two breast US datasets show that BIRADS-SSDL ranked the best among the four networks, with a classification accuracy of around 94.23 ± 3.33% and 84.38 ± 3.11% on two datasets. In the case of experiments across two datasets collected from two different institutions/and US devices, the developed BIRADS-SSDL is generalizable across the different US devices and institutions without overfitting to a single dataset and achieved satisfactory results. Furthermore, we investigate the performance of the proposed method by varying the model training strategies, lesion boundary accuracy, and Gaussian filter parameters. The experimental results showed that a pre-training strategy can help to speed up model convergence during training but with no improvement of the classification accuracy on the testing dataset. The classification accuracy decreases as the segmentation accuracy decreases. The proposed BIRADS-SSDL achieves the best results among the compared methods in each case and has the capacity to deal with multiple different datasets under one model. Compared with state-of-the-art methods, BIRADS-SSDL could be promising for effective breast US computer-aided diagnosis using small datasets.

    View details for DOI 10.1088/1361-6560/ab7e7d

    View details for Web of Science ID 000542229100001

    View details for PubMedID 32155605

  • POD-DOSI: A dedicated dosimetry system for GammaPod commissioning and quality assurance MEDICAL PHYSICS Parsons, D., Zhang, Y., Gu, X., Lu, W. 2020; 47 (8): 3647–57

    Abstract

    GammaPod, a stereotactic partial breast irradiator allowing highly conformal radiation dose delivery, has its unique mechanical design and treatment planning system (TPS). However, the uniqueness of the system poses challenges regarding initial GammaPod system commissioning and routine quality assurance (QA). In this study, we report POD-DOSI, a dedicated dosimetry system for accurate and efficient commissioning and QA of GammaPod.The POD-DOSI system consists of two subsystems, POD-Scanner and POD-Calculator. The POD-Scanner is an automatic ion-chamber positioning system driven by two translational stepper motors for anterior-posterior, longitudinal, and lateral beam scanning. The stepper motors are controlled by a microcomputer through an in-house-developed graphical user interface, which can be remotely accessed by a laptop via wireless connection. The POD-Calculator is a commissioned GPU-based Monte Carlo dose calculation engine, which calculates dose by transporting particles from phase space constructed for GammaPod. In our institution, the POD-DOSI system was used for GammaPod TPS commissioning and dose verification. The POD-Calculator was further developed as a secondary dose calculation tool performing patient-specific plan QA before each treatment.The POD-DOSI system has been fully evaluated and tested, both mechanically and dosimetrically, and applied successfully to drive the commissioning of our GammaPod system. The POD-Scanner achieved 0.1 mm accuracy in ion-chamber positioning tests. The POD-Calculator generated dose profiles matched well with water phantom measurements and TPS calculations to <0.5 mm accuracy. For end-to-end test on 56 different treatment plans, in-water point dose measurements by POD-Scanner were within ±2.20% of the doses calculated by POD-Calculator (range: -2.01% to 2.20%, mean: 0.04%, std_dev: 1.10%). Correspondingly, when switching the calculation medium from water to breast tissue, the point doses calculated by the POD-Calculator were within ±1.60% of the point doses calculated by the GammaPOD TPS (range: -1.59% to 1.51%, mean: -0.02%, std_dev: 0.73%). The average three-dimensional gamma passing rate between the GammaPod TPS doses and the POD-Calculator doses was 97.10% under the 2%/1 mm gamma criteria. The POD-DOSI system substantially shortened the GammaPod dosimetry commissioning time from weeks to days.The developed POD-DOSI system resolves the challenges and streamlines the process of GammaPod commissioning and QA. It improves the efficiency and accuracy for both GammaPod commissioning and routine patient-specific QA.

    View details for DOI 10.1002/mp.14221

    View details for Web of Science ID 000535190500001

    View details for PubMedID 32367556

  • A web-based brain metastases segmentation and labeling platform for stereotactic radiosurgery MEDICAL PHYSICS Yang, Z., Liu, H., Liu, Y., Stojadinovic, S., Timmerman, R., Nedzi, L., Dan, T., Wardak, Z., Lu, W., Gu, X. 2020; 47 (8): 3263–76

    Abstract

    Stereotactic radiosurgery (SRS) has become a standard of care for patients' with brain metastases (BMs). However, the manual multiple BMs delineation can be time-consuming and could create an efficiency bottleneck in SRS workflow. There is a clinical need for automatic delineation and quantitative evaluation tools. In this study, building on our previous developed deep learning-based segmentation algorithms, we developed a web-based automated BMs segmentation and labeling platform to assist the SRS clinical workflow.This platform was developed based on the Django framework, including a web client and a back-end server. The web client enables interactions as database access, data import, and image viewing. The server performs the segmentation and labeling tasks including: skull stripping; deep learning-based BMs segmentation; and affine registration-based BMs labeling. Additionally, the client can display BMs contours with corresponding atlas labels, and allows further postprocessing tasks including: (a) adjusting window levels; (b) displaying/hiding specific contours; (c) removing false-positive contours; (d) exporting contours as DICOM RTStruct files; etc. RESULTS: We evaluated this platform on 10 clinical cases with BMs number varied from 12-81 per case. The overall operation took about 4-5 min per patient. The segmentation accuracy was evaluated between the manual contour and automatic segmentation with several metrics. The averaged center of mass shift was 1.55 ± 0.36 mm, the Hausdorff distance was 2.98 ± 0.63 mm, the mean of surface-to-surface distance (SSD) was 1.06 ± 0.31 mm, and the standard deviation of SSD was 0.80 ± 0.16 mm. In addition, the initial averaged false-positive over union (FPoU) and false-negative rate (FNR) were 0.43 ± 0.19 and 0.15 ± 0.10 respectively. After case-specific postprocessing, the averaged FPoU and FNR were 0.19 ± 0.10 and 0.15 ± 0.10 respectively.The evaluated web-based BMs segmentation and labeling platform can substantially improve the clinical efficiency compared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow-up.

    View details for DOI 10.1002/mp.14201

    View details for Web of Science ID 000534819700001

    View details for PubMedID 32333797

    View details for PubMedCentralID PMC7567132

  • Radiation Therapy for Pediatric Brain Tumors using Robotic Radiation Delivery System and Intensity Modulated Proton Therapy PRACTICAL RADIATION ONCOLOGY Lin, M., Yang, M., Dougherty, J., Tasson, A., Zhang, Y., Mohamad, O., Dan, T., Yan, Y., Gu, X., Timmerman, R., Laack, N., Beltran, C. 2020; 10 (3): E173–E182

    Abstract

    This study recruited 2 centers with expertise in treating pediatric brain tumors with robotic radiation delivery system photon therapy and proton therapy, respectively, to study the plan quality and dose deposition characteristics of robotic radiation delivery system photon therapy and intensity modulated proton therapy (IMPT) plans.A total of 18 patients clinically treated with the robotic radiation delivery system were planned with IMPT. Cases were planned per the standard of care of each institution but respected the same planning objectives. The comparison included 3 aspects: plan quality, dose fall-off characteristics around the target volume, and the volume of the high-, intermediate-, and low-dose baths.All robotic radiation delivery system and IMPT plans met the planning objectives. However, IMPT significantly reduced the maximum dose to organs at risk away from the planning target volume (PTV), such as the cochlea and eye (P < .05), and the mean dose to the normal brain (P < .05). No statistically significant difference was observed in the maximum dose to the optical pathway and brain stem. Robotic radiation delivery system plans demonstrated a sharper dose fall-off within 5 mm around the PTV (P < .05), whereas IMPT significantly lowered the dose to the normal tissue beyond 10 mm from the PTV (P < .05). The robotic radiation delivery system offers a smaller high-dose bath whereas IMPT offers a smaller low-dose bath (P < .05). However, the difference in intermediate dose is not statistically significant.In general, robotic radiation delivery system plans exhibit reduced high-dose exposure to normal tissue, and IMPT plans have considerably smaller volumes of low-dose exposure with differences in medium-range dose baths increasingly favoring protons as tumor size increases.

    View details for DOI 10.1016/j.prro.2019.09.008

    View details for Web of Science ID 000534515800008

    View details for PubMedID 31542454

  • Electron modulated arc therapy (EMAT) using photon MLC for postmastectomy chest wall treatment I: Monte Carlo-based dosimetric characterizations PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS Ma, C., Parsons, D., Chen, M., Jiang, S., Hou, Q., Gu, X., Lu, W. 2019; 67: 1–8

    Abstract

    To study the dosimetric properties of electron arc beams delivered by photon-beam multi-leaf collimators (pMLC) in electron modulated arc therapy (EMAT) for postmastectomy chest wall treatments.Using the Monte Carlo method, we simulated a 2100EX Varian linear accelerator and verified the beam models in a water tank. Dosimetric characterizations were performed on cylindrical water phantoms of elliptical bases with various field sizes, arc ranges and source-to-surface distances (SSDs) for 6, 9 and 12 MeV beam energy.The arc beam has a higher bremsstrahlung dose than the static beam at the isocenter due to crossfire, but choosing a field size greater than 5 cm effectively reduces the bremsstrahlung dose. The depths of the 90% maximum dose located at 1.7, 2.8 and 4.1 cm for 6, 9 and 12 MeV, respectively, are similar to those of the static beams and independent of the field size and arc range.Based on the study, we recommend using the 5 cm field width for electron arc beams considering both bremsstrahlung dose at the isocenter and the arc profile penumbra. To ensure sufficient PTV edge coverage, we recommend a field length extension of at least 4 cm from PTV's edge for all beam energies and an arc extension of around 7°, 5°, and 5° for beam energies 6, 9, and 12 MeV, respectively. These dosimetric characterizations are the basis of pMLC-delivered EMAT treatment planning for postmastectomy chest wall patients.

    View details for DOI 10.1016/j.ejmp.2019.10.018

    View details for Web of Science ID 000503163400001

    View details for PubMedID 31606657

    View details for PubMedCentralID PMC6925626

  • Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy PHYSICS IN MEDICINE AND BIOLOGY Liang, X., Chen, L., Dan Nguyen, Zhou, Z., Gu, X., Yang, M., Wang, J., Jiang, S. 2019; 64 (12): 125002

    Abstract

    Throughout the course of delivering a radiation therapy treatment, which may take several weeks, a patient's anatomy may change drastically, and adaptive radiation therapy (ART) may be needed. Cone-beam computed tomography (CBCT), which is often available during the treatment process, can be used for both patient positioning and ART re-planning. However, due to the prominent amount of noise, artifacts, and inaccurate Hounsfield unit (HU) values, the dose calculation based on CBCT images could be inaccurate for treatment planning. One way to solve this problem is to convert CBCT images to more accurate synthesized CT (sCT) images. In this work, we have developed a cycle-consistent generative adversarial network framework (CycleGAN) to synthesize CT images from CBCT images. This model is capable of image-to-image translation using unpaired CT and CBCT images in an unsupervised learning setting. The sCT images generated from CBCT through this CycleGAN model are visually and quantitatively similar to real CT images with decreased mean absolute error (MAE) from 69.29 HU to 29.85 HU for head-and-neck (H&N) cancer patients. The dose distributions calculated on the sCT by CycleGAN demonstrated a higher accuracy than those on CBCT in a 3D gamma index analysis with increased gamma index pass rate from 86.92% to 96.26% under 1 mm/1% criteria, when using the deformed planning CT image (dpCT) as the reference. We also compared the CycleGAN model with other unsupervised learning methods, including deep convolutional generative adversarial networks (DCGAN) and progressive growing of GANs (PGGAN), and demonstrated that CycleGAN outperformed the other two models. A phantom study has been conducted to compare sCT with dpCT, and the increase of structural similarity index from 0.91 to 0.93 shows that CycleGAN performed better than DIR in terms of preserving anatomical accuracy.

    View details for DOI 10.1088/1361-6560/ab22f9

    View details for Web of Science ID 000471166400002

    View details for PubMedID 31108465

  • A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning SCIENTIFIC REPORTS Dan Nguyen, Long, T., Jia, X., Lu, W., Gu, X., Iqbal, Z., Jiang, S. 2019; 9: 1076

    Abstract

    With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average Dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in [max, mean] dose is found to be under 5% of the prescription dose, specifically for each structure is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%, 1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%, 1.62%](Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient's PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.

    View details for DOI 10.1038/s41598-018-37741-x

    View details for Web of Science ID 000457287000064

    View details for PubMedID 30705354

    View details for PubMedCentralID PMC6355802

  • A recursive ensemble organ segmentation (REOS) framework: application in brain radiotherapy PHYSICS IN MEDICINE AND BIOLOGY Chen, H., Lu, W., Chen, M., Zhou, L., Timmerman, R., Tu, D., Nedzi, L., Wardak, Z., Jiang, S., Zhen, X., Gu, X. 2019; 64 (2): 025015

    Abstract

    The aim of this work is to develop a novel recursive ensemble OARs segmentation (REOS) framework for accurate organs-at-risk (OARs) automatic segmentation. The REOS recursively segment individual OARs by ensembling images features extracted from an organ localization module and a contour detection module. Both modules are based on a 3D U-Net architecture. The organ localization module is trained for rough segmentation to localize a region of interest (ROI) that encompasses the to-be-delineated OAR, while the contour detection module is trained to segment the OAR within the identified ROI. In this study, the developed REOS framework is applied for brain radiotherapy on segmenting six OARs including the eyes, the brainstem (BS), the optical nerves and the chiasm. Eighty T1-weighted magnetic resonance images (MRI) from 80 brain cancer patients' cases with OARs' gold standard contours were collected for training and testing REOS. On 20 testing cases, the REOS achieve a high segmentation accuracy with Dice similarity coefficient (DSC) mean and standard deviation of 93.9%  ±  1.4%, 94.5%  ±  2.0%, 90.6%  ±  2.7%, on the left and right eyes and the BS, respectively. On small and segmentation-challenging organs, the left and right optical nerves and the chiasm, the REOS achieves DSC of 78.0%  ±  10.5%, 82.2%  ±  5.9% and 71.1%  ±  9.1%. The satisfactory performances demonstrated the effectiveness of the REOS in OARs segmentation.

    View details for DOI 10.1088/1361-6560/aaf83c

    View details for Web of Science ID 000455823300012

    View details for PubMedID 30540975

  • Flattening filter free in intensity-modulated radiotherapy (IMRT) - Theoretical modeling with delivery efficiency analysis MEDICAL PHYSICS Ma, C., Chen, M., Long, T., Parsons, D., Gu, X., Jiang, S., Hou, Q., Lu, W. 2019; 46 (1): 34–44

    Abstract

    The flattening filter (FF) has traditionally been used to flatten beams or create uniform fields in conformal and intensity-modulated radiation therapy (IMRT) but reduces the dose rate. Many studies have confirmed improvement in delivery efficiency by removing the FF, also known as flattening filter free (FFF), especially for small field stereotactic body radiation therapy (SBRT); but it is unclear if large treatment fields still favor the FFF beam. We propose a novel, unified approach to quantify delivery efficiency of the FFF and flattened beams.We modeled the FF effect by inverse conical filters and systematically studied delivery efficiency (beam-on time, BOT) by varying the filter thickness, including the FF and FFF mode. We formulated the BOT of different beams for any arbitrary fluence map in linear programming to solve the optimal inverse conical filter that minimizes the BOT. One-dimensional optimal filters of minimum BOT were also derived in closed form for conical fluence to gain insight for arbitrary clinical fluence maps. We evaluated the BOT of the FFF beam and flattened beam for conformal treatment fields of various dimensions ranging from 5 cm × 5 cm to 25 cm × 25 cm. We also analyzed the BOT for 698 clinical IMRT prostate fluence maps of field size 10 cm × 10 cm, 17 head-and-neck fluence maps of field size 15 cm × 15 cm, and additional realistic test data from 90° rotation and up to 40 cm × 40 cm enlargement of these clinical fluence maps, which were all initially generated with flattened beams.The FFF beam minimized the BOT for A field size less than 20 cm in single leaf pair cases and for conformal fields of dimension less than 20 cm × 20 cm. The FFF beam also minimized the BOT for all tested prostate and head-and-neck cases. The median BOT ratios of the FFF beam to the flattened beam were 0.56 and 0.61 for prostate and head-and-neck cases, respectively. The FFF beam minimized the BOT for field size up to 30 cm × 30 cm and had similar BOTs to those of the flattened beam for field size greater than 30 cm × 30 cm in those clinically realistic test data.The filter modeling and BOT calculation enable us to quantify delivery efficiency of the FFF beam and flattened beam in a unified approach. The FFF beam minimized the BOT both theoretically and in simulations for all clinically relevant field sizes and fluence maps in IMRT. The results for conformal fields imply that the FFF beam requires less BOT than the flattened beam for volumetric modulated arc therapy (VMAT) treatments. The delivery efficiency consideration favors the FFF beam in intensity-modulated treatments and may eventually lead to removal of the FF in all future linear accelerator head designs.

    View details for DOI 10.1002/mp.13267

    View details for Web of Science ID 000455029900006

    View details for PubMedID 30371944

  • Deep-learning based surface region selection for deep inspiration breath hold (DIBH) monitoring in left breast cancer radiotherapy PHYSICS IN MEDICINE AND BIOLOGY Chen, H., Chen, M., Lu, W., Zhao, B., Jiang, S., Zhou, L., Kim, N., Spangler, A., Rahimi, A., Zhen, X., Gu, X. 2018; 63 (24): 245013

    Abstract

    Deep inspiration breath hold (DIBH) with surface supervising is a common technique for cardiac dose reduction in left breast cancer radiotherapy. Surface supervision accuracy relies on the characteristics of surface region. In this study, a convolutional neural network (CNN) based automatic region-of-interest (ROI) selection method was proposed to select an optimal surface ROI for DIBH surface monitoring. The curvature entropy and the normal of each vertex on the breast cancer patient surface were calculated and formed as representative maps for ROI selection learning. 900 ROIs were randomly extracted from each patient's surface representative map, and the corresponding rigid ROI registration errors (REs) were calculated. The VGG-16 (a 16-layer network structure developed by Visual Geometry Group(VGG) from University of Oxford) pre-trained on a large natural image database ImageNet were fine-tuned using 27 thousand extracted ROIs and the corresponding REs from thirty patients. The RE prediction accuracy of the trained model was validated on additional ten patients. Satisfactory RE predictive accuracies were achieved with the root mean square error (RMSE)/mean absolute error (MAE) smaller than 1 mm/0.7 mm in translations and 0.45°/0.35° in rotations, respectively. The REs of the model selected ROIs on ten testing cases is close to the minimal predicted RE with mean RE differences  <1 mm and  <0.5° for translation and rotation, respectively. The proposed RE predictive model can be utilized for selecting a quasi-optimal ROI in left breast cancer DIBH radiotherapy (DIBH-RT).

    View details for DOI 10.1088/1361-6560/aaf0d6

    View details for Web of Science ID 000453061000004

    View details for PubMedID 30523967

  • Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy RADIATION ONCOLOGY Chen, J., Chen, H., Zhong, Z., Wang, Z., Hrycushko, B., Zhou, L., Jiang, S., Albuquerque, K., Gu, X., Zhen, X. 2018; 13: 125

    Abstract

    Better knowledge of the dose-toxicity relationship is essential for safe dose escalation to improve local control in cervical cancer radiotherapy. The conventional dose-toxicity model is based on the dose volume histogram, which is the parameter lacking spatial dose information. To overcome this limit, we explore a comprehensive rectal dose-toxicity model based on both dose volume histogram and dose map features for accurate radiation toxicity prediction.Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively studied, including 12 with Grade ≥ 2 rectum toxicity and 30 patients with Grade 0-1 toxicity (non-toxicity patients). The cumulative equivalent 2-Gy rectal surface dose was deformably summed using the deformation vector fields obtained through a recent developed local topology preserved non-rigid point matching algorithm. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectum surface dose map (RSDM). The dose volume parameters (DVPs) were calculated from the 3D rectum surface, while the texture features and the dose geometric parameters (DGPs) were extracted from the 2D RSDM. Representative features further computed from DVPs, textures and DGPs by principle component analysis (PCA) and statistical analysis were respectively fed into a support vector machine equipped with a sequential feature selection procedure. The predictive powers of the representative features were compared with the GEC-ESTRO dosimetric parameters D0.1/1/2cm3.Satisfactory predictive accuracy of sensitivity 74.75 and 84.75%, specificity 72.67 and 79.87%, and area under the receiver operating characteristic curve (AUC) 0.82 and 0.91 were respectively achieved by the PCA features and statistical significant features, which were superior to the D0.1/1/2cm3 (AUC 0.71). The relative area in dose levels of 64Gy, 67Gy, 68Gy, 87Gy, 88Gy and 89Gy, perimeters in dose levels of 89Gy, as well as two texture features were ranked as the important factors that were closely correlated with rectal toxicity.Our extensive experimental results have demonstrated the feasibility of the proposed scheme. A future large patient cohort study is still needed for model validation.

    View details for DOI 10.1186/s13014-018-1068-0

    View details for Web of Science ID 000437974400001

    View details for PubMedID 29980214

    View details for PubMedCentralID PMC6035458

  • Use of 5-alpha-reductase inhibitors as alternatives to luteinizing-hormone releasing hormone (LHRH) analogs or anti-androgens for prostate downsizing before brachytherapy PRACTICAL RADIATION ONCOLOGY Chiu, T., Tan, J., Brenner, M., Gu, X., Yang, M., Westover, K., Strom, T., Sher, D., Jiang, S., Zhao, B. 2018; 8 (3): E167–E174

    Abstract

    Custom tissue compensators provide dosimetric advantages for treating superficial or complex anatomy, but currently available fabrication technology is expensive or impractical for most clinical operations and yields compensators that are difficult for patients to tolerate. We aimed to develop an inexpensive, clinically feasible workflow for generating patient-specific, soft, custom silicone boluses (SCSBs) for head-and-neck (HN) radiation therapy.We developed a method using 3-dimensional printed parts for generating SCSBs for the treatment of HN cancers. The clinical workflow for generation of SCSBs was characterized inclusive of patient simulation to treatment in terms of resource time and cost. Dosimetric properties such as percentage depth dose and dose profiles were measured for SCSBs using GaF films. Comprehensive measurements were also conducted on an HN phantom. SCSBs were generated and used for electron or photon based radiation treatments of 7 HN patients with lesions at nose, cheek, eye, or ears. In vivo dose measurements with optically simulated luminescence dosimeters were performed.Total design and fabrication time from patient simulation to radiation treatment start required approximately 1 week, with fabrication constituting 1 to 2 working days depending on bolus surface area, volume, and complexity. Computed tomography and dosimetric properties of the soft bolus were similar to water. In vivo dose measurements on 7 treated patients confirmed that the dose deposition conformed to planned doses. Material costs were lower than currently available hard plastic boluses generated with 3-dimensional printing technology. All treated patients tolerated SCSBs for the duration of therapy.Generation and use of SCSBs for clinical use is feasible and effective for the treatment of HN cancers.

    View details for DOI 10.1016/j.prro.2017.11.001

    View details for Web of Science ID 000432348600011

    View details for PubMedID 29452869

  • Prototype volumetric ultrasound tomography image guidance system for prone stereotactic partial breast irradiation: proof-of-concept PHYSICS IN MEDICINE AND BIOLOGY Chiu, T. D., Parsons, D., Zhang, Y., Hrycushko, B., Zhao, B., Chopra, R., Kim, N., Spangler, A., Rahimi, A., Timmerman, R., Jiang, S. B., Lu, W., Gu, X. 2018; 63 (5): 055004

    Abstract

    Accurate dose delivery in stereotactic partial breast irradiation (S-PBI) is challenging because of the target position uncertainty caused by breast deformation, the target volume changes caused by lumpectomy cavity shrinkage, and the target delineation uncertainty on simulation computed tomography (CT) images caused by poor soft tissue contrast. We have developed a volumetric ultrasound tomography (UST) image guidance system for prone position S-PBI. The system is composed of a novel 3D printed rotation water tank, a patient-specific resin breast immobilization cup, and a 1D array ultrasound transducer. Coronal 2D US images were acquired in 5° increments over a 360° range, and planes were acquired every 2 mm in elevation. A super-compounding technique was used to reconstruct the image volume. The image quality of UST was evaluated with a BB-1 breast phantom and BioZorb surgical marker, and the results revealed that UST offered better soft tissue contrast than CT and similar image quality to MR. In the evaluated plane, the size and location of five embedded objects were measured and compared to MR, which is considered as the ground truth. Objects' diameters and the distances between objects in UST differ by approximately 1 to 2 mm from those in MR, which showed that UST offers the image quality required for S-PBI. In future work we will develop a robotic system that will be ultimately implemented in the clinic.

    View details for DOI 10.1088/1361-6560/aaad1f

    View details for Web of Science ID 000426580100001

    View details for PubMedID 29405123

  • Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy SCIENTIFIC REPORTS Chen, H., Zhong, Z., Yang, Y., Chen, J., Zhou, L., Zhen, X., Gu, X. 2018; 8: 3677

    Abstract

    The aim of this study is to develop an internal-external correlation model for internal motion estimation for lung cancer radiotherapy. Deformation vector fields that characterize the internal-external motion are obtained by respectively registering the internal organ meshes and external surface meshes from the 4DCT images via a recently developed local topology preserved non-rigid point matching algorithm. A composite matrix is constructed by combing the estimated internal phasic DVFs with external phasic and directional DVFs. Principle component analysis is then applied to the composite matrix to extract principal motion characteristics, and generate model parameters to correlate the internal-external motion. The proposed model is evaluated on a 4D NURBS-based cardiac-torso (NCAT) synthetic phantom and 4DCT images from five lung cancer patients. For tumor tracking, the center of mass errors of the tracked tumor are 0.8(±0.5)mm/0.8(±0.4)mm for synthetic data, and 1.3(±1.0)mm/1.2(±1.2)mm for patient data in the intra-fraction/inter-fraction tracking, respectively. For lung tracking, the percent errors of the tracked contours are 0.06(±0.02)/0.07(±0.03) for synthetic data, and 0.06(±0.02)/0.06(±0.02) for patient data in the intra-fraction/inter-fraction tracking, respectively. The extensive validations have demonstrated the effectiveness and reliability of the proposed model in motion tracking for both the tumor and the lung in lung cancer radiotherapy.

    View details for DOI 10.1038/s41598-018-22023-3

    View details for Web of Science ID 000426151800010

    View details for PubMedID 29487330

    View details for PubMedCentralID PMC5829085

  • Predicting severe hematologic toxicity from extended-field chemoradiation of para-aortic nodal metastases from cervical cancer PRACTICAL RADIATION ONCOLOGY Yan, K., Ramirez, E., Xie, X., Gu, X., Xi, Y., Albuquerque, K. 2018; 8 (1): 13–19

    Abstract

    The purpose of this study was to determine factors predictive for severe hematologic toxicity (HT) in cervical cancer patients with para-aortic lymph node metastasis treated with concurrent cisplatin chemoradiation to an extended field (EFCRT).Thirty-eight patients with cervical cancer and para-aortic lymph node metastasis who underwent EFCRT were analyzed. Active bone marrow was defined as the region within irradiated total bone marrow (BMTOT) with a standard uptake value on 18F-fluorodeoxyglucose positron emission tomography/computed tomography greater than the mean standard uptake value for BMTOT. Serial weekly blood counts from the beginning to the end of radiation treatment were evaluated for HT using Common Terminology Criteria for Adverse Events, version 4.0.Nineteen patients had grade 3 or higher hematologic toxicity (HT3+), not including lymphocyte toxicity. Obese patients (n = 12) were less likely to get HT3+ (P = .03) despite getting equivalent doses of chemotherapy. Volumes of BMTOT and active bone marrow receiving doses of 20, 30, and 45 Gy and body mass index significantly predicted HT3+. Patients who had HT3+ had prolonged treatment time (62 vs 53 days, P < .001).For patients receiving EFCRT, bone marrow irradiation parameters and patient body mass index were associated with HT3+. A simplified nomogram has been created to predict HT3+ in these patients, allowing the potential to explore bone marrow-sparing delivery techniques.

    View details for DOI 10.1016/j.prro.2017.07.001

    View details for Web of Science ID 000419077100006

    View details for PubMedID 28865761

  • Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study PHYSICS IN MEDICINE AND BIOLOGY Zhen, X., Chen, J., Zhong, Z., Hrycushko, B., Zhou, L., Jiang, S., Albuquerque, K., Gu, X. 2017; 62 (21): 8246–63

    Abstract

    Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT  +  BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D 0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

    View details for DOI 10.1088/1361-6560/aa8d09

    View details for Web of Science ID 000413137000004

    View details for PubMedID 28914611

  • A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery PLOS ONE Liu, Y., Stojadinovic, S., Hrycushko, B., Wardak, Z., Lau, S., Lu, W., Yan, Y., Jiang, S. B., Zhen, X., Timmerman, R., Nedzi, L., Gu, X. 2017; 12 (10): e0185844

    Abstract

    Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

    View details for DOI 10.1371/journal.pone.0185844

    View details for Web of Science ID 000412493000011

    View details for PubMedID 28985229

    View details for PubMedCentralID PMC5630188

  • Inversed-Planned Respiratory Phase Gating in Lung Conformal Radiation Therapy INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Modiri, A., Sabouri, P., Gu, X., Timmerman, R., Sawant, A. 2017; 99 (2): 317–24

    Abstract

    To assess whether the optimal gating window for each beam during lung radiation therapy with respiratory gating will be dependent on a variety of patient-specific factors, such as tumor size and location and the extent of relative tumor and organ motion.To create optimal gating treatment plans, we started from an optimized clinical plan, created a plan per respiratory phase using the same beam arrangements, and used an inverse planning optimization approach to determine the optimal gating window for each beam and optimal beam weights (ie, monitor units). Two pieces of information were used for optimization: (1) the state of the anatomy at each phase, extracted from 4-dimensional computed tomography scans; and (2) the time spent in each state, estimated from a 2-minute monitoring of the patient's breathing motion. We retrospectively studied 15 lung cancer patients clinically treated by hypofractionated conformal radiation therapy, for whom 45 to 60 Gy was administered over 3 to 15 fractions using 7 to 13 beams. Mean gross tumor volume and respiratory-induced tumor motion were 82.5 cm3 and 1.0 cm, respectively.Although patients spent most of their respiratory cycle in end-exhalation (EE), our optimal gating plans used EE for only 34% of the beams. Using optimal gating, maximum and mean doses to the esophagus, heart, and spinal cord were reduced by an average of 15% to 26%, and the beam-on times were reduced by an average of 23% compared with equivalent single-phase EE gated plans (P<.034, paired 2-tailed t test).We introduce a personalized respiratory-gating technique in which inverse planning optimization is used to determine patient- and beam-specific gating phases toward enhancing dosimetric quality of radiation therapy treatment plans.

    View details for DOI 10.1016/j.ijrobp.2017.05.039

    View details for Web of Science ID 000408690000019

    View details for PubMedID 28871981

    View details for PubMedCentralID PMC5656395

  • Comprehensive target geometric errors and margin assessment in stereotactic partial breast irradiation RADIATION ONCOLOGY Zhen, X., Zhao, B., Wang, Z., Timmerman, R., Spangler, A., Kim, N., Rahimi, A., Gu, X. 2017; 12: 151

    Abstract

    Recently developed stereotactic partial breast irradiation (S-PBI) allows delivery of a high biologically potent dose to the target while sparing adjacent critical organs and normal tissue. With S-PBI tumoricidal doses, accurate and precise dose delivery is critical to achieve high treatment quality. This study is to investigate both rigid and non-rigid components of target geometric error and their corresponding margins in S-PBI and identify correlated clinical factors.Forty-three early-stage breast cancer patients with implanted gold fiducial markers were enrolled in the study. Fiducial positions recorded on the orthogonal kV images on a Cyberknife system during treatment were used to estimate intra-fraction errors and composite errors (including intra-fraction errors and residual errors after patient setup). Both rigid and non-rigid components of intra-fraction and composite errors were analyzed and used to estimate rigid and non-rigid margins, respectively. Univariate and multivariate linear regressions were conducted to evaluate correlations between clinical factors and errors.For the study group, the intra-fraction rigid and non-rigid errors are 2.0 ± 0.6 mm and 0.3 ± 0.2 mm, respectively. The composite rigid and non-rigid errors are 2.3 ± 0.5 mm and 1.3 ± 0.8 mm, respectively. The rigid margins in the left-right, anterior-posterior, and superior-inferior directions are estimated as 2.1, 2.4, and 2.3 mm, respectively. The estimated non-rigid margin, assumed to be isotropic, is 1.7 mm. The outer breast quadrants are more susceptible to composite errors occurrence than the inner breast quadrants. The target to chest wall distance is the clinical factor correlated with target geometric errors.This is the first comprehensive analysis of breast target geometric rigid and non-rigid errors in S-PBI. Upon the estimation, the non-rigid margin is comparable to rigid margin, and therefore should be included in planning target volume as it cannot be accounted for by the Cyberknife system. Treatment margins selection also need to consider the impact of relevant clinical factor.

    View details for DOI 10.1186/s13014-017-0889-6

    View details for Web of Science ID 000410314600001

    View details for PubMedID 28893302

    View details for PubMedCentralID PMC5594509

  • An anthropomorphic abdominal phantom for deformable image registration accuracy validation in adaptive radiation therapy MEDICAL PHYSICS Liao, Y., Wang, L., Xu, X., Chen, H., Chen, J., Zhang, G., Lei, H., Wang, R., Zhang, S., Gu, X., Zhen, X., Zhou, L. 2017; 44 (6): 2369–78

    Abstract

    To design and construct a three-dimensional (3D) anthropomorphic abdominal phantom for geometric accuracy and dose summation accuracy evaluations of deformable image registration (DIR) algorithms for adaptive radiation therapy (ART).Organ molds, including liver, kidney, spleen, stomach, vertebra, and two metastasis tumors, were 3D printed using contours from an ovarian cancer patient. The organ molds were molded with deformable gels made of different mixtures of polyvinyl chloride (PVC) and the softener dioctyl terephthalate. Gels with different densities were obtained by a polynomial fitting curve that described the relation between the Hounsfield unit (HU) and PVC-softener blending ratio. The rigid vertebras were constructed by molding of white cement and cellulose pulp. The final abdominal phantom was assembled by arranging all the fabricated organs inside a hollow dummy according to their anatomies, and sealed by deformable gel with averaged HU of muscle and fat. Fiducial landmarks were embedded inside the phantom for spatial accuracy and dose accumulation accuracy studies. Two channels were excavated to facilitate ionization chamber insertion for dosimetric measurements. Phantom properties such as deformable gel elasticity and HU stability were studied. The dosimetric measurement accuracy in the phantom was performed, and the DIR accuracies of three DIR algorithms available in the open source DIR toolkit-DIRART were also validated.The constructed deformable gel showed elastic behavior and was stable in HU values over times, proving to be a practical material for the deformable phantom. The constructed abdominal phantom consisted of realistic anatomies in terms of both anatomical shapes and densities when compared with its reference patient. The dosimetric measurements showed a good agreement with the calculated doses from the treatment planning system. Fiducial-based accuracy analysis conducted on the constructed phantom demonstrated the feasibility of applying the phantom for organ-wise DIR accuracy assessment.We have designed and constructed an anthropomorphic abdominal deformable phantom with satisfactory elastic property, realistic organ density, and anatomy. This physical phantom can be used for routine validations of DIR geometric accuracy and dose accumulation accuracy in ART.

    View details for DOI 10.1002/mp.12229

    View details for Web of Science ID 000408033400030

    View details for PubMedID 28317122

  • A novel geometry-dosimetry label fusion method in multi-atlas segmentation for radiotherapy: a proof-of-concept study PHYSICS IN MEDICINE AND BIOLOGY Chang, J., Tian, Z., Lu, W., Gu, X., Chen, M., Jiang, S. B. 2017; 62 (9): 3656–67

    Abstract

    Multi-atlas segmentation (MAS) has been widely used to automate the delineation of organs at risk (OARs) for radiotherapy. Label fusion is a crucial step in MAS to cope with the segmentation variabilities among multiple atlases. However, most existing label fusion methods do not consider the potential dosimetric impact of the segmentation result. In this proof-of-concept study, we propose a novel geometry-dosimetry label fusion method for MAS-based OAR auto-contouring, which evaluates the segmentation performance in terms of both geometric accuracy and the dosimetric impact of the segmentation accuracy on the resulting treatment plan. Differently from the original selective and iterative method for performance level estimation (SIMPLE), we evaluated and rejected the atlases based on both Dice similarity coefficient and the predicted error of the dosimetric endpoints. The dosimetric error was predicted using our previously developed geometry-dosimetry model. We tested our method in MAS-based rectum auto-contouring on 20 prostate cancer patients. The accuracy in the rectum sub-volume close to the planning tumor volume (PTV), which was found to be a dosimetric sensitive region of the rectum, was greatly improved. The mean absolute distance between the obtained contour and the physician-drawn contour in the rectum sub-volume 2 mm away from PTV was reduced from 3.96 mm to 3.36 mm on average for the 20 patients, with the maximum decrease found to be from 9.22 mm to 3.75 mm. We also compared the dosimetric endpoints predicted for the obtained contours with those predicted for the physician-drawn contours. Our method led to smaller dosimetric endpoint errors than the SIMPLE method in 15 patients, comparable errors in 2 patients, and slightly larger errors in 3 patients. These results indicated the efficacy of our method in terms of considering both geometric accuracy and dosimetric impact during label fusion. Our algorithm can be applied to different tumor sites and radiation treatments, given a specifically trained geometry-dosimetry model.

    View details for DOI 10.1088/1361-6560/aa5ed9

    View details for Web of Science ID 000399212500016

    View details for PubMedID 28379850

  • Comprehensive evaluation of ten deformable image registration algorithms for contour propagation between CT and cone-beam CT images in adaptive head & neck radiotherapy PLOS ONE Li, X., Zhang, Y., Shi, Y., Wu, S., Xiao, Y., Gu, X., Zhen, X., Zhou, L. 2017; 12 (4): e0175906

    Abstract

    Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) for propagating contours between planning computerized tomography (CT) images and treatment CT/cone-beam CT (CBCT) images to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contour mapping, ten intensity-based DIR strategies, which were classified into four categories-optical flow-based, demons-based, level-set-based and spline-based-were tested on planning CT and fractional CBCT images acquired from twenty-one head & neck (H&N) cancer patients who underwent 6~7-week intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e., the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), were employed to measure the agreement between the propagated contours and the physician-delineated ground truths of four OARs, including the vertebra (VTB), the vertebral foramen (VF), the parotid gland (PG) and the submandibular gland (SMG). It was found that the evaluated DIRs in this work did not necessarily outperform rigid registration. DIR performed better for bony structures than soft-tissue organs, and the DIR performance tended to vary for different ROIs with different degrees of deformation as the treatment proceeded. Generally, the optical flow-based DIR performed best, while the demons-based DIR usually ranked last except for a modified demons-based DISC used for CT-CBCT DIR. These experimental results suggest that the choice of a specific DIR algorithm depends on the image modality, anatomic site, magnitude of deformation and application. Therefore, careful examinations and modifications are required before accepting the auto-propagated contours, especially for automatic re-planning ART systems.

    View details for DOI 10.1371/journal.pone.0175906

    View details for Web of Science ID 000399874800076

    View details for PubMedID 28414799

    View details for PubMedCentralID PMC5393623

  • Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications PHYSICS IN MEDICINE AND BIOLOGY Liu, Y., Stojadinovic, S., Hrycushko, B., Wardak, Z., Lu, W., Yan, Y., Jiang, S. B., Timmerman, R., Abdulrahman, R., Nedzi, L., Gu, X. 2016; 61 (24): 8440–61

    Abstract

    The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8.8  ±  12.6 mm, an MSSD of 1.5  ±  3.2 mm, and an SDSSD of 1.8  ±  3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.

    View details for DOI 10.1088/0031-9155/61/24/8440

    View details for Web of Science ID 000388688800003

    View details for PubMedID 27845915

  • Cardiac dosimetric evaluation of deep inspiration breath-hold level variances using computed tomography scans generated from deformable image registration displacement vectors MEDICAL DOSIMETRY Harry, T., Rahn, D., Semenov, D., Gu, X., Yashar, C., Einck, J., Jiang, S., Cervino, L. 2016; 41 (1): 22–27

    Abstract

    There is a reduction in cardiac dose for left-sided breast radiotherapy during treatment with deep inspiration breath-hold (DIBH) when compared with treatment with free breathing (FB). Various levels of DIBH may occur for different treatment fractions. Dosimetric effects due to this and other motions are a major component of uncertainty in radiotherapy in this setting. Recent developments in deformable registration techniques allow displacement vectors between various temporal and spatial patient representations to be digitally quantified. We propose a method to evaluate the dosimetric effect to the heart from variable reproducibility of DIBH by using deformable registration to create new anatomical computed tomography (CT) scans. From deformable registration, 3-dimensional deformation vectors are generated with FB and DIBH. The obtained deformation vectors are scaled to 75%, 90%, and 110% and are applied to the reference image to create new CT scans at these inspirational levels. The scans are then imported into the treatment planning system and dose calculations are performed. The average mean dose to the heart was 2.5Gy (0.7 to 9.6Gy) at FB, 1.2Gy (0.6 to 3.8Gy, p < 0.001) at 75% inspiration, 1.1Gy (0.6 to 3.1Gy, p = 0.004) at 90% inspiration, 1.0Gy (0.6 to 3.0Gy) at 100% inspiration or DIBH, and 1.0Gy (0.6 to 2.8Gy, p = 0.019) at 110% inspiration. The average mean dose to the left anterior descending artery (LAD) was 19.9Gy (2.4 to 46.4Gy), 8.6Gy (2.0 to 43.8Gy, p < 0.001), 7.2Gy (1.9 to 40.1Gy, p = 0.035), 6.5Gy (1.8 to 34.7Gy), and 5.3Gy (1.5 to 31.5Gy, p < 0.001), correspondingly. This novel method enables numerous anatomical situations to be mimicked and quantifies the dosimetric effect they have on a treatment plan.

    View details for DOI 10.1016/j.meddos.2015.06.004

    View details for Web of Science ID 000370206800005

    View details for PubMedID 26206154

  • 4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling PHYSICS IN MEDICINE AND BIOLOGY Zhong, Z., Gu, X., Mao, W., Wang, J. 2016; 61 (3): 996–1020

    Abstract

    A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.

    View details for DOI 10.1088/0031-9155/61/3/996

    View details for Web of Science ID 000369517000007

    View details for PubMedID 26758496

    View details for PubMedCentralID PMC5026392

  • SCORE System for Online Adaptive Radiotherapy GRAPHICS PROCESSING UNIT-BASED HIGH PERFORMANCE COMPUTING IN RADIATION THERAPY Tian, Z., Gautier, Q., Gu, X., Men, C., Peng, F., Zarepisheh, M., Graves, Y., Uribe-Sanchez, A., Jia, X., Jiang, S. B., Jia, Jiang, S. B. 2016: 351–65
  • Patient-specific dosimetric endpoints based treatment plan quality control in radiotherapy PHYSICS IN MEDICINE AND BIOLOGY Song, T., Staub, D., Chen, M., Lu, W., Tian, Z., Jia, X., Li, Y., Zhou, L., Jiang, S. B., Gu, X. 2015; 60 (21): 8213–27

    Abstract

    In intensity modulated radiotherapy (IMRT), the optimal plan for each patient is specific due to unique patient anatomy. To achieve such a plan, patient-specific dosimetric goals reflecting each patient's unique anatomy should be defined and adopted in the treatment planning procedure for plan quality control. This study is to develop such a personalized treatment plan quality control tool by predicting patient-specific dosimetric endpoints (DEs). The incorporation of patient specific DEs is realized by a multi-OAR geometry-dosimetry model, capable of predicting optimal DEs based on the individual patient's geometry. The overall quality of a treatment plan is then judged with a numerical treatment plan quality indicator and characterized as optimal or suboptimal. Taking advantage of clinically available prostate volumetric modulated arc therapy (VMAT) treatment plans, we built and evaluated our proposed plan quality control tool. Using our developed tool, six of twenty evaluated plans were identified as sub-optimal plans. After plan re-optimization, these suboptimal plans achieved better OAR dose sparing without sacrificing the PTV coverage, and the dosimetric endpoints of the re-optimized plans agreed well with the model predicted values, which validate the predictability of the proposed tool. In conclusion, the developed tool is able to accurately predict optimally achievable DEs of multiple OARs, identify suboptimal plans, and guide plan optimization. It is a useful tool for achieving patient-specific treatment plan quality control.

    View details for DOI 10.1088/0031-9155/60/21/8213

    View details for Web of Science ID 000366108500004

    View details for PubMedID 26447829

  • A Pilot Evaluation of a 4-Dimensional Cone-Beam Computed Tomographic Scheme Based on Simultaneous Motion Estimation and Image Reconstruction INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Dang, J., Gu, X., Pan, T., Wang, J. 2015; 91 (2): 410–18

    Abstract

    To evaluate the performance of a 4-dimensional (4-D) cone-beam computed tomographic (CBCT) reconstruction scheme based on simultaneous motion estimation and image reconstruction (SMEIR) through patient studies.The SMEIR algorithm contains 2 alternating steps: (1) motion-compensated CBCT reconstruction using projections from all phases to reconstruct a reference phase 4D-CBCT by explicitly considering the motion models between each different phase and (2) estimation of motion models directly from projections by matching the measured projections to the forward projection of the deformed reference phase 4D-CBCT. Four lung cancer patients were scanned for 4 to 6 minutes to obtain approximately 2000 projections for each patient. To evaluate the performance of the SMEIR algorithm on a conventional 1-minute CBCT scan, the number of projections at each phase was reduced by a factor of 5, 8, or 10 for each patient. Then, 4D-CBCTs were reconstructed from the down-sampled projections using Feldkamp-Davis-Kress, total variation (TV) minimization, prior image constrained compressive sensing (PICCS), and SMEIR. Using the 4D-CBCT reconstructed from the fully sampled projections as a reference, the relative error (RE) of reconstructed images, root mean square error (RMSE), and maximum error (MaxE) of estimated tumor positions were analyzed to quantify the performance of the SMEIR algorithm.The SMEIR algorithm can achieve results consistent with the reference 4D-CBCT reconstructed with many more projections per phase. With an average of 30 to 40 projections per phase, the MaxE in tumor position detection is less than 1 mm in SMEIR for all 4 patients.The results from a limited number of patients show that SMEIR is a promising tool for high-quality 4D-CBCT reconstruction and tumor motion modeling.

    View details for DOI 10.1016/j.ijrobp.2014.10.029

    View details for Web of Science ID 000348045400023

    View details for PubMedID 25636763

  • Automated landmark-guided deformable image registration PHYSICS IN MEDICINE AND BIOLOGY Kearney, V., Chen, S., Gu, X., Chiu, T., Liu, H., Jiang, L., Wang, J., Yordy, J., Nedzi, L., Mao, W. 2015; 60 (1): 101–16

    Abstract

    The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency.

    View details for DOI 10.1088/0031-9155/60/1/101

    View details for Web of Science ID 000346479700009

    View details for PubMedID 25479095

  • Breaking bad IMRT QA practice JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS Stojadinovic, S., Ouyang, L., Gu, X., Pompos, A., Bao, Q., Solberg, T. D. 2015; 16 (3): 154–65

    Abstract

    Agreement between planned and delivered dose distributions for patient-specific quality assurance in routine clinical practice is predominantly assessed utilizing the gamma index method. Several reports, however, fundamentally question current IMRT QA practice due to poor sensitivity and specificity of the standard gamma index implementation. An alternative is to employ dose volume histogram (DVH)-based metrics. An analysis based on the AAPM TG 53 and ESTRO booklet No.7 recommendations for QA of treatment planning systems reveals deficiencies in the current "state of the art" IMRT QA, no matter which metric is selected. The set of IMRT benchmark plans were planned, delivered, and analyzed by following guidance of the AAPM TG 119 report. The recommended point dose and planar dose measurements were obtained using a PinPoint ionization chamber, EDR2 radiographic film, and a 2D ionization chamber array. Gamma index criteria {3% (global), 3 mm} and {3% (local), 3 mm} were used to assess the agreement between calculated and delivered planar dose distributions. Next, the AAPM TG 53 and ESTRO booklet No.7 recommendations were followed by dividing dose distributions into four distinct regions: the high-dose (HD) or umbra region, the high-gradient (HG) or penumbra region, the medium-dose (MD) region, and the low-dose (LD) region. A different gamma passing criteria was defined for each region, i.e., a "divide and conquer" (D&C) gamma method was utilized. The D&C gamma analysis was subsequently tested on 50 datasets of previously treated patients. Measured point dose and planar dose distributions compared favorably with TG 119 benchmark data. For all complex tests, the percentage of points passing the conventional {3% (global), 3 mm} gamma criteria was 97.2% ± 3.2% and 95.7% ± 1.2% for film and 2D ionization chamber array, respectively. By dividing 2D ionization chamber array dose measurements into regions and applying 3mm isodose point distance and variable local point dose difference criteria of 7%, 15%, 25%, and 40% for HD, HG, MD, and LD regions, respectively, a 93.4% ± 2.3% gamma passing rate was obtained. Identical criteria applied using the D&C gamma technique on 50 clinical treatment plans resulted in a 97.9% ± 2.3% gamma passing score. Based on the TG 119 standard, meeting or exceeding the benchmark results would indicate an exemplary IMRT QA program. In contrast to TG 119 analysis, a different scrutiny on the same set of data, which follows the AAPM TG 53 and ESTRO booklet No.7 guidelines, reveals a much poorer agreement between calculated and measured dose distributions with large local point dose differences within different dose regions. This observation may challenge the conventional wisdom that an IMRT QA program is producing acceptable results.

    View details for DOI 10.1120/jacmp.v16i3.5242

    View details for Web of Science ID 000354950100015

    View details for PubMedID 26103484

    View details for PubMedCentralID PMC5690124

  • Deformation vector fields (DVF)-driven image reconstruction for 4D-CBCT JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY Dang, J., Luo, O., Gu, X., Wang, J. 2015; 23 (1): 11–23

    Abstract

    High quality 4D-CBCT can be obtained by deforming a planning CT (pCT), where the deformation vector fields (DVF) are estimated by matching the forward projections of pCT and 4D-CBCT projections. The matching metric used in the previous study is the sum of squared intensity differences (SSID). The scatter signal level in CBCT projections is much higher than pCT, the SSID metric may not lead to optimal DVF.To improve the DVF estimation accuracy, we develop a new matching metric that is less sensitive to the intensity level difference caused by the scatter signal.The negative logarithm of correlation coefficient (NLCC) is used as the matching metric. A non-linear conjugate gradient optimization algorithm is used to estimate the DVF. A 4D NCAT phantom and an anthropomorphic thoracic phantom were used to evaluate the NLCC-based algorithm.In the NCAT phantom study, the relative reconstruction error is reduced from 18.0% in SSID to 14.13% in NLCC. In the thoracic phantom study, the root mean square error of the tumor motion is reduced from 1.16 mm in SSID to 0.43 mm in NLCC.NLCC metric can improve the image reconstruction and motion estimation accuracy of DVF-driven image reconstruction for 4D-CBCT.

    View details for DOI 10.3233/XST-140466

    View details for Web of Science ID 000347450700002

    View details for PubMedID 25567403

  • Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT MEDICAL PHYSICS Wang, J., Gu, X. 2013; 40 (10): 101912

    Abstract

    Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR).The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT.Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively.The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

    View details for DOI 10.1118/1.4821099

    View details for Web of Science ID 000325394400028

    View details for PubMedID 24089914

  • High-quality four-dimensional cone-beam CT by deforming prior images PHYSICS IN MEDICINE AND BIOLOGY Wang, J., Gu, X. 2013; 58 (2): 231–46

    Abstract

    Due to a limited number of projections at each phase, severe view aliasing artifacts are present in four-dimensional cone beam computed tomography (4D-CBCT) when reconstruction is performed using conventional algorithms. In this work, we aim to obtain high-quality 4D-CBCT of lung cancer patients in radiation therapy by deforming the planning CT. The deformation vector fields (DVF) to deform the planning CT are estimated through matching the forward projection of the deformed prior image and measured on-treatment CBCT projection. The estimation of the DVF is formulated as an unconstrained optimization problem, where the objective function to be minimized is the sum of the squared difference between the forward projection of the deformed planning CT and the measured 4D-CBCT projection. A nonlinear conjugate gradient method is used to solve the DVF. As the number of the variables in the DVF is much greater than the number of measurements, the solution to such a highly ill-posed problem is very sensitive to the initials during the optimization process. To improve the estimation accuracy of DVF, we proposed a new strategy to obtain better initials for the optimization. In this strategy, 4D-CBCT is first reconstructed by total variation minimization. Demons deformable registration is performed to register the planning CT and the 4D-CBCT reconstructed by total variation minimization. The resulted DVF from demons registration is then used as the initial parameters in the optimization process. A 4D nonuniform rotational B-spline-based cardiac-torso (NCAT) phantom and a patient 4D-CBCT are used to evaluate the algorithm. Image quality of 4D-CBCT is substantially improved by using the proposed strategy in both NCAT phantom and patient studies. The proposed method has the potential to improve the temporal resolution of 4D-CBCT. Improved 4D-CBCT can better characterize the motion of lung tumors and will be a valuable tool for image-guided adaptive radiation therapy.

    View details for DOI 10.1088/0031-9155/58/2/231

    View details for Web of Science ID 000312674400005

    View details for PubMedID 23257113

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