Browsing by Subject "Radiotherapy Planning, Computer-Assisted"
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Item Addressing Label Variability in Deep Learning-Based Segmentation for Radiation Therapy(December 2021) Balagopal, Anjali; Wang, Jing; Jiang, Steve B.; Nguyen, Dan; Lin, Mu-Han; Fei, BaoweiAccurate segmentation of tumor and surrounding organs-at-risk (OARs) is important for radiotherapy treatment(RT) planning. Manual segmentation by physicians currently used in clinical practice is time consuming and highly depends on the physicians' skill and experience, leading to large inter-and intra-observer variation. Deep learning(DL) algorithms, such as those used for image recognition, has been promising in the development of automated segmentation tools for medical imaging. Contouring in RT has some specific challenges as opposed to semantic segmentation in other spaces. DL segmentation models are trained with large, annotated datasets. The annotations play such an important role since these labels are the supervisory signals teaching the model how to segment. Majority of the times, a very high-quality dataset is unavailable. The datasets available are usually biased, noisy, and sometimes scarcely annotated. One of the primary sources of noise in RT datasets is annotation variations. This dissertation addresses the challenge of label variability and investigates different methods to deal with various kinds of label variability. The thesis studies the impact that label variability has on automation performance as well as patient outcome and devices ways to reduce or to respect this variability. Computed Tomography based segmentation model for intact and post-operative prostate cancer RT planning are proposed and developed for clinical use. Uncertainty in the automated label prediction for CTV and OARs are evaluated in detail. Impact of inter-physician variability on patient outcome for post-operative prostate cancer is investigated and instead of segmenting a general CTV , PSA-Net is proposed that respects style variations across physicians as well as across institutions. For dealing with systematic label variability in some structures, a prior-guided deep difference meta-learner is proposed that can segment a structure in a new labelling style from just a handful of prior segmented patients. A multi-modality IPA segmentation model is proposed to reduce label variability due to expertise differences among physicians in clinical trials. This model can effectively help inexperienced physicians in producing expert segmentations. The contributions of this thesis are expected to facilitate better understanding of label variability in RT and help in avoiding/respecting label variability when developing deep learning models for RT.Item Efficient and Intelligent Radiotherapy Planning and Adaptation(2022-05) Ma, Lin; Gu, Xuejun; Lu, Weiguo; Jiang, Steve B.; Jia, Xun; Wang, JingTreatment planning--inverse planning on volumetric tomography images--is the foundation of modern radiotherapy. Contouring planning structures and optimizing plan dose distributions are the two most important components of treatment planning. Treatment planning happens in two stages. The initial treatment plan is first designed before the commencement of treatment course. With the on-line images acquired before each fraction of treatment, treatment plan may be adapted off-line/on-line accounting for inter/intra-fractional changes. This dissertation presents innovative research to improve the efficiency and intelligence of treatment planning, with a special focus on its application in on-line adaptive planning. Four standalone, but related, techniques are developed in this dissertation. * Registration-guided on-line image segmentation. This technique targets the segmentation of on-line images. Based on the previous methods for on-line image contouring such as registration-based contour adaptation and deep learning-based image segmentation, we proposed a technique that can combine the merits of individual method. * Volumetric dose extension and isodose tuning. Interactive plan dose tuning is essential to on-line plan adaptation. We developed a dose painting algorithm that can output volumetric dose distribution from two isodose surfaces in real time. Then we applied the algorithm to interactive plan dose tuning, which allows for tuning volumetric dose by dragging isodose lines. * Fluence map prediction. Plan optimization is the central part of inverse planning. We proposed a deep learning-based fluence map prediction method, to achieve inverse planning without optimization. Compared to fluence map optimization, fluence map prediction is as accurate and much faster because of its nature as a direct inference calculation. * On-line proton range verification. Proton therapy is sensitive to motion and anatomy variation. For the previously proposed range-guided proton therapy strategy, we conducted an end-to-end Monte-Carlo simulation of the on-line proton measurement process. The key parameters of range measurement, such as the mapping from measurement result to range and measurement uncertainty, are obtained by simulation. Clinical data has been used to train and evaluate each technique. The results show the feasibility, generalizability, as well as limitations of the developed techniques. Future directions for on-line planning were discussed.Item Improving Cone-Beam Computed Tomography Based Adaptive Radiation Therapy with Deep Learning(2023-05-01T05:00:00.000Z) Liang, Xiao; Nguyen, Dan; Jiang, Steve B.; Lin, Mu-Han; Wang, Jing; Lu, WeiguoDuring radiation therapy, patient anatomical changes may compromise treatment quality if the treatment plan, prepared prior to therapy, remains unchanged throughout the course. Adaptive radiation therapy (ART) has been developed to address this issue by adapting the treatment plan based on up-to-date patient anatomy. The widespread availability of cone-beam computed tomography (CBCT) and its capability for the 3D imaging of patient anatomy has made CBCT-based ART an emerging and increasingly popular technology in the field of radiation oncology. However, numerous challenges persist in the current workflow. One challenge involves generating synthetic CT (sCT) images that retain CBCT anatomy while maintaining CT image quality. Clinically used sCT is typically obtained through deformable image registration (DIR) between pre-planning CT (pCT) and CBCT; however, this method often inadequately preserves CBCT anatomy due to DIR errors. Another challenge stems from the truncation issue in CBCT images, resulting from size limitations of imaging panels, which consequently leads to inaccuracies in CBCT-based dose calculations. Additionally, auto-segmentation of CBCT is impeded by low image quality and a lack of training labels. Although addressing this issue is difficult, enhancing auto-segmentation is essential, as manual segmentation is highly time-consuming. This dissertation aims to improve CBCT-based ART by leveraging deep learning (DL) technologies. First, an unsupervised DL model is proposed to directly convert CBCT images into sCT images with reduced artifacts and more accurate Hounsfield Unit values, as found in CT scans. Second, the model's generalizability is investigated and discussed, along with potential solutions to address the generalizability problem. Third, two DL models are designed to extract and combine information from pCT and CBCT, in order to inpaint axial and longitudinal truncations in CBCT images. Through these three studies, the predicted truncation-free sCT images hold the potential to enhance ART workflows, enabling more accurate dose calculations compared to DIR-generated sCT. The dissertation then shifts its focus to CBCT auto-segmentation. Initially, an unsupervised DL-based DIR model is proposed to predict the deformation vector field between pCT and CBCT, enabling pCT structure propagation as the segmentation on CBCT. To further improve segmentation accuracy, a DL-based direct segmentation model assisted by DIR is proposed, which outperforms state-of-the-art DIR-based segmentation results. The contributions of this thesis are expected to enhance the accuracy and efficiency of sCT generation and CBCT auto-segmentation in the CBCT-based ART workflow.Item Safety and Accuracy of Employing Active Breathing Coordinator (ABC) Based Deep Inspiration Breath Hold (DIBH) Technique in Delivery of Radiation Therapy for Locally Advanced Left Sided Breast Cancer(2020-05-01T05:00:00.000Z) Maxwell, Christian Travis Wade; Kim, D. Nathan; Zhao, Bo; Chiu, Tsuicheng DavidBACKGROUND: Patients with locally advanced breast cancer typically require complex field matching technique for radiation treatment delivery. While field matching has been demonstrated to be safe and effective in free breathing patients, its safety and accuracy in the setting of deep inspiration breath hold (DIBH) with active breathing coordinator technology (ABC) use has not been reported. OBJECTIVE: The purpose of this study is to determine the safety of ABC technology in DIBH of breast cancer patients requiring multi-field matching techniques. METHODS: 202 patients treated with DIBH/ABC technique at UT Southwestern between 2013-2018 were reviewed for this study. The amount of overlap or gap between the supraclavicular field and the chest wall field were measured and recorded for each treatment fraction prospectively to determine accuracy of setup. Optically stimulated luminescent dosimeter (OSLD) readings were taken at 1 cm above, below, and at the junction on at least 3 different fractions from 10 patients to determine accuracy of treatment dose. Acute and delayed skin toxicities, as defined by the common terminology criteria for adverse events v4.0, were collected and analyzed as a measure of treatment safety. RESULTS: 4973 fractions with gap/overlap measurements were analyzed. The average gap/overlap measured at junction was 0.28 mm+/-0.13 mm. 72% of fractions had a perfect match with 0 mm measurement, while 5.6% had an overlap and 22.7% had a measurable gap. Moreover, gap/overlap measurements neither improved nor worsened as patients received more treatment fractions. The median OSLD dose at 1 cm above the junction was 106%+/-7% of planned dose (range 94% to 116%); 1 cm below the junction, 114%+/-11% of planned dose (range 95% to 131%); at the junction, 106%+/-16.3% of planned dose (range 86% to 131%). These range of values appear to be within acceptable limits, since OSLD itself has a 10% calibration error, along with a likely minimal setup error. No significant acute or delayed skin toxicity at the matchline occurred in any patient. CONCLUSION: This work suggests that ABC assisted DIBH is a safe method of delivering radiation therapy in the setting of complex matching field technique for breast and regional nodal treatments.