Respiration introduces large tumor motion in the thoracic region which influences treatment outcome for lung cancer patients. Tumor motion management techniques require characterization of temporal tumor motions because tumor motion varies patient to patient, day to day and cycle to cycle. This work develops a markerless algorithm to estimate 3 dimensional (3D) lung-tumor trajectories on free breathing cone beam computed tomography (CBCT) projections, which are 2 dimensional (2D) sequential images rotating about an axis and are used to reconstruct 3D CBCT images. A gold standard tumor trajectory is required to guide the algorithm development and estimate the tumor detection accuracy for markerless tracking algorithms. However, a sufficient strategy to validate markerless tracking algorithms is lacking. A validation framework is developed based on fiducial markers. Markers are segmented and marker trajectories are xiv obtained. The displacement of the tumor to the marker is calculated and added to the segmented marker trajectory to generate reference tumor trajectory. Markerless tumor trajectory estimation (MLTM) algorithm is developed and improved to acquire tumor trajectory with clinical acceptable accuracy for locally advanced lung tumors. The development is separate into two parts. The first part considers none tumor deformation. It investigates shape and appearance of the template, moreover, a constraint method is introduced to narrow down the template matching searching region for more precise matching results. The second part is to accommodate tumor deformation near the end of the treatment. The accuracy of MLTM is calculated and compared against 4D CBCT, which is the current standard of care. In summary, a validation framework based on fiducial markers is successfully built. MLTM is successfully developed with or without the consideration of tumor deformation with promising accuracy. MLTM outperforms 4D CBCT in temporal tumor trajectory estimation.

Lung cancer is the most deadly cancer in the United States. Radiation therapy uses ionizing radiation with high energy to destroy lung tumor cells by damaging their genetic material, preventing those cells from reproducing. The most challenging aspect of modern radiation therapy for lung cancer is the motion of lung tumors caused by patient breathing during treatment. Most gating based radiotherapy derives the tumor motion from external surrogates and generates a respiratory signal to trigger the beam. We propose a method that monitors internal diaphragm motion, which can provide a respiratory signal that is more highly correlated to lung tumor motion compared to the external surrogates. We also investigate direct tracking of the tumor in fluoroscopic video imagery. We tracked fixed tumor contours in fluoroscopic videos for 5 patients. The predominant tumor displacements are well tracked based on optical flow. Some tumors or nearby anatomy features exhibit severe nonrigid deformation, especially in the supradiaphragmatic region. By combining Active Shape Models and the respiratory signal, the deformed contours are tracked within a range defined in the training period. All the tracking results are validated by a human expert and the proposed methods are promising for applications in radiotherapy. Another important aspect of lung patient treatment is patient setup verification, which is needed to reduce inter- and intra-fractions geometry uncertainties and ensure precise dose delivery. Currently, there is no universally accepted method for lung patient verification. We propose to register 4DCT and 2D x-ray images taken before treatment to derive the couch shifts necessary for precise radiotherapy. The proposed technique leads to improved patient care.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18877号 / 医博第3988号 / 新制||医||1008(附属図書館) / 31828 / 京都大学大学院医学研究科医学専攻 / (主査)教授 武田 俊一, 教授 富樫 かおり, 教授 増永 慎一郎 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19562号 / 医博第4069号 / 新制||医||1013(附属図書館) / 32598 / 京都大学大学院医学研究科医学専攻 / (主査)教授 富樫 かおり, 教授 坂井 義治, 教授 鈴木 実 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

The effectiveness of cancer treatment is compromised by the need to reduce the uncertainties originating from a variety of factors including tumor volume delineation, patient setup, and irregular physiologic motion. In particular, effective yet practical tumor motion management remains a major challenge in current external beam radiation therapy. Many strategies such as motion encompassment, breath-hold techniques, and respiratory gating have been proposed in the literature and implemented clinically. These methods have shown success in certain situations with different limitations. With the advent of image guided radiation therapy, real-time tumor tracking methods have become popular in clinics to proactively address the challenge with on-board tumor localization. Nevertheless, such techniques rely on surrogate signals and have been reported vulnerable to errors. In this dissertation, EGRT is proposed as a new modality for effective and practical management strategy of cancer treatment uncertainties. One implementation of EGRT is to use PET emissions in real-time for direct tumor tracking during radiation delivery. Radiation beamlets are delivered along PET lines of response by a fast rotating ring therapy unit consisting of a linear accelerator and PET detectors. A complete treatment scheme with capabilities of accurate tumor tracking and dose planning is proposed to implement this EGRT concept. Simulation studies with physical phantom, 4D digital patient model, and clinical patient datasets are carefully designed to evaluate the feasibility and performance of EGRT. We show that with the capabilities of achieving both tumor tracking and sophisticated intensity modulation, EGRT has the potential to enable an effective implementation of 4D radiation therapy with true biological targeting and other advantages.

Objectives. The goal in radiotherapy is to deliver adequate radiation to the tumor volume while limiting damage to the surrounding healthy tissue. However, this goal is challenged by respiratory-induced motion. The objective of this work was to identify whether motion in electronic portal images can be tracked with an optical flow algorithm and whether a neural network can predict tumor motion. Methods. A multi-resolution optical flow algorithm that incorporates weighting based on the differences between image frames was used to automatically sample the vectors corresponding to the motion. The global motion was obtained by computing the average weighted mean from the set of vectors. The algorithm was evaluated using tumor trajectories taken from seven lung cancer patients, a 3D printed patient tumor and a virtual dynamic multi-leaf collimator (DMLC) system. The feasibility of detecting and tracking motion at the field edge was examined with a proof-of-concept implementation that included (1) an algorithm that detected local motion, and (2) a control algorithm that adapted the virtual MLC. To compensate for system latency, a generalized neural network, using both offline (treatment planning data) and online (during treatment delivery) learning, was implemented for tumor motion prediction. Results and Conclusions. The algorithm tracked the global motion of the target with an accuracy of around 0.5 mm. While the accuracy is similar to other methods, this approach does not require manual delineation of the target and can, therefore, provide real-time autonomous motion estimation during treatment. Motion at the treatment field edge was tracked with an accuracy of -0.4 ± 0.3 mm. This proof-of-concept simulation demonstrated that it is possible to adapt MLC leaves based on the motion detected at the field edges. Unplanned intrusions of external organs-at-risk could be shielded. A generalized network with a prediction error of 0.59 mm, and a shorter initial learning period (compared to previous studies) was achieved. This network may be used as a plug-and-play predictor in which tumor position could be predicted at the start of treatment and the need for pretreatment data and optimization for individual patients may be avoided. / February 2017

Accurate tumor tracking remains as a major challenge in radiation therapy. Large margins are added to the clinical target volume (CTV) to ensure the treatment of tumor in presence of patient setup uncertainty and that caused by intra-motion. Fiducial seeds and calypso markers are commonly implanted into the disease sites to further reduce the dose delivery error due to tumor motion. For more accurate dose delivery and improved patient comfort, the use of radioactive tracers in positron emission tomography (PET) as non-invasive tumor markers has been proposed - a concept called emission-guided radiation therapy (EGRT). Instead of using images obtained from a stand-alone PET scanner for treatment guidance, we mount a positron imaging system on a radiation therapy machine. Such an EGRT system is able to track the tumor in real time based on the lines of response (LOR) of the tumor positron events, and perform radiation therapy simultaneously. In this work, we illustrate the EGRT concept using computer simulations and propose a typical treatment scheme. EGRT's advantage on increased dose delivery accuracy is demonstrated using a pancreas tumor case and a lung tumor case without the setup margin and motion margin. The emission process is simulated by Geant4 Application for Tomographic Emission package and Linac dose delivery is simulated using a voxel-based Monte Carlo algorithm. The tumor tracking error can be controlled within 2 mm which indicates margins can be significantly reduced. The dose distributions show that the proposed EGRT can accurately deliver the prescribed dose to the CTV with much less margins. Although still in a preliminary research stage, EGRT has the potential to substantially reduce tumor location uncertainties and to greatly increase the performance of current radiation therapy.

京都大学 / 新制・課程博士 / 博士(人間健康科学) / 甲第24542号 / 人健博第113号 / 新制||人健||8(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 中尾 恵, 教授 杉本 直三, 教授 黒田 知宏 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

京都大学 / 新制・論文博士 / 博士(人間健康科学) / 乙第13589号 / 論人健博第13号 / 新制||人健||8(附属図書館) / 名古屋大学大学院医学系研究科医療技術学専攻 / (主査)教授 中尾 恵, 教授 杉本 直三, 教授 中本 裕士 / 学位規則第4条第2項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19553号 / 医博第4060号 / 新制||医||1012(附属図書館) / 32589 / 京都大学大学院医学研究科医学専攻 / (主査)教授 増永 慎一郎, 教授 富樫 かおり, 教授 武田 俊一 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM