Multivariate Statistical Techniques for Accurately and Noninvasively Localizing Tumors Subject to Respiration-Induced Motion

Tumors in the lung, liver, and pancreas can move considerably with normal respiration. The tumor motion extent, path, and baseline position change over time. This creates a complex "moving target" for external beam radiation and is a major obstacle to treating cancer. Real-time tumor motion compensation systems have emerged, but device performance is limited by tumor localization accuracy. Direct tumor tracking is not feasible for these tumors, but tumor displacement can be predicted from surrogate measurements of respiration. In this dissertation, we have developed a series of multivariate statistical techniques for reliably and accurately localizing tumors from respiratory surrogate markers affixed to the torso surface. Our studies utilized radiographic tumor localizations measured concurrently with optically tracked respiratory surrogates during 176 lung, liver, and pancreas radiation treatment and dynamic MR imaging sessions. We identified measurement precision, tumor-surrogate correlation, training data selection, inter-patient variations, and algorithm design as factors impacting localization accuracy. Training data timing was particularly important, as tumor localization errors increased over time in 63% of 30-min treatments. This was a result of the changing relationship between surrogate signals and tumor motion. To account for these changes, we developed a method for detecting and correcting large localization errors. By monitoring the surrogate-to-surrogate and surrogate-to-model relationships, tumor localization errors exceeding 3 mm could be detected at a sensitivity of 95%. The method that we have proposed and validated in this dissertation leads to 69% fewer treatment interruptions than conventional respiratory surrogate model monitoring techniques. Finally, we extended respiratory surrogate-based tumor motion prediction to the otherwise time-consuming process of contouring respiratory-correlated computed tomography scans. This dissertation clarifies the scope and significance of problems underlying existing surrogate-based tumor localization models. Furthermore, it presents novel solutions that make it possible to improve radiation delivery to tumors without increasing the time required to plan and deliver radiation treatments

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Aerospace Medicine and Biology: A continuing bibliography (supplement 160)

This bibliography lists 166 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1976

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 127, April 1974

This special bibliography lists 279 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1974

Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 129, June 1974

This special bibliography lists 280 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1974

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified