Download or read book Prediction and Classification of Respiratory Motion written by Suk Jin Lee and published by Springer. This book was released on 2013-10-25 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin. In the first chapter following the Introduction to this book, we review three prediction approaches of respiratory motion: model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the following chapter, we present a phantom study—prediction of human motion with distributed body sensors—using a Polhemus Liberty AC magnetic tracker. Next we describe respiratory motion estimation with hybrid implementation of extended Kalman filter. The given method assigns the recurrent neural network the role of the predictor and the extended Kalman filter the role of the corrector. After that, we present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. We have evaluated the new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier in the last chapter.

Download or read book Prediction of Respiratory Motion for Radiotherapy Applications written by Asad Rasheed and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Prediction of Respiratory Motion written by Suk Jin Lee and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients' breathing patterns validated the proposed irregular breathing classifier.

Download or read book Adaptive Modelling and Prediction of Respiratory Motion in External Beam Radiotherapy written by Majdi Rashed S. Alnowami and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy written by Jan Ehrhardt and published by Springer Science & Business Media. This book was released on 2013-05-30 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Respiratory motion causes an important uncertainty in radiotherapy planning of the thorax and upper abdomen. The main objective of radiation therapy is to eradicate or shrink tumor cells without damaging the surrounding tissue by delivering a high radiation dose to the tumor region and a dose as low as possible to healthy organ tissues. Meeting this demand remains a challenge especially in case of lung tumors due to breathing-induced tumor and organ motion where motion amplitudes can measure up to several centimeters. Therefore, modeling of respiratory motion has become increasingly important in radiation therapy. With 4D imaging techniques spatiotemporal image sequences can be acquired to investigate dynamic processes in the patient’s body. Furthermore, image registration enables the estimation of the breathing-induced motion and the description of the temporal change in position and shape of the structures of interest by establishing the correspondence between images acquired at different phases of the breathing cycle. In radiation therapy these motion estimations are used to define accurate treatment margins, e.g. to calculate dose distributions and to develop prediction models for gated or robotic radiotherapy. In this book, the increasing role of image registration and motion estimation algorithms for the interpretation of complex 4D medical image sequences is illustrated. Different 4D CT image acquisition techniques and conceptually different motion estimation algorithms are presented. The clinical relevance is demonstrated by means of example applications which are related to the radiation therapy of thoracic and abdominal tumors. The state of the art and perspectives are shown by an insight into the current field of research. The book is addressed to biomedical engineers, medical physicists, researchers and physicians working in the fields of medical image analysis, radiology and radiation therapy.

Download or read book A Respiratory Motion Prediction Based on Time Variant Seasonal Autoregressive Model for Real Time Image Guided Radiotherapy written by Kei Ichiji and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A Respiratory Motion Prediction Based on Time-Variant Seasonal Autoregressive Model for Real-Time Image-Guided Radiotherapy.

Download or read book Predicting Respiratory Motion for Active Canceling During Percutaneous Needle Insertion written by and published by . This book was released on 2001 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prediction of bodily motion due to respiration was investigated preparatory to implementation of active compensation for respiration in a robot-assisted system for percutaneous kidney surgery. Data for preliminary testing were recorded from the chest wall of a subject using an optical displacement sensor. The weighted-frequency Fourier linear combiner algorithm, an adaptive modeling algorithm, was used to model and predict respiratory movement. Preliminary results are presented, in which the algorithm is shown to track a 0.86 mm rms respiration signal with 0.11 mm rms error. The general robotic system and compensation scheme are also described.

Download or read book Prediction and Characterization of Lung Tissue Motion During Quiet Respiration written by Benjamin Michael White and published by . This book was released on 2013 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Purpose: The purpose of this dissertation is to quantitatively characterize and predict lung tissue motion with the goal of improving the local control of lung cancer. This is accomplished by producing a biomechanical model of lung tissue motion during quiet respiration. This dissertation proposes the development of algorithms and protocols for the analysis of motion information in 4DCT images. Methods: A cohort of 50 patients was acquired with a 16-slice CT scanner. This data was used throughout the dissertation. Based on the law of volume conservation, a relationship between the tidal volume and the geometric expansion of the torso was devised and used to improve breathing motion studies. The breathing patterns of these patients were used to characterize breathing patterns based on the measured external surrogate information with the aim of improving the efficiency of linear accelerator gating windows. A characteristic breath was defined as an average breath for use in generating patterns representative of realistic motion for breathing motion studies. A prospective gating algorithm was developed to allow the acquisition of user specified breathing phases with a relatively simple model to accurately predict respiratory phase occurrence in order to reduce the number of scans necessary to obtain sufficient data for breathing motion modeling. A new term to the breathing motion model to account for cardiac induced lung tissue motion was developed to improve the accuracy of the model. Results: Breathing studies can be optimized by placing the surrogate device between the third and fourth lumbar vertebra. Three types of breathing patterns were observed in the patient cohort. The hysteresis component of lung tissue trajectories was shown to be between 8 - 18 % of the volume filling component of motion. A simple prediction algorithm was shown to be a significant improvement over commercially available software. An additional term was devised to account for cardiac-induced lung motion and was shown to be accurate. Conclusion: This dissertation has demonstrated new quantitative methods to characterize lung tissue motion. Future work includes incorporating the work described in this dissertation into a new fast helical CT image acquisition protocol for breathing motion modeling.

Download or read book Surface Guided Radiation Therapy written by Jeremy David Page Hoisak and published by CRC Press. This book was released on 2020-02-13 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surface Guided Radiation Therapy provides a comprehensive overview of optical surface image guidance systems for radiation therapy. It serves as an introductory teaching resource for students and trainees, and a valuable reference for medical physicists, physicians, radiation therapists, and administrators who wish to incorporate surface guided radiation therapy (SGRT) into their clinical practice. This is the first book dedicated to the principles and practice of SGRT, featuring: Chapters authored by an internationally represented list of physicists, radiation oncologists and therapists, edited by pioneers and experts in SGRT Covering the evolution of localization systems and their role in quality and safety, current SGRT systems, practical guides to commissioning and quality assurance, clinical applications by anatomic site, and emerging topics including skin mark-less setups. Several dedicated chapters on SGRT for intracranial radiosurgery and breast, covering technical aspects, risk assessment and outcomes. Jeremy Hoisak, PhD, DABR is an Assistant Professor in the Department of Radiation Medicine and Applied Sciences at the University of California, San Diego. Dr. Hoisak’s clinical expertise includes radiosurgery and respiratory motion management. Adam Paxton, PhD, DABR is an Assistant Professor in the Department of Radiation Oncology at the University of Utah. Dr. Paxton’s clinical expertise includes patient safety, motion management, radiosurgery, and proton therapy. Benjamin Waghorn, PhD, DABR is the Director of Clinical Physics at Vision RT. Dr. Waghorn’s research interests include intensity modulated radiation therapy, motion management, and surface image guidance systems. Todd Pawlicki, PhD, DABR, FAAPM, FASTRO, is Professor and Vice-Chair for Medical Physics in the Department of Radiation Medicine and Applied Sciences at the University of California, San Diego. Dr. Pawlicki has published extensively on quality and safety in radiation therapy. He has served on the Board of Directors for the American Society for Radiology Oncology (ASTRO) and the American Association of Physicists in Medicine (AAPM).

Download or read book Selected Papers written by Herbert Robbins and published by . This book was released on 1985 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Respiratory Motion Tracking in Magnetic Resonance Imaging with Pilot Tone Technology written by Mary C. Lenk and published by . This book was released on 2018 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis explores the hypothesis that Pilot Tone (PT) technology can encode respiratory induced motion of the heart to improve cardiac magnetic resonance (MR) imaging. Pilot tone technology is advantageous due to its high sampling rate to provide high temporal resolution in tracking and predicting respiration. Also, the PT signal has the potential to provide motion information without interrupting the pulse sequence to perform motion compensated scans. A prediction model is hypothesized to account for in-plane and through-plane motions due to respiration. A proof-of-concept experiment was designed to explore the ability of the PT signal to encode respiratory-induced motion. The PT signal was processed retrospectively offline and compared to a reference for respiratory motion. The two signals had a high correlation and show preliminary success for the PT to detect respiration. A linear filter was then designed to predict motion from a training phase using the same reference signal. The linear filter was successful with peak/trough locations between the prediction and the reference signal having a correlation coefficient of 0.9999 for end-expiration and end-inspiration prediction. Furthermore, a PT transmitter was designed and constructed for implementation of additional experiments. The transmitter was designed to be programmable, battery-powered, MR-safe, and portable for placement at various locations in the bore during scans.

Download or read book Machine Learning in Radiation Oncology written by Issam El Naqa and published by Springer. This book was released on 2015-06-19 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Download or read book Novel Techniques for Respiratory Motion Estimation and Modelling from Magnetic Resonance Data written by Christian Buerger and published by . This book was released on 2011 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Imaging such as Magnetic Resonance (MR) imaging and Positron Emission Tomography (PET) are commonly used in the diagnosis and treatment follow-up of cancer. Respiratory motion, however, introduces motion blurring and degrades image quality. As a solution, motion models have been proposed but remain challenging due to the following problems. First, common motion estimation algorithms are computationally complex (and consequently time-consuming) and often have poor robustness when large deformations are present. Second, due to technical limitations of current MR systems, prospective acquisitions are not capable of acquiring images with both high temporal and spatial resolution, which are required for accurate motion estimations. In this thesis, both challenges are addressed. A new registration algorithm has been developed which estimates complex non-rigid motion by a combination of multiple local affine components. This algorithm allows fast and accurate motion estimations and is robust against large deformations due to its adaptive hierarchical structure. Furthermore, a new reconstruction scheme has been developed which retrospectively combines raw data acquired from free-breathing acquisitions to reconstruct multiple images covering the complete range of the breathing cycle. This reconstruction method overcomes the spatial-temporal trade-off and produces near motion-free respiratory images with high isotropic resolution. Both methods are combined to model the continuous deformation of the abdomen during an average breathing cycle, with errors in model predictions of comparable magnitude to the image resolution. A modification of this work is used to allow highly efficient motion compensated reconstructions from short acquisitions under free-breathing.

Download or read book Novel Techniques for Respiratory Motion Estimation and Modeling from MR Data written by Christian Buerger and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Imaging such as Magnetic Resonance (MR) imaging and Positron Emission Tomography (PET) are commonly used in the diagnosis and treatment follow-up of cancer. Respiratory motion, however, introduces motion blurring and degrades image quality. As a solution, motion models have been proposed but remain challenging due to the following problems. First, common motion estimation algorithms are computationally complex (and consequently time-consuming) and often have poor robustness when large deformations are present. Second, due to technical limitations of current MR systems, prospective acquisitions are not capable of acquiring images with both high temporal and spatial resolution, which are required for accurate motion estimations. In this thesis, both challenges are addressed. A new registration algorithm has been developed which estimates complex non-rigid motion by a combination of multiple local affine components. This algorithm allows fast and accurate motion estimations and is robust against large deformations due to its adaptive hierarchical structure. Furthermore, a new reconstruction scheme has been developed which retrospectively combines raw data acquired from free-breathing acquisitions to reconstruct multiple images covering the complete range of the breathing cycle. This reconstruction method overcomes the spatial-temporal trade-off and produces near motion-free respiratory images with high isotropic resolution. Both methods are combined to model the continuous deformation of the abdomen during an average breathing cycle, with errors in model predictions of comparable magnitude to the image resolution. A modification of this work is used to allow highly efficient motion compensated reconstructions from short acquisitions under free-breathing.

Download or read book NETLAB written by Ian Nabney and published by Springer Science & Business Media. This book was released on 2002 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Getting the most out of neural networks and related data modelling techniques is the purpose of this book. The text, with the accompanying Netlab toolbox, provides all the necessary tools and knowledge. Throughout, the emphasis is on methods that are relevant to the practical application of neural networks to pattern analysis problems. All parts of the toolbox interact in a coherent way, and implementations and descriptions of standard statistical techniques are provided so that they can be used as benchmarks against which more sophisticated algorithms can be evaluated. Plenty of examples and demonstration programs illustrate the theory and help the reader understand the algorithms and how to apply them.

Download or read book Adaptive Motion Compensation in Radiotherapy written by Martin J. Murphy and published by CRC Press. This book was released on 2011-12-14 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: External-beam radiotherapy has long been challenged by the simple fact that patients can (and do) move during the delivery of radiation. Recent advances in imaging and beam delivery technologies have made the solution-adapting delivery to natural movement-a practical reality. Adaptive Motion Compensation in Radiotherapy provides the first detailed

Download or read book Oxford Textbook of Critical Care written by Webb and published by Oxford University Press. This book was released on 2020-01-10 with total page 1961 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in paperback, the second edition of the Oxford Textbook of Critical Care is a comprehensive multi-disciplinary text covering all aspects of adult intensive care management. Uniquely this text takes a problem-orientated approach providing a key resource for daily clinical issues in the intensive care unit. The text is organized into short topics allowing readers to rapidly access authoritative information on specific clinical problems. Each topic refers to basic physiological principles and provides up-to-date treatment advice supported by references to the most vital literature. Where international differences exist in clinical practice, authors cover alternative views. Key messages summarise each topic in order to aid quick review and decision making. Edited and written by an international group of recognized experts from many disciplines, the second edition of the Oxford Textbook of Critical Careprovides an up-to-date reference that is relevant for intensive care units and emergency departments globally. This volume is the definitive text for all health care providers, including physicians, nurses, respiratory therapists, and other allied health professionals who take care of critically ill patients.