Download or read book Deformable Lung Registration for Pulmonary Image Analysis of MRI and CT Scans written by Mattias Paul Heinrich and published by . This book was released on 2013 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The First International Workshop on Pulmonary Image Analysis written by and published by Lulu.com. This book was released on 2008 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the First International Workshop on Pulmonary Image Analysis, held on September 6, 2008 in New York as part of the MICCAI workshop program.
Download or read book Lung Imaging and Computer Aided Diagnosis written by Ayman El-Baz and published by CRC Press. This book was released on 2016-04-19 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer remains the leading cause of cancer-related deaths worldwide. Early diagnosis can improve the effectiveness of treatment and increase a patient's chances of survival. Thus, there is an urgent need for new technology to diagnose small, malignant lung nodules early as well as large nodules located away from large diameter airways because
Download or read book Medical Image Registration written by Joseph V. Hajnal and published by CRC Press. This book was released on 2001-06-27 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid
Download or read book Development of a Biomechanical Basis for Lung Image Registration written by Hamed Minaeizaeim and published by . This book was released on 2019 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Respiratory disease places an exceptionally high economic and social burden on society. Due to limitations of pulmonary function tests, most lung abnormalities are diagnosed using imaging techniques such as computed tomography and chest X-ray. In the clinical setting, in order to diagnose, prognose and evaluate lung diseases and overcome limitations of each imaging technique, multiple images may be taken at different volumes or postures. To improve interpretation, these images need to be accurately mapped together to relate information in one to another. Although a number of image registration techniques have been developed, either based on image intensity or landmarks, these techniques are not robust when significant changes in lung volume or postural changes occur, as the lung is highly deformable. Furthermore, they are not typically constrained to physical tissue deformation, so their results can be non-physical. In this thesis, a physics-based lung image registration using finite element method was developed and incorporated into an existing intensity based free form registration. When we breathe, the shape of the lung changes non-uniformly, as most of the lung is constrained by the chest wall but the diaphragm moves more freely. This deformation plays an important role in the physiology and mechanics of breathing. However, no biophysical accurate model has been published on the effect of the pleural cavity shape changes during breathing or posture changes. In this study, quantitative measurement of how the shape of pleural cavity for the left lung changes between two different postures and volumes were made. Then, cavity shape changes were incorporated in a biophysically based model of lung tissue deformation. Both left and right lungs deform significantly during the breathing cycle and both lungs have similar physical structure, and so likely material properties. In this study, the left lung was selected as it has more complex deformation than the right lung due to the location of the heart, which has been proposed to interact with the lung and influence its deformation. The model was assessed in healthy subjects imaged at functional residual capacity and total lung capacity in supine posture and ten healthy subjects imaged at total lung capacity in supine and prone postures. The biophysical model of the lung was used to develop a physics-based lung registration that can map the material points between a source and target image. This method can register lung images despite different postures and volumes. Furthermore, a hybrid method combining the biophysical model and free form deformation were developed to create a robust registration methodology that can rely on both the physics of the lung and image intensity. This novel registration technique was examined in two case studies of clinical interest. The first is in an adult population where multiple high-resolution computed tomography (CT) images are available, a cohort with idiopathic pulmonary fibrosis to register multiple CT images in the same subjects at different time points. Idiopathic pulmonary fibrosis can be difficult to assess as patients may find it difficult to breath to reproducible volumes at repeat visits, in this study we show how registration can help to quantitatively evaluate progression of disease features in imaging by mapping data to a consistent lung volume. The second case focuses on a more challenging population, a cohort of children with cystic fibrosis, for whom both high-resolution computed tomography and X-ray images are acquired to monitor disease status. These images are typically analysed qualitatively or quantitatively without applying registration. In this study, a biophysically based model of the left lung was created using a CT image acquired in the supine position. Then, deformation of lung tissue in the upright position was computed and areas with abnormalities mapped to an X-ray image. A machine learning method was then employed to automatically differentiate between normal and abnormal areas in X-ray. The methodologies presented a tool for mapping abnormal regions between images to identify locations where abnormalities potentially change, and for multimodal and multidimensional registration.
Download or read book Pulmonary Functional Imaging written by Yoshiharu Ohno and published by Springer Nature. This book was released on 2020-12-11 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the basics of pulmonary functional imaging using new CT and MR techniques and describes the clinical applications of these techniques in detail. The intention is to equip readers with a full understanding of pulmonary functional imaging that will allow optimal application of all relevant techniques in the assessment of a variety of diseases, including COPD, asthma, cystic fibrosis, pulmonary thromboembolism, pulmonary hypertension, lung cancer and pulmonary nodule. Pulmonary functional imaging has been promoted as a research and diagnostic tool that has the capability to overcome the limitations of morphological assessments as well as functional evaluation based on traditional nuclear medicine studies. The recent advances in CT and MRI and in medical image processing and analysis have given further impetus to pulmonary functional imaging and provide the basis for future expansion of its use in clinical applications. In documenting the utility of state-of-the-art pulmonary functional imaging in diagnostic radiology and pulmonary medicine, this book will be of high value for chest radiologists, pulmonologists, pulmonary surgeons, and radiation technologists.
Download or read book Biomedical Image Registration written by Sebastien Ourselin and published by Springer. This book was released on 2014-06-05 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Workshop on Biomedical Image Registration, WBIR 2014, held in London, UK, in July 2014. The 16 full papers and 8 poster papers included in this volume were carefully reviewed and selected from numerous submitted papers. The full papers are organized in the following topical sections: computational efficiency, model based regularisation, optimisation, reconstruction, interventional application and application specific measures of similarity.
Download or read book Image Processing in Radiation Therapy written by Kristy K. Brock and published by CRC Press. This book was released on 2016-04-19 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Images from CT, MRI, PET, and other medical instrumentation have become central to the radiotherapy process in the past two decades, thus requiring medical physicists, clinicians, dosimetrists, radiation therapists, and trainees to integrate and segment these images efficiently and accurately in a clinical environment. Image Processing in Radiation
Download or read book An Algorithm to Improve Deformable Image Registration Accuracy in Challenging Cases of Locally advanced Non small Cell Lung Cancer written by Christopher Logan Guy and published by . This book was released on 2017 with total page 727 pages. Available in PDF, EPUB and Kindle. Book excerpt: A common co-pathology of large lung tumors located near the central airways is collapse of portions of lung due to blockage of airflow by the tumor. Not only does the lung volume decrease as collapse occurs, but fluid from capillaries also fills the space no longer occupied by air, greatly altering tissue appearance. During radiotherapy, typically administered to the patient over multiple weeks, the tumor can dramatically shrink in response to the treatment, restoring airflow to the lung sections which were collapsed when therapy began. While return of normal lung function is a positive development, the change in anatomy presents problems for future radiation sessions since the treatment was planned on lung geometry which is no longer accurate. The treatment must be adapted to the new lung state so that the radiation continues to accurately target the tumor while safely avoiding healthy tissue. However, to account for the dose delivered previously, correspondences of anatomy between the former image when the lung was collapsed and the re-expanded lung in a current image must be obtained. This process, known as deformable image registration, is performed by registration software. Most registration algorithms assume that identical anatomy is contained in the images and that intensities of corresponding image elements are similar; both assumptions are untrue when collapsed lung re-expands. This work was to develop an algorithm which accurately registers images in the presence of lung expansion. The lung registration method matched CT images of patients aided by vessel enhancement and information of individual lobe boundaries. The algorithm was tested on eighteen patients with lung collapse using physician-specified correspondences to measure registration error. The image registration algorithm developed in this work which was designed for challenging lung patients resulted in accuracy comparable to that of other methods when large lung changes are absent.
Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-11-23 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Download or read book Adaptive Radiation Therapy written by X. Allen Li and published by CRC Press. This book was released on 2011-01-27 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern medical imaging and radiation therapy technologies are so complex and computer driven that it is difficult for physicians and technologists to know exactly what is happening at the point-of-care. Medical physicists responsible for filling this gap in knowledge must stay abreast of the latest advances at the intersection of medical imaging an
Download or read book Lung Imaging and Computer Aided Diagnosis written by Ayman El-Baz and published by CRC Press. This book was released on 2011-08-23 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer remains the leading cause of cancer-related deaths worldwide. Early diagnosis can improve the effectiveness of treatment and increase a patient’s chances of survival. Thus, there is an urgent need for new technology to diagnose small, malignant lung nodules early as well as large nodules located away from large diameter airways because the current technology—namely, needle biopsy and bronchoscopy—fail to diagnose those cases. However, the analysis of small, indeterminate lung masses is fraught with many technical difficulties. Often patients must be followed for years with serial CT scans in order to establish a diagnosis, but inter-scan variability, slice selection artifacts, differences in degree of inspiration, and scan angles can make comparing serial scans unreliable. Lung Imaging and Computer Aided Diagnosis brings together researchers in pulmonary image analysis to present state-of-the-art image processing techniques for detecting and diagnosing lung cancer at an early stage. The book addresses variables and discrepancies in scans and proposes ways of evaluating small lung masses more consistently to allow for more accurate measurement of growth rates and analysis of shape and appearance of the detected lung nodules. Dealing with all aspects of image analysis of the data, this book examines: Lung segmentation Nodule segmentation Vessels segmentation Airways segmentation Lung registration Detection of lung nodules Diagnosis of detected lung nodules Shape and appearance analysis of lung nodules Contributors also explore the effective use of these methodologies for diagnosis and therapy in clinical applications. Arguably the first book of its kind to address and evaluate image-based diagnostic approaches for the early diagnosis of lung cancer, Lung Imaging and Computer Aided Diagnosis constitutes a valuable resource for biomedical engineers, researchers, and clinicians in lung disease imaging.
Download or read book DICOM Structured Reporting written by David A. Clunie and published by PixelMed Publishing. This book was released on 2000 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Regional Pulmonary Function Analysis Using Image Registration and 4DCT written by Kaifang Du and published by . This book was released on 2013 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current radiation therapy (RT) planning for limiting lung toxicity is based on a uniform lung function with little consideration to the spatial and temporal pattern of lung function. Establishment of relationships between radiation dose and changes in pulmonary function can help predict and reduce the RT-induced pulmonary toxicity. Baseline measurement uncertainty of pulmonary function across scans needs to be assessed, and there is a great interest to compensate the pulmonary function for respiratory effort variations. Respiratory-gated 4DCT imaging and image registration can be used to estimate the regional lung volume change by a transformation-based ventilation metric which is computed directly from the deformation field, or a intensity-based metric which is based on CT density change in the registered image pair.
Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2007 written by Nicholas Ayache and published by Springer. This book was released on 2007-11-22 with total page 1044 pages. Available in PDF, EPUB and Kindle. Book excerpt: This title is part of a two-volume set that constitute the refereed proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007. Coverage in this first volume includes diffusion tensor imaging and computing, cardiac imaging and robotics, image segmentation and classification, image guided intervention and robotics, innovative clinical and biological applications, brain atlas computing, and simulation of therapy.
Download or read book Statistical Methodology for Computed Tomography Scans of the Lung written by Sarah Marie Ryan and published by . This book was released on 2018 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: As computed tomography (CT) scans of the lung become more widely used for clinical research, methods that can meaningfully identify population-level features across lung images are needed. Approaches to characterize lung CT scans have traditionally relied on visual assessments, making it challenging to objectively identify quantitative patterns across scans. This dissertation advances quantitative image analysis for lung CT scans three-fold. First, we create the first publicly available standard lung template to enable spatial registrations between lung scans. Next, we develop a population-level spatial modeling approach for the analysis of lung CT scans, called spatial voxel-based morphometry, which identifies spatial regions with significant associations between the radiodensity from the scans and a set of covariates of interest. Lastly, we extend an unsupervised machine learning framework by both clustering medical imaging data and visualizing cluster-specific activation maps, providing a data-driven approach to uncover novel subtypes of lung disease based purely on CT scan presentation. We apply these methodologies to a population of patients with pulmonary sarcoidosis from the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis study, resulting in improved clinical understanding of this disease. Together, these methodologies advance the field of lung imaging by providing publicly available software and state-of-the-art methodologies to perform population-level inference and clustering for lung CT scans, and lay the groundwork for further methodological and clinical advancements for other lung diseases.
Download or read book Lung Motion Model Validation Experiments Free Breathing Tissue Densitometry and Ventilation Mapping Using Fast Helical CT Imaging written by Tai H. Dou and published by . This book was released on 2016 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: The uncertainties due to respiratory motion present significant challenges to accurate characterization of cancerous tissues both in terms of imaging and treatment. Currently available clinical lung imaging techniques are subject to inferior image quality and incorrect motion estimation, with consequences that can systematically impact the downstream treatment delivery and outcome. The main objective of this thesis is the development of the techniques of fast helical computed tomography (CT) imaging and deformable image registration for the radiotherapy applications in accurate breathing motion modeling, lung tissue density modeling and ventilation imaging. Fast helical CT scanning was performed on 64-slice CT scanner using the shortest available gantry rotation time and largest pitch value such that scanning of the thorax region amounts to just two seconds, which is less than typical breathing cycle in humans. The scanning was conducted under free breathing condition. Any portion of the lung anatomy undergoing such scanning protocol would be irradiated for only a quarter second, effectively removing any motion induced image artifacts. The resulting CT data were pristine volumetric images that record the lung tissue position and density in a fraction of the breathing cycle. Following our developed protocol, multiple fast helical CT scans were acquired to sample the tissue positions in different breathing states. To measure the tissue displacement, deformable image registration was performed that registers the non-reference images to the reference one. In modeling breathing motion, external breathing surrogate signal was recorded synchronously with the CT image slices. This allowed for the tissue-specific displacement to be modeled as parametrization of the recorded breathing signal using the 5D lung motion model. To assess the accuracy of the motion model in describing tissue position change, the model was used to simulate the original high-pitch helical CT scan geometries, employed as ground truth data. Image similarity between the simulated and ground truth scans was evaluated. The model validation experiments were conducted in a patient cohort of seventeen patients to assess the model robustness and inter-patient variation. The model error averaged over multiple tracked positions from several breathing cycles was found to be on the order of one millimeter. In modeling the density change under free breathing condition, the determinant of Jacobian matrix from the registration-derived deformation vector field yielded volume change information of the lung tissues. Correlation of the Jacobian values to the corresponding voxel Housfield units (HU) reveals that the density variation for the majority of lung tissues can be very well described by mass conservation relationship. Different tissue types were identified and separately modeled. Large trials of validation experiments were performed. The averaged deviation between the modeled and the reference lung density was 30 HU, which was estimated to be the background CT noise level. In characterizing the lung ventilation function, a novel method was developed to determine the extent of lung tissue volume change. Information on volume change was derived from the deformable image registration of the fast helical CT images in terms of Jacobian values with respect to a reference image. Assuming the multiple volume change measurements are independently and identically distributed, statistical formulation was derived to model ventilation distribution of each lung voxels and empirical minimum and maximum probability distribution of the Jacobian values was computed. Ventilation characteristic was evaluated as the difference of the expectation value from these extremal distributions. The resulting ventilation map was compared with an independently obtained ventilation image derived directly from the lung intensities and good correlation was found using statistical test. In addition, dynamic ventilation characterization was investigated by estimating the voxel-specific ventilation distribution. Ventilation maps were generated at different percentile levels using the tissue volume expansion metrics.
