Deformable Lung Registration for Pulmonary Image Analysis of MRI and CT Scans PDF Download
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Author: Mattias Paul Heinrich
Publisher:
ISBN:
Category : Biomedical engineering
Languages : en
Pages : 462
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Book Description
Author: Mattias Paul Heinrich
Publisher:
ISBN:
Category : Biomedical engineering
Languages : en
Pages : 462
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Book Description
Author: Ayman El-Baz
Publisher: CRC Press
ISBN: 1439845581
Category : Medical
Languages : en
Pages : 473
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Book Description
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
Author:
Publisher: Lulu.com
ISBN: 1435759524
Category : Computers
Languages : en
Pages : 318
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Book Description
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.
Author: Joseph V. Hajnal
Publisher: CRC Press
ISBN: 1420042475
Category : Medical
Languages : en
Pages : 394
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Book Description
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
Author: Hamed Minaeizaeim
Publisher:
ISBN:
Category : Diagnostic imaging
Languages : en
Pages : 170
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Book Description
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.
Author: Yoshiharu Ohno
Publisher: Springer Nature
ISBN: 3030435393
Category : Medical
Languages : en
Pages : 363
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Book Description
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.
Author: Sebastien Ourselin
Publisher: Springer
ISBN: 3319085549
Category : Computers
Languages : en
Pages : 252
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Book Description
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.
Author: X. Allen Li
Publisher: CRC Press
ISBN: 1439816352
Category : Medical
Languages : en
Pages : 404
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Book Description
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
Author: S. Kevin Zhou
Publisher: Academic Press
ISBN: 0323858880
Category : Computers
Languages : en
Pages : 544
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Book Description
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
Author: Christopher Logan Guy
Publisher:
ISBN:
Category :
Languages : en
Pages : 727
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Book Description
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.
