Download or read book Algorithms for magnetic resonance imaging in radiotherapy written by Jens Sjölund and published by Linköping University Electronic Press. This book was released on 2018-02-21 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential. Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning approach to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs. Cancerous tissue has a different structure from normal tissue. This affects molecular diffusion, which can be measured using MRI. The prototypical diffusion encoding sequence has recently been challenged with the introduction of more general gradient waveforms. One such example is diffusional variance decomposition (DIVIDE), which allows non-invasive mapping of parameters that reflect variable cell eccentricity and density in brain tumors. To take full advantage of such more general gradient waveforms it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints. We demonstrate that, by using the optimized gradient waveforms, it is technically feasible to perform whole-brain diffusional variance decomposition at clinical MRI systems with varying performance. The last part of the thesis is devoted to estimation of diffusion MRI models from measurements. We show that, by using a machine learning framework called Gaussian processes, it is possible to perform diffusion spectrum imaging using far fewer measurements than ordinarily required. This has the potential of making diffusion spectrum imaging feasible even though the acquisition time is limited. A key property of Gaussian processes, which is a probabilistic model, is that it comes with a rigorous way of reasoning about uncertainty. This is pursued further in the last paper, in which we propose a Bayesian reinterpretation of several of the most popular models for diffusion MRI. Thanks to the Bayesian interpretation it possible to quantify the uncertainty in any property derived from these models. We expect this will be broadly useful, in particular in group analyses and in cases when the uncertainty is large.
Download or read book Algorithms for Magnetic Resonance Imaging in Radiotherapy written by Jens Sjölund and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential. Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning approach to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs. Cancerous tissue has a different structure from normal tissue. This affects molecular diffusion, which can be measured using MRI. The prototypical diffusion encoding sequence has recently been challenged with the introduction of more general gradient waveforms. One such example is diffusional variance decomposition (DIVIDE), which allows non-invasive mapping of parameters that reflect variable cell eccentricity and density in brain tumors. To take full advantage of such more general gradient waveforms it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints. We demonstrate that, by using the optimized gradient waveforms, it is technically feasible to perform whole-brain diffusional variance decomposition at clinical MRI systems with varying performance. The last part of the thesis is devoted to estimation of diffusion MRI models from measurements. We show that, by using a machine learning framework called Gaussian processes, it is possible to perform diffusion spectrum imaging using far fewer measurements than ordinarily required. This has the potential of making diffusion spectrum imaging feasible even though the acquisition time is limited. A key property of Gaussian processes, which is a probabilistic model, is that it comes with a rigorous way of reasoning about uncertainty. This is pursued further in the last paper, in which we propose a Bayesian reinterpretation of several of the most popular models for diffusion MRI. Thanks to the Bayesian interpretation it possible to quantify the uncertainty in any property derived from these models. We expect this will be broadly useful, in particular in group analyses and in cases when the uncertainty is large.
Download or read book Magnetic Resonance Imaging for Radiation Therapy written by Ning Wen and published by Frontiers Media SA. This book was released on 2020-06-04 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Bhabesh Deka and published by Springer. This book was released on 2018-12-29 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need for the CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly useful for researchers who have just entered into the exciting field of CS-MRI and would like to quickly go through the developments to date without diving into the detailed mathematical analysis. Finally, it also discusses recent trends and future research directions for implementation of CS-MRI in clinical practice, particularly in Bio- and Neuro-informatics applications.
Download or read book Novel Compressed Sensing Algorithms with Applications to Magnetic Resonance Imaging written by Yue Hu and published by . This book was released on 2014 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Magnetic Resonance Imaging (MRI) is a widely used non-invasive clinical imaging modality. Unlike other medical imaging tools, such as X-rays or computed tomography (CT), the advantage of MRI is that it uses non-ionizing radiation. In addition, MRI can provide images with multiple contrast by using different pulse sequences and protocols. However, acquisition speed, which remains the main challenge for MRI, limits its clinical application. Clinicians have to compromise between spatial resolution, SNR, and scan time, which leads to sub-optimal performance. The acquisition speed of MRI can be improved by collecting fewer data samples. However, according to the Nyquist sampling theory, undersampling in k-space will lead to aliasing artifacts in the recovered image. The recent mathematical theory of compressed sensing has been developed to exploit the property of sparsity for signals/images. It states that if an image is sparse, it can be accurately reconstructed using a subset of the k-space data under certain conditions. Generally, the reconstruction is formulated as an optimization problem. The sparsity of the image is enforced by using a sparsifying transform. Total variation (TV) is one of the commonly used methods, which enforces the sparsity of the image gradients and provides good image quality. However, TV introduces patchy or painting-like artifacts in the reconstructed images. We introduce novel regularization penalties involving higher degree image derivatives to overcome the practical problems associated with the classical TV scheme. Motivated by novel reinterpretations of the classical TV regularizer, we derive two families of functionals, which we term as isotropic and anisotropic higher degree total variation (HDTV) penalties, respectively. The numerical comparisons of the proposed scheme with classical TV penalty, current second order methods, and wavelet algorithms demonstrate the performance improvement. Specifically, the proposed algorithms minimize the staircase and ringing artifacts that are common with TV schemes and wavelet algorithms, while better preserving the singularities. Higher dimensional MRI is also challenging due to the above mentioned trade-offs. We propose a three-dimensional (3D) version of HDTV (3D-HDTV) to recover 3D datasets. One of the challenges associated with the HDTV framework is the high computational complexity of the algorithm. We introduce a novel computationally efficient algorithm for HDTV regularized image recovery problems. We find that this new algorithm improves the convergence rate by a factor of ten compared to the previously used method. We demonstrate the utility of 3D-HDTV regularization in the context of compressed sensing, denoising, and deblurring of 3D MR dataset and fluorescence microscope images. We show that 3D-HDTV outperforms 3D-TV schemes in terms of the signal to noise ratio (SNR) of the reconstructed images and its ability to preserve ridge-like details in the 3D datasets. To address speed limitations in dynamic MR imaging, which is an important scheme in multi-dimensional MRI, we combine the properties of low rank and sparsity of the dataset to introduce a novel algorithm to recover dynamic MR datasets from undersampled k-t space data. We pose the reconstruction as an optimization problem, where we minimize a linear combination of data consistency error, non-convex spectral penalty, and non-convex sparsity penalty. The problem is solved using an iterative, three step, alternating minimization scheme. Our results on brain perfusion data show a signicant improvement in SNR and image quality compared to classical dynamic imaging algorithms"--Page vii-ix.
Download or read book Using Pattern Recognition Algorithms in Dynamic Contrast enhanced Magnetic Resonance Imaging written by Dipal Patel and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Soft-tissue sarcoma is a rare cancer that has high metastatic potential to the lung with poor prognosis at 3-year survival for patients that develop lung metastasis. As certain prognostic factors such as necrosis and poor perfusion due to abnormal blood supply may be targets for novel strategies, research in imaging techniques that can characterize the tumour microenvironment can allow for prediction of patient response to treatment. Dynamic Contrast-Enhanced (DCE) MRI is a functional imaging technique that can visualize perfusion in biological tissue by acquiring multiple images following the injection of a contrast agent through the cardiovascular system to analyze the contrast uptake within a given tissue. DCE-MRI has a complex 4-dimensional dataset consisting of thousands of pixels per image across multiple timepoints. DCE-MRI analysis tends to aim towards building semi-quantitative methods and model-based approaches to describe the kinetics of the contrast agent. However, the implementation of these techniques often requires a-priori information that puts constraints on the data. In this thesis, a data-driven technique to DCE-MRI analysis is proposed using non-negative matrix factorization (NMF), a dimensionality reduction technique that can isolate signal patterns in the data and provide visualization of the spatial distribution of these patterns. Using a DCE-MRI dataset consisting of sarcoma tumours over the course of radiotherapy, we show that the alternating non-negative least squares using block pivot principle (ANLS-BPP) NMF framework can find high and low signal enhancement curves in this data and generate weight maps for each perfusion curve that can be superimposed to visualize the heterogenous spatial distribution of high and low perfusion in these tumours. While these signal enhancement time-course patterns are consistent across patients and over the course of the radiotherapy, the weight maps across several timepoints carry the changes in perfusion distribution in response to radiotherapy over the course of treatment. However, these weight maps vary according to the random initialization of the NMF algorithm due to the non-uniqueness of the solutions to the algorithm as it tends to converge onto local minima. For this reason, we proposed a multi-NMF algorithm that performs an averaging of the weight maps produced by the algorithm using a distance minimization function with multiple tolerances to obtain the most representative weight map for each sarcoma tumour. This algorithm could reduce the variability in the weight maps produced by the NMF algorithm, thereby increasing the robustness of this technique to produce repeatable perfusion distributions in sarcoma tumours. These results have significant implications in the development of model-free approaches to DCE-MRI analysis as the ANLS-BPP NMF algorithm can find consistent signal enhancement patterns in the data and generate weight maps that can spatially visualize the perfusion distributions. Furthermore, the proposed multi-NMF algorithm can, in principle, be applied to any NMF algorithm to reduce the variability of the perfusion maps. Future work aims to test the ANLS-BPP algorithm on different types of solid tumours and investigate the ability for the proposed multi-NMF algorithm to improve the variation issues on other frameworks of the NMF algorithm"--
Download or read book Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology written by Alan Jackson and published by Springer Science & Business Media. This book was released on 2005-11-02 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic contrast-enhanced MRI is now established as the methodology of choice for the assessment of tumor microcirculation in vivo. The method assists clinical practitioners in the management of patients with solid tumors and is finding prominence in the assessment of tumor treatments, including anti-angiogenics, chemotherapy, and radiotherapy. Here, leading authorities discuss the principles of the methods, their practical implementation, and their application to specific tumor types. The text is an invaluable single-volume reference that covers all the latest developments in contrast-enhanced oncological MRI.
Download or read book Signal Processing for Magnetic Resonance Imaging and Spectroscopy written by Hong Yan and published by CRC Press. This book was released on 2002-02-20 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: This reference/text contains the latest signal processing techniques in magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) for more efficient clinical diagnoses-providing ready-to-use algorithms for image segmentation and analysis, reconstruction and visualization, and removal of distortions and artifacts for increased detec
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 Quantitative MRI in Cancer written by Thomas E. Yankeelov and published by Taylor & Francis. This book was released on 2011-09-13 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Propelling quantitative MRI techniques from bench to bedside, Quantitative MRI in Cancer presents a range of quantitative MRI methods for assessing tumor biology. It includes biophysical and theoretical explanations of the most relevant MRI techniques as well as examples of these techniques in cancer applications.The introductory part of the book c
Download or read book Machine Learning With Radiation Oncology Big Data written by Jun Deng and published by Frontiers Media SA. This book was released on 2019-01-21 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Clinical Development of Prostate Magnetic Resonance Imaging Only Simulation for Radiation Therapy written by Kamal Singhrao and published by . This book was released on 2020 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging-only (MRI-only) simulation for external beam radiation therapy treatment planning of prostate cancer has seen increased clinical use. The use of a single imaging modality for simulation imaging brings benefits to radiation therapy workflows such as the elimination of systematic positional errors associated with multimodal image registration during treatment planning. However, several challenges remain for the widespread clinical adoption of MRI-only simulation imaging for radiation therapy such as the lack of robust pre-treatment alignment methods and dedicated quality assurance testing equipment. In the MRI-only simulation imaging workflow, synthetic computed tomography (CT) images are created for a variety of uses including providing tissue electron density information for dose calculations. Synthetic CT image generation algorithms are typically trained using patient data and are highly sensitive to human tissue contrast and geometry. Most institutions that treat patients with MRI-only simulation images cannot use commercially available phantoms to quality assurance test processes such as synthetic CT image generation. This is because most commercially available phantoms do not mimic human tissue geometry and tissue imaging characteristics for both MRI/CT modalities. The absence of MRI/CT compatible end-to-end quality assurance testing instruments could potentially lead to systematic errors in treatments using MRI-only simulation imaging because of the lack of imaging and dosimetric benchmarking standards. Studies on the commissioning of MRI-only simulation imaging for radiation therapy of prostate cancers have recommended the use of intraprostatic fiducial markers for pre-treatment patient positioning and alignment. However, fiducial markers appear as dark signal voids in MRI and are challenging to manually localize without the aid of CT imaging. Other intraprostatic objects such as calcifications produce similar signal voids to fiducial markers in MRI images. There is currently no consensus on the optimal fiducial marker or MRI sequence to detect fiducial markers with a high level of sensitivity and specificity in MRI-only simulation images. Additionally, there are no clinically available automatic marker detection workflows available to aid in the clinical transition to MRI-only simulation imaging. This thesis presents work undertaken to meet the challenges of the clinical development of MRI-only simulation imaging for radiation therapy of prostate cancers. In the presented work, the author describes the development of a novel system of multimodal tissue mimicking materials for MRI and CT imaging. The aforementioned system of materials was adapted into a novel 3D-printed anthropomorphic phantom for quality assurance testing of MRI-only simulation procedures. To address the issues with patient positioning and alignment, a human and phantom study was conducted to quantitatively evaluate the optimal fiducial marker and MRI sequence for patients receiving MRI-only radiation therapy simulation imaging. Finally, an automatic deep-learning based fiducial marker detection algorithm is presented to aid with the clinical transition of CT-based to MRI-only radiation therapy simulation workflow.
Download or read book A Practical Guide to MR Linac written by Indra J. Das and published by Springer Nature. This book was released on with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Colorectal Cancer MRI Image Segmentation Using Image Processing Techniques written by Arjun Nelikanti and published by . This book was released on 2015-02-02 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2014 in the subject Medicine - Biomedical Engineering, grade: 76, course: Image processing, language: English, abstract: Colorectal cancer is the third most commonly diagnosed cancer and the second leading cause of cancer death in men and women. Magnetic resonance imaging (MRI) established itself as the primary method for detection and staging in patients with colorectal cancer. MRI images of Colorectal cancer are used to detect the area and mean values of tumor area and distance from tumor area to other parts. The thesis describes algorithms for preprocessing, clustering and post processing of MRI images. Implemented algorithm for preprocessing using image enhancement techniques, clustering is done using adaptive k-means algorithm and post processing using image processing techniques in MATLAB.
Download or read book Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Sumit Datta and published by . This book was released on 2019 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need for the CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly useful for researchers who have just entered into the exciting field of CS-MRI and would like to quickly go through the developments to date without diving into the detailed mathematical analysis. Finally, it also discusses recent trends and future research directions for implementation of CS-MRI in clinical practice, particularly in Bio- and Neuro-informatics applications.
Download or read book Machine and Deep Learning in Oncology Medical Physics and Radiology written by Issam El Naqa and published by Springer Nature. This book was released on 2022-02-02 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Download or read book Acquisition and Reconstruction Algorithms for Highly Efficient Magnetic Resonance Imaging written by Daniel Polak and published by . This book was released on 2020* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
