Download or read book Improved Time Series Reconstruction for Dynamic Magnetic Resonance Imaging written by Uygar Sümbül and published by . This book was released on 2009 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Development of Deep Learning Methods for Magnetic Resonance Imaging Reconstruction and Analysis written by Yuhua Chen and published by . This book was released on 2021 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance image (MRI) is a widely used non-invasive radiation-free imaging technique that uniquely provides structural and functional information for disease detection, diagnosis, and treatment planning. However, the conventional MRI imaging techniques are typically slow and low in spatial or time resolution, resulting in long scan times and more susceptibility to motion artifacts. Moreover, a fast MRI scan usually comes in a low spatial resolution, making it less desirable for clinical application. A recently proposed technique, Multi-tasking MRI (MTMRI), significantly improves the scan efficiency with high temporal resolution. Nevertheless, the iterative reconstruction requires a lot of computational resources and takes a long time to process, making it challenging to fit in the clinical routine. Additionally, when doing image post-processing with MRI, despite MRI providing a good contrast of soft tissues, the variety in weighted contrast MRI's intensity values makes it challenging to extract image features compared with other quantitative imaging techniques. The most significant contribution of this dissertation's work is to address the three limitations above by developing a unified multi-purpose structure with deep-learning (DL) techniques. We achieved three primary goals in three different areas: 1) A general framework for highly accelerated MRI scanning without sacrificing spatial resolution, 2) reduce reconstruction time for motion-resolved free-breathing MRI technique, 3) accurately fully automated segmentation for abdominal MRI for fast image post-processing. All technical improvements utilize DL techniques to improve MRI in different aspects: to improve image quality in fast MRI scans, reduce reconstruction time in motion-resolved MRI, and reduce tedious human labors in abdominal MRI. First, a DL-based Super-Resolution (SR) technique is developed and evaluated in both brain MRI and coronary MR Angiography (MRA). SR can recover the image quality and structural details from a 4x and 16x low-resolution fast MRI scan. For brain MRI, several SR networks have been developed. The proposed network (mDCSRN) has successfully recovered the brain structural details from a 4x low-resolution fast scan. It is developed and evaluated on an open access high-resolution T1w brain MRI with 1131 healthy volunteers. Quantitative results show that it can achieve 4x acceleration in scan while keeping similar image quality. For coronary MRA, introducing a domain adaptive network (DRAGAN) jointly trained on both coronary and brain MRA to overcome catastrophic failures commonly in training a GAN in a small dataset, we successfully accelerated the MRA acquisition by a factor of 16. Second, DL networks are developed to accelerate the reconstruction of a 5-dimensional (5D) Multitasking MRI (MTMRI). The MTMRI is a respiratory and cardiac-motion-resolved, high-temporal-resolution technique that provides quantitative T1 mapping. However, the massive size of many dynamic MRI problems prevents deep learning networks from directly exploiting global temporal relationships. By applying deep neural networks inside a priori calculated temporal feature spaces, we enable deep learning reconstruction with global temporal modeling even for image sequences with >40,000 frames. One proposed variation of our approach using dilated multi-level Densely Connected Network (mDCN) speeds up feature space coordinate calculation by 3000x compared to conventional iterative methods, from 20 minutes to 0.39 seconds. Thus, the combination of low-rank tensor and deep learning models makes large-scale dynamic MRI feasible and practical for routine clinical application. Third, we developed Automated deep Learning-based Abdominal Multi-Organ segmentation (ALAMO) technique based on 2D U-net and a densely connected network structure with tailored design in data augmentation and training procedures. The model takes in multi-slice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. ALAMO generated segmentation labels in good agreement with the manual results. Specifically, among the 10 OARs, 9 achieved high Dice Similarity Coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. Overall, the ALAMO model matched the state-of-the-art techniques in performance.

Download or read book Magnetic Resonance Image Reconstruction written by Mehmet Akcakaya and published by Academic Press. This book was released on 2022-11-04 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. Explains the underlying principles of MRI reconstruction, along with the latest research“/li> Gives example codes for some of the methods presented Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction

Download or read book Frontiers Of Medical Imaging written by Chi Hau Chen and published by World Scientific. This book was released on 2014-09-16 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been great progress and increase in demand for medical imaging. The aim of this book is to capture all major developments in all aspects of medical imaging. As such, this book consists of three major parts: medical physics which includes 3D reconstructions, image processing and segmentation in medical imaging, and medical imaging instruments and systems. As the field is very broad and growing exponentially, this book will cover major activities with chapters prepared by leaders in the field.This book takes a balanced approach in providing coverage of all major work done in the field, and thus provides readers a clear view of the frontier activities in the field. Other books may only focus on instrumentation, physics or computer algorithms. In contrast, this book contains all components so that the readers will obtain a full picture of the field. At the same time, readers can gain some deep insights into certain special topics such as 3D reconstruction and image enhancement software systems involving MRI, ultrasound, X-ray and other medical imaging modalities.

Download or read book Development of Efficient Dynamic Magnetic Resonance Imaging Methods With Application to Breast Cancer Detection and Diagnosis written by and published by . This book was released on 1995 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this predoctoral fellowship research project is to improve the temporal and spatial resolutions in dynamic contrast-enhanced magnetic resonance imaging of the breast by optimizing the Reduced-encoding Imaging by Generalized-series Reconstruction (RIGR) method. Specifically, we investigated the use of non-Fourier encoding for collecting the reduced encoding dynamic data sets. The conclusion from our study was that the current SVD encoding method biases the results towards reproducing the known features in the reference image and, therefore, is not appropriate for dynamic imaging applications. For that reason, we continue to acquire the dynamic data using Fourier encoding. Next, we incorporated dynamic information into the basis functions of the generalized-series model used by the RIGR algorithm. The TRIGR method resulted from incorporating information about the dynamic changes into the basis functions. Explicit edge constraints derived from the reference image were then used along with the contrast information from the dynamic data to inject dynamic information into the basis functions for both RIGR and TRIGR. Of these, the TRIGR method works better for contrast-enhanced imaging because the active reference image can be used for the edge extraction step.

Download or read book Compressed Sensing for Magnetic Resonance Image Reconstruction written by Angshul Majumdar and published by Cambridge University Press. This book was released on 2015-02-26 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expecting the reader to have some basic training in liner algebra and optimization, the book begins with a general discussion on CS techniques and algorithms. It moves on to discussing single channel static MRI, the most common modality in clinical studies. It then takes up multi-channel MRI and the interesting challenges consequently thrown up in signal reconstruction. Off-line and on-line techniques in dynamic MRI reconstruction are visited. Towards the end the book broadens the subject by discussing how CS is being applied to other areas of biomedical signal processing like X-ray, CT and EEG acquisition. The emphasis throughout is on qualitative understanding of the subject rather than on quantitative aspects of mathematical forms. The book is intended for MRI engineers interested in the brass tacks of image formation; medical physicists interested in advanced techniques in image reconstruction; and mathematicians or signal processing engineers.

Download or read book Reduced encoding Dynamic Imaging written by Jill Marie Hanson and published by . This book was released on 1997 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research addresses the problem of acquiring a time series of magnetic resonance images with both high spatial and temporal resolutions. Specifically, we systematically investigate the advantages and limitations of reduced-encoding imaging using a priori constraints. This study reveals that if the available a priori information is a reference image, direct use of this information to 'optimize' data acquisition using the existing wavelet transform or singular value decomposition schemes can undermine the capability to detect new image features. However, proper incorporation of the a priori information in the image reconstruction step can significantly reduce the resolution loss associated with reduced-encoding. For Fourier encoded data, we have shown that the Generalized-Series (GS) model is an effective mathematical framework for carrying out the constrained reconstruction step. Several techniques are proposed in this dissertation to improve the basis functions of the GS model by introducing dynamic information. The two reference reduced-encoding imaging by generalized-series reconstruction (TRIGR) method suppresses background information through the use of a second high resolution reference image. A second technique injects information from the dynamic data into the GS basis functions, as opposed to deriving them solely from the reference information. These techniques allow the GS basis functions to more accurately represent the areas of dynamic change. Finally, motion that occurs between the acquisition of the reference and dynamic data sets can render the reference information useless as a constraint for image reconstruction. A motion compensation method is proposed which uses a similarity norm to accurately detect the motion in spite of contrast changes and the low resolution nature of the dynamic data.

Download or read book Toward Improved Characterization of Brain Network Temporal Properties with Functional Magnetic Resonance Imaging written by Catherine Elizabeth Chang and published by Stanford University. This book was released on 2011 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast is a powerful technique for non-invasive measurement of brain activity. Recent fMRI studies have revealed that the spontaneous BOLD fluctuations of the human brain organize into distributed, temporally-coherent networks ("resting-state networks"; RSNs). Examination of RSNs has yielded valuable insight into neural organization and development, and demonstrates potential as a biomarker for conditions such as Alzheimer's disease and depression. However, the accuracy by which the spatio-temporal properties of RSNs can be delineated using fMRI is compromised by the presence of physiological (cardiac and respiratory) noise and vascular hemodynamic variability. Further, our present understanding of how RSNs may interact and support cognitive function has been limited by the fact that the vast majority of studies to-date analyze RSNs in a manner that assumes temporal stationarity. Here, we describe efforts to correct for non-neural physiological influences on the BOLD signal, as well as investigations into the dynamic character of resting-state network connectivity. It is found that low-frequency variations in cardiac and respiratory processes account for significant noise across widespread gray matter regions, and that a constrained deconvolution approach may prove effective for modeling and reducing their effects. Application of the proposed noise-reduction procedure is observed to yield negative correlations between the spontaneous fluctuations of two major RSNs. The relationship between respiratory volume changes and the BOLD signal is further examined by simultaneously monitoring and comparing chest expansion data, end-tidal gas concentrations, and spontaneous BOLD fluctuations. The use of a breath-holding task is proposed for quantifying regional differences in BOLD signal timing that arise from local vasomotor response delays; such non-neural timing delays are found to impact inferences of resting-state connectivity and causality. Finally, a preliminary analysis of non-stationary connectivity between RSNs is performed using wavelet and sliding-window approaches, and it is observed that interactions between networks may reconfigure on time-scales of seconds to minutes.

Download or read book Image Reconstruction in Dynamic Contrast Enhanced Magnetic Resonance Imaging written by Liyong Chen and published by . This book was released on 2012 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Regularized Image Reconstruction in Parallel MRI with MATLAB written by Joseph Suresh Paul and published by CRC Press. This book was released on 2019-11-05 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.

Download or read book Analysis of Dynamic Contrast Enhanced Mri Datasets written by Olga Kubassova and published by LAP Lambert Academic Publishing. This book was released on 2010-06 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives an insight into algorithms used for analysis and interpretation Magnetic Resonance Imaging (MRI) and specifically dynamic contrast - enhanced MRI data. It discusses state of the art and cutting edge segmentation and patient motion correction methods, evaluation and analysis techniques and their application in research and clinical routine. The book presents a comprehensive solution for fully automated objective assessment of data acquired from patients with inflammatory conditions. We show how data can be interpreted using a novel model-based approach, which permits understanding of the behaviour of tissues undergoing the medical procedure, and allows for robust and accurate extraction of various parameters that quantify the extent of inflammation. The author and her colleagues took this scientific work further and developed a platform for analysis of MRI and dynamic MRI data DYNAMIKA, www.dynamika-ra.com, www.imageanalysis.org.uk which became a standard in processing data acquired from patients with inflammatory conditions such as rheumatoid arthritis and cancer.

Download or read book Improved Image Reconstruction Methods for Parallel Magnetic Resonance Imaging written by Kaiyu Zheng and published by . This book was released on 2010 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Signal Processing Techniques for Improving Image Reconstruction of Parallel Magnetic Resonance Imaging and Dynamic Magnetic Resonance Imaging written by Huajun She and published by . This book was released on 2015 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Applying Machine Learning and Deep Learning for Improved Acquisition Reconstruction and Quantification in MRI written by Enhao Gong and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Imaging, MRI, is a powerful imaging modality that is frequently used in both clinical and academic settings. With its advantages of flexibility in signal encoding, we can use MRI to non-invasively visualize various soft-tissue contrasts, showing not only anatomical but also metabolic and functional information. In addition, MRI is a radiation-free modality which makes it favorable in numbers of clinical applications because of the reduced radiation-risk compared with other radiology modalities such as X-ray, Computed Tomography (CT), Positron Emission Tomography (PET) etc. Despite the advantages of MRI techniques, there are still several challenges preventing MRI from becoming more efficient and accessible. First, the scan time for MRI is usually longer than other modalities such as X-ray and CT, since it requires enough measurements to resolve high-quality images for diagnostic tasks. In order to accelerate MRI, various fast-imaging techniques, such as Parallel Imaging (PI) and Compressed Sensing (CS) have been proposed to speed up MRI acquisition using under-sampling. However, it is still unclear what is the best approach to conduct the under-sampling as different under-sampling patterns may result in different reconstruction quality. Second, the reconstruction methods for under-sampled MRI need further improvement. The reconstruction algorithms are formed as nonlinear optimization problems using iterative optimization that can be time-consuming. Fixed and handcrafted penalty terms are usually used to regularize the optimization, which are hard to tune. There are often trade-offs between the speed of the algorithm and the quality of resulting images. In many cases, the imperfect artifact suppression or over-smoothing slows down the clinical adoption of these fast-imaging techniques. Third, MR images are typically not quantitative. Most clinical MRI protocols used nowadays are contrast-weighted sequences, which incorporate the tissue contrasts in qualitative ways. Therefore, the resulting MR images may vary a lot between different protocols and scanners, which makes it very difficult for radiologists to conduct quantitative analysis or longitudinal comparison. In this work, we propose to resolve these remaining challenges to further improve MRI technologies. We utilized state-of-the-art Machine Learning and Deep Learning algorithms to significantly improve these three essential components in MRI: faster acquisition, better reconstruction, and more accurate qualification. Specifically, we firstly propose a machine learning based method to optimize the undersampling pattern for accelerated acquisition. The results, validated on in-vivo multi-contrast brain and prostate MRI datasets, demonstrate that the proposed method can generalize well for different anatomy. It enables efficient (5sec-10sec) and adaptive under-sampling pattern optimization at per-subject/per-scan level, and achieves 30%-50% lower PI+CS reconstruction error at the same acceleration factor. To improve MRI acquisition with a safer protocol and lower contrast dose, a deep learning model is developed to enhance the MRI. The proposed Deep Learning method yielded significant (N=50, p 0.001) improvements over the low-dose (10%) images ( 5dB PSNR gains and > 11.0% SSIM). Ratings on image quality and contrast enhancement are significantly (N=20, p

Download or read book Improved Acquisition and Reconstruction for Cardiac and Susceptibility Magnetic Resonance Imaging written by Bryan Michael Kressler and published by . This book was released on 2008 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mathematics and Physics of Emerging Biomedical Imaging written by Committee on the Mathematics and Physics of Emerging Dynamic Biomedical Imaging and published by National Academies Press. This book was released on 1996-03-13 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This cross-disciplinary book documents the key research challenges in the mathematical sciences and physics that could enable the economical development of novel biomedical imaging devices. It is hoped that the infusion of new insights from mathematical scientists and physicists will accelerate progress in imaging. Incorporating input from dozens of biomedical researchers who described what they perceived as key open problems of imaging that are amenable to attack by mathematical scientists and physicists, this book introduces the frontiers of biomedical imaging, especially the imaging of dynamic physiological functions, to the educated nonspecialist. Ten imaging modalities are covered, from the well-established (e.g., CAT scanning, MRI) to the more speculative (e.g., electrical and magnetic source imaging). For each modality, mathematics and physics research challenges are identified and a short list of suggested reading offered. Two additional chapters offer visions of the next generation of surgical and interventional techniques and of image processing. A final chapter provides an overview of mathematical issues that cut across the various modalities.