Application Tailored Accelerated Magnetic Resonance Imaging Methods
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Application Tailored Accelerated Magnetic Resonance Imaging Methods
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Author : Ziwu Zhou
language : en
Publisher:
Release Date : 2018
Application Tailored Accelerated Magnetic Resonance Imaging Methods written by Ziwu Zhou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Magnetic resonance imaging (MRI) is a powerful diagnostic medical imaging technique that provides very high spatial resolution. By manipulating the signal evolution through careful imaging sequence design, MRI can generate a wide range of soft-tissue contrast unique to individual application. However, imaging speed remains an issue for many applications. In order to increase scan output without compromising the image quality, the data acquisition and image reconstruction methods need to be designed to fit each application to achieve maximum efficiency. This dissertation concerns several application-tailored accelerated imaging methods through improved sequence design, efficient k-space traverse, as well as tailored image reconstruction algorithm, all together aiming to exploit the full potential of data acquisition and image reconstruction in each application. The first application is ferumoxtyol-enhanced 4D multi-phase cardiovascular MRI on pediatric patients with congenital heart disease. By taking advantage of the high signal-to-noise ratio (SNR) results from contrast enhancement, we introduced two methods to improve the scan efficiency with maintained clinical utility: one with reduced scan time and one with improved temporal resolution. The first method used prospective Poisson-disc under-sampling in combination with graphics processing unit accelerated parallel imaging and compressed sensing combined reconstruction algorithm to reduce scan time by approximately 50% while maintaining highly comparable image quality to un-accelerated acquisition in a clinically practical reconstruction time. The second method utilized a motion weighted reconstruction technique to increase temporal resolution of acquired data, and thus permits improved cardiac functional assessment. Compared with existing acceleration method, the proposed method has nearly three times lower computation burden and six times faster reconstruction speed, all with equal image quality. The second application is noncontrast-enhanced 4D intracranial MR angiography with arterial spin labeling (ASL). Considering the inherently low SNR of ASL signal, we proposed to sample k-space with the efficient golden-angle stack-of-stars trajectory and reconstruct images using compressed sensing with magnitude subtraction as regularization. The acquisition and reconstruction strategy in combination produces images with detailed vascular structures and clean background. At the same time, it allows a reduced temporal blurring delineation of the fine distal arteries when compared with the conventional k-space weighted image contrast (KWIC) reconstruction. Stands upon on this, we further developed an improved stack-of-stars radial sampling strategy for reducing streaking artifacts in general volumetric MRI. By rotating the radial spokes in a golden angle manner along the partition-encoding direction, the aliasing pattern due to under-sampling is modified, resulting in improved image quality for gridding and more advanced reconstruction methods. The third application is low-latency real-time imaging. To achieve sufficient frame rate, real-time MRI typically requires significant k-space under-sampling to accelerate the data acquisition. At the same time, many real-time application, such as interventional MRI, requires user interaction or decision making based on image feedback. Therefore, low-latency on-the-fly reconstruction is highly desirable. We proposed a parallel imaging and convolutional neural network combined image reconstruction framework for low-latency and high quality reconstruction. This is achieved by compacting gradient descent steps resolved from conventional parallel imaging reconstruction as network layers and interleaved with convolutional layers in a general convolutional neural network. Once all parameters of the network are determined during the off-line training process, it can be applied to unseen data with less than 100ms reconstruction time per frame, while more than 1s is usually needed for conventional parallel imaging and compressed sensing combined reconstruction.
Magnetic Resonance Imaging In Tissue Engineering
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Author : Mrignayani Kotecha
language : en
Publisher: John Wiley & Sons
Release Date : 2017-03-06
Magnetic Resonance Imaging In Tissue Engineering written by Mrignayani Kotecha and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-06 with Technology & Engineering categories.
Magnetic Resonance Imaging in Tissue Engineering provides a unique overview of the field of non-invasive MRI assessment of tissue engineering and regenerative medicine Establish a dialogue between the tissue-engineering scientists and imaging experts and serves as a guide for tissue engineers and biomaterial developers alike Provides comprehensive details of magnetic resonance imaging (MRI) techniques used to assess a variety of engineered and regenerating tissues and organs Covers cell-based therapies, engineered cartilage, bone, meniscus, tendon, ligaments, cardiovascular, liver and bladder tissue engineering and regeneration assessed by MRI Includes a chapter on oxygen imaging method that predominantly is used for assessing hypoxia in solid tumors for improving radiation therapy but has the ability to provide information on design strategies and cellular viability in tissue engineering regenerative medicine
Accelerated Imaging Techniques For Chemical Shift Magnetic Resonance Imaging
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Author : Curtis N. Wiens
language : en
Publisher:
Release Date : 2013
Accelerated Imaging Techniques For Chemical Shift Magnetic Resonance Imaging written by Curtis N. Wiens and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Chemical shift imaging is a magnetic resonance imaging technique that separates the signal from two or more chemical species. The cost of chemical shift encoding is increased acquisition time as multiple acquisitions are required at different echo times. Image accelera tion techniques, typically parallel imaging, are often used to improve coverage and resolution. This thesis describes a new technique for estimating the signal to noise ratio for parallel imaging reconstruction s and proposes new image reconstructions for a ccelerated chemical shift imaging using compressed sensing and/or parallel imaging for two applications: water- at separation and metabolic imaging of hyperpolarized [1-13C] pyruvate. Spatially varying noise in parallel imaging reconstructions makes measurements of the signal to noise ratio, a commonly used metric for image for image quality, difficult. Existing approaches have limitations: they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A signal to noise ratio estimation technique is proposed that does not exhibit these limitations. Water-fat imaging of highly undersampled datasets from the liver, calf, knee, and abdominal cavity are demonstrated using a customized IDEAL-SPGR pulse sequence and an integrated compressed sensing, parallel imaging, water-fat reconstruction. This method offer s image quality comparable to fully sampled reference images for a range of acceleration factors. At high acceleration factors, this method offers improved image quality when compared to the current standard of parallel imaging. Accelerated metabolic imaging of hyperpolarized [1-13C] pyruvate and its metabolic by-products lactate, alanine, and bicarbonate is demonstrated using an integrated compressed sensing, metabolite separation reconstruction. Phantoms are used to validate this technique while retrospectively and prospectively accelerated 3D in vivo datasets are used to demonstrate feasibility. An alternative approach to accelerated metabolic imaging is demonstrated using high performance magnetic field gradient set. This thesis addresses the inherently slow acquisition times of chemical shift imaging by examining the role compressed sensing and parallel imaging can play in chemical shift imaging. An approach to SNR assessment for parallel imaging reconstruction is proposed and approaches to accelerated chemical shift imaging are described for applications in water-fat imaging and metabolic imaging of hyperpolarized [1-13C] pyruvate.
Magnetic Resonance Imaging Principles Techniques And Clinical Applications
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Author : Zachary Garcia
language : en
Publisher: American Medical Publishers
Release Date : 2021-11-16
Magnetic Resonance Imaging Principles Techniques And Clinical Applications written by Zachary Garcia and has been published by American Medical Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-16 with Medical categories.
The technique that is used in radiology to form pictures of the physiological processes and the anatomy of the body is known as magnetic resonance imaging (MRI). Magnetic field gradients, radio waves and strong magnetic fields are used in it to generate images of the organs in the body. Magnetic resonance imaging is different from CT scan and PET scan and does not involve X-rays and the use of ionizing radiation. In hospitals and clinics, magnetic resonance imaging is primarily used for staging of disease, medical diagnosis, and follow-up without getting body exposed to radiation. MRI is widely used in cardiovascular imaging, spinal imaging, neuroimaging, angiography, etc. This book contains some path-breaking studies in the field of magnetic resonance imaging. It strives to provide a fair idea about this discipline and to help develop a better understanding of the latest advances within this field. This book aims to equip students and experts with the advanced topics and upcoming concepts in this area.
Development And Application Of Quantitative Magnetic Resonance Imaging Methods
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Author : Melissa Schall
language : en
Publisher:
Release Date : 2019
Development And Application Of Quantitative Magnetic Resonance Imaging Methods written by Melissa Schall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Magnetic Resonance Imaging
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Author : Pottumarthi V. Prasad
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-02-02
Magnetic Resonance Imaging written by Pottumarthi V. Prasad and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-02 with Medical categories.
Leading experts in the use of MRI explain its basic principles and demonstrate its power to understand biological processes with numerous cutting-edge applications. To illustrate its capability to reveal exquisite anatomical detail, the authors discuss MRI applications to developmental biology, mouse phenotyping, and fiber architecture. MRI can also provide information about organ and tissue function based on endogenous cantrast mechanisms. Examples of brain, kidney, and cardiac function are included, as well as applications to neuro and tumor pathophysiology. In addition, the volume demonstrates the use of exogenous contrast material in functional assessment of the lung, noninvasive evaluation of tissue pH, the imaging of metabolic activity or gene expression that occur on a molecular level, and cellular labeling using superparamagnetic iron oxide contrast agents.
Magnetic Resonance Image Reconstruction
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Author : Mehmet Akcakaya
language : en
Publisher: Academic Press
Release Date : 2022-11-04
Magnetic Resonance Image Reconstruction written by Mehmet Akcakaya and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-04 with Science categories.
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
Development Of Deep Learning Methods For Magnetic Resonance Imaging Reconstruction And Analysis
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Author : Yuhua Chen
language : en
Publisher:
Release Date : 2021
Development Of Deep Learning Methods For Magnetic Resonance Imaging Reconstruction And Analysis written by Yuhua Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
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.
Development And Optimization Of Methods For Accelerated Magnetic Resonance Imaging
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Author : Tom Hilbert
language : en
Publisher:
Release Date : 2018
Development And Optimization Of Methods For Accelerated Magnetic Resonance Imaging written by Tom Hilbert and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Mots-clés de l'auteur: magnetic resonance imaging; quantitative imaging; acquisition acceleration; model-based reconstruction.
Parallel Magnetic Resonance Imaging Methods And Applications
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Author : Mark Griswold
language : en
Publisher:
Release Date : 2002
Parallel Magnetic Resonance Imaging Methods And Applications written by Mark Griswold and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.