Download or read book Reduced data Magnetic Resonance Imaging Reconstruction Methods written by Lei Hou Hamilton and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Imaging speed is very important in magnetic resonance imaging (MRI), especially in dynamic cardiac applications, which involve respiratory motion and heart motion. With the introduction of reduced-data MR imaging methods, increasing acquisition speed has become possible without requiring a higher gradient system. But these reduced-data imaging methods carry a price for higher imaging speed. This may be a signal-to-noise ratio (SNR) penalty, reduced resolution, or a combination of both. Many methods sacrifice edge information in favor of SNR gain, which is not preferable for applications which require accurate detection of myocardial boundaries. The central goal of this thesis is to develop novel reduced-data imaging methods to improve reconstructed image performance. This thesis presents a novel reduced-data imaging method, PINOT (Parallel Imaging and NOquist in Tandem), to accelerate MR imaging. As illustrated by a variety of computer simulated and real cardiac MRI data experiments, PINOT preserves the edge details, with flexibility of improving SNR by regularization. Another contribution is to exploit the data redundancy from parallel imaging, rFOV and partial Fourier methods. A Gerchberg Reduced Iterative System (GRIS), implemented with the Gerchberg-Papoulis (GP) iterative algorithm is introduced. Under the GRIS, which utilizes a temporal band-limitation constraint in the image reconstruction, a variant of Noquist called iterative implementation iNoquist (iterative Noquist) is proposed. Utilizing a different source of prior information, first combining iNoquist and Partial Fourier technique (phase-constrained iNoquist) and further integrating with parallel imaging methods (PINOT-GRIS) are presented to achieve additional acceleration gains.
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 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 Data Sampling and Reconstruction Strategies for Rock Core Magnetic Resonance Imaging written by Dan Xiao and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is uniquely well suited for studies of sedimentary rocks as it allows direct non-invasive detection of fluid content and fluid interactions in the pore space. Pure phase encoding MRI methods have proven to be robust in their ability to generate quantitative images in porous media. However, the sensitivity is low for pure phase encoding, especially with low magnetic field MRI systems that are common for porous media studies. Novel sampling strategies and data reconstruction methods, described in this thesis, improve measurement sensitivity with no hardware modifications required. Pure phase encode MRI methods acquire a single k-space data point with each radio frequency (RF) excitation. Reducing the number of acquired data points will significantly increase the measurement sensitivity. The goal is to look for data sampling and image reconstruction methods that ensure good image quality with reduced data. These methods are based on the inherent sparsity of MRI data, either in k-space or in transformed image spaces. Sample geometry based restricted sampling exploits k-space redundancy, with simple and reliable linear image reconstruction. The sampling patterns that collect regions of high intensity signal while neglecting low intensity regions can be naturally applied to a wide variety of pure phase encoding measurements. An important application is T 2 mapping spin-echo single point imaging (SES PI) that reveals different bedding plane structures within the rock core plug sample. In compressed sensing, spatial or spatiotemporal correlations of the static and dynamic MR images are exploited by transforming the images to sparse representations. Incoherent sampling and non-linear reconstruction are required. Imaging speed can also be improved by more efficient data collection. This can be achieved by combining phase and frequency encodings. A novel k-space trajectory, with rapid and accurate linear image reconstruction, is employed for high quality quantitative density images. In this thesis, new MRI data sampling and image reconstruction methods, for application to porous media, have been developed. These methods significantly improve the measurement sensitivity of quantitative MR imaging.
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 Reconstruction Methods for Accelerated Magnetic Resonance Imaging written by Tao Zhang and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is a powerful medical imaging modality widely used in clinical practice. MRI provides excellent soft-tissue contrast, and does not involve ionizing radiation. In an ideal clinical setting for MRI, several requirements have to be met. First of all, diagnostic image quality has to be achieved. Second, fast image reconstruction is required, so that the radiologists can review the images before releasing the patients. Third, fast data acquisition is desired. Short scan time can not only improve patient comfort, but also reduce many imaging artifacts and improve image quality. While advanced methods, such as parallel imaging and compressed sensing, can accelerate MRI data acquisition to some extent, the achievable scan time is still very limited for several MR applications. Meanwhile, the reconstruction time for these advanced methods can take up to hours, and become clinically infeasible. This dissertation describes approaches to maintain a clinically feasible reconstruction time for advanced reconstructions, and approaches to further accelerate MRI applications, specifically MR parameter mapping and dynamic contrast-enhanced (DCE) MRI. The ultimate goal of this work is to make MRI more clinically practical. To maintain a clinically feasible reconstruction time for advanced reconstructions with large coil arrays, a geometric-decomposition coil compression method is proposed. The proposed method exploits the spatially varying data redundancy of large coil arrays, and can compress the raw data from original coils into very few virtual coils. The advanced reconstruction can be directly performed on the virtual coils instead of the original coils. The reconstruction time for large 3D datasets, acquired with 32-channel coils and reconstructed by a combined parallel imaging compressed sensing method, can be reduced to under a minute. The proposed method has been implemented in Lucile Packard Children's Hospital at Stanford. The clinical evaluation suggests that the proposed method can achieve very fast reconstruction without compromising overall image quality and delineation of anatomical structures. MR parameter mapping is a promising approach to characterize intrinsic tissue-dependent information. To accelerate lengthy MR parameter mapping, which can take up to half an hour or more, a locally low-rank method has been proposed. The proposed method has been combined with parallel imaging to achieve further acceleration. Based on preliminary result, the combined parallel imaging locally low-rank method can accelerate variable flip angle T1 mapping by factor of 6, without obvious imaging artifacts. DCE MRI is a standard component of abdominal MRI exams, most commonly used to detect and characterize mass lesions and assess renal function. 3D DCE MRI is often limited compromised spatiotemporal resolution and motion artifacts. In this work, a combined locally low-rank parallel imaging method with soft gating is proposed. The proposed method can significantly reduce motion artifacts for completely free-breathing acquisition and remove the need for deep anesthesia. The high spatiotemporal resolution achieved by the proposed method can also capture the rapid contrast hemodynamics. The proposed method has been deployed clinically in Lucile Packard Children's Hospital at Stanford. Preliminary clinical evaluation results suggest that the proposed method can achieve an image quality very close to a respiratory-triggered data acquisition, but with much higher spatiotemporal resolution.
Download or read book Medical Image Reconstruction written by Gengsheng Lawrence Zeng and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-07-04 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook introduces the essential concepts of tomography in the field of medical imaging. The medical imaging modalities include x-ray CT (computed tomography), PET (positron emission tomography), SPECT (single photon emission tomography) and MRI. In these modalities, the measurements are not in the image domain and the conversion from the measurements to the images is referred to as the image reconstruction. The work covers various image reconstruction methods, ranging from the classic analytical inversion methods to the optimization-based iterative image reconstruction methods. As machine learning methods have lately exhibited astonishing potentials in various areas including medical imaging the author devotes one chapter to applications of machine learning in image reconstruction. Based on college level in mathematics, physics, and engineering the textbook supports students in understanding the concepts. It is an essential reference for graduate students and engineers with electrical engineering and biomedical background due to its didactical structure and the balanced combination of methodologies and applications,
Download or read book Acquisition and Reconstruction Methods for Magnetic Resonance Imaging written by Itthi Chatnuntawech and published by . This book was released on 2016 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging modality that has a wide range of applications in both diagnostic clinical imaging and medical research. MRI has progressively gained in importance in clinical use because of its ability to produce high quality images of soft tissue throughout the body without subjecting the patient to any ionizing radiation. In addition to exquisite anatomical detail obtained from the conventional MRI, complementary physiological information is also available through emerging specialized applications of MRI such as magnetic resonance spectroscopic imaging, quantitative susceptibility mapping, functional MRI, and diffusion MRI. Despite its great versatility, MRI is limited by the long time required to acquire the data needed to form an image. Since a typical MRI protocol consists of multiple scans of the same patient, the total scan time is commonly extended beyond half an hour. During the session, the patient must remain perfectly still within a tight and closed environment, raising difficulties for certain populations such as children and patients with claustrophobia. The long acquisition time of MRI not only reduces the availability of the MRI scanner, but also results in patient discomfort, which often leads to motion that degrades image quality. Therefore, reducing the acquisition time of MRI is a well-motivated problem. This thesis proposes acquisition and reconstruction methods that increase the imaging efficiency of MRI and two of its emerging specialized applications, magnetic resonance spectroscopic imaging and quantitative susceptibility mapping. In particular, each of the proposed methods increases the imaging efficiency by achieving at least one of two aims: reduction of total scan time and improved image quality by mitigating image artifacts, while minimizing reconstruction time.
Download or read book Application Tailored Accelerated Magnetic Resonance Imaging Methods written by Ziwu Zhou and published by . This book was released on 2018 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Principles of Magnetic Resonance Imaging written by Zhi-Pei Liang and published by Wiley-IEEE Press. This book was released on 2000 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, "father of the MRI", and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles. The authors use a signal processing approach to describe the fundamentals of magnetic resonance imaging. You will find a clear and rigorous discussion of these carefully selected essential topics: Mathematical fundamentals Signal generation and detection principles Signal characteristics Signal localization principles Image reconstruction techniques Image contrast mechanisms Image resolution, noise, and artifacts Fast-scan imaging Constrained reconstruction Complete with a comprehensive set of examples and homework problems, Principles of Magnetic Resonance Imaging is the must-read book to improve your knowledge of this revolutionary technique.
Download or read book Accelerating Magnetic Resonance Imaging by Unifying Sparse Models and Multiple Receivers written by Daniel (Daniel Stuart) Weller and published by . This book was released on 2012 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is an increasingly versatile diagnostic tool for a variety of medical purposes. During a conventional MRI scan, samples are acquired along a trajectory in the spatial Fourier transform domain (called k-space) and the image is reconstructed using an inverse discrete Fourier transform. The affordability, availability, and applications of MRI remain limited by the time required to sample enough points of k-space for the desired field of view (FOV), resolution, and signal-to-noise ratio (SNR). GRAPPA, an accelerated parallel imaging method, and compressed sensing (CS) have been successfully employed to accelerate the acquisition process by reducing the number of k-space samples required. GRAPPA leverages the different spatial weightings of each receiver coil to undo the aliasing from the reduction in FOV induced by undersampling k-space. However, accelerated parallel imaging reconstruction methods like GRAPPA amplify the noise present in the data, reducing the SNR by a factor greater than that due to only the level of undersampling. Completely separate from accelerated parallel imaging, which capitalizes on observing data with multiple receivers, CS leverages the sparsity of the object along with incoherent sampling and nonlinear reconstruction algorithms to recover the image from fewer samples. In contrast to parallel imaging, CS actually denoises the result, because noise typically is not sparse. When reconstructing brain images, the discrete wavelet transform and finite differences are effective in producing an approximately sparse representation of the image. Because parallel imaging utilizes the multiple receiver coils and CS takes advantage of the sparsity of the image itself, these methods are complementary, and a combination of these methods would be expected to enable further acceleration beyond what is achievable using parallel imaging or CS alone. This thesis investigates three approaches to leveraging both multiple receiver coils and image sparsity. The first approach involves an optimization framework for jointly optimizing the fidelity to the GRAPPA result and the sparsity of the image. This technique operates in the nullspace of the data observation matrix, preserving the acquired data without resorting to techniques for constrained optimization. While this framework is presented generally, the effectiveness of the implementation depends on the choice of sparsifying transform, sparsity penalty function, and undersampling pattern. The second approach involves modifying the kernel estimation step of GRAPPA to promote sparsity in the reconstructed image and mitigate the noise amplification typically encountered with parallel imaging. The third approach involves imposing a sparsity prior on the coil images and estimating the full k-space from the observations using Bayesian techniques. This third method is extended to jointly estimate the GRAPPA kernel weights and the full k-space together. These approaches represent different frameworks for accelerating MRI imaging beyond current methods. The results presented suggest that these practical reconstruction and post-processing methods allow for greater acceleration with conventional Cartesian acquisitions.
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 Methods for Reducing 3D Non Cartesian Reconstruction Time written by Zachary Miller and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is a powerful imaging modality. Its flexibility allows for both diagnostic and functional imaging with unparalleled soft tissue contrast. In the brain, MRI is the go-to imaging technique for many structural and functional applications. The same, however, cannot be said for the body where computed tomography (CT) remains the imaging modality of choice. This difference is in part a result of MR's slow acquisition speed making it sensitive to the complex, non-rigid motions seen in the body during minutes long scans. CT, on the other hand, is relatively insensitive to these motions, acquiring high resolution images within seconds. Non-Cartesian sampling trajectories combined with retrospective motion correction and efficient reconstruction techniques have the potential to change this. Compared to Cartesian scans, non-Cartesian trajectories efficiently sample k-space in all dimensions, have intrinsic motion robustness, and generate noise-like aliases when under-sampled making them optimal for applications that require reconstructions with high spatiotemporal resolution [1]. For these reasons, non-Cartesian acquisitions are being developed for free breathing pulmonary [2] and dynamic contrast enhanced imaging [1] (among others). Despite the promise of non-Cartesian trajectories for rapid body imaging, they have seen limited use clinically. In the first part of this thesis, I take steps toward making non-Cartesian acquisitions easier to integrate into clinical workflows. The first part of this work addresses the lengthy iterative reconstruction times (on the order of 30 minutes to an hour on state of the art GPUs) seen with 3D non-Cartesian acquisitions by developing methods to allow robust deep learning methods to be applied to these high dimensional acquisitions. To do this, I address two primary challenges to applying DL to these datasets: extreme GPU memory demand (>250 GB) and lack of supervision. In the second part of this dissertation, I work towards improving the quality and dynamics captured by time resolved reconstructions for high spatial resolution non-Cartesian acquisitions. Building on the work of [1], I incorporate motion compensation into large scale time-resolved multi-scale low rank reconstructions in a technique called MoCo-MSLR. Although these reconstructions are computationally and memory intensive, and remain difficult to integrate into clinical workflows, simply demonstrating the ability to capture such high temporal resolution dynamics with high fidelity is a step forward.
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 Parallel Magnetic Resonance Imaging Reconstruction Problems Using Wavelet Representations written by Lotfi Chaari (enseignant-chercheur en informatique).) and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: To reduce scanning time or improve spatio-temporal resolution in some MRI applications, parallel MRI acquisition techniques with multiple coils have emerged since the early 90's as powerful methods. In these techniques, MRI images have to be reconstructed from acquired undersampled « k-space » data. To this end, several reconstruction techniques have been proposed such as the widely-used SENSitivity Encoding (SENSE) method. However, the reconstructed images generally present artifacts due to the noise corrupting the observed data and coil sensitivity profile estimation errors. In this work, we present novel SENSE-based reconstruction methods which proceed with regularization in the complex wavelet domain so as to promote the sparsity of the solution. These methods achieve accurate image reconstruction under degraded experimental conditions, in which neither the SENSE method nor standard regularized methods (e.g. Tikhonov) give convincing results. The proposed approaches relies on fast parallel optimization algorithms dealing with convex but non-differentiable criteria involving suitable sparsity promoting priors. Moreover, in contrast with most of the available reconstruction methods which proceed by a slice by slice reconstruction, one of the proposed methods allows 4D (3D + time) reconstruction exploiting spatial and temporal correlations. The hyperparameter estimation problem inherent to the regularization process has also been addressed from a Bayesian viewpoint by using MCMC techniques. Experiments on real anatomical and functional data show that the proposed methods allow us to reduce reconstruction artifacts and improve the statistical sensitivity/specificity in functional MRI.
Download or read book Machine Learning for Medical Image Reconstruction written by Nandinee Haq and published by Springer Nature. This book was released on 2022-09-22 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with MICCAI 2022, in September 2022, held in Singapore. The 15 papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
Download or read book Magnetic Resonance Elastography written by Sudhakar K. Venkatesh and published by Springer. This book was released on 2014-10-01 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book to cover the groundbreaking development and clinical applications of Magnetic Resonance Elastography, this book is essential for all practitioners interested in this revolutionary diagnostic modality. The book is divided into three sections. The first covers the history of MRE. The second covers technique and clinical applications of MRE in the liver with respect to fibrosis, liver masses, and other diseases. Case descriptions are presented to give the reader a hands-on approach. The final section presents the techniques, sequence and preliminary results of applications in other areas of the body including muscle, brain, lung, heart, and breast.
