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 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 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 MRI written by Angshul Majumdar and published by CRC Press. This book was released on 2018-09-03 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of magnetic resonance imaging (MRI) has developed rapidly over the past decade, benefiting greatly from the newly developed framework of compressed sensing and its ability to drastically reduce MRI scan times. MRI: Physics, Image Reconstruction, and Analysis presents the latest research in MRI technology, emphasizing compressed sensing-based image reconstruction techniques. The book begins with a succinct introduction to the principles of MRI and then: Discusses the technology and applications of T1rho MRI Details the recovery of highly sampled functional MRIs Explains sparsity-based techniques for quantitative MRIs Describes multi-coil parallel MRI reconstruction techniques Examines off-line techniques in dynamic MRI reconstruction Explores advances in brain connectivity analysis using diffusion and functional MRIs Featuring chapters authored by field experts, MRI: Physics, Image Reconstruction, and Analysis delivers an authoritative and cutting-edge treatment of MRI reconstruction techniques. The book provides engineers, physicists, and graduate students with a comprehensive look at the state of the art of MRI.

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 Reconstruction Methods for Fast Magnetic Resonance Imaging written by Philip James Beatty and published by . This book was released on 2006 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Image Acquisition and Reconstruction Methods for Fast Magnetic Resonance Imaging written by Jin Hyung Lee and published by . This book was released on 2004 with total page 234 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 Development and Optimization of Methods for Accelerated Magnetic Resonance Imaging written by Tom Hilbert and published by . This book was released on 2018 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: magnetic resonance imaging; quantitative imaging; acquisition acceleration; model-based reconstruction.

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 Novel Adaptive Reconstruction Schemes for Accelerated Myocardial Perfusion Magnetic Resonance Imaging written by Sajan Goud Lingala and published by . This book was released on 2013 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation deals with developing novel image reconstruction methods in conjunction with non-Cartesian sampling for the reconstruction of dynamic MRI data from highly accelerated / under-sampled Fourier measurements. The reconstruction methods are based on adaptive signal models to represent the dynamic data using few model coefficients. Three novel adaptive reconstruction methods are developed and validated: (a) low rank and sparsity based modeling, (b) blind compressed sensing, and (c) motion compensated compressed sensing. The developed methods are applicable to a wide range of dynamic imaging problems. In the context of MR-MPI, this dissertation show feasibilities that the developed methods can enable free breathing myocardial perfusion MRI acquisitions with high spatio-temporal resolutions (

Download or read book Under sampled Reconstruction Techniques for Accelerated Magnetic Resonance Imaging written by Mohammad Hosain Kayvanrad and published by . This book was released on 2013 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to physical and biological constraints and requirements on the minimum resolution and SNR, the acquisition time is relatively long in magnetic resonance imaging (MRI). Consequently, a limited number of pulse sequences can be run in a clinical MRI session because of constraints on the total acquisition time due to patient comfort and cost considerations. Therefore, it is strongly desired to reduce the acquisition time without compromising the reconstruction quality. This thesis concerns under-sampled reconstruction techniques for acceleration of MRI acquisitions, i.e., parallel imaging and compressed sensing. While compressed sensing MRI reconstructions are commonly regularized by penalizing the decimated wavelet transform coefficients, it is shown in this thesis that the visual artifacts, associated with the lack of translation-invariance of the wavelet basis in the decimated form, can be avoided by penalizing the undecimated wavelet transform coefficients, i.e., the stationary wavelet transform (SWT). An iterative SWT thresholding algorithm for combined SWT-regularized compressed sensing and parallel imaging reconstruction is presented. Additionally, it is shown that in MRI applications involving multiple sequential acquisitions, e.g., quantitative T1/T2 mapping, the correlation between the successive acquisitions can be incorporated as an additional constraint for joint under-sampled reconstruction, resulting in improved reconstruction performance. While quantitative measures of quality, e.g., reconstruction error with respect to the fully-sampled reference, are commonly used for performance evaluation and comparison of under-sampled reconstructions, this thesis shows that such quantitative measures do not necessarily correlate with the subjective quality of reconstruction as perceived by radiologists and other expert end users. Therefore, unless accompanied by subjective evaluations, quantitative quality measurements/comparisons will be of limited clinical impact. The results of experiments aimed at subjective evaluation/comparison of different under-sampled reconstructions for specific clinical neuroimaging MRI applications are presented in this thesis. One motivation behind the current work was to reduce the acquisition time for relaxation mapping techniques DESPOT1 and DESPOT2. This work also includes a modification to the Driven Equilibrium Single Pulse Observation of T1 with high-speed incorporation of RF field inhomogeneities (DESPOT1-HIFI), resulting in more accurate estimation of T1 values at high strength (3T and higher) magnetic fields.

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 Nonlinear Reconstruction Methods for Parallel Magnetic Resonance Imaging written by Martin Uecker and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Image Reconstruction in Accelerated Magnetic Resonance Imaging written by Zhaolin Chen and published by . This book was released on 2007 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Machine Learning for Medical Image Reconstruction written by Nandinee Haq and published by Springer Nature. This book was released on 2021-09-29 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.