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Author: Peng Qu Publisher: Open Dissertation Press ISBN: 9781361470374 Category : Languages : en Pages :
Book Description
This dissertation, "Advances in Parallel Imaging Reconstruction Techniques" by Peng, Qu, 瞿蓬, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Advances in Parallel Imaging Reconstruction Techniques submitted by Qu Peng for the degree of Doctor of Philosophy at The University of Hong Kong in February 2006 In recent years, a new approach to magnetic resonance imaging (MRI), known as "parallel imaging," has revolutionized the field of fast MRI. By using sensitivity information from an RF coil array to perform some of the spatial encoding which is traditionally accomplished by magnetic field gradient, parallel imaging techniques allow reduction of phase encoding steps and consequently decrease the scan time. This thesis presents the author's investigations in the reconstruction techniques of parallel MRI. After reviewing the conventional methods, such as the image-domain-based sensitivity encoding (SENSE), the k-space-based simultaneous acquisition of spatial harmonics (SMASH), generalized auto-calibrating partially parallel acquisition (GRAPPA), and the iterative SENSE method which is applicable to arbitrary k-space trajectories, the author proposes several advanced reconstruction strategies to enhance the performance of parallel imaging in terms of signal-to-noise (SNR), the power of aliasing artifacts, and computational efficiency. First, the conventional GRAPPA technique is extended in that the data interpolation scheme is tailored and optimized for each specific reconstruction. This novel approach extracts a subset of signal points corresponding to the most linearly independent base vectors in the coefficient matrix for the fit procedure, effectively preventing incorporating redundant signals which only bring noise into reconstruction with little contribution to the exactness of fit. Phantom and in vivo MRI experiments demonstrate that this subset selection strategy can reduce residual artifacts for GRAPPA reconstruction. Second, a novel discrepancy-based method for regularization parameter choice is introduced into GRAPPA reconstruction. By this strategy, adaptive regularization in GRAPPA can be realized which can automatically choose nearly optimal parameters for the reconstructions so as to achieve good compromise between SNR and artifacts. It is demonstrated by MRI experiments that the discrepancy-based parameter choice strategy significantly outperforms those based on the L-curve or on a fixed singular value threshold. Third, the convergence behavior of the iterative non-Cartesian SENSE reconstruction is analyzed, and two different strategies are proposed to make reconstructions more stable and robust. One idea is to stop the iteration process in due time so that artifacts and SNR are well balanced and fine overall image quality is achieved; as an alternative, the inner-regularization method, in combination with the Lanczos iteration process, is introduced into non-Cartesian SENSE to mitigate the ill-conditioning effect and improve the convergence behavior. Finally, a novel multi-resolution successive iteration (MRSI) algorithm for non-Cartesian parallel imaging is proposed. The conjugate gradient (CG) iteration is performed in several successive phases with increasing resolution. It is demonstrated by spiral MRI results that the total reconstruction time can be reduced by over 30% by using low resolution in initial stages of iteration. In sum, the author describes several developments in image reconstruction for sensitivity-encoded MRI. The great potential of parallel imaging in modern applications can be further enh
Author: Peng Qu Publisher: Open Dissertation Press ISBN: 9781361470374 Category : Languages : en Pages :
Book Description
This dissertation, "Advances in Parallel Imaging Reconstruction Techniques" by Peng, Qu, 瞿蓬, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Advances in Parallel Imaging Reconstruction Techniques submitted by Qu Peng for the degree of Doctor of Philosophy at The University of Hong Kong in February 2006 In recent years, a new approach to magnetic resonance imaging (MRI), known as "parallel imaging," has revolutionized the field of fast MRI. By using sensitivity information from an RF coil array to perform some of the spatial encoding which is traditionally accomplished by magnetic field gradient, parallel imaging techniques allow reduction of phase encoding steps and consequently decrease the scan time. This thesis presents the author's investigations in the reconstruction techniques of parallel MRI. After reviewing the conventional methods, such as the image-domain-based sensitivity encoding (SENSE), the k-space-based simultaneous acquisition of spatial harmonics (SMASH), generalized auto-calibrating partially parallel acquisition (GRAPPA), and the iterative SENSE method which is applicable to arbitrary k-space trajectories, the author proposes several advanced reconstruction strategies to enhance the performance of parallel imaging in terms of signal-to-noise (SNR), the power of aliasing artifacts, and computational efficiency. First, the conventional GRAPPA technique is extended in that the data interpolation scheme is tailored and optimized for each specific reconstruction. This novel approach extracts a subset of signal points corresponding to the most linearly independent base vectors in the coefficient matrix for the fit procedure, effectively preventing incorporating redundant signals which only bring noise into reconstruction with little contribution to the exactness of fit. Phantom and in vivo MRI experiments demonstrate that this subset selection strategy can reduce residual artifacts for GRAPPA reconstruction. Second, a novel discrepancy-based method for regularization parameter choice is introduced into GRAPPA reconstruction. By this strategy, adaptive regularization in GRAPPA can be realized which can automatically choose nearly optimal parameters for the reconstructions so as to achieve good compromise between SNR and artifacts. It is demonstrated by MRI experiments that the discrepancy-based parameter choice strategy significantly outperforms those based on the L-curve or on a fixed singular value threshold. Third, the convergence behavior of the iterative non-Cartesian SENSE reconstruction is analyzed, and two different strategies are proposed to make reconstructions more stable and robust. One idea is to stop the iteration process in due time so that artifacts and SNR are well balanced and fine overall image quality is achieved; as an alternative, the inner-regularization method, in combination with the Lanczos iteration process, is introduced into non-Cartesian SENSE to mitigate the ill-conditioning effect and improve the convergence behavior. Finally, a novel multi-resolution successive iteration (MRSI) algorithm for non-Cartesian parallel imaging is proposed. The conjugate gradient (CG) iteration is performed in several successive phases with increasing resolution. It is demonstrated by spiral MRI results that the total reconstruction time can be reduced by over 30% by using low resolution in initial stages of iteration. In sum, the author describes several developments in image reconstruction for sensitivity-encoded MRI. The great potential of parallel imaging in modern applications can be further enh
Author: Angshul Majumdar Publisher: CRC Press ISBN: 1482298899 Category : Technology & Engineering Languages : en Pages : 222
Book Description
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.
Author: Mehmet Akcakaya Publisher: Academic Press ISBN: 012822746X Category : Science Languages : en Pages : 518
Book Description
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
Author: Stefan O. Schönberg Publisher: Springer Science & Business Media ISBN: 354068879X Category : Medical Languages : en Pages : 548
Book Description
This book presents the first in-depth introduction to parallel imaging techniques and, in particular, to the application of parallel imaging in clinical MRI. It will provide readers with a broader understanding of the fundamental principles of parallel imaging and of the advantages and disadvantages of specific MR protocols in clinical applications in all parts of the body at 1.5 and 3 Tesla.
Author: Tao Zhang Publisher: ISBN: Category : Languages : en Pages :
Book Description
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.
Author: Joseph Suresh Paul Publisher: CRC Press ISBN: 135102924X Category : Medical Languages : en Pages : 271
Book Description
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.
Author: Mark Murphy Publisher: ISBN: Category : Languages : en Pages : 250
Book Description
Magnetic Resonance Imaging (MRI) is a non-invasive and highly flexible medical imaging modality that does not expose patients ionizing radiation. MR Image acquisitions can be designed by varying a large number of contrast-generation parameters, and many clinical diagnostic applications exist. However, imaging speed is a fundamental limitation to many potential applications. Traditionally, MRI data have been collected at Nyquist sampling rates to produce alias-free images. However, many recent scan acceleration techniques produce sub-Nyquist samplings. For example, Parallel Imaging is a well-established acceleration technique that receives the MR signal simultaneously from multiple receive channels. Compressed sensing leverages randomized undersampling and the compressibility (e.g. via Wavelet transforms or Total-Variation) of medical images to allow more aggressive undersampling. Reconstruction of clinically viable images from these highly accelerated acquisitions requires powerful, usually iterative algorithms. Non-Cartesian pulse sequences that perform non-equispaced sampling of k-space further increase computational intensity of reconstruction, as they preclude direct use of the Fast Fourier Transform (FFT). Most iterative algorithms can be understood by considering the MRI reconstruction as an inverse problem, where measurements of un-observable parameters are made via an observation function that models the acquisition process. Traditional direct reconstruction methods attempt to invert this observation function, whereas iterative methods require its repeated computation and computation of its adjoint. As a result, na\"ive sequential implementations of iterative reconstructions produce unfeasibly long runtimes. Their computational intensity is a substantial barrier to their adoption in clinical MRI practice. A powerful new family of massively parallel microprocessor architectures has emerged simultaneously with the development of these new reconstruction techniques. Due to fundamental limitations in silicon fabrication technology, sequential microprocessors reached the power-dissipation limits of commodity cooling systems in the early 2000's. The techniques used by processor architects to extract instruction-level parallelism from sequential programs face ever-diminishing returns, and further performance improvement of sequential processors via increasing clock-frequency has become impractical. However, circuit density and process feature sizes still improve at Moore's Law rates. With every generation of silicon fabrication technology, a larger number of transistors are available to system architects. Consequently, all microprocessor vendors now exclusively produce multi-core parallel processors. Additionally, the move towards on-chip parallelism has allowed processor architects a larger degree of freedom in the design of multi-threaded pipelines and memory hierarchies. Many of the inefficiencies inherent in superscalar out-of-order design are being replaced by the high efficiency afforded by throughput-oriented designs. The move towards on-chip parallelism has resulted in a vast increase in the amount of computational power available in commodity systems. However, this move has also shifted the burden of computational performance towards software developers. In particular, the highly efficient implementation of MRI reconstructions on these systems requires manual parallelization and optimization. Thus, while ubiquitous parallelism provides a solution to the computational intensity of iterative MRI reconstructions, it also poses a substantial software productivity challenge. In this thesis, we propose that a principled approach to the design and implementation of reconstruction algorithms can ameliorate this software productivity issue. We draw much inspiration from developments in the field of computational science, which has faced similar parallelization and software development challenges for several decades. We propose a Software Architecture for the implementation of reconstruction algorithms, which composes two Design Patterns that originated in the domain of massively parallel scientific computing. This architecture allows for the most computationally intense operations performed by MRI reconstructions to be implemented as re-usable libraries. Thus the software development effort required to produce highly efficient and heavily optimized implementations of these operations can be amortized over many different reconstruction systems. Additionally, the architecture prescribes several different strategies for mapping reconstruction algorithms onto parallel processors, easing the burden of parallelization. We describe the implementation of a complete reconstruction, $\ell_1$-SPIRiT, according to these strategies. $\ell_1$-SPIRiT is a general reconstruction framework that seamlessly integrates all three of the scan acceleration techniques mentioned above. Our implementation achieves substantial performance improvement over baseline, and has enabled substantial clinical evaluation of its approach to combining Parallel Imaging and Compressive Sensing. Additionally, we include an in-depth description of the performance optimization of the non-uniform Fast Fourier Transform (nuFFT), an operation used in all non-Cartesian reconstructions. This discussion complements well our description of $\ell_1$-SPIRiT, which we have only implemented for Cartesian samplings.
Author: Frank H. Miller Publisher: Elsevier Health Sciences ISBN: 0323987362 Category : Medical Languages : en Pages : 289
Book Description
Advances in Clinical Radiology reviews the year’s most important findings and updates within the field in order to provide radiologists with the current clinical information they need for everyday practice. A distinguished editorial board, led by Dr. Frank H. Miller, identifies key areas of major progress and controversy and invites preeminent specialists to contribute original articles devoted to these topics. These insightful overviews in radiology inform and enhance clinical practice by bringing concepts to a clinical level and exploring their everyday impact on patient care. Contains a variety of articles on such topics as accelerating abdominopelvic MRI; image-guided biopsy: an algorithmic approach for optimizing results in the age of precision medicine; COVID in the abdomen; and advances in imaging of cystic renal masses: appraisal of emerging evidence from Bosniak version 2019 to artificial intelligence. Provides in-depth, clinical reviews in radiology, providing actionable insights for clinical practice. Presents the latest information in the field under the leadership of an experienced editorial team. Authors synthesize and distill the latest research and practice guidelines to create these timely topic-based reviews.
Author: Ernest Nanjung Yeh Publisher: ISBN: Category : Languages : en Pages : 180
Book Description
(cont.) Second, two matrix inversion strategies are presented which, respectively, exploit physical properties of coil encoding and the phase information of the magnetization. While the former allows stable and distributable matrix inversion using the k-space locality principle, the latter integrates parallel image reconstruction with conjugate symmetry. Third, a numerical strategy is presented for computing noise statistics of parallel MRI techniques which involve magnitude image combination, enabling quantitative image comparison. In addition, fundamental limits on the performance of parallel image reconstruction are derived using the Cramer-Rao bounds. Lastly, the practical applications of techniques developed in this thesis are demonstrated by a case study in improved coronary angiography.
Author: Peng Qu
Author: Peng Qu
Author: Angshul Majumdar
Author: Mehmet Akcakaya
Author: Stefan O. Schönberg
Author: Tao Zhang
Author: Arjun Arunachalam
Author: Joseph Suresh Paul
Author: Mark Murphy
Author: Frank H. Miller
Author: Ernest Nanjung Yeh