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Author: 易哲源
Publisher:
ISBN:
Category : Magnetic resonance imaging
Languages : en
Pages : 135
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Book Description
Author: 易哲源
Publisher:
ISBN:
Category : Magnetic resonance imaging
Languages : en
Pages : 135
Get Book Here
Book Description
Author: Tao Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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: Stefan O. Schönberg
Publisher: Springer Science & Business Media
ISBN: 354068879X
Category : Medical
Languages : en
Pages : 548
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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: Joseph Suresh Paul
Publisher: CRC Press
ISBN: 135102924X
Category : Medical
Languages : en
Pages : 271
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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: Lei Hou Hamilton
Publisher:
ISBN:
Category : Diagnostic imaging
Languages : en
Pages :
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Book Description
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.
Author: Mehmet Akcakaya
Publisher: Academic Press
ISBN: 012822746X
Category : Science
Languages : en
Pages : 518
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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 - 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: Mark Murphy
Publisher:
ISBN:
Category :
Languages : en
Pages : 250
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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: Ziwu Zhou
Publisher:
ISBN:
Category :
Languages : en
Pages : 180
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Book Description
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.
Author: Angshul Majumdar
Publisher: Cambridge University Press
ISBN: 1107103762
Category : Computers
Languages : en
Pages : 227
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Book Description
"Discusses different ways to use existing mathematical techniques to solve compressed sensing problems"--Provided by publisher.
Author: Donald W. McRobbie
Publisher: Cambridge University Press
ISBN: 1316688259
Category : Medical
Languages : en
Pages : 405
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Book Description
MR is a powerful modality. At its most advanced, it can be used not just to image anatomy and pathology, but to investigate organ function, to probe in vivo chemistry, and even to visualise the brain thinking. However, clinicians, technologists and scientists struggle with the study of the subject. The result is sometimes an obscurity of understanding, or a dilution of scientific truth, resulting in misconceptions. This is why MRI from Picture to Proton has achieved its reputation for practical clarity. MR is introduced as a tool, with coverage starting from the images, equipment and scanning protocols and traced back towards the underlying physics theory. With new content on quantitative MRI, MR safety, multi-band excitation, Dixon imaging, MR elastography and advanced pulse sequences, and with additional supportive materials available on the book's website, this new edition is completely revised and updated to reflect the best use of modern MR technology.
