Influence of partial k-space filling on the quality of magnetic resonance images

Jornada, Tiago da Silva; Murata, Camila H.; Medeiros, Regina Bitelli

doi:10.1590/0100-3984.2015.0028

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…One way of reducing image acquisition time is to reduce the number of profiles that populate the k-space. However, adopting this measure without further provision leads to negative effects on image quality [12].…”

Section: Motivationmentioning

confidence: 99%

Parallelization and Deep Learning Techniques for the Registration and Reconstruction of Dynamic Cardiac Magnetic Resonance Imaging

Martín González

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On the one hand, a parallel framework for GWCS is explored. GWCS is a highly parallelizable problem, so we will make use of the OpenCLIPER framework, another previous work of ours, for its implementation. This solution is motion compensated to increase the sparse character of the solution, so we have to parallelize: 1) the groupwise registration algorithm to estimate the motion, which will be done using FFDs with cubic B-splines, and 2) the optimization algorithm of the reconstruction itself, for which we will use NESTA. Results obtained with and without motion estimation and compensation are analyzed to conclude that the solution is clinically viable in terms of execution times, and suitable for any computing device which has an OpenCL implementation.On the other hand, we propose a GWCS-like approach that leverages deep learning to enhance the reconstruction process. Our approach eliminates the need of optimization steps and utilizes deep learning techniques instead to speed up reconstructions and reduce computational complexity.We first create a fast solution for registration with unsupervised DL, called dGW, and then a self-supervised DL solution for motion-compensated reconstruction (SSMoComp) that relies on the previously trained registration. Regarding dGW, we found that it achieved comparable accuracy to traditional optimization-based approaches, but with significantly reduced registration runtimes. As for SSMoComp, we conducted a comparative analysis with a state-of-the-art solution and observed that our design outperformed it, yielding superior results. A modified version of the cine DL solution was additionally adapted for first-pass perfusion, called SECRET.Compared with state-of-the-art approaches, the SECRET method maintains good quality reconstructions for higher acceleration rates, with low training and very fast reconstruction times.

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Section: Motivationmentioning

confidence: 99%

Parallelization and Deep Learning Techniques for the Registration and Reconstruction of Dynamic Cardiac Magnetic Resonance Imaging

Martín González

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Radial sequences and compressed sensing in pediatric body magnetic resonance imaging

Bardo

Rubert

2021

Pediatr Radiol

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State-of-the-art magnetic resonance imaging sequences for pediatric body imaging

et al. 2022

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Influence of partial k-space filling on the quality of magnetic resonance images

Cited by 5 publications

References 9 publications

Parallelization and Deep Learning Techniques for the Registration and Reconstruction of Dynamic Cardiac Magnetic Resonance Imaging

Parallelization and Deep Learning Techniques for the Registration and Reconstruction of Dynamic Cardiac Magnetic Resonance Imaging

Radial sequences and compressed sensing in pediatric body magnetic resonance imaging

State-of-the-art magnetic resonance imaging sequences for pediatric body imaging

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