Accelerated parallel magnetic resonance imaging with compressed sensing using structured sparsity.

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Medical Imaging Pub Date : 2024-05-01 Epub Date: 2024-06-26 DOI:10.1117/1.JMI.11.3.033504

Nicholas Dwork, Jeremy W Gordon, Erin K Englund

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Abstract

Purpose: We present a method that combines compressed sensing with parallel imaging that takes advantage of the structure of the sparsifying transformation.

Approach: Previous work has combined compressed sensing with parallel imaging using model-based reconstruction but without taking advantage of the structured sparsity. Blurry images for each coil are reconstructed from the fully sampled center region. The optimization problem of compressed sensing is modified to take these blurry images into account, and it is solved to estimate the missing details.

Results: Using data of brain, ankle, and shoulder anatomies, the combination of compressed sensing with structured sparsity and parallel imaging reconstructs an image with a lower relative error than does sparse SENSE or L1 ESPIRiT, which do not use structured sparsity.

Conclusions: Taking advantage of structured sparsity improves the image quality for a given amount of data as long as a fully sampled region centered on the zero frequency of the appropriate size is acquired.

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利用结构稀疏性压缩传感加速并行磁共振成像。

目的：我们提出了一种将压缩传感与并行成像相结合的方法，该方法利用了稀疏变换的结构：方法：之前的研究利用基于模型的重建将压缩传感与并行成像相结合，但没有利用结构稀疏性。每个线圈的模糊图像都是从完全采样的中心区域重建的。对压缩传感的优化问题进行了修改，将这些模糊图像考虑在内，并通过求解来估计缺失的细节：结果：利用大脑、脚踝和肩部解剖数据，与不使用结构稀疏性的稀疏 SENSE 或 L1 ESPIRiT 相比，将压缩传感与结构稀疏性和并行成像相结合，重建的图像相对误差更小：结论：利用结构稀疏性的优势，只要获取以适当大小的零频率为中心的完全采样区域，就能提高给定数据量下的图像质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.10

自引率

4.20%

发文量

期刊介绍： JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.