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Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI.快速变密度泊松圆盘采样生成与方向变化的磁共振成像压缩感知。
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Rapid compressed sensing reconstruction of 3D non-Cartesian MRI.三维非笛卡尔 MRI 的快速压缩感知重建。
Magn Reson Med. 2018 May;79(5):2685-2692. doi: 10.1002/mrm.26928. Epub 2017 Sep 23.
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P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data.P-LORAKS：利用并行成像数据对局部k空间邻域进行低秩建模。
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Highly accelerated real-time cardiac cine MRI using k-t SPARSE-SENSE.基于 k-t SPARSE-SENSE 的高速实时心脏电影磁共振成像。
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Parallel MR imaging.并行磁共振成像。
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Fast l₁-SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime.快速 l₁-SPIRiT 压缩感知并行成像 MRI：可扩展的并行实现和临床可行的运行时间。
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基于结构化稀疏性的压缩感知加速并行磁共振成像

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

作者信息

Dwork Nicholas, Gordon Jeremy W, Englund Erin K

机构信息

University of Colorado-Anschutz Medical Campus, Department of Biomedical Informatics, Aurora, Colorado, United States.

University of Colorado-Anschutz Medical Campus, Department of Radiology, Aurora, Colorado, United States.

出版信息

J Med Imaging (Bellingham). 2024 May;11(3):033504. doi: 10.1117/1.JMI.11.3.033504. Epub 2024 Jun 26.

DOI:10.1117/1.JMI.11.3.033504
PMID:38938501
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11205977/
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.

摘要

目的

我们提出一种将压缩感知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

先前的工作已将压缩感知与基于模型重建的并行成像相结合，但未利用结构化稀疏性。从完全采样的中心区域重建每个线圈的模糊图像。修改压缩感知的优化问题以考虑这些模糊图像，并求解该问题以估计缺失的细节。

结果

使用大脑、脚踝和肩部解剖结构的数据，与不使用结构化稀疏性的稀疏SENSE或L1 ESPIRiT相比，将压缩感知与结构化稀疏性及并行成像相结合重建的图像具有更低的相对误差。

结论

只要获取以适当大小的零频率为中心的完全采样区域，利用结构化稀疏性就能在给定数据量的情况下提高图像质量。