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首页> 外文会议>Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Undersampling Strategies for Compressed Sensing Accelerated MR Spectroscopic Imaging
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Undersampling Strategies for Compressed Sensing Accelerated MR Spectroscopic Imaging

机译：压缩传感加速MR光谱成像的欠采样策略

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Compressed sensing (CS) can accelerate magnetic resonance spectroscopic imaging (MRSI), facilitating its widespread clinical integration. The objective of this study was to assess the effect of different undersampling strategy on CS-MRSI reconstruction quality. Phantom data were acquired on a Philips 3 T Ingenia scanner. Four types of undersampling masks, corresponding to each strategy, namely, low resolution, variable density, iterative design, and a priori were simulated in Matlab and retrospectively applied to the test 1X MRSI data to generate undersampled datasets corresponding to the 2X - 5X, and 7X accelerations for each type of mask. Reconstruction parameters were kept the same in each case(all masks and accelerations) to ensure that any resulting differences can be attributed to the type of mask being employed. The reconstructed datasets from each mask were statistically compared with the reference 1X, and assessed using metrics like the root mean square error and metabolite ratios. Simulation results indicate that both the a priori and variable density undersampling masks maintain high fidelity with the 1X up to five-fold acceleration. The low resolution mask based reconstructions showed statistically significant differences from the 1X with the reconstruction failing at 3X, while the iterative design reconstructions maintained fidelity with the 1X till 4X acceleration. In summary, a pilot study was conducted to identify an optimal sampling mask in CS-MRSI. Simulation results demonstrate that the a priori and variable density masks can provide statistically similar results to the fully sampled reference. Future work would involve implementing these two masks prospectively on a clinical scanner.
机译：压缩检测（CS）可以加速磁共振光谱成像（MRSI），促进其广泛的临床一体化。本研究的目的是评估不同欠采样策略对CS-MRSI重建质量的影响。在飞利浦3 T Ingenia扫描仪上获得了幻像数据。在MATLAB中模拟了对应于每个策略，即低分辨率，可变浓度，迭代设计和先验的四种类型的下采样掩模，并回顾性地应用于测试1x MRSI数据以生成对应于2x-5x的下采样数据集，以及每种类型的面罩的7x加速度。在每种情况下重建参数（所有助手和加速）保持相同，以确保任何导致的差异都可以归因于所采用的掩模类型。从每个掩模的重建数据集与参考1x进行统计数据，并使用像根均方误差和代谢物比例的度量评估。仿真结果表明，先验和可变密度下采样掩模都与高达五倍的加速度保持高保真度。基于低分辨率的掩模的重建显示出与1x的统计学上显着的差异，其中重建在3倍时失效，而迭代设计重建将保真度保持了1倍，直到4x加速度。总之，进行了试验研究以识别CS-MRSI中的最佳采样掩模。仿真结果表明，先验和可变密度掩模可以提供与完全采样的参考的统计上类似的结果。未来的工作将涉及前瞻性地在临床扫描仪上实施这两个面具。

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