Eslami Ramin, Jacob Mathews
Department of Biomedical Engineering, University of Rochester, Rochester, NY 14627, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6663-6. doi: 10.1109/IEMBS.2009.5334516.
We propose a novel spatial spectral model for the reconstruction of magnetic resonance spectroscopic imaging (MRSI) signal. We penalize the compartmentalized spatial total variation norm of the signal to exploit the spatial properties of the metabolite peaks. The spectral signal is modeled as a sparse linear combination of spikes and polynomials to capture the peaks and baseline induced by unsuppressed water and lipids. We also use the high-resolution map of the magnetic field distribution within the slice to model the image acquisition, thus correcting for intra-voxel line shape distortions. The spectral model enables the stable recovery of the signal even in challenging spatial regions, while the spatial model suppresses the spectral leakage from extra-cranial fat and inter-voxel crosstalk. We acquire the MRSI signal using EPSI, while the high-resolution 3-D MRI information is derived using Dixon scans. The reconstruction of phantom and in vivo MRSI data demonstrate a significant improvement in spectral quality and accuracy over classical MRSI schemes.
我们提出了一种用于磁共振波谱成像（MRSI）信号重建的新型空间光谱模型。我们对信号的分区空间全变差范数进行惩罚，以利用代谢物峰的空间特性。光谱信号被建模为尖峰和多项式的稀疏线性组合，以捕获未抑制的水和脂质引起的峰和基线。我们还使用切片内磁场分布的高分辨率地图对图像采集进行建模，从而校正体素内线形状失真。光谱模型即使在具有挑战性的空间区域也能实现信号的稳定恢复，而空间模型则抑制了来自颅外脂肪的光谱泄漏和体素间串扰。我们使用回波平面光谱成像（EPSI）获取MRSI信号，而高分辨率三维磁共振成像（MRI）信息则通过狄克逊扫描获得。体模和体内MRSI数据的重建表明，与传统MRSI方案相比，光谱质量和准确性有显著提高。
