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首页> 美国卫生研究院文献>Biomedical Optics Express >Direct regularization from co-registered anatomical images for MRI-guided near-infrared spectral tomographic image reconstruction
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Direct regularization from co-registered anatomical images for MRI-guided near-infrared spectral tomographic image reconstruction

机译：从共配准的解剖图像直接进行正则化以进行MRI引导的近红外光谱层析图像重建

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摘要

Combining anatomical information from high resolution imaging modalities to guide near-infrared spectral tomography (NIRST) is an efficient strategy for improving the quality of the reconstructed spectral images. A new approach for incorporating image information directly into the inversion matrix regularization was examined using Direct Regularization from Images (DRI), which encodes the gray-scale data into the NIRST image reconstruction problem. This process has the benefit of eliminating user intervention such as image segmentation of distinct regions. Specifically, the Dynamic Contrast Enhanced Magnetic Resonance (DCE-MR) image intensity value differences within the anatomical image were used to implement an exponentially-weighted regularization function between the image pixels. The algorithm was validated using simulated reconstructions with noise, and the results showed that spatial resolution and robustness of the reconstructed images were significantly improved by appropriate choice of the regularization weight parameters. The proposed approach was also tested on in vivo breast data acquired in a recent clinical trial combining NIRST / MRI for cancer tumor characterization. Relative to the standard “no priors” diffuse recovery, the contrast of the tumor to the normal surrounding tissue increased from 2.4 to 3.6, and the difference between the tumor size segmented from DCE-MR images and reconstructed optical images decreased from 18% to 6%, while there was an overall decrease in surface artifacts.
机译：结合来自高分辨率成像模态的解剖学信息以指导近红外光谱层析成像（NIRST）是提高重建光谱图像质量的有效策略。研究了一种使用图像直接正则化（DRI）将图像信息直接合并到反矩阵正则化中的新方法，该方法将灰度数据编码为NIRST图像重建问题。此过程的好处是消除了用户干预，例如不同区域的图像分割。具体而言，解剖图像内的动态对比度增强磁共振（DCE-MR）图像强度值差异用于实现图像像素之间的指数加权正则化功能。使用带有噪声的模拟重建对该算法进行了验证，结果表明，通过适当选择正则化权重参数，可以显着提高重建图像的空间分辨率和鲁棒性。还针对结合了NIRST / MRI的最新临床试验中获得的体内乳腺数据对所提议的方法进行了测试，以用于癌症肿瘤的表征。相对于标准的“无先例”弥漫性恢复，肿瘤与正常周围组织的对比度从2.4增加到3.6，从DCE-MR图像和重建的光学图像分割出的肿瘤大小之间的差异从18％减少到6 ％，而表面伪影总体上减少了。

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