Zhang Limin, Zhao Yan, Jiang Shudong, Pogue Brian W, Paulsen Keith D
Thayer School of Engineering, Dartmouth College, Hanover NH 03755, USA ; College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China ; Tianjin Key Laboratory of Biomedical Detecting Techniques and Instrument, Tianjin 300072, China.
Thayer School of Engineering, Dartmouth College, Hanover NH 03755, USA.
Biomed Opt Express. 2015 Aug 27;6(9):3618-30. doi: 10.1364/BOE.6.003618. eCollection 2015 Sep 1.
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.
将来自高分辨率成像模态的解剖学信息相结合以指导近红外光谱断层扫描（NIRST）是提高重建光谱图像质量的有效策略。使用图像直接正则化（DRI）研究了一种将图像信息直接纳入反演矩阵正则化的新方法，该方法将灰度数据编码到NIRST图像重建问题中。此过程的好处是消除了诸如不同区域的图像分割等用户干预。具体而言，利用解剖图像内的动态对比增强磁共振（DCE-MR）图像强度值差异来实现图像像素之间的指数加权正则化函数。该算法通过有噪声的模拟重建进行了验证，结果表明，通过适当选择正则化权重参数，重建图像的空间分辨率和稳健性得到了显著提高。所提出的方法还在最近一项结合NIRST/MRI用于癌症肿瘤特征表征的临床试验中获取的体内乳腺数据上进行了测试。相对于标准的“无先验”扩散恢复，肿瘤与周围正常组织的对比度从2.4提高到3.6，从DCE-MR图像分割的肿瘤大小与重建光学图像之间的差异从18%降至6%，同时表面伪影总体减少。
