Lin Sung-Han, Ma Junjie, Park Jae Mo
University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
GE Healthcare, Jersey City, NJ 07302, USA.
IEEE Access. 2024;12:164315-164324. doi: 10.1109/access.2024.3491592. Epub 2024 Nov 4.
The achievable spatial resolution of C metabolic images acquired with hyperpolarized C-pyruvate is worse than H images typically by an order of magnitude due to the rapidly decaying hyperpolarized signals and the low gyromagnetic ratio of C. This study is to develop and characterize a volumetric patch-based super-resolution reconstruction algorithm that enhances spatial resolution C cardiac MRI by utilizing structural information from H MRI. The reconstruction procedure comprises anatomical segmentation from high-resolution H MRI, calculation of a patch-based weight matrix, and iterative reconstruction of high-resolution multi-slice C MRI. The method was tested with a multi-compartmental digital phantom for optimizing the patch size and an anthropomorphic cardiac MR phantom for validating the performance. Finally, the method was applied to human cardiac C images, acquired with an injection of hyperpolarized [1-C]pyruvate. The phantom studies demonstrated that high-resolution multi-slice C images, reconstructed from a single-slice low-resolution input C image, retained the signal intensity range. The reconstruction accuracy was asymptotically improved as the patch size increased whereas intra-segmental spatial fluctuations were preserved better with smaller patches. However, a structurally non-identified tissue region was not restored regardless of the patch size. The cardiac MR phantom and the human cardiac images demonstrated improved spatial resolutions in the reconstructed images (10 × 10 × 30 mm/voxel to 2 × 2 × 5 mm/voxel). The volumetric patch-based super-resolution method reconstructs multi-slice high-resolution of C images, enhancing the cardiac structure, while preserving the quantitative accuracy. The proposed method is applicable to other multi-modal images that suffer from limited spatial resolution.
由于超极化信号快速衰减以及碳的低旋磁比，用超极化碳 - 丙酮酸采集的碳代谢图像可实现的空间分辨率通常比氢图像差一个数量级。本研究旨在开发并表征一种基于体积块的超分辨率重建算法，该算法通过利用氢磁共振成像（H MRI）的结构信息来提高碳心脏磁共振成像（C cardiac MRI）的空间分辨率。重建过程包括从高分辨率H MRI进行解剖分割、计算基于块的权重矩阵以及对高分辨率多层C MRI进行迭代重建。该方法在多室数字体模上进行测试以优化块大小，并在拟人化心脏磁共振体模上进行验证以评估性能。最后，该方法应用于通过注射超极化[1 - C]丙酮酸获取的人体心脏碳图像。体模研究表明，从单切片低分辨率输入碳图像重建的高分辨率多层碳图像保留了信号强度范围。随着块大小增加，重建精度逐渐提高，而较小的块能更好地保留段内空间波动。然而，无论块大小如何，结构未明确的组织区域都无法恢复。心脏磁共振体模和人体心脏图像显示重建图像的空间分辨率有所提高（从10×10×30毫米/体素提高到2×2×5毫米/体素）。基于体积块的超分辨率方法重建多层高分辨率碳图像，增强了心脏结构，同时保留了定量准确性。所提出的方法适用于其他空间分辨率受限的多模态图像。
