Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices.
Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices.
复制标题
通过从平面内高分辨率切片训练的过完备字典进行单个各向异性 3-D MR 图像上采样。
DOI：
10.1109/jbhi.2015.2470682
复制
发表时间：
2016-11
影响因子：
7.7
通讯作者：
Warfield SK
中科院分区：
文献类型：
--
作者：
Jia Y;He Z;Gholipour A;Warfield SK
在磁共振 (MR) 中，硬件限制、扫描时间和患者舒适度通常会导致采集各向异性 3D MR 图像。提高图像分辨率是人们所期望的，但在医学图像处理中却非常具有挑战性。基于稀疏表示和过完备字典的超分辨率（SR）重建最近被用来解决这个问题；然而，这些方法需要额外的训练集，而这些训练集可能并不总是可用。本文提出了一种新颖的单各向异性 3D MR 图像上采样方法，该方法通过稀疏表示和超完备字典进行训练，从面内高分辨率切片训练到面外维度上采样。因此，所提出的方法不需要额外的训练集。对模拟和临床脑部 MR 图像进行的大量实验表明，所提出的方法比经典插值法更准确。与最近基于非局部均值方法的上采样方法相比，所提出的方法在模拟图像的低上采样因子下没有显示出改进的结果，但在临床案例中产生了具有更好计算效率的可比结果。因此，所提出的方法可以有效地实现并常规用于对平面外视图中的 MR 图像进行上采样，以进行放射学评估和采集后处理。
In Magnetic Resonance (MR), hardware limitation, scanning time, and patient comfort often result in the acquisition of anisotropic 3D MR images. Enhancing image resolution is desired but has been very challenging in medical image processing. Super resolution (SR) reconstruction based on sparse representation and over-complete dictionary has been lately employed to address this problem; however, these methods require extra training sets, which may not be always available. This paper proposes a novel single anisotropic 3D MR image upsampling method via sparse representation and over-complete dictionary that is trained from in-plane high resolution slices to upsample in the out-of-plane dimensions. The proposed method, therefore, does not require extra training sets. Abundant experiments, conducted on simulated and clinical brain MR images, show that the proposed method is more accurate than classical interpolation. When compared to a recent upsampling method based on the non-local means approach, the proposed method did not show improved results at low upsampling factors with simulated images, but generated comparable results with much better computational efficiency in clinical cases. Therefore, the proposed approach can be efficiently implemented and routinely used to upsample MR images in the out-of-planes views for radiologic assessment and post-acquisition processing.