Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning
Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning
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DOI：
10.1109/tmi.2018.2807451
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发表时间：
2018-08-01
影响因子：
10.6
通讯作者：
Odille, Freddy
中科院分区：
文献类型：
--
作者：
Bustin, Aurelien;Voilliot, Damien;Odille, Freddy
各向同性三维(3D)采集是磁共振成像(MRI)中一项具有挑战性的任务。特别是在心脏MRI中，由于硬件和时间的限制，目前的3D采集受到低分辨率的限制，特别是在穿透平面方向上，导致该维度的图像质量较差。为了克服这一问题，人们提出了从多个各向异性采集重建单个各向同性三维体的超分辨率(SR)技术。以前，像全变分这样的局部正则化技术已经被应用来限制噪声放大，同时保留图像中的尖锐边缘和小特征。受基于面片重建的最新进展的启发，我们提出了一种新的各向同性三维重建方案，该方案综合了来自3D面片邻域的非局部和自相似信息。通过对具有相似结构的3D块进行分组，增强了MR图像的自然稀疏性，这种稀疏性可以用低阶结构来表示，从而在高信噪比的情况下实现了稳健的图像重建。提出了该问题的增广拉格朗日公式，以有效地将优化分解为低秩体噪声去噪和随机共振重建。在仿真、脑成像和临床心脏MRI中的实验结果表明，所提出的联合SR和自相似学习框架的性能优于目前最先进的方法。所提出的各向同性3D体积重建可能对心脏应用特别有用，例如通过晚期Gd增强MRI评估心肌梗死疤痕。
Isotropic three-dimensional (3D) acquisition is a challenging task in magnetic resonance imaging (MRI). Particularly in cardiac MRI, due to hardware and time limitations, current 3D acquisitions are limited by low-resolution, especially in the through-plane direction, leading to poor image quality in that dimension. To overcome this problem, super-resolution (SR) techniques have been proposed to reconstruct a single isotropic 3D volume from multiple anisotropic acquisitions. Previously, local regularization techniques such as total variation have been applied to limit noise amplification while preserving sharp edges and small features in the images. In this paper, inspired by the recent progress in patch-based reconstruction, we propose a novel isotropic 3D reconstruction scheme that integrates non-local and self-similarity information from 3D patch neighborhoods. By grouping 3D patches with similar structures, we enforce the natural sparsity of MR images, which can be expressed by a low-rank structure, leading to robust image reconstruction with high signal-to-noise ratio efficiency. An Augmented Lagrangian formulation of the problem is proposed to efficiently decompose the optimization into a low-rank volume denoising and a SR reconstruction. Experimental results in simulations, brain imaging and clinical cardiac MRI, demonstrate that the proposed joint SR and self-similarity learning framework outperforms current state-of-the-art methods. The proposed reconstruction of isotropic 3D volumes may be particularly useful for cardiac applications, such as myocardial infarction scar assessment by late gadolinium enhancement MRI.