Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints. Thus acquired views suffer from poor out-of-plane resolution and affect downstream volumetric image analysis that typically requires isotropic 3D scans. Combining different views of multi-contrast scans into high-resolution isotropic 3D scans is challenging due to the lack of a large training cohort, which calls for a subject-specific framework.This work proposes a novel solution to this problem leveraging Implicit Neural Representations (INR). Our proposed INR jointly learns two different contrasts of complementary views in a continuous spatial function and benefits from exchanging anatomical information between them. Trained within minutes on a single commodity GPU, our model provides realistic super-resolution across different pairs of contrasts in our experiments with three datasets. Using Mutual Information (MI) as a metric, we find that our model converges to an optimum MI amongst sequences, achieving anatomically faithful reconstruction. Code is available at: https://github.com/jqmcginnis/multi_contrast_inr.

翻译：临床常规诊疗和回顾性队列研究常采用多参数磁共振成像，但由于信噪比和扫描时间的限制，这些图像大多以不同方向的各向异性二维视图获取。此类视图存在平面外分辨率不足的问题，影响通常需要各向同性三维扫描的体素级图像分析。由于缺乏大规模训练队列，将多对比扫描的不同视图整合为高分辨率各向同性三维扫描极具挑战性，这催生了针对个体受试者的专用框架。本文提出一种基于隐式神经表征的创新解决方案。我们提出的INR模型通过连续空间函数联合学习互补视图的两种对比度，并促进两者间解剖信息的交互。该模型在单块商用GPU上仅需数分钟训练即可实现三组数据集实验中不同对比度对的逼真超分辨率效果。以互信息作为评估指标，我们发现模型收敛后序列间的MI达到最优值，从而获得解剖结构保真度极高的重建结果。代码开源地址：https://github.com/jqmcginnis/multi_contrast_inr