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通常包含多参数磁共振成像，但由于信噪比和扫描时间的限制，这些图像通常以不同的各向异性二维视角采集。此类视角存在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下游体积图像分析。由于缺乏大规模训练队列，将多对比度扫描的不同视角结合为高分辨率各向同性三维扫描极具挑战性，这需要开发基于个体被试的框架。本研究提出一种利用隐式神经表示的创新解决方案。我们提出的INR方法在连续空间函数中联合学习互补视角的两种不同对比度，并通过交换解剖信息相互增益。模型在单块商用GPU上数分钟内即可完成训练，在三组数据集的实验中为不同对比度对提供了真实的超分辨率重建。以互信息作为评估指标，我们发现模型在序列间收敛至最优互信息值，实现了解剖学保真重建。代码见：https://github.com/jqmcginnis/multi_contrast_inr/