Motion artifacts in Magnetic Resonance Imaging (MRI) arise due to relatively long acquisition times and can compromise the clinical utility of acquired images. Traditional motion correction methods often fail to address severe motion, leading to distorted and unreliable results. Deep Learning (DL) alleviated such pitfalls through generalization with the cost of vanishing structures and hallucinations, making it challenging to apply in the medical field where hallucinated structures can tremendously impact the diagnostic outcome. In this work, we present an instance-wise motion correction pipeline that leverages motion-guided Implicit Neural Representations (INRs) to mitigate the impact of motion artifacts while retaining anatomical structure. Our method is evaluated using the NYU fastMRI dataset with different degrees of simulated motion severity. For the correction alone, we can improve over state-of-the-art image reconstruction methods by $+5\%$ SSIM, $+5\:db$ PSNR, and $+14\%$ HaarPSI. Clinical relevance is demonstrated by a subsequent experiment, where our method improves classification outcomes by at least $+1.5$ accuracy percentage points compared to motion-corrupted images.

翻译：磁共振成像（MRI）中因相对较长的采集时间而产生的运动伪影会损害所获图像的临床实用性。传统运动校正方法往往无法处理严重运动，导致结果失真且不可靠。深度学习（DL）通过泛化能力缓解了此类缺陷，但代价是结构消失与幻觉伪影，这在医学领域中难以应用，因为幻觉结构可能严重影响诊断结果。本研究提出一种实例级运动校正流程，利用运动引导的隐式神经表示（INRs）在保留解剖结构的同时减轻运动伪影的影响。我们的方法使用NYU fastMRI数据集在不同模拟运动严重程度下进行评估。仅就校正性能而言，我们能够将最先进的图像重建方法的指标提升$+5\%$ SSIM、$+5\:db$ PSNR和$+14\%$ HaarPSI。通过后续实验证明了临床相关性：与运动伪影图像相比，我们的方法将分类结果准确率提升了至少$+1.5$个百分点。