IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural Representations
This addresses motion artifacts in MRI for medical imaging, offering a method that reduces hallucinations and preserves anatomical structures, though it appears incremental as it builds on existing implicit neural representation techniques.
The paper tackles motion artifacts in MRI by proposing a self-supervised motion correction pipeline using motion-guided implicit neural representations, achieving improvements of +5% SSIM, +5 dB PSNR, and +14% HaarPSI over state-of-the-art methods and enhancing classification accuracy by at least +1.5 percentage points.
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.