  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.
