AI RESEARCH
Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation
arXiv CS.LG
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ArXi:2605.16476v1 Announce Type: cross Through-plane resolution in clinical MRI is typically much coarser than in-plane resolution, limiting diagnostic utility. This work investigates deep learning approaches to interpolate intermediate MRI slices in prostate imaging, effectively doubling through-plane resolution. I evaluated five architectures (CNN, U-Net, two GAN variants, and DDPM) and discovered that problem formulation has dramatically impact than architectural complexity.