On the public ReMIND dataset, a systematic benchmark of six synthesis models across 48 experiments finds LPIPS correlates with downstream segmentation utility while SSIM does not, with SynDiff-2.5D performing best.
IEEE Transactions on Medical Imaging 41(10), 2598–2614 (2022)
3 Pith papers cite this work, alongside 541 external citations. Polarity classification is still indexing.
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HA-DSB uses a diffusion Schrödinger bridge with vision-language model region embeddings and PET-guided noise modulation to translate whole-body MRI while preserving lesion fidelity.
FGSB is a two-stage neural Schrödinger bridge that generates missing MRI modalities from limited paired data and preserves lesions via expert priors.
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On the public ReMIND dataset, a systematic benchmark of six synthesis models across 48 experiments finds LPIPS correlates with downstream segmentation utility while SSIM does not, with SynDiff-2.5D performing best.
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Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation
HA-DSB uses a diffusion Schrödinger bridge with vision-language model region embeddings and PET-guided noise modulation to translate whole-body MRI while preserving lesion fidelity.
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FGSB is a two-stage neural Schrödinger bridge that generates missing MRI modalities from limited paired data and preserves lesions via expert priors.