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Dense Temporal Contrast Synthesis via Conditioned Latent Transport

Research Medical/Healthcare AI

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TL;DR - A conditioned latent transport model synthesizes virtual contrast-enhanced breast MRI in a single forward pass, potentially reducing or eliminating gadolinium contrast agents. It matters because it targets a real clinical bottleneck with external-cohort validation and a radiologist reader study.

  • Anchors the latent trajectory to pre-contrast anatomy with continuous time conditioning, enabling patient-specific enhancement at any acquisition time without slow iterative sampling.
  • Outperforms baselines and prior SOTA on spatial, perceptual, temporal, and distributional metrics; robust to scanner noise and differing acquisition protocols on an independent external cohort.
  • Downstream tumor segmentation improved 22.4% relative Dice (0.60 vs 0.49 pre-contrast, p < 0.01) with >39% lower boundary error.
  • Reader study with four breast radiologists over 40 cases: 70% of synthesized sequences supported the same management decisions as real DCE-MRI.

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Dense Temporal Contrast Synthesis via Conditioned Latent Transport

arXiv cs.CV Smriti Joshi, Apostolia Tsirikoglou, Daniel M. Lang, Richard Osuala, Noah Márquez Varaa, Alejandro Guzman, Grzegorz Skorupko, Sebastian Ibarra Arregui, Lidia Garrucho, Akane Ohashi, Dimitra Ntoula, Eugen Divjak, Oğuz Lafcı, Jan C. Peeken, Julia A. Schnabel, Fredrik Strand, Oliver Diaz, Karim Lekadir 2026-07-31 arXiv:2607.29394
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-24 14:28:52.941197 UTC

TL;DR - A conditioned latent transport model synthesizes virtual contrast-enhanced breast MRI in a single forward pass, potentially reducing or eliminating gadolinium contrast agents. It matters because it targets a real clinical bottleneck with external-cohort validation and a radiologist reader study.

  • Anchors the latent trajectory to pre-contrast anatomy with continuous time conditioning, enabling patient-specific enhancement at any acquisition time without slow iterative sampling.
  • Outperforms baselines and prior SOTA on spatial, perceptual, temporal, and distributional metrics; robust to scanner noise and differing acquisition protocols on an independent external cohort.
  • Downstream tumor segmentation improved 22.4% relative Dice (0.60 vs 0.49 pre-contrast, p < 0.01) with >39% lower boundary error.
  • Reader study with four breast radiologists over 40 cases: 70% of synthesized sequences supported the same management decisions as real DCE-MRI.
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