Claudia Giardina, Montse Pardàs, Verónica Vilaplana · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31523 · open-access PDF
A ressonância magnética biparamétrica sintética (bpMRI) pode ajudar a aliviar a disponibilidade limitada de conjuntos de dados de ressonância magnética da próstata grandes e representativos para aplicações de aprendizagem profunda. Neste trabalho, apresentamos um quadro de difusão 3D condicional para a síntese condicionada pela anatomia de bpMRI da próstata completa a partir de máscaras anatômicas. O quadro suporta a síntese sequencial, na qual as modalidades de T2w e difusão são geradas por modelos treinados separadamente, e a síntese conjunta, na qual todas as modalidades de bpMRI são geradas simultaneamente dentro de um único modelo.
Machine translation (Meta M2M100); the English original follows.
Synthetic biparametric MRI (bpMRI) may help alleviate the limited availability of large and representative prostate MRI datasets for deep learning applications. In this work, we present a conditional 3D diffusion framework for anatomy-conditioned synthesis of complete prostate bpMRI from anatomical masks. The framework supports sequential synthesis, in which T2w and diffusion modalities are generated by separately trained models, and joint synthesis, in which all bpMRI modalities are generated simultaneously within a single model. Five configurations were trained using PI-CAI, FastMRI Prostate
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