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New diffusion framework synthesizes quantitative DCE-MRI from incomplete sequences

Researchers have developed Spatio-Temporal Mixture-of-Modality-Experts (ST-MoME), a novel diffusion framework designed to synthesize quantitative dynamic contrast-enhanced MRI (DCE-MRI) parameter maps from incomplete MR sequences. This method addresses the challenge of unavailable DCE-MRI data due to contrast-agent risks and protocol variability by fusing features from various MRI modalities. ST-MoME utilizes a spatio-temporal gating network to generate voxel-wise weights, guiding the denoising process in image space with a Swin-based backbone. Evaluated on a cohort of 386 brain tumor patients across 16 different data availability scenarios, ST-MoME demonstrated superior performance in reconstructing DCE parameters, particularly within the critical tumor region. AI

IMPACT This research could improve tumor assessment by enabling the synthesis of crucial quantitative MRI data from incomplete scans.

RANK_REASON The cluster contains a research paper detailing a new method for medical image synthesis.

Read on arXiv cs.CV →

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New diffusion framework synthesizes quantitative DCE-MRI from incomplete sequences

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Junhyeok Lee, Kyu Sung Choi ·

    Spatio-Temporal Mixture-of-Modality-Experts Diffusion for Quantitative DCE-MRI Synthesis from Incomplete MR Sequences

    arXiv:2606.25535v1 Announce Type: new Abstract: Quantitative maps from dynamic contrast-enhanced MRI (DCE-MRI) are essential for tumor assessment but are often unavailable due to contrast-agent risks and protocol variability. Prior methods predict these maps from other MRI modali…

  2. arXiv cs.CV TIER_1 English(EN) · Kyu Sung Choi ·

    Spatio-Temporal Mixture-of-Modality-Experts Diffusion for Quantitative DCE-MRI Synthesis from Incomplete MR Sequences

    Quantitative maps from dynamic contrast-enhanced MRI (DCE-MRI) are essential for tumor assessment but are often unavailable due to contrast-agent risks and protocol variability. Prior methods predict these maps from other MRI modalities, yet most assume fixed, fully observed inpu…