ResDCE-Diff : Dynamic contrast enhanced MRI translation in prostate cancer using residual denoising diffusion models

Kishore Kumar, Sriprabha Ramanarayanan, Keerthi Ram, Harsh Kumar Agarwal, Ramesh Venkatesan, Mohanasankar Sivaprakasam

Keywords: Prostate cancer, Dynamic contrast enhanced MRI (DCE-MRI), Diffusion model, Medical image-to-image translation

TL;DR: Dynamic contrast enhanced MRI synthesis using diffusion models from multi-modal inputs

Abstract: Dynamic contrast enhanced MRI (DCE-MRI) identifies early perfusion patterns of aggressive prostate tumors, but its reliance on gadolinium contrast agents limits wider clinical adoption due to safety concerns. Recently, diffusion models offers a potential solution to synthesize contrast-enhanced images directly from non-contrast MRI. Previous diffusion models for prostate DCE-MRI require long inference times as they need hundreds or thousands of sampling steps limiting practical use. Moreover, the reverse generation process for DCE-MRI synthesis starts from pure noise without explicitly utilizing the prior information present in the non-contrast inputs in the diffusion process. We propose ResDCE-diff, a residual denoising diffusion model to synthesize early and late phase DCE-MRI images from non-contrast multimodal inputs (T2, Apparent diffusion coefficient and pre-contrast MRI). The diffusion process shifts anatomical, micro-structurally relevant and physics-informed residual features between the non-contrast inputs and DCE-MRI targets. Extensive experiments using PROSTATEx dataset shows that ResDCE-diff, (i) consistently outperforms previous methods across early and late DCE-MRI phases with improvement margins of +1.29 db and +1.17 dB in PSNR, +0.04 and +0.03 in SSIM respectively, (ii) requires significantly lesser diffusion steps (~15) compared to the baseline diffusion model, and (iii) exhibits relatively higher diagnostically relevant synthesis quality.

Primary Subject Area: Image Synthesis

Secondary Subject Area: Generative Models

Registration Requirement: Yes

Reproducibility: https://anonymous.4open.science/r/KSK_RDD/

Visa & Travel: Yes

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 207