AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI Synthesis
Journal:
arXiv
Published Date:
Feb 4, 2025
Abstract
Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a medical
imaging technique that plays a crucial role in the detailed visualization and
identification of tissue perfusion in abnormal lesions and radiological
suggestions for biopsy. However, DCE-MRI involves the administration of a
Gadolinium based (Gad) contrast agent, which is associated with a risk of
toxicity in the body. Previous deep learning approaches that synthesize DCE-MR
images employ unimodal non-contrast or low-dose contrast MRI images lacking
focus on the local perfusion information within the anatomy of interest. We
propose AAD-DCE, a generative adversarial network (GAN) with an aggregated
attention discriminator module consisting of global and local discriminators.
The discriminators provide a spatial embedded attention map to drive the
generator to synthesize early and late response DCE-MRI images. Our method
employs multimodal inputs - T2 weighted (T2W), Apparent Diffusion Coefficient
(ADC), and T1 pre-contrast for image synthesis. Extensive comparative and
ablation studies on the ProstateX dataset show that our model (i) is agnostic
to various generator benchmarks and (ii) outperforms other DCE-MRI synthesis
approaches with improvement margins of +0.64 dB PSNR, +0.0518 SSIM, -0.015 MAE
for early response and +0.1 dB PSNR, +0.0424 SSIM, -0.021 MAE for late
response, and (ii) emphasize the importance of attention ensembling. Our code
is available at https://github.com/bhartidivya/AAD-DCE.
