Abstract: Diffusion magnetic resonance imaging (DMRI) is a key tool for non-invasively studying brain microstructure and connectivity, but its clinical utility is limited by low signal- to-noise ratio, long scan times, and hardware constraints. While recent deep learning methods have improved DMRI quality through denoising and non-linear mapping, they lack flexibility and efficiency.

This talk will present FastDTI, a 3D scale-arbitrary super-resolution autoencoder with a residual dense network architecture for diffusion tensor imaging. Using curriculum learning, FastDTI enables non-integer upscaling and produces high-fidelity clinical maps — mean diffusivity (MD), fractional anisotropy (FA), and principal diffusion direction (D-Maps) from just six diffusion-weighted images. On paired 3T/7T HCP Young Adult data, FastDTI delivers a 35% PSNR improvement over traditional methods and achieves a 6x inference speedup over state-of-the-art deep learning models via its single-pass design, combining both accuracy and efficiency.

All are cordially invited.

Event Details
Title: Fast-DTI: A 3D Scale-arbitrary Super-resolution Autoencoder Residual Dense Network for DTI
Date: April 21, 2026 at 2:30 PM
Venue: Google Meet (https://meet.google.com/bqi-okxk-yfi)
Speaker: Mr. Arihant Jain (EE21S061)
Guide: Dr. Mohanasankar S
Type: MS seminar

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