Paper ID: 2310.08608

BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI

Shubham Singh, Dr. Mrunal Bewoor, Ammar Ranapurwala, Satyam Rai, Sheetal Patil

The study presents a deep learning framework aimed at synthesizing 3D MRI volumes from three-dimensional ultrasound images of the brain utilizing the Pix2Pix GAN model. The process involves inputting a 3D volume of ultrasounds into a UNET generator and patch discriminator, generating a corresponding 3D volume of MRI. Model performance was evaluated using losses on the discriminator and generator applied to a dataset of 3D ultrasound and MRI images. The results indicate that the synthesized MRI images exhibit some similarity to the expected outcomes. Despite challenges related to dataset size, computational resources, and technical complexities, the method successfully generated MRI volume with a satisfactory similarity score meant to serve as a baseline for further research. It underscores the potential of deep learning-based volume synthesis techniques for ultrasound to MRI conversion, showcasing their viability for medical applications. Further refinement and exploration are warranted for enhanced clinical relevance.

Submitted: Oct 11, 2023