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.