BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI
This research addresses the modality disparity between ultrasound and MRI for medical diagnostics and treatment planning in neuroimaging, but it is incremental as it builds on existing GAN methods.
The work tackled the problem of synthesizing 3D MRI volumes from 3D ultrasound brain images using a modified Pix2Pix GAN, resulting in MRI volumes with a satisfactory similarity score but not meeting practical deployment standards due to dataset and computational constraints.
The work proposes a novel deep-learning framework for the synthesis of three-dimensional MRI volumes from corresponding 3D ultrasound images of the brain, leveraging a modified iteration of the Pix2Pix Generative Adversarial Network (GAN) model. Addressing the formidable challenge of bridging the modality disparity between ultrasound and MRI, this research holds promise for transformative applications in medical diagnostics and treatment planning within the neuroimaging domain. While the findings reveal a discernible degree of similarity between the synthesized MRI volumes and anticipated outcomes, they fall short of practical deployment standards, primarily due to constraints associated with dataset scale and computational resources. The methodology yields MRI volumes with a satisfactory similarity score, establishing a foundational benchmark for subsequent investigations.