  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.
