Hybrid Deep Learning Framework for Bidirectional Medical Image Synthesis

Despite the valuable insights offered by both MRI and CT scans for brain diagnosis and treatment, limitations like cost, radiation exposure, and modality unavailability can restrict their combined use. This work addresses this challenge by proposing a novel unsupervised deep learning framework for the bidirectional synthesis of brain cross-sectional images between MRI and CT modalities. The method is evaluated on a large dataset of 2500 unpaired CT and MRI brain images. This research aligns with advancements in medical image translation, holding immense potential to streamline diagnostics, improve treatment planning, and enhance patient care by revolutionizing how medical professionals interpret diagnostic imaging and potentially reducing repeat scans and unnecessary radiation exposure.