  We introduce a new framework called DiffGEPCI for cross-modality generation
in magnetic resonance imaging (MRI) using a 2.5D conditional diffusion model.
DiffGEPCI can synthesize high-quality Fluid Attenuated Inversion Recovery
(FLAIR) and Magnetization Prepared-Rapid Gradient Echo (MPRAGE) images, without
acquiring corresponding measurements, by leveraging multi-Gradient-Recalled
Echo (mGRE) MRI signals as conditional inputs. DiffGEPCI operates in a two-step
fashion: it initially estimates a 3D volume slice-by-slice using the axial
plane and subsequently applies a refinement algorithm (referred to as 2.5D) to
enhance the quality of the coronal and sagittal planes. Experimental validation
on real mGRE data shows that DiffGEPCI achieves excellent performance,
surpassing generative adversarial networks (GANs) and traditional diffusion
models.
