  Multi-modal images play a crucial role in comprehensive evaluations in
medical image analysis providing complementary information for identifying
clinically important biomarkers. However, in clinical practice, acquiring
multiple modalities can be challenging due to reasons such as scan cost,
limited scan time, and safety considerations. In this paper, we propose a model
based on the latent diffusion model (LDM) that leverages switchable blocks for
image-to-image translation in 3D medical images without patch cropping. The 3D
LDM combined with conditioning using the target modality allows generating
high-quality target modality in 3D overcoming the shortcoming of the missing
out-of-slice information in 2D generation methods. The switchable block, noted
as multiple switchable spatially adaptive normalization (MS-SPADE), dynamically
transforms source latents to the desired style of the target latents to help
with the diffusion process. The MS-SPADE block allows us to have one single
model to tackle many translation tasks of one source modality to various
targets removing the need for many translation models for different scenarios.
Our model exhibited successful image synthesis across different source-target
modality scenarios and surpassed other models in quantitative evaluations
tested on multi-modal brain magnetic resonance imaging datasets of four
different modalities and an independent IXI dataset. Our model demonstrated
successful image synthesis across various modalities even allowing for
one-to-many modality translations. Furthermore, it outperformed other
one-to-one translation models in quantitative evaluations.
