# Empirical Bayesian Mixture Models for Medical Image Translation

## TL;DR

This paper introduces an interpretable Bayesian mixture model for medical image translation that can predict missing imaging modalities from limited data, demonstrating effectiveness across multiple clinical scenarios.

## Contribution

It presents a novel probabilistic generative model capable of handling missing data and training on small datasets for medical image translation tasks.

## Key findings

- Effective prediction of missing MR contrasts and CT images.
- Model performs well with limited training data.
- Validated on three clinically relevant scenarios.

## Abstract

Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpretable generative modelling approach to medical image translation. By allowing a common model for group-wise normalisation and segmentation of brain scans to handle missing data, the model allows for predicting entirely missing modalities from one, or a few, MR contrasts. Furthermore, the model can be trained on a fairly small number of subjects. The proposed model is validated on three clinically relevant scenarios. Results appear promising and show that a principled, probabilistic model of the relationship between multi-channel signal intensities can be used to infer missing modalities -- both MR contrasts and CT images.