# Structural Similarity based Anatomical and Functional Brain Imaging Fusion

## TL;DR

This paper introduces an unsupervised CNN method that fuses MRI-PET images using SSIM as a loss function, enhancing visualization and quantitative assessment of fused medical images for better diagnosis.

## Contribution

A novel end-to-end unsupervised CNN approach utilizing SSIM for effective multimodal brain image fusion and visualization.

## Key findings

- Improved visual perception of fused images.
- Favorable quantitative assessment compared to previous methods.
- Effective visualization of input contributions.

## Abstract

Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image. One of the pivotal clinical applications of medical image fusion is the merging of anatomical and functional modalities for fast diagnosis of malignant tissues. In this paper, we present a novel end-to-end unsupervised learning-based Convolutional Neural Network (CNN) for fusing the high and low frequency components of MRI-PET grayscale image pairs, publicly available at ADNI, by exploiting Structural Similarity Index (SSIM) as the loss function during training. We then apply color coding for the visualization of the fused image by quantifying the contribution of each input image in terms of the partial derivatives of the fused image. We find that our fusion and visualization approach results in better visual perception of the fused image, while also comparing favorably to previous methods when applying various quantitative assessment metrics.