# A Multimodal Deep Network for the Reconstruction of T2W MR Images

## TL;DR

This paper introduces a deep learning model that reconstructs high-quality T2W MR images from subsampled data, reducing acquisition time and motion artifacts in brain lesion imaging.

## Contribution

It presents a novel multimodal deep network that accelerates MR image reconstruction by effectively utilizing reduced k-space data.

## Key findings

- High-quality reconstruction of subsampled MR images
- Significant reduction in MR acquisition time
- Effective preservation of lesion visibility

## Abstract

Multiple sclerosis is one of the most common chronic neurological diseases affecting the central nervous system. Lesions produced by the MS can be observed through two modalities of magnetic resonance (MR), known as T2W and FLAIR sequences, both providing useful information for formulating a diagnosis. However, long acquisition time makes the acquired MR image vulnerable to motion artifacts. This leads to the need of accelerating the execution of the MR analysis. In this paper, we present a deep learning method that is able to reconstruct subsampled MR images obtained by reducing the k-space data, while maintaining a high image quality that can be used to observe brain lesions. The proposed method exploits the multimodal approach of neural networks and it also focuses on the data acquisition and processing stages to reduce execution time of the MR analysis. Results prove the effectiveness of the proposed method in reconstructing subsampled MR images while saving execution time.