# Systematic Review of Techniques in Brain Image Synthesis using Deep Learning

arXiv: https://arxiv.org/abs/2309.04511 · HTML: https://mlsanity.com/paper/2309.04511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [Developing Brain Atlas through Deep Learning](https://mlsanity.com/paper/1807.03440/) (2018)

## Built on by (most cited first, 1 of 1)

- [BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI](https://mlsanity.com/paper/2310.08608/) (2023)

## Abstract (arXiv)

This review paper delves into the present state of medical imaging, with a specific focus on the use of deep learning techniques for brain image synthesis. The need for medical image synthesis to improve diagnostic accuracy and decrease invasiveness in medical procedures is emphasized, along with the role of deep learning in enabling these advancements. The paper examines various methods and techniques for brain image synthesis, including 2D to 3D constructions, MRI synthesis, and the use of transformers. It also addresses limitations and challenges faced in these methods, such as obtaining well-curated training data and addressing brain ultrasound issues. The review concludes by exploring the future potential of this field and the opportunities for further advancements in medical imaging using deep learning techniques. The significance of transformers and their potential to revolutionize the medical imaging field is highlighted. Additionally, the paper discusses the potential solutions to the shortcomings and limitations faced in this field. The review provides researchers with an updated reference on the present state of the field and aims to inspire further research and bridge the gap between the present state of medical imaging and the future possibilities offered by deep learning techniques.