  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.
