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.

该综述论文介绍了医学影像学的现状，重点关注深度学习技术在脑图像合成中的应用。强调了医学图像合成在提高诊断准确性和减少医疗过程中侵入性方面的需求，并介绍了深度学习在实现这些进展中的作用。论文探讨了各种脑图像合成方法和技术，包括二维到三维构建、MRI 合成和变换器的使用。还讨论了这些方法面临的限制和挑战，例如获取经过精心筛选的训练数据和解决脑超声问题。综述通过探索这一领域的未来潜力和使用深度学习技术在医学影像学中的进一步发展机会来总结。强调了变换器的重要性及其在医学影像领域中革命性的潜力。此外，论文还讨论了解决该领域的缺点和限制的潜在解决方案。该综述为研究人员提供了关于该领域现状的更新参考资料，并旨在激发进一步研究，弥合医学影像学的当前状态与深度学习技术所提供的未来可能性之间的差距。