Adversarial learning helps generative models translate MRI from source to
target sequence when lacking paired samples. However, implementing MRI
synthesis with adversarial learning in clinical settings is challenging due to
training instability and mode collapse. To address this issue, we leverage
intermediate sequences to estimate the common latent space among multi-sequence
MRI, enabling the reconstruction of distinct sequences from the common latent
space. We propose a generative model that compresses discrete representations
of each sequence to estimate the Gaussian distribution of vector-quantized
common (VQC) latent space between multiple sequences. Moreover, we improve the
latent space consistency with contrastive learning and increase model stability
by domain augmentation. Experiments using BraTS2021 dataset show that our
non-adversarial model outperforms other GAN-based methods, and VQC latent space
aids our model to achieve (1) anti-interference ability, which can eliminate
the effects of noise, bias fields, and artifacts, and (2) solid semantic
representation ability, with the potential of one-shot segmentation. Our code
is publicly available.

使用中间序列估计多序列 MRI 之间的共同潜空间，我们的非对抗模型在 BraTS2021 数据集中表现优于其他基于 GAN 的方法，VQC 潜空间可以实现 (1) 抗干扰能力，可消除噪声、偏差和伪影的影响，以及 (2) 坚实的语义表示能力，具备一次性分割的潜力。

非对抗学习：多序列 MRI 的量化共同潜在空间

Non-Adversarial Learning: Vector-Quantized Common Latent Space for  Multi-Sequence MRI

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。

深度学习在脑影像合成技术中的系统回顾

Systematic Review of Techniques in Brain Image Synthesis using Deep  Learning

Multi-sequence MRI is valuable in clinical settings for reliable diagnosis
and treatment prognosis, but some sequences may be unusable or missing for
various reasons. To address this issue, MRI synthesis is a potential solution.
Recent deep learning-based methods have achieved good performance in combining
multiple available sequences for missing sequence synthesis. Despite their
success, these methods lack the ability to quantify the contributions of
different input sequences and estimate the quality of generated images, making
it hard to be practical. Hence, we propose an explainable task-specific
synthesis network, which adapts weights automatically for specific sequence
generation tasks and provides interpretability and reliability from two sides:
(1) visualize the contribution of each input sequence in the fusion stage by a
trainable task-specific weighted average module; (2) highlight the area the
network tried to refine during synthesizing by a task-specific attention
module. We conduct experiments on the BraTS2021 dataset of 1251 subjects, and
results on arbitrary sequence synthesis indicate that the proposed method
achieves better performance than the state-of-the-art methods. Our code is
available at https://github.com/fiy2W/mri_seq2seq.

本文提出一种可解释的任务特定综合网络，通过训练可调节的任务特定加权平均模块和任务特定注意模块，可视化每个输入序列的贡献并突出网络尝试在合成过程中完善的区域。在 BraTS2021 数据集上的实验证明了该方法在任意序列合成方面具有优异的性能，是当前最先进方法的一步之遥。