Generative AI models hold great potential in creating synthetic brain MRIs
that advance neuroimaging studies by, for example, enriching data diversity.
However, the mainstay of AI research only focuses on optimizing the visual
quality (such as signal-to-noise ratio) of the synthetic MRIs while lacking
insights into their relevance to neuroscience. To gain these insights with
respect to T1-weighted MRIs, we first propose a new generative model,
BrainSynth, to synthesize metadata-conditioned (e.g., age- and sex-specific)
MRIs that achieve state-of-the-art visual quality. We then extend our
evaluation with a novel procedure to quantify anatomical plausibility, i.e.,
how well the synthetic MRIs capture macrostructural properties of brain
regions, and how accurately they encode the effects of age and sex. Results
indicate that more than half of the brain regions in our synthetic MRIs are
anatomically accurate, i.e., with a small effect size between real and
synthetic MRIs. Moreover, the anatomical plausibility varies across cortical
regions according to their geometric complexity. As is, our synthetic MRIs can
significantly improve the training of a Convolutional Neural Network to
identify accelerated aging effects in an independent study. These results
highlight the opportunities of using generative AI to aid neuroimaging research
and point to areas for further improvement.

生成式人工智能模型在创建改进神经影像研究的合成脑 MRI 方面具有巨大潜力，但目前主要研究仅侧重于优化合成 MRI 的视觉质量，缺乏对其与神经科学的关联性的深入理解。然而，通过我们提出的新的生成模型 BrainSynth，我们首先实现了在 T1 加权 MRI 方面实现最先进视觉质量的元数据条件下（例如年龄和性别具体），并通过一种新的程序扩展了我们的评估方法，以量化解剖合理性，即合成 MRI 多少地捕捉到脑区的宏观结构特性，并且准确地编码了年龄和性别的影响。结果表明，我们合成的 MRI 中有一半以上的脑区在解剖上是准确的，即真实 MRI 与合成 MRI 之间存在很小的效应。此外，解剖合理性在皮层区域之间根据其几何复杂性而异。根据现有情况，我们的合成 MRI 可以显著改善卷积神经网络在独立研究中识别加速衰老效应的训练。这些结果突出了使用生成式人工智能来辅助神经影像研究的机会，并指出了进一步改进的方向。

元数据条件生成模型合成解剖合理的 3D 脑核磁共振图像

Metadata-Conditioned Generative Models to Synthesize  Anatomically-Plausible 3D Brain MRIs

This paper introduces an innovative methodology for producing high-quality 3D
lung CT images guided by textual information. While diffusion-based generative
models are increasingly used in medical imaging, current state-of-the-art
approaches are limited to low-resolution outputs and underutilize radiology
reports' abundant information. The radiology reports can enhance the generation
process by providing additional guidance and offering fine-grained control over
the synthesis of images. Nevertheless, expanding text-guided generation to
high-resolution 3D images poses significant memory and anatomical
detail-preserving challenges. Addressing the memory issue, we introduce a
hierarchical scheme that uses a modified UNet architecture. We start by
synthesizing low-resolution images conditioned on the text, serving as a
foundation for subsequent generators for complete volumetric data. To ensure
the anatomical plausibility of the generated samples, we provide further
guidance by generating vascular, airway, and lobular segmentation masks in
conjunction with the CT images. The model demonstrates the capability to use
textual input and segmentation tasks to generate synthesized images. The
results of comparative assessments indicate that our approach exhibits superior
performance compared to the most advanced models based on GAN and diffusion
techniques, especially in accurately retaining crucial anatomical features such
as fissure lines, airways, and vascular structures. This innovation introduces
novel possibilities. This study focuses on two main objectives: (1) the
development of a method for creating images based on textual prompts and
anatomical components, and (2) the capability to generate new images
conditioning on anatomical elements. The advancements in image generation can
be applied to enhance numerous downstream tasks.

基于文本信息和解剖组成的方法生成基于文本提示的图像，并应用于多种下游任务。

MedSynV1：基于文本引导的解剖感知高保真度 3D CT 图像合成

MedSynV1: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT  Images

Today, deep convolutional neural networks (CNNs) have demonstrated
state-of-the-art performance for medical image segmentation, on various imaging
modalities and tasks. Despite early success, segmentation networks may still
generate anatomically aberrant segmentations, with holes or inaccuracies near
the object boundaries. To enforce anatomical plausibility, recent research
studies have focused on incorporating prior knowledge such as object shape or
boundary, as constraints in the loss function. Prior integrated could be
low-level referring to reformulated representations extracted from the
ground-truth segmentations, or high-level representing external medical
information such as the organ's shape or size. Over the past few years,
prior-based losses exhibited a rising interest in the research field since they
allow integration of expert knowledge while still being architecture-agnostic.
However, given the diversity of prior-based losses on different medical imaging
challenges and tasks, it has become hard to identify what loss works best for
which dataset. In this paper, we establish a benchmark of recent prior-based
losses for medical image segmentation. The main objective is to provide
intuition onto which losses to choose given a particular task or dataset. To
this end, four low-level and high-level prior-based losses are selected. The
considered losses are validated on 8 different datasets from a variety of
medical image segmentation challenges including the Decathlon, the ISLES and
the WMH challenge. Results show that whereas low-level prior-based losses can
guarantee an increase in performance over the Dice loss baseline regardless of
the dataset characteristics, high-level prior-based losses can increase
anatomical plausibility as per data characteristics.

本文建立了一个医学图像分割的基准，比较了四种先验损失函数的性能。结果表明，低级别的先验损失函数可以提高性能，而高级别的先验损失函数可以提高解剖可信度。