The emergence of clinical data warehouses (CDWs), which contain the medical
data of millions of patients, has paved the way for vast data sharing for
research. The quality of MRIs gathered in CDWs differs greatly from what is
observed in research settings and reflects a certain clinical reality.
Consequently, a significant proportion of these images turns out to be unusable
due to their poor quality. Given the massive volume of MRIs contained in CDWs,
the manual rating of image quality is impossible. Thus, it is necessary to
develop an automated solution capable of effectively identifying corrupted
images in CDWs. This study presents an innovative transfer learning method for
automated quality control of 3D gradient echo T1-weighted brain MRIs within a
CDW, leveraging artefact simulation. We first intentionally corrupt images from
research datasets by inducing poorer contrast, adding noise and introducing
motion artefacts. Subsequently, three artefact-specific models are pre-trained
using these corrupted images to detect distinct types of artefacts. Finally,
the models are generalised to routine clinical data through a transfer learning
technique, utilising 3660 manually annotated images. The overall image quality
is inferred from the results of the three models, each designed to detect a
specific type of artefact. Our method was validated on an independent test set
of 385 3D gradient echo T1-weighted MRIs. Our proposed approach achieved
excellent results for the detection of bad quality MRIs, with a balanced
accuracy of over 87%, surpassing our previous approach by 3.5 percent points.
Additionally, we achieved a satisfactory balanced accuracy of 79% for the
detection of moderate quality MRIs, outperforming our previous performance by 5
percent points. Our framework provides a valuable tool for exploiting the
potential of MRIs in CDWs.

使用迁移学习方法，本研究提出了一种创新的方案，通过模拟图像伪影，自动进行临床数据仓库中三维梯度回波 T1 加权脑部 MRI 图像的质量控制，达到了优秀的识别质量差的 MRI 图像的结果，对中等质量的 MRI 图像也取得了令人满意的表现，为充分利用临床数据仓库中的 MRI 图像提供了有价值的工具。

基于仿真伪影的迁移学习方法的临床数据仓库中脑部 T1 加权 MRI 自动质量评估

Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical  Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation

In this work, we present X-Diffusion, a cross-sectional diffusion model
tailored for Magnetic Resonance Imaging (MRI) data. X-Diffusion is capable of
generating the entire MRI volume from just a single MRI slice or optionally
from few multiple slices, setting new benchmarks in the precision of
synthesized MRIs from extremely sparse observations. The uniqueness lies in the
novel view-conditional training and inference of X-Diffusion on MRI volumes,
allowing for generalized MRI learning. Our evaluations span both brain tumour
MRIs from the BRATS dataset and full-body MRIs from the UK Biobank dataset.
Utilizing the paired pre-registered Dual-energy X-ray Absorptiometry (DXA) and
MRI modalities in the UK Biobank dataset, X-Diffusion is able to generate
detailed 3D MRI volume from a single full-body DXA. Remarkably, the resultant
MRIs not only stand out in precision on unseen examples (surpassing
state-of-the-art results by large margins) but also flawlessly retain essential
features of the original MRI, including tumour profiles, spine curvature, brain
volume, and beyond. Furthermore, the trained X-Diffusion model on the MRI
datasets attains a generalization capacity out-of-domain (e.g. generating knee
MRIs even though it is trained on brains). The code is available on the project
website this https URL .

X-Diffusion 是一种为磁共振成像 (MRI) 数据量身定制的横截面扩散模型，能够从一张或少量多张 MRI 切片中生成完整的 MRI 体积，实现了从极度稀疏的观测得到合成 MRI 的精度方面的新突破。

X-Diffusion：使用横截面扩散模型从单张图像生成详细的 3D MRI 体积

X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image  Using Cross-Sectional Diffusion Models

Brain magnetic resonance imaging (MRI) has been extensively employed across
clinical and research fields, but often exhibits sensitivity to site effects
arising from nonbiological variations such as differences in field strength and
scanner vendors. Numerous retrospective MRI harmonization techniques have
demonstrated encouraging outcomes in reducing the site effects at image level.
However, existing methods generally suffer from high computational requirements
and limited generalizability, restricting their applicability to unseen MRIs.
In this paper, we design a novel disentangled latent energy-based style
translation (DLEST) framework for unpaired image-level MRI harmonization,
consisting of (1) site-invariant image generation (SIG), (2) site-specific
style translation (SST), and (3) site-specific MRI synthesis (SMS).
Specifically, the SIG employs a latent autoencoder to encode MRIs into a
low-dimensional latent space and reconstruct MRIs from latent codes. The SST
utilizes an energy-based model to comprehend the global latent distribution of
a target domain and translate source latent codes toward the target domain,
while SMS enables MRI synthesis with a target-specific style. By disentangling
image generation and style translation in latent space, the DLEST can achieve
efficient style translation. Our model was trained on T1-weighted MRIs from a
public dataset (with 3,984 subjects across 58 acquisition sites/settings) and
validated on an independent dataset (with 9 traveling subjects scanned in 11
sites/settings) in 4 tasks: (1) histogram and clustering comparison, (2) site
classification, (3) brain tissue segmentation, and (4) site-specific MRI
synthesis. Qualitative and quantitative results demonstrate the superiority of
our method over several state-of-the-arts.

设计了一种新颖的分离的基于潜在能量的风格翻译（DLEST）框架，用于未配对的图像级 MRI 协调，包括（1）无场地相关图像生成（SIG），（2）场地特定风格翻译（SST）和（3）场地特定 MRI 合成（SMS）。