The UltraCortex repository (this https URL) houses magnetic
resonance imaging data of the human brain obtained at an ultra-high field
strength of 9.4 T. It contains 86 structural MR images with spatial resolutions
ranging from 0.6 to 0.8 mm. Additionally, the repository includes segmentations
of 12 brains into gray and white matter compartments. These segmentations have
been independently validated by two expert neuroradiologists, thus establishing
them as a reliable gold standard. This resource provides researchers with
access to high-quality brain imaging data and validated segmentations,
facilitating neuroimaging studies and advancing our understanding of brain
structure and function. Existing repositories do not accommodate field
strengths beyond 7 T, nor do they offer validated segmentations, underscoring
the significance of this new resource.

这篇研究论文介绍了一个新的资源，即 UltraCortex 数据库，其中包含了经过专家验证的高品质脑成像数据和分割，对于神经影像研究和推动我们对脑结构和功能的理解具有重要意义。

UltraCortex：亚毫米级超高场 9.4 T1 脑 MR 图像采集与手动大脑皮层分割

UltraCortex: Submillimeter Ultra-High Field 9.4 T1 Brain MR Image  Collection and Manual Cortical Segmentations

Surface-based cortical registration is an important topic in medical image
analysis and facilitates many downstream applications. Current approaches for
cortical registration are mainly driven by geometric features, such as sulcal
depth and curvature, and often assume that registration of folding patterns
leads to alignment of brain function. However, functional variability of
anatomically corresponding areas across subjects has been widely reported,
particularly in higher-order cognitive areas. In this work, we present JOSA, a
novel cortical registration framework that jointly models the mismatch between
geometry and function while simultaneously learning an unbiased
population-specific atlas. Using a semi-supervised training strategy, JOSA
achieves superior registration performance in both geometry and function to the
state-of-the-art methods but without requiring functional data at inference.
This learning framework can be extended to any auxiliary data to guide
spherical registration that is available during training but is difficult or
impossible to obtain during inference, such as parcellations, architectonic
identity, transcriptomic information, and molecular profiles. By recognizing
the mismatch between geometry and function, JOSA provides new insights into the
future development of registration methods using joint analysis of the brain
structure and function.

JOSA 是一种新颖的皮层注册框架，它同时建模几何和功能之间的不匹配，以及学习一个无偏的人口特异性地图，从而在几何和功能方面实现了优于现有方法的注册性能。