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WarpDrive:通过手动细化改进空间归一化

WarpDrive: Improving spatial normalization using manual refinements.

作者信息

Oxenford Simón, Ríos Ana Sofía, Hollunder Barbara, Neudorfer Clemens, Boutet Alexandre, Elias Gavin J B, Germann Jurgen, Loh Aaron, Deeb Wissam, Salvato Bryan, Almeida Leonardo, Foote Kelly D, Amaral Robert, Rosenberg Paul B, Tang-Wai David F, Wolk David A, Burke Anna D, Sabbagh Marwan N, Salloway Stephen, Chakravarty M Mallar, Smith Gwenn S, Lyketsos Constantine G, Okun Michael S, Anderson William S, Mari Zoltan, Ponce Francisco A, Lozano Andres, Neumann Wolf-Julian, Al-Fatly Bassam, Horn Andreas

机构信息

Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

出版信息

Med Image Anal. 2024 Jan;91:103041. doi: 10.1016/j.media.2023.103041. Epub 2023 Nov 19.

Abstract

Spatial normalization-the process of mapping subject brain images to an average template brain-has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.

摘要

空间归一化——将受试者脑图像映射到平均模板脑的过程——在过去20多年里已发展成为一种可靠的方法,有助于跨患者、中心和模态比较脑成像结果。虽然总体上是成功的,但有时这个自动过程会产生次优结果,特别是在处理具有广泛神经退行性变和萎缩模式的大脑时,或者在需要特定区域的高精度时。在这里,我们介绍WarpDrive,一种用于自动配准后手动优化图像对齐的新型工具。我们表明,该工具应用于接受深部脑刺激手术的阿尔茨海默病患者队列中,有助于创建更准确的数据表示以及有意义的模型来解释患者的预后。该工具旨在处理任何类型的3D成像数据,还允许在高分辨率成像中进行优化,包括组织学和多种模态,以精确地将多个数据源聚合在一起。

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本文引用的文献

1
A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI.
Sci Rep. 2023 Apr 24;13(1):6657. doi: 10.1038/s41598-023-33781-0.
2
Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks.
Neuroimage. 2023 Mar;268:119862. doi: 10.1016/j.neuroimage.2023.119862. Epub 2023 Jan 5.
3
4
Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.
IEEE Trans Med Imaging. 2023 Mar;42(3):697-712. doi: 10.1109/TMI.2022.3213983. Epub 2023 Mar 2.
5
Diminishing Uncertainty Within the Training Pool: Active Learning for Medical Image Segmentation.
IEEE Trans Med Imaging. 2021 Oct;40(10):2534-2547. doi: 10.1109/TMI.2020.3048055. Epub 2021 Sep 30.
6
A high-resolution in vivo magnetic resonance imaging atlas of the human hypothalamic region.
Sci Data. 2020 Sep 15;7(1):305. doi: 10.1038/s41597-020-00644-6.
8
Deep brain stimulation: Imaging on a group level.
Neuroimage. 2020 Oct 1;219:117018. doi: 10.1016/j.neuroimage.2020.117018. Epub 2020 Jun 4.
9
7 Tesla MRI of the ex vivo human brain at 100 micron resolution.
Sci Data. 2019 Oct 30;6(1):244. doi: 10.1038/s41597-019-0254-8.
10
An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases.
Sci Data. 2019 Oct 17;6(1):210. doi: 10.1038/s41597-019-0217-0.

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