Increased brain volumetric measurement precision from multi-site 3D T1-weighted 3 T magnetic resonance imaging by correcting geometric distortions

Nanayakkara, Nuwan D.; Arnott, Stephen R.; Scott, Christopher J.; Solovey, Igor; Liang, Shuai; Fonov, Vladimir; Gee, Tom; Broberg, Dana N; Haddad, Seyyed Mohammad Hassan; Ramirez, Joel; Berezuk, Courtney; Holmes, Melissa F.; Adamo, Sabrina; Ozzoude, Miracle; Theyers, Athena; Sujanthan, Sujeevini; Zamyadi, Mojdeh; Casaubon, Leanne K.; Dowlatshahi, Dar; Mandzia, Jennifer; Sahlas, Demetrios J.; Saposnik, Gustavo; Hassan, Ayman; Swartz, Richard H.; Strother, Stephen C.; Szilagyi, Gregory M.; Black, Sandra E.; Symons, Sean; Investigators, Ondri; Bartha, Robert

doi:10.1016/j.mri.2022.06.005

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…One of the main libraries is Alzheimer's Disease Neuroimaging Initiative (ADNI) [118], a large longitudinal study aiming to develop novel biomarkers to detect AD and monitor disease progression. The original ADNI cohort was collected from 2004 to 2010 and contains T1-weighted MRI [119], FDG-PET, blood, and CSF biomarkers from 800 subjects [120]. Additional cohorts, ADNI-Go and ADNI-2, extended the longitudinal study of ADNI-1 while also encompassing a broader range of the stages of AD, adding 200 new subjects with early MCI [121].…”

Section: Sources Of Datamentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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