This dataset contains synthetic data created by a generative AI model.

This dataset is a collection that includes the 6448 synthetic aging brain T1 MRI scans derived from two data sets by our proposed methodology (the following paper [1]). We augmented the HEALTHY longitudinal brain MRI data with corresponding segmentations to simulate the access of a scan per subject every 6 months in these cohorts.

Keywords: Radiology, Annotated, Brain, MRI, Synthetic, Brain aging, Synthetic brain aging, Medical image generation.

Sample images

Sample images with reduced image quality. Please click to preview.

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Dataset information

Short name	SHBAMRI
Origin	Synthetic
Cite as	Jingru Fu, Antonios Tzortzakakis, José Barroso, Eric Westman, Daniel Ferreira, and Rodrigo Moreno (2023) Synthetic healthy brain aging MRIs with segmentation masks doi:10.23698/aida/synthetic/shbamri
Field	Radiology
Organ	Brain
Age span
Title	Synthetic healthy brain aging MRIs with segmentation masks
Author	Jingru Fu Antonios Tzortzakakis José Barroso Eric Westman Daniel Ferreira Rodrigo Moreno
Year	2023
DOI	doi:10.23698/aida/synthetic/shbamri
Status	Completed
Version	1.0.0
Scans	6448
Annotations	6448
Size	52.4GB
Resolution	160x160x192, 1X1X1 mm
Modality	MR
Scanner
Stain
Phase
References	Fast 3D image generation for healthy brain aging using diffeomorphic registration. Fu, Jingru and Tzortzakakis, Antonios and Barroso, José and Westman, Eric and Ferreira, Daniel and Moreno, Rodrigo and for the Alzheimer's Disease Neuroimaging Initiative, 2022. doi: 10.1002/hbm.26165 OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease. Pamela J LaMontagne, Tammie L.S. Benzinger, John C. Morris, Sarah Keefe, Russ Hornbeck, Chengjie Xiong, Elizabeth Grant, Jason Hassenstab, Krista Moulder, Andrei Vlassenko, Marcus E. Raichle, Carlos Cruchaga, Daniel Marcus, 2019. medRxiv. doi: 10.1101/2019.12.13.19014902 The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Jack Jr C R, Bernstein M A, Fox N C, et al. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2008, 27(4): 685-691. Muehlboeck J S, Westman E, Simmons A. TheHiveDB image data management and analysis framework[J]. Frontiers in neuroinformatics, 2014, 7: 49. doi: 10.3389/fninf.2013.00049
Copyright	Copyright 2023 KTH, Jingru Fu, KTH, Rodrigo Moreno
Access	Available under the following licenses, described in the License section below. Controlled access Free for use in legal and ethical medical diagnostics research and education. AIDA BY license Free for use within AIDA with attribution.

Annotation

All original segmentations were collected by using FreeSurfer (aparc+aseg.mgz). Synthetic images are segmented based on ground truth segmentations using registration, more information can be found in [1].

License

Controlled access

Free for use in legal and ethical medical diagnostics research and education. Please contact the dataset provider for terms of access.

You are invited to send an access request email from your institutional account.

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To: jingruf@kth.se
cc: rodmore@kth.se,daniel.ferreira.padilla@ki.se,aida-data@nbis.se
Subject: Requesting access to dataset doi:10.23698/aida/synthetic/shbamri
Hi!

I work at INSTITUTION in COUNTRY, emailing from my institutional account.

I would like to access the dataset doi:10.23698/aida/synthetic/shbamri, for use in ethical and legal medical diagnostics research. Could you please send me a data sharing agreement template?

Our planned use of the data can be summarized as:

BRIEF_DESCRIPTION_OF_PLANNED_ACTIVITIES

Dataset: https://datahub.aida.scilifelab.se/10.23698/aida/synthetic/shbamri

Example agreement template: https://datahub.aida.scilifelab.se/sharing/templates/

Template placeholders:

Name of research PI ("Recipient Scientist"): PI_NAME (cc here)
Title of research PI: PI_TITLE (ph d or better, in relevant field)

Name of institution: INSTITUTION_NAME
Name of department: DEPARTMENT_NAME
Institution postal address: POSTAL_ADDRESS

Name of authorized signatory: SIGNATORY_NAME (cc here)
Title of authorized signatory: SIGNATORY_TITLE

Also, I would like to ALTERNATIVELY_WOULD_NOT_LIKE_TO be included in the public record of data sharing facilitated by AIDA:

https://docs.google.com/spreadsheets/d/1fl2BwZJ4rivOKzOCy5pAnxU8N1CyoF86BTCnH-rBV04

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(If "not", then only institution/department information will be included.)

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AIDA BY license

Copyright 2023 KTH, Jingru Fu, KTH, Rodrigo Moreno

Permission to use, copy, modify, and/or distribute this data within Analytic Imaging Diagnostics Arena (AIDA) for the purpose of medical diagnostics research with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies, and that publications resulting from the use of this data cite the following works:

Jingru Fu, Antonios Tzortzakakis, José Barroso, Eric Westman, Daniel Ferreira, and Rodrigo Moreno (2023) Synthetic healthy brain aging MRIs with segmentation masks doi:10.23698/aida/synthetic/shbamri.

Fast 3D image generation for healthy brain aging using diffeomorphic registration. Fu, Jingru and Tzortzakakis, Antonios and Barroso, José and Westman, Eric and Ferreira, Daniel and Moreno, Rodrigo and for the Alzheimer’s Disease Neuroimaging Initiative, 2022. doi: 10.1002/hbm.26165

OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease. Pamela J LaMontagne, Tammie L.S. Benzinger, John C. Morris, Sarah Keefe, Russ Hornbeck, Chengjie Xiong, Elizabeth Grant, Jason Hassenstab, Krista Moulder, Andrei Vlassenko, Marcus E. Raichle, Carlos Cruchaga, Daniel Marcus, 2019. medRxiv. doi: 10.1101/2019.12.13.19014902

The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Jack Jr C R, Bernstein M A, Fox N C, et al. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2008, 27(4): 685-691.

Muehlboeck J S, Westman E, Simmons A. TheHiveDB image data management and analysis framework[J]. Frontiers in neuroinformatics, 2014, 7: 49. doi: 10.3389/fninf.2013.00049

THE DATA IS PROVIDED “AS IS” AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS DATA INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR CHARACTERISTICS OF THIS DATA.