PREDICTOM MR Protocol: Rationales, Design and Initial Validation
Poster No:
110
Submission Type:
Abstract Submission
Authors:
Brice Fernandez1, Ana Beatriz Solana2, David Lythgoe3, Flavio Dell'Acqua3, Fernando Zelaya3, Steven Williams3, Ashwin Venkataraman3, Maarten Naeyaert4, Peter Van Schuerbeek4, Hubert Raeymaekers4, Ralph Noeske5, Julie Poujol1, Brian Burns6, David Shin7, Rachid Mahdjoub1, Suchandrima Banerjee7, PREDICTOM Consortium8
Institutions:
1GE HealthCare, Buc, France, 2GE HealthCare, Munich, Germany, 3King's College London, London, United Kingdom, 4UZ Brussel, Brussels, Belgium, 5GE HealthCare, Berlin, Germany, 6GE HealthCare, Seattle, WA, 7GE HealthCare, Menlo Park, CA, 8IHI JU No 101132356, UKRI No 10083467, KCL 10083181, Exeter No 10091560, Geneva SERI No 113152304, Stavanger, Norway
First Author:
Co-Author(s):
PREDICTOM Consortium
IHI JU No 101132356, UKRI No 10083467, KCL 10083181, Exeter No 10091560, Geneva SERI No 113152304
Stavanger, Norway
IHI JU No 101132356, UKRI No 10083467, KCL 10083181, Exeter No 10091560, Geneva SERI No 113152304
Stavanger, Norway
Introduction:
The PREDICTOM (Prediction of Neurodegenerative Disease using an AI driven Screening Platform – www.predictom.eu) project is an EU funded project aimed at developing new biomarkers to identify people at high risk of developing Alzheimer Disease (AD) (Brem 2024).
The primary objective is to test the utility of individual or combined existing and novel biomarkers, collected remotely (e.g. at home behavioral cognitive tests, etc) and in primary care clinics including digital, physiological, biofluids and imaging data.
Here, we aim to describe the rationale behind the MR protocol that will be used at the seven different clinical centers across Europe in the PREDICTOM study.
The primary objective is to test the utility of individual or combined existing and novel biomarkers, collected remotely (e.g. at home behavioral cognitive tests, etc) and in primary care clinics including digital, physiological, biofluids and imaging data.
Here, we aim to describe the rationale behind the MR protocol that will be used at the seven different clinical centers across Europe in the PREDICTOM study.
Methods:
The PREDICTOM study will collect MR data from 615 subjects, downselected from a 4000 subject cohort tested at home (level 1) in a mix of high and low of developing AD based on level 1 (target ratio: 80/20% at higher/lower risk). A lumbar puncture and/or amyloid PET will be performed in the 615 subjects to confirm or rule out AD using established biomarkers. The targeted population will be healthy and over 50 years old.
Seven clinical centers across Europe equipped with 3T MR scanners from two different vendors and six different MR models will acquire the MR data.
Built on ADNI4 (Arani, 2024), the PREDICTOM MR protocol is designed to target only modern clinical MR scanners (3T, 32+ channels coils). It is intended to be a comprehensive protocol containing the necessary MR data for extracting the most promising anatomical and functional features for Alzheimer Disease (i.e. cortical thickness, atrophy, QSM, connectivity, myelin, etc) (Struyfs 2020, Esrael 2021, Paul 2024). It will contain the necessary data for modern surface-based processing (Glasser 2013) to explore structural vascular, functional, microstructural and metabolic aspects that might provide insights into early onset of the disease.
As PREDICTOM is a multicenter study, the protocol must be harmonized to reduce the bias across centers/scanners to a reasonable level. The total exam time will not exceed 1 hour including the subject positioning.
Seven clinical centers across Europe equipped with 3T MR scanners from two different vendors and six different MR models will acquire the MR data.
Built on ADNI4 (Arani, 2024), the PREDICTOM MR protocol is designed to target only modern clinical MR scanners (3T, 32+ channels coils). It is intended to be a comprehensive protocol containing the necessary MR data for extracting the most promising anatomical and functional features for Alzheimer Disease (i.e. cortical thickness, atrophy, QSM, connectivity, myelin, etc) (Struyfs 2020, Esrael 2021, Paul 2024). It will contain the necessary data for modern surface-based processing (Glasser 2013) to explore structural vascular, functional, microstructural and metabolic aspects that might provide insights into early onset of the disease.
As PREDICTOM is a multicenter study, the protocol must be harmonized to reduce the bias across centers/scanners to a reasonable level. The total exam time will not exceed 1 hour including the subject positioning.
Results:
PREDICTOM MRI protocol contains eight different datatypes:
(1) 3D T1w MPRAGE
(2) 3D T2w FSE
(3) 3D T2w FLAIR
(4) 3D T2*w for QSM/SWI
(5) Single voxel spectroscopy semiLASER
(6) 2D resting state fMRI using SMS EPI and acquisitions for distortion correction
(7) 3D Pseudo Continuous Arterial Spin Labelling
(8) 2D Multi-shell Diffusion-weighted Imaging and acquisitions for distortion correction.
Some illustrations and key acquisition parameters are given in figs. 1 and 2.
The protocol starts with a 1mm 3D-T1w followed by a 3D-T2w and T2w-FLAIR.
The T2*w-QSM acquisition is aligned with the QSM Consensus recommendation (Bilgic 2024). The same data can be used to generate SWI if needed. The choice of QSM was driven by its performance to better detect microbleeds (Lee 2023).
The single voxel proton MRS acquisition has been included due to consistent evidence of alterations in N-acetylaspartate and myo-Inositol in the posterior cingulate cortex in early stages of AD (Maul 2020).
The rsfMRI is performed at 2.7mm isotropic (1s temporal resolution) to be very close to the mean cortical thickness (Glasser 2016) and can be achieved by most modern scanners even though the scanner with weaker gradient performance might have to use partial Fourier. This gives a homogenous resolution across the fleet.
The multi-shell diffusion is similar to ADNI4 but the number of directions for b-value 500 was increased to 12 to better capture fast diffusion components. For the scanners with lower gradient performance, the b=2000 shell is removed.
(1) 3D T1w MPRAGE
(2) 3D T2w FSE
(3) 3D T2w FLAIR
(4) 3D T2*w for QSM/SWI
(5) Single voxel spectroscopy semiLASER
(6) 2D resting state fMRI using SMS EPI and acquisitions for distortion correction
(7) 3D Pseudo Continuous Arterial Spin Labelling
(8) 2D Multi-shell Diffusion-weighted Imaging and acquisitions for distortion correction.
Some illustrations and key acquisition parameters are given in figs. 1 and 2.
The protocol starts with a 1mm 3D-T1w followed by a 3D-T2w and T2w-FLAIR.
The T2*w-QSM acquisition is aligned with the QSM Consensus recommendation (Bilgic 2024). The same data can be used to generate SWI if needed. The choice of QSM was driven by its performance to better detect microbleeds (Lee 2023).
The single voxel proton MRS acquisition has been included due to consistent evidence of alterations in N-acetylaspartate and myo-Inositol in the posterior cingulate cortex in early stages of AD (Maul 2020).
The rsfMRI is performed at 2.7mm isotropic (1s temporal resolution) to be very close to the mean cortical thickness (Glasser 2016) and can be achieved by most modern scanners even though the scanner with weaker gradient performance might have to use partial Fourier. This gives a homogenous resolution across the fleet.
The multi-shell diffusion is similar to ADNI4 but the number of directions for b-value 500 was increased to 12 to better capture fast diffusion components. For the scanners with lower gradient performance, the b=2000 shell is removed.
·Figure 1
·Figure 2
Conclusions:
The protocol has been tested on 4 scanners (2 vendors, 3 models) and the harmonisation is still in progress. Parameter adjustments are still possible to meet the scan time requirements. The PREDICTOM MR protocol was designed to allow the exploration of existing and novel MRI biomarkers for early identification of people at high risk of developing AD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging 2
Neuroinformatics and Data Sharing:
Databasing and Data Sharing
Keywords:
Acquisition
Aging
Degenerative Disease
MR SPECTROSCOPY
MRI
STRUCTURAL MRI
Other - Alzheimer's disease
1|2Indicates the priority used for review
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Provide references using APA citation style.
Bilgic, B., Costagli, M., Chan, K. S., et al. (2024). Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magnetic resonance in medicine, 91(5), 1834–1862. https://doi.org/10.1002/mrm.30006
Brem, A. K., Khan, Z., Ashworth, M et al. (2024) Screening for Alzheimer’s disease in primary care using an AI driven screening platform: design of the PREDICTOM study, in Alzheimer’s Association International Conference (AAIC), Philadelphia, USA, July 28th 2024. https://alz.confex.com/alz/2024/meetingapp.cgi/Paper/87279
Esrael, S.M.A.M., Hamed, A.M.M., Khedr, E.M. et al. Application of diffusion tensor imaging in Alzheimer’s disease: quantification of white matter microstructural changes. Egypt J Radiol Nucl Med 52, 89 (2021). https://doi.org/10.1186/s43055-021-00460-x
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., et al., & WU-Minn HCP Consortium (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. https://doi.org/10.1016/j.neuroimage.2013.04.127
Glasser, M. F., Smith, S. M., Marcus, D. S.,et al.. (2016). The Human Connectome Project's neuroimaging approach. Nature neuroscience, 19(9), 1175–1187. https://doi.org/10.1038/nn.4361
Lee K, Ellison B, Selim M, Long NH, et al. Quantitative susceptibility mapping improves cerebral microbleed detection relative to susceptibility-weighted images. (2023) J Neuroimaging, 33(1):138-146. https://doi.org/10.1111/jon.13054
Maul S, Giegling I. & Rujescu D. Proton Magnetic Resonance Spectroscopy in Common Dementias—Current Status and Perspectives (2020) Frontiers in Psychiatry, 11. https://doi.org/10.3389/fpsyt.2020.00769
Paul SJ, Arun RT, Raghavan S, Kesavadas C, Comparative analysis of quantitative susceptibility mapping in preclinical dementia detection (2024) European Journal of Radiology, 178:11598. https://doi.org/10.1016/j.ejrad.2024.111598
Struyfs H, Sima DM, et al.. (2020) Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm. Neuroimage Clin. 2020;26:102243. https://doi.org/10.1016/j.nicl.2020.102243