Comparative shape analysis of 3D hippocampal reconstructions across MRI field strengths and methods
Poster No:
1562
Submission Type:
Abstract Submission
Authors:
Luis Echeverry Quiceno1, Katherine Koenig2, Álvaro Heredia Lidón3, Xavier Sevillano3, Neus Martínez Abadías4
Institutions:
1University of Barcelona, Barcelona, Cataluña, 2The Cleveland Clinic, Cleveland, OH, 3La Salle - Universitat Ramon Llull, Barcelona, Cataluña, 4University of Barcelona, Barcelona, Spain
First Author:
Co-Author(s):
Introduction:
3-Tesla (3T) magnetic resonance imaging (MRI) has been widely used to investigate brain anatomy (Alvarez-Linera, 2008; Pukenas, 2011). Quantitative measures of cortical and subcortical structures from 3T images were proposed as reliable biomarkers to improve diagnosis in neurodegenerative (Pennington et al., 2003; Durrleman et al., 2014) and psychiatric disorders (Zhu et al., 2023). However, advances using ultra-high-field 7-Tesla (7T) MRI are showing greater potential for understanding diseases such as epilepsy (Salehi et al., 2022) and Alzheimer (Perera Molligoda et al., 2023). With increased signal-to-noise and contrast-to-noise ratios, 7T MRI enables more detailed visualizations of brain structures as compared to 3T MRI (Springer et al., 2016). Voxel-based 2D analyses of subcortical regions showed improved performance at 7T (Theysohn et al., 2008; Springer et al., 2016), but 3D anatomical reconstructions of subcortical regions extracted from 3T and 7T MRI have not yet been compared. Here, we analyzed the hippocampus, a structure prone to atrophy and shape alterations in neurological disorders with memory impairment. We compared 3D hippocampal reconstructions across MRI field strengths to validate these technologies for neuroanatomy research.
Methods:
Whole-brain T1-weighted scans were acquired for six participants on a 3T Siemens Prisma (MPRAGE, 1mm³ voxel size) and a 7T Siemens Terra (MP2RAGE, 0.83mm³ voxel size). For all participants, hippocampal morphology was reconstructed using four methods: a) directly using the vtk mesh file generated by FSL FIRST (FSL), b) AFNI isosurface extraction using the hippocampal volumetric segmentation by FSL, c) FSL manually edited by an expert following the HARP protocol (Frisoni et al, 2015), and d) FreeSurfer 7.4.1. The 3D coordinates of 60 landmarks and semi-landmarks were automatically recorded on all the hippocampal 3D meshes (N=48) using deep learning methods. Geometric morphometrics and multivariate statistical analyses were conducted using R and MorphoJ to compare the hippocampal morphology from 3T and 7T MRI using the different reconstruction methods.
Results:
When the reconstructions were grouped by subject, the Procrustes distance revealed significant differences in hippocampal morphology among all subjects (p < 0.0001*) (Figure 1), indicating that regardless of the reconstruction methodology or magnetic field strength, the 3D anatomical morphology of the hippocampus can be precisely captured and quantified for individual or group comparisons. The meshes obtained by the different reconstruction methods showed qualitative and quantitative differences, in particular the meshes generated by FreeSurfer (p < 0.0001*). Finally, when hippocampi were grouped by magnetic field strength (3T vs. 7T), no statistically significant differences were detected (p = 0.9245) (Figure 2), suggesting that 3T MRI is sufficient to accurately capture the 3D hippocampal shape.
Conclusions:
Our findings showed that the 3D morphology of the whole hippocampus can be reliably captured across MRI field strengths and reconstruction methods, with 3T MRI offering resolution comparable to 7T. While 7T MRI offers higher resolution and may enhance the analysis of small brain structures, this technology is associated to higher costs, limited availability and increased patient discomfort (Theysohn et al., 2008). Our results underscore the robustness of 3T MRI for 3D hippocampal shape assessment for detecting individual and pathological changes.
This work was supported with a mobility grant from the University of Barcelona and Montcelimar Foundation; Fundación Álvaro Entrecanales-Lejeune and Joan Oró predoctoral grants (2024 FI-3 00160); and grants from Fondation Jérôme Lejeune (2020b cycle-Project No.2001), AGAUR (2021 SGR00706 and SGR0139), and Spanish Ministry of Science, Innovation, and Universities (PID2020-113609RB-C21/AEI/10.13039/501100011033).
This work was supported with a mobility grant from the University of Barcelona and Montcelimar Foundation; Fundación Álvaro Entrecanales-Lejeune and Joan Oró predoctoral grants (2024 FI-3 00160); and grants from Fondation Jérôme Lejeune (2020b cycle-Project No.2001), AGAUR (2021 SGR00706 and SGR0139), and Spanish Ministry of Science, Innovation, and Universities (PID2020-113609RB-C21/AEI/10.13039/501100011033).
Modeling and Analysis Methods:
Methods Development 1
Segmentation and Parcellation 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Keywords:
Acquisition
Computing
Data analysis
Data Registration
Morphometrics
MRI
Segmentation
STRUCTURAL MRI
Sub-Cortical
Other - Hippocampus
1|2Indicates the priority used for review
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Provide references using APA citation style.
Durrleman, S., Prastawa, M., Charon, N., Korenberg, J. R., Joshi, S., Gerig, G., & Trouvé, A. (2014). Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage, 101, 35–49. https://doi.org/10.1016/j.neuroimage.2014.06.043
Frisoni, G. B. et al. (2015). The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimer's & dementia: the journal of the Alzheimer's Association, 11(2), 111–125. https://doi.org/10.1016/j.jalz.2014.05.1756
Pennington, B. F., Moon, J., Edgin, J., Stedron, J., & Nadel, L. (2003). The neuropsychology of Down syndrome: evidence for hippocampal dysfunction. Child development, 74(1), 75–93. https://doi.org/10.1111/1467-8624.00522
Perera Molligoda Arachchige, A. S., & Garner, A. K. (2023). Seven Tesla MRI in Alzheimer's disease research: State of the art and future directions: A narrative review. AIMS neuroscience, 10(4), 401–422. https://doi.org/10.3934/Neuroscience.2023030
Pukenas, B. (2011). Normal brain anatomy on magnetic resonance imaging. Magnetic resonance imaging clinics of North America, 19(3), 429–vii. https://doi.org/10.1016/j.mric.2011.05.015
Salehi, F., Nadeem, I. M., Kwan, B. Y. M., et al. (2022). Ultra-High Field 7-Tesla Magnetic Resonance Imaging and Electroencephalography Findings in Epilepsy. Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes, 73(2), 396–402. https://doi.org/10.1177/08465371211031802
Springer, E., Dymerska, B., Cardoso, P. L., Robinson, S. D., Weisstanner, C., Wiest, R., Schmitt, B., & Trattnig, S. (2016). Comparison of Routine Brain Imaging at 3 T and 7 T. Investigative radiology, 51(8), 469–482. https://doi.org/10.1097/RLI.0000000000000256
Theysohn, J. M., Maderwald, S., Kraff, O. et al. (2008). Subjective acceptance of 7 Tesla MRI for human imaging. Magnetic Resonance Materials in Physics, 21, 63. https://doi.org/10.1007/s10334-007-0095-x
Zhu, Z., Lei, D., Qin, K. et al. (2023). Cortical and subcortical structural differences in psychostimulant-free ADHD youth with and without a family history of bipolar I disorder: a cross-sectional morphometric comparison. Translational Psychiatry, 13, 368. https://doi.org/10.1038/s41398-023-02667-0