Normative T1 mapping highlights individual epileptic pathologies in the Australian Epilepsy Project
Poster No:
1867
Submission Type:
Abstract Submission
Authors:
Negin Yaghmaie1, David Vaughan2,3,4, Anaïs Burrus5,6,7, Veronica Ravano5,6,7, Bénédicte Maréchal5,6,7, Tom Hilbert5,6,7, Tobias Kober8, Molly Ireland2, David Abbott2, Heath Pardoe2,3, Daniel Staeb1, Kieran O’Brien9, Graeme Jackson2,3,4
Institutions:
1Siemens Healthcare Pty Ltd, Melbourne, Australia, 2The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, 3Faculty of Medicine, University of Melbourne, Melbourne, Australia, 4Department of Neurology, Austin Health, Melbourne, Australia, 5Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 6Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 7LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 8Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany, 9Siemens Healthcare Pty Ltd, Brisbane, Australia
First Author:
Co-Author(s):
David Vaughan
The Florey Institute of Neuroscience and Mental Health|Faculty of Medicine, University of Melbourne|Department of Neurology, Austin Health
Melbourne, Australia|Melbourne, Australia|Melbourne, Australia
The Florey Institute of Neuroscience and Mental Health|Faculty of Medicine, University of Melbourne|Department of Neurology, Austin Health
Melbourne, Australia|Melbourne, Australia|Melbourne, Australia
Anaïs Burrus
Advanced Clinical Imaging Technology, Siemens Healthcare AG|Department of Radiology, Lausanne University Hospital and University of Lausanne|LTS5, Ecole Polytechnique Fédérale de Lausanne
Lausanne, Switzerland|Lausanne, Switzerland|Lausanne, Switzerland
Advanced Clinical Imaging Technology, Siemens Healthcare AG|Department of Radiology, Lausanne University Hospital and University of Lausanne|LTS5, Ecole Polytechnique Fédérale de Lausanne
Lausanne, Switzerland|Lausanne, Switzerland|Lausanne, Switzerland
Veronica Ravano
Advanced Clinical Imaging Technology, Siemens Healthcare AG|Department of Radiology, Lausanne University Hospital and University of Lausanne|LTS5, Ecole Polytechnique Fédérale de Lausanne
Lausanne, Switzerland|Lausanne, Switzerland|Lausanne, Switzerland
Advanced Clinical Imaging Technology, Siemens Healthcare AG|Department of Radiology, Lausanne University Hospital and University of Lausanne|LTS5, Ecole Polytechnique Fédérale de Lausanne
Lausanne, Switzerland|Lausanne, Switzerland|Lausanne, Switzerland
Bénédicte Maréchal
Advanced Clinical Imaging Technology, Siemens Healthcare AG|Department of Radiology, Lausanne University Hospital and University of Lausanne|LTS5, Ecole Polytechnique Fédérale de Lausanne
Lausanne, Switzerland|Lausanne, Switzerland|Lausanne, Switzerland
Advanced Clinical Imaging Technology, Siemens Healthcare AG|Department of Radiology, Lausanne University Hospital and University of Lausanne|LTS5, Ecole Polytechnique Fédérale de Lausanne
Lausanne, Switzerland|Lausanne, Switzerland|Lausanne, Switzerland
Tom Hilbert
Advanced Clinical Imaging Technology, Siemens Healthcare AG|Department of Radiology, Lausanne University Hospital and University of Lausanne|LTS5, Ecole Polytechnique Fédérale de Lausanne
Lausanne, Switzerland|Lausanne, Switzerland|Lausanne, Switzerland
Advanced Clinical Imaging Technology, Siemens Healthcare AG|Department of Radiology, Lausanne University Hospital and University of Lausanne|LTS5, Ecole Polytechnique Fédérale de Lausanne
Lausanne, Switzerland|Lausanne, Switzerland|Lausanne, Switzerland
Tobias Kober
Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG
Erlangen, Germany
Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG
Erlangen, Germany
Heath Pardoe, PhD
The Florey Institute of Neuroscience and Mental Health|Faculty of Medicine, University of Melbourne
Melbourne, Australia|Melbourne, Australia
The Florey Institute of Neuroscience and Mental Health|Faculty of Medicine, University of Melbourne
Melbourne, Australia|Melbourne, Australia
Graeme Jackson
The Florey Institute of Neuroscience and Mental Health|Faculty of Medicine, University of Melbourne|Department of Neurology, Austin Health
Melbourne, Australia|Melbourne, Australia|Melbourne, Australia
The Florey Institute of Neuroscience and Mental Health|Faculty of Medicine, University of Melbourne|Department of Neurology, Austin Health
Melbourne, Australia|Melbourne, Australia|Melbourne, Australia
Introduction:
Epilepsy is the second most burdensome neurological condition in Australia. The Australian Epilepsy Project (AEP) (https://www.epilepsyproject.org.au) is a dedicated study to collect qualitative and quantitative MRI data in a large cohort of people with epilepsy and healthy controls. Quantitative mapping of relaxation times in MRI enhances tissue characterization, providing insights beyond qualitative imaging. T1 mapping, with normative atlases, shows promise as quantitative technique for diagnosis and monitoring of several conditions (Piredda et al., 2020, Ravano et al., 2024). In this work, we used the AEP data to create a study specific normative T1 atlas to explore the feasibility of detecting various pathologies in patients with epilepsy at the single subject level.
Methods:
The AEP study imaging protocol includes a CS 3D MP2RAGE research sequence (Marques et al., 2010, Mussard,et al., 2020) for T1 mapping, as part of a comprehensive protocol intended for both clinical diagnosis and research. Participant scans were obtained on a 3T scanner (MAGNETOM PrismaFit, Siemens Healthineers, Forchheim, Germany), with MP2RAGE parameters of voxel size 1.0mm3, TE 2.88ms, TI 700ms/2500ms, TR 5000ms, FA 4°/5°, BW 240Hz/px, 4.6x undersampling, TA 4:03 min. Other clinically relevant sequences (T1w MPRAGE, 3D FLAIR, 2D coronal T2w TSE, etc.) were reported by a subspecialist epilepsy neuroradiologist, independent of the T1 map findings.
A normative voxel-wise T1 atlas was generated using AEP data from 147 healthy controls (HC), age range: 18-67, mean age 41.5 ± 13.3 years, using the approach described in (Piredda et al., 2020). Skull-stripped T1 maps were registered to a common study specific template (SST), and used to compute voxel wise regression models for T1 using sex (binarized as 0:F, 1:M), age, and age-squared (both mean centred). Exemplar atlas slices are demonstrated in Figure 1.
Patient T1 maps were registered to the common SST with Elastix (Klein et al., 2010) and voxelmorph (Balakrishnan et al., 2019). Z-score maps were computed for each patient, comparing the measured T1 values and the expected normative values. The single-subject analysis was limited to areas where the coefficient of variability (COV=RMSE/Intercept) was below 10% in the normative atlas, resulting in the exclusion of regions with high inter-subject variability such as cortical regions.
A subset of patients (n=8) was selected from the AEP cohort to demonstrate a range of epileptogenic pathologies. For each patient, the highest z-score cluster was identified and visually inspected to assess its concordance with the clinico-radiological cause of their epilepsy.
A normative voxel-wise T1 atlas was generated using AEP data from 147 healthy controls (HC), age range: 18-67, mean age 41.5 ± 13.3 years, using the approach described in (Piredda et al., 2020). Skull-stripped T1 maps were registered to a common study specific template (SST), and used to compute voxel wise regression models for T1 using sex (binarized as 0:F, 1:M), age, and age-squared (both mean centred). Exemplar atlas slices are demonstrated in Figure 1.
Patient T1 maps were registered to the common SST with Elastix (Klein et al., 2010) and voxelmorph (Balakrishnan et al., 2019). Z-score maps were computed for each patient, comparing the measured T1 values and the expected normative values. The single-subject analysis was limited to areas where the coefficient of variability (COV=RMSE/Intercept) was below 10% in the normative atlas, resulting in the exclusion of regions with high inter-subject variability such as cortical regions.
A subset of patients (n=8) was selected from the AEP cohort to demonstrate a range of epileptogenic pathologies. For each patient, the highest z-score cluster was identified and visually inspected to assess its concordance with the clinico-radiological cause of their epilepsy.
Results:
The voxel-wise T1 atlas characterized normative brain T1 values in HC (Fig 1), representing the population variability in both local tissue properties and large-scale brain morphology. In epilepsy patients, the peak z-score cluster for each individual successfully highlighted clinically relevant pathology. Identified epileptogenic pathologies included chronic infarction (#1, #3), hippocampal sclerosis (#2, #4), primary and developmental tumours (#4, #7), cavernous haemangioma (#6 with a focal decrease in z-score), and grey matter heterotopia (#5, #8). Constraining the analysis to high-reliability voxels reduced false-positive detections but led to partial coverage of the superficial cortical and periventricular pathologies. Nevertheless, this method clearly highlights rare pathologies ("double cortex", #5) that can be otherwise easily missed in clinical practice.
·Figure 1
·Figure 2
Conclusions:
Generating a voxel-wise normative T1 atlas from a cohort of healthy volunteers enables calculation of single-subject T1 z-score maps for patient cohorts. In a selected group of people with focal epilepsy, this approach highlighted the most clinically relevant brain regions and detected a diverse range of epileptogenic pathologies. Overall, this technique appears both feasible and promising as a potential aid to neuroimaging diagnosis in focal epilepsy.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Novel Imaging Acquisition Methods:
Anatomical MRI 1
Imaging Methods Other
Keywords:
Data analysis
Epilepsy
Modeling
MRI
Other - Normative Atlases, T1 Mapping, Quantitative MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hilbert, T., et al., (2018). Accelerated T2 mapping combining parallel MRI and model‐based reconstruction: GRAPPATINI. Journal of Magnetic Resonance Imaging, 48(2), 359-368.
Klein S., et al. (2010) elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29:196–205.
Marques, J. P., et al. (2010). MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage, 49(2), 1271-1281.
Mussard, E., et al. (2020). Accelerated MP2RAGE imaging using Cartesian phyllotaxis readout and compressed sensing reconstruction. Magnetic Resonance in Medicine, 84(4), 1881-1894.
Piredda, G. F., et al. (2020). Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology. Magnetic Resonance in Medicine, 83(1), 337-351.