Standard predictors of outcome after cardiac arrest (CA) have substantial limitations in terms of reliability and generalizability. By providing brain structural connectivity maps, or connectomes, advanced MRI techniques, operating through high-strength magnetic field (HF; 1.5 to 3-T), have precisely revealed white and grey brain matter damages induced by CA, and have demonstrated the high sensitivity and specificity of these indicators for predicting neurological outcome after CA. However, HF MRI requires rigid safety precautions, highly trained technicians and patient transport to dedicated hospital imaging suites, hindering the implementation of these promising neuroimaging techniques in the setting of critical illness.
Interestingly, a recent report demonstrates the capability of a proof-of-concept very low-field (VLF; 0.064-T) mobile MRI to obtain neuroimaging at the bedside in critically ill patients. Nevertheless, the spatial resolution of VLF-MRI seems low and there is no available evidence about the use of VLF-MRI to extract highly needed new predictors of neurological recovery based on critical brain structural connectomes.
The CUBE project holds the promise of providing a radical paradigm shift in the field of neuroprognostication of anoxic coma patients. The current proposal is a "proof-of concept" study which aims to compare for the first time, HF, VLF and enhanced VLF (recon-VLF) structural connectomes from anoxic coma patients and healthy subjects across the time (3 paired HF and VLF brain scan across the first two weeks after CA). To obtain recon-VLF data, the Investigators will use an ensemble of ground-breaking methods to increase the native spatial resolution of VLF-MRI data. The whole brain imaging dataset will be used to prepare future neuroprognostication studies based on fully bedside assessment of brain structural integrity after CA.
Adult patients (male or female ≥ 18 years).
Coma, as indicated by a Glasgow Coma Scale (GCS) ≤ 8 (motor score ≤ 2) immediately after CA resuscitation and before sedation onset.
Written informed consent from patient's legal representative.
Affiliation to the French social security system
Brain death.
Coma explained by other cause than CA.
Pregnancy.
MRI contraindication: medical material not MRI compatible.
Parallel
High-field and very-low-field MRI acquisitions
Diagnostic testMRI will be early acquired, as soon as possible after patient hospital admission and always in coma state for patients. For both patients and controls each MRI scanning session will encompass pairs of HF and VLF MRI acquisitions, that will be collected the same day. The main MRI sequences will be: T1, T2, FLAIR and DWI. The VLF spatial resolution recommended by the constructor for all these sequences correspond to voxel size is around 1.5mm/1.5mm/5mm. The total brain scanning time is estimated at 30 minutes.
Differences between whole-brain white (WWM-FA) and grey (GMM) matter global metrics independently acquired by HF and VLF-MRI scans very early after coma onset (D0)
White matter will be assessed by computing the White Matter Fractional Anisotropy (WWM-FA), which ranges from 0 to 1 with lower values related to worse outcomes. Grey matter will be evaluated using Global brain Matter Morphometry (GMM), which encompass both cortical thickness (mm) and deep grey matter volumetry (mm3) assessments, with lower values being associated with poorer outcomes. Both WWM-FA and GMM data will be normalized using brain MRI data obtained from healthy controls (z-scores).
Differences between HF and VLF white (WWM-FA=White Matter Fractional Anisotropy ) and grey (GMM=Global brain Matter Morphometry) matter z-scores.
White matter will be assessed by computing the White Matter Fractional Anisotropy (WWM-FA), which ranges from 0 to 1 with lower values related to worse outcomes. Grey matter will be evaluated using Global brain Matter Morphometry (GMM), which encompass both cortical thickness (mm) and deep grey matter volumetry (mm3) assessments, with lower values being associated with poorer outcomes. Both WWM-FA and GMM data will be normalized using brain MRI data obtained from healthy controls (z-scores).
Differences between HF and VLF white (WWM-FA=White Matter Fractional Anisotropy ) and grey (GMM=Global brain Matter Morphometry) matter z-score
White matter will be assessed by computing the White Matter Fractional Anisotropy (WWM-FA), which ranges from 0 to 1 with lower values related to worse outcomes. Grey matter will be evaluated using Global brain Matter Morphometry (GMM), which encompass both cortical thickness (mm) and deep grey matter volumetry (mm3) assessments, with lower values being associated with poorer outcomes. Both WWM-FA and GMM data will be normalized using brain MRI data obtained from healthy controls (z-scores).
Differences between HF and VLF white (WWM-FA=White Matter Fractional Anisotropy ) and grey (GMM=Global brain Matter Morphometry) matter z-score
White matter will be assessed by computing the White Matter Fractional Anisotropy (WWM-FA), which ranges from 0 to 1 with lower values related to worse outcomes. Grey matter will be evaluated using Global brain Matter Morphometry (GMM), which encompass both cortical thickness (mm) and deep grey matter volumetry (mm3) assessments, with lower values being associated with poorer outcomes. Both WWM-FA and GMM data will be normalized using brain MRI data obtained from healthy controls (z-scores).
Paired comparison between recon-VLF, VLF and HF MRI image's quality, using radiometric information metrics (PSNR, SSIM, ERGAS)
PSNR (Peak Signal-to-Noise Ratio) is a ratio between the maximum possible signal and the noise. The PSNR theoretically can approach infinity if there is no difference between the compared images and go down to 0, meaning in that case that there is significant distortion between the visual dataset. The SSIM (Structure Similarity Index) considers changes in structural information, luminance, and texture, making it more aligned with human visual perception. The maximum value of SSIM is 1, which indicates that the two images are identical. The minimum SSIM value is 0, which means no similarity between the two images. The ERGAS (Error-Related Global Accuracy Statistic) evaluates the quality of super-resolution tasks. The minimum value for ERGAS is 0, which indicates perfect fusion with the reference image. The maximum value for ERGAS is theoretically infinity, as the error can be arbitrarily large if there is a significant mismatch between the compared images.
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University Hospital, Toulouse
Lead sponsor
National Research Agency, France
Collaborator
Institut National de la Santé Et de la Recherche Médicale, France
Collaborator
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