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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Biomarkers.

Ava Farnan1, Annabel Sorby-Adams1, Jennifer Guo1

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Portable low-field MRI (LF-MRI) combined with machine learning accurately quantifies brain changes in Alzheimer's disease (AD) patients. This accessible neuroimaging approach enables effective disease monitoring in outpatient settings.

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Neurology

Background:

  • Magnetic resonance imaging (MRI) is crucial for monitoring Alzheimer's disease (AD) progression.
  • Portable, low-field MRI (LF-MRI) offers point-of-care assessment and improved patient access.
  • A machine learning (ML) pipeline was developed to quantify brain morphometry using LF-MRI for AD patient monitoring.

Purpose of the Study:

  • To assess the feasibility of using LF-MRI with ML for brain morphometry quantification in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients.
  • To compare brain volume measurements derived from LF-MRI against conventional high-field MRI (HF-MRI).
  • To evaluate LF-MRI's potential for differentiating between AD/MCI patients and those with vascular comorbidities (VC).

Main Methods:

  • Enrolled patients with MCI, AD, and VC from outpatient memory disorder clinics.
  • Acquired LF-MRI (0.064 T) and HF-MRI (1.5-3 T) scans.
  • Processed T2 FLAIR sequences using a FreeSurfer-based pipeline (WMH-SynthSeg) to quantify brain volumes in various regions.

Main Results:

  • High correlations (p < 0.05) were observed between HF-MRI and LF-MRI derived brain volumes across all regions.
  • Cortex, white matter, lateral ventricle, hippocampus, and amygdala showed the highest correlations.
  • MCI and AD cohorts exhibited significant regional atrophy compared to the VC cohort, particularly in the cortex, hippocampus, and amygdala (p < 0.001).

Conclusions:

  • LF-MRI acquisition is feasible for point-of-care assessment in MCI or AD patients.
  • ML algorithms applied to LF-MRI data generate brain volumes comparable to HF-MRI.
  • LF-MRI serves as an accessible neuroimaging tool for monitoring cognitive impairment and differentiating patient subgroups.