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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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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Biomarkers.

Gaurav V Bhalerao1, Pawel J Markiewicz2, David L Thomas3

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Summary
This summary is machine-generated.

Harmonizing brain MRI scans across different scanners is crucial for multi-site studies. The ComBat method effectively reduced variability while preserving biological signals, outperforming other tested harmonization techniques.

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Area of Science:

  • Neuroimaging
  • Medical Imaging Analysis
  • Biomarker Development

Background:

  • The DPUK PET-MR Harmonisation Study addresses variability in brain scans from multiple PET-MR scanners.
  • Optimizing clinical trial design and evaluating PET-MR methods are key objectives.
  • This research focuses on imaging-derived phenotypes (IDPs) from T1-weighted MRI scans in healthy elderly participants.

Purpose of the Study:

  • To quantify within-site and across-site variability in brain scans acquired from different PET-MR scanners.
  • To evaluate the effectiveness of various harmonisation methods for T1-weighted MRI scans.
  • To improve the consistency of imaging biomarkers for early dementia diagnosis.

Main Methods:

  • T1-weighted (T1w) scans were processed using the UK Biobank (UKB) pipeline to extract volumetric IDPs.
  • Three harmonisation approaches were assessed: Image Quality Metric (IQM)-based regression, IDP-based ComBat adjustment, and image-based histogram matching or SynthSR.
  • Within-subject variability was quantified using the Coefficient of Variation (CoV); cross-subject variability was also assessed.

Main Results:

  • Visual inspection revealed scan quality significantly impacts within-subject variability.
  • Longitudinal ComBat effectively reduced CoV (e.g., brain volume: 3.7±1.2% to 1.4±0.8%) while preserving cross-subject variability.
  • IQM regression reduced variability but also decreased cross-subject variability; image-based methods showed inconsistent or incompatible results.

Conclusions:

  • ComBat demonstrated effectiveness in reducing IDP variability but requires specific batch/site variable definitions.
  • IQM regression offers flexibility but needs refinement to maintain cross-subject variability.
  • Further development is needed for image-based harmonisation methods; quantifying diverse variability aspects is vital for multi-site MRI studies.