Quality control in clinical raster-scan optoacoustic mesoscopy

Area of Science:

• Biomedical optics
• Medical imaging technology
• Acoustic imaging

Background:

• Optoacoustic mesoscopy (RSOM) offers deep, high-resolution imaging beyond optical microscopy.
• Image quality in RSOM can be compromised by motion and noise, hindering reliable clinical application.
• Standardized quality control is crucial for reproducible RSOM data, especially for machine learning and biomarker analysis.

Purpose of the Study:

• To introduce a quality control scheme for raster-scan optoacoustic mesoscopy (RSOM) data.
• To develop a method for assessing image quality and motion in RSOM scans.
• To enable reproducible, high-quality RSOM datasets for clinical and biomedical research.

Main Methods:

• A reference scan of a phantom was used to characterize system noise.
• Motion estimation algorithms were developed from RSOM data.
• A quality assessment index (QASIN) was derived to classify raw data quality and select high-fidelity images.

Main Results:

• Simulations demonstrated a selection criterion based on QASIN for compiling consistent RSOM datasets.
• Analysis of 160 RSOM measurements from healthy volunteers showed QASIN-selected images were of higher quality and fidelity.
• The proposed scheme effectively identifies and filters lower-quality RSOM data.

Conclusions:

• The developed quality control scheme, QASIN, provides a reliable method for standardizing RSOM image quality.
• This standardization is essential for the reliable application of RSOM in clinical diagnostics and biomedical research.
• The QASIN method facilitates reproducible data acquisition for advanced analyses like machine learning and biomarker quantification.