Carotid artery wall motion analysis from B-mode ultrasound using adaptive block matching: in silico evaluation and in...

Area of Science:

• Biomedical Engineering
• Medical Imaging Analysis
• Cardiovascular Research

Background:

• Carotid atherosclerotic plaque risk stratification is vital for preventing cerebrovascular events.
• Motion analysis (MA) offers insights into arterial wall dynamics, but identifying motion-based risk markers is challenging.
• The accuracy of MA depends on motion estimators (MEs) that can adapt to changing target appearances.

Purpose of the Study:

• To investigate the potential of adaptive block matching (ABM) motion estimators for enhanced MA in carotid artery disease.
• To optimize and validate ABM algorithms within an in silico framework for MA accuracy.
• To identify novel, motion-based risk markers for carotid atherosclerotic plaques using advanced MA techniques.

Main Methods:

• Development and optimization of adaptive block matching (ABM) motion estimators, including ABM(FIRF2), within a specialized in silico framework.
• Evaluation of ABM algorithms' accuracy in motion estimation for arterial wall dynamics.
• In vivo application of the most effective ABM algorithm for identifying potential plaque-related risk markers.

Main Results:

• ABM(FIRF2) demonstrated a 47% accuracy increase over conventional block matching by leveraging arterial wall motion periodicity.
• Five potential risk markers were identified in vivo: low radial and longitudinal wall motion amplitude adjacent to plaques (RMA(PWL), LMA(PWL)), high radial motion amplitude of the plaque top surface (RMA(PTS)), and high relative radial strain (RSI(PL)) and longitudinal shear strain (LSSI(PL)) between plaque surfaces.
• In silico validated MEs, OF(LK(WLS)) and ABM(KF-K2), reproduced the in vivo findings, confirming the clinical value of the identified markers.

Conclusions:

• Adaptive block matching (ABM) motion estimators, particularly ABM(FIRF2), significantly enhance motion analysis accuracy for carotid plaques.
• Novel motion-based risk markers associated with plaque and adjacent wall dynamics have been identified, offering potential for improved cerebrovascular event prediction.
• The study validates the clinical relevance of these markers and the utility of advanced MEs, paving the way for future clinical investigations.