Implications of computed tomography reconstruction algorithms on coronary atheroma composition: A head-to-head...

To evaluate the performance of various computed tomography angiography (CTA) reconstruction methods in assessing coronary plaque composition using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) as the reference standard.

Fifteen patients with chronic coronary syndrome underwent CTA and 3-vessel NIRS-IVUS imaging. CTA datasets were reconstructed using a medium-smooth b40f kernel with two slice thicknesses, 0.75 ​mm and 0.50 ​mm, and three strengths of advanced model-based iterative reconstruction (ADMIRE). Plaque components on CTA were classified using fixed and adaptive Hounsfield unit (HU) thresholds while NIRS-IVUS classification employed a deep learning method validated against histology. Matched CTA and NIRS-IVUS images were analyzed to quantify fibrotic tissue (FT), necrotic core (NC), and calcific (Ca) volumes and areas at both segment- and lesion-level. The intraclass correlation coefficient (ICC) and Surface Under the Cumulative Ranking Curve (SUCRA) scores were used to determine the best-performing reconstruction approach.

Fifty vessels were included in the final analysis. CTA showed weak correlation with NIRS-IVUS for FT (ICC <0.43), good correlation for Ca (ICC 0.42-0.83), and poor correlation for NC, except when using reconstruction approach ADMIRE 2, 0.50 ​mm slice thickness, and fixed HU cutoffs, which demonstrated moderate correlation for NC (segment-level ICC ​= ​0.67; lesion-level ICC ​= ​0.61). This approach ranked highest on SUCRA plots.

CTA reconstruction parameters influence plaque composition analysis. The combination of an intermediate-strength IR, thin slice thickness, and fixed HU cutoffs yields the most accurate tissue characterization compared to NIRS-IVUS as the reference standard.