The Reverse Calcification Technique (RCT): A Quantitative Parametric Model of Aortic Stenosis Progression

Aortic stenosis (AS) develops from calcific aortic valve disease (CAVD), which narrows the aortic valve opening as leaflet stiffness increases due to calcium deposition. This study extends the Reverse Calcification Technique (RCT) by incorporating a time dimension to develop a quantitative parametric model for patient-specific prediction of CAVD progression from sequential CT scans.

Seventeen pre-transcatheter aortic valve replacement (TAVR) patients underwent sequential CT scans (1.2-6.5 years); baseline aortic valve calcification (AVC) volumes: $\approx$ 250 to $\approx$ 1,600 mm³ (cohort mean $\approx$  730 mm³ at the first scan and $\approx$ 920 mm³ at the follow-up scan). A parametric model was developed using two approaches: forward prediction (mild to severe stages) and backward reconstruction (from severe to moderate stages). 34 test cases were assessed through alternating calibration and verification, with performance evaluated using Bland-Altman analysis, paired t-tests, and relative error calculations.

For scan intervals under 3 years, forward prediction achieved a mean absolute error of 77 mm³ (7.0% relative to a mean target volume of 918 mm³) and backward reconstruction achieved 53 mm³ (8.4% relative to 542 mm³) within the inherent CT measurement uncertainty of 8-12%. For longer intervals (> 3 years), relative errors increased to 16.8-21.6%. Individual errors ranged from 0.02% to 36.8%, with no systematic bias detected.

The quantitative parametric RCT model demonstrates feasibility for patient-specific estimation of CAVD progression and shows promise for optimizing follow-up intervals. External validation in larger independent cohorts and incorporation of patient-specific risk factors are required before clinical implementation.