Improved bias and reproducibility of coronary artery calcification features using deconvolution

Abstract

Purpose: Our long-range goal is to improve whole-heart CT calcium scores by extracting quantitative features from individual calcifications. Here, we perform deconvolution to improve bias/reproducibility of small calcification assessments, which can be degraded at the normal CT calcium score image resolution.Approach: We analyzed features of individual calcifications on repeated standard (2.5 mm) and thin (1.25 mm) slice scans from QRM-Cardio phantom, cadaver hearts, and CARDIA study participants. Preprocessing to improve the resolution involved of Lucy-Richardson deconvolution with a measured point spread function (PSF) or three-dimensional blind deconvolution in which the PSF was iteratively optimized on high detail structures such as calcifications in images.Results: Using QRM with inserts having known mg-calcium, we determined that both blind and conventional deconvolution improved mass measurements nearly equally well on standard images. Further, deconvolved thin images gave an excellent recovery of actual mass scores, suggesting that such processing could be our gold standard. For CARDIA images, blind deconvolution greatly improved results on standard slices. Bias across 33 calcifications (without, with deconvolution) was (23%, 9%), (18%, 1%), and (−19%, −1%) for Agatston, volume, and mass scores, respectively. Reproducibility was (0.13, 0.10), (0.12, 0.08), and (0.11, 0.06), respectively. Mass scores were more reproducible than Agatston scores or volume scores. For many other calcification features, blind deconvolution improved reproducibility in 21 out of 24 features. Cadaver images showed similar improvements in bias/reproducibility and slightly better results with a measured PSF.Conclusions: Deconvolution improves bias and reproducibility of multiple features extracted from individual calcifications in CT calcium score exams. Blind deconvolution is useful for improving feature assessments of coronary calcification in archived datasets.

Prediction of major adverse cardiovascular events using comprehensive AI analysis of calcifications and fat depots in CT calcium score images

“…We investigated traditional univariate calcium and fat Cox models to compare with our proposed models (Table 2). Agatston score is the standard calcium score model in literature; however, mass score is considered more reproducible than Agatston 15,16 . Both models (lines 1 and 2) reported comparable 2-year AUC results, as in such a small cohort, high percentage of zero Agatston score cases produced non-desirable high correlation for total scores such as Agatston, mass and volume.…”

Enhancing cardiovascular risk prediction through AI-enabled calcium-omics

“…Comparing Agatston (line 1) and mass score (line 2), we determined that mass score had a slightly higher C-index and AUC at 2 years. As the mass score is generally considered more reproducible than Agatston 5 , 13 , we used it in subsequent evaluations. When we examined territorial mass scores (line 3), we found improved discrimination (C-index and AUC) compared to a whole heart mass score particularly for the LAD, which has the lowest p -value and the highest HR, indicating that an equivalent mass in the LAD was more predictive of MACE than that in another territory.…”

“…Univariate and multivariable Cox models in Table 2 offer explanation as to why calcium-omics does better, per the following observations. (1) Summing over the entire heart, mass score slightly improves prediction compared with Agatston, potentially due to its improved reproducibility 5 , 13 . (2) When we simply add the dense calcification (> 1000 HU) feature to mass, there is improvement as compared to mass or Agatston alone.…”

“…The Agatston method simply uses a non-linear weighted sum of the areas of coronary artery calcium (CAC) with densities above 130 HU. A calcium mass score is known to be more reproducible 5 . Importantly, current CAC scoring approach ignores a plethora of other CAC features that may be pathophysiologically important, including density, distribution, geometry, and others.…”