Integrating AI quantitative CT, photon-counting CT, and FFR-CT brings coronary plaque burden and ischemia assessment into one CCTA workflow

AI quantitative CT turns plaque quantification from time-consuming manual segmentation into a reproducible automated workflow that yields outcome-linked quantitative metrics. Plaque quantification previously relied on manual or semiautomated delineation, taking 25-45 minutes per case with interobserver coefficients of variation of 8%-15%; the multicenter validation summarized in the review shows deep learning platforms achieve 81%-86% diagnostic accuracy versus expert consensus and 0.92-0.95 correlation with IVUS, with analysis time falling to typically under 60 seconds. Evidence comes from multiple multicenter validation studies and large cohorts, such as Lin and colleagues' n=1196 study reporting 84% accuracy, r=0.94 correlation with IVUS, and time reduced from 28 minutes to 45 seconds; the review also states clearly that vendors differ in algorithmic architecture, training data, and HU thresholds, with noncalcified plaque defined at 30-350 HU and low-attenuation components ranging from below 30 HU to below 60 HU, producing 20%-30% inter-platform differences in reported plaque volumes.

Plaque quantification metrics predict events independently of stenosis severity across several large prospective cohorts, supporting risk stratification by plaque burden rather than stenosis alone. CCTA evaluation has traditionally centered on stenosis detection, whereas the evidence summarized in the review shows whole-vessel plaque metrics retain independent prognostic value after adjusting for stenosis severity and clinical risk factors, and exceed the prognostic strength of diabetes or hypertension. SCOT-HEART showed low-attenuation plaque burden >4% conferred an HR of 4.65 (95% CI 2.06-10.5) for myocardial infarction or cardiac death, and at 10-year follow-up CCTA-guided management reduced coronary heart disease death or non-fatal MI from 8.2% to 6.6% (HR 0.79, 95% CI 0.63-0.99); PROMISE (n=4415) reported HR 4.31 (95% CI 2.25-8.26) for high-risk plaque features in non-obstructive disease, adjusted HR 1.72; CONFIRM (n=3547) reported adjusted HR 3.3 (95% CI 2.2-4.8) for segment involvement score >5; ISCHEMIA (n=3711) reported HR 4.37 (95% CI 2.51-7.62) for percent atheroma volume >7.5%.

Photon-counting detector CT improves lumen and plaque assessment with ultra-high resolution and spectral capability, particularly in the presence of calcium and stents. Conventional energy-integrating detector CT overestimates stenosis and plaque volume because of calcium blooming and beam-hardening; PCD-CT's ultra-high resolution and calcium-removal reconstruction reduce these artifacts, and the review reports reclassification of some cases to lower CAD-RADS categories and improved reproducibility of low-attenuation plaque quantification. Studies cited in the review report a 49%-54% reclassification rate to lower CAD-RADS categories with ultra-high-resolution PCD-CT, total plaque volume quantification reduced by almost one-third compared with conventional multidetector CT, and, for coronary stent patency versus invasive coronary angiography, sensitivity of 100%, specificity of 87%, accuracy of 89%, and negative predictive value of 100%. The review also notes that because of resolution differences, PCD-CT-specific detection thresholds for plaque components may need to be redefined.

FFR-CT adds lesion-specific physiologic assessment beyond anatomy and, combined with plaque quantification, improves risk stratification and intervention decisions. CCTA lacks specificity for intermediate (40%-90%) stenoses, and only about half of stenoses identified on CCTA are functionally significant by invasive FFR; FFR-CT improves specificity while maintaining high sensitivity, and delta FFR-CT reflects lesion-specific hemodynamic impact, addressing the influence of diffuse disease and microvascular resistance on distal FFR-CT values. In the NXT trial FFR-CT raised specificity from 34% to 79% and AUC from 0.81 to 0.90 versus CCTA; in the ADVANCE registry FFR-CT altered treatment strategy in two-thirds of patients, and those with normal FFR-CT (>0.80) had a 5.8% risk of cardiovascular death or MI versus 38.4% for abnormal values, while normal lesion-specific FFR-CT corresponded to annual event rates below 0.5%. The review notes that major validation trials used CFD-based FFR-CT requiring off-site analysis, whereas on-site AI-based data come mainly from smaller cohorts, such as the MACHINE consortium's 78% per-vessel and 85% per-patient accuracy (AUC 0.84), and the TARGET trial (n=1216) confirmed safe implementation but observed no improvement in symptoms, quality of life, or MACE.

This is a review focused on CT evaluation of coronary artery disease, intended for cardiovascular imaging and clinical physicians interpreting CCTA, as well as researchers and department leaders interested in deploying AI quantitative tools. Its conclusions apply to noninvasive assessment of symptomatic or suspected CAD patients, including plaque burden and composition quantification, stenosis grading, lesion-specific physiologic assessment, and lumen assessment in calcified and stented lesions; for CCTA-guided primary prevention in asymptomatic individuals, the review frames this as a question being tested by SCOT-HEART 2 (n>5000). The review also notes that the practical value of plaque quantification for optimizing and titrating lipid-lowering therapy still requires further study.

Several open questions remain for the careful reader: the optimal plaque metric is not yet established and metric selection likely differs by clinical context; vendor HU threshold differences produce 20%-30% inter-platform differences in reported plaque volumes, and in serial scanning the limits of agreement reach +50% for total plaque volume and +100% for low-attenuation plaque, with scan parameters such as contrast concentration and tube voltage also altering plaque attenuation; plaque validation lacks a universally accepted reference standard, and IVUS, OCT, histopathology, and expert reading each have limitations; the robustness and reproducibility of AI algorithms still need large multicenter trials, and photon-counting CT requires retraining and recalibration of existing algorithms that have mostly been trained on energy-integrating detector data. In addition, this is a review text in which the content of Figures 1-3 is presented as captions; the images themselves are not directly viewable in the text, so readers who need to judge specific case appearances from image detail should consult the original figure plates.