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Background Compared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort. Methods Three hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments. Results ML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%–79.5%] vs. 54.8% [45.7%–63.8%]), LAD (79.3 [73.9–84.0] vs. 59.6 [53.5–65.6]), LCX (84.1 [76.0–90.3] vs. 63.7 [54.1–72.6]), proximal (81.5 [74.6–87.1] vs. 63.8 [55.9–71.2]), middle (81.2 [75.7–85.9] vs. 59.4 [53.0–65.6]) and distal stenosis location (67.4 [57.0–76.6] vs. 51.6 [41.1–62.0]). Conclusion In a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.

Objectives This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning–based coronary computed tomography (CT) angiography (cCTA)–derived fractional flow reserve (CT-FFR). Background CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated. Methods Four hundred eighty-two vessels from 314 patients (62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to Results The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥ 400) and high performance in low-to-intermediate Agatston scores (CAC >0 to 0 to Conclusions Machine-learning–based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621).

Purpose This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFRML) for the detection of lesion-specific ischemia. Method Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR ≤ 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis ≥50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. Results In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72–84), 79% (95%CI 73–84), 75% (95%CI 69–79), and 82% (95%CI: 76–86) in men vs. 75% (95%CI 58–88), 81 (95%CI 72–89), 61% (95%CI 50–72) and 89% (95%CI 82–94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79–0.87] vs. 0.83 [95%CI 0.75–0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75–0.89) vs. 0.74 (95%CI: 0.65–0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79–0.87) vs. 0.76 (95%CI: 0.71–0.80), p = 0.007]. Conclusions Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.

Background Coronary computed tomography angiography (CCTA) is increasingly used to detect coronary artery disease (CAD), but long-term follow-up studies are still scarce. Purpose To evaluate the prognostic value of CCTA in patients with suspected CAD. Material and Methods A total of 1205 consecutive CCTA patients with chest pain were classified as normal coronary arteries, non-obstructive CAD, or obstructive CAD. The primary outcome was major adverse cardiac event (MACE), defined as a composite outcome including cardiac death, myocardial infarction, unstable angina pectoris, or late revascularization (after >90 days). Results Over 7.5 years follow-up (median = 3.1 years), Kaplan–Meier estimates demonstrated a MACE in 1.0%, 4.6%, and 20.7% in normal coronary arteries, non-obstructive CAD, and obstructive CAD, respectively. Log rank test for pairwise comparisons showed significant differences between non-obstructive CAD and normal coronary arteries ( P = 0.023) and between obstructive CAD and normal coronary arteries ( P Conclusion Patients with normal coronary arteries have excellent long-term prognosis, but the risk for MACE increases with non-obstructive and obstructive CAD. Both non-obstructive and obstructive CAD are independently associated with future ischemic events.

Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is a noninvasive application to evaluate the hemodynamic impact of coronary artery disease by simulating invasively measured FFR based on CT data. CT-FFR is based on the assumption of a normal coronary microvascular response. We assessed the diagnostic performance of a machine-learning based application for on-site computation of CT-FFR in patients with and without diabetes mellitus with suspected coronary artery disease. The study population included 75 diabetic and 276 nondiabetic patients who were enrolled in the MACHINE consortium. The overall diagnostic performance of coronary CT angiography alone and in combination with CT-FFR were analyzed with direct invasive FFR comparison in 110 coronary vessels of the diabetic group and in 415 coronary vessels of the nondiabetic group. Per-vessel discrimination of lesion-specific ischemia by CT-FFR was assessed by the area under the receiver operating characteristic curves. The overall diagnostic accuracy of CT-FFR in diabetic patients was 83% and in nondiabetic patients 75% (p = 0.088), showing improvement over the diagnostic accuracy of coronary CT angiography, which was 58% and 65% (p = 0.223), respectively. In addition, the diagnostic accuracy of CT-FFR was similar between diabetic and nondiabetic patients per stratified CT-FFR group (CT-FFR < 0.6, 0.6 to 0.69, 0.7 to 0.79, 0.8 to 0.89, ≥0.9). The area under the curves for diabetic and nondiabetic patients were also comparable, 0.88 and 0.82 (p = 0.113), respectively. In conclusion, on-site machine-learning CT-FFR analysis improved the diagnostic performance of coronary CT angiography and accurately discriminated lesion-specific ischemia in both diabetic and nondiabetic patients suspected of coronary artery disease.

Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease. Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent ( R =0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; P Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

BackgroundCardiac Computed Tomography Angiography (CCTA) is becoming increasingly useful in the work‐up of coronary artery disease (CAD). Several potential methods for increasing the diagnostic yie ...

Background Computed tomography (CT) is becoming increasingly popular as a non-invasive method for visualizing the coronary arteries but patient radiation doses are still an issue. Postprocessing filters such as 2D adaptive non-linear filters might help to reduce the dose without loss of image quality. Purpose To investigate whether the use of a 2D, non-linear adaptive noise reduction filter can improve image quality in cardiac computed tomography angiography (CCTA). Material and Methods CCTA examinations were performed in 36 clinical patients on a dual source CT using two patient dose levels: maximum dose during diastole and reduced dose (20% of maximum dose) during systole. One full-dose and one reduced-dose image were selected from each of the examinations. The reduced-dose image was duplicated and one copy postprocessed using a 2D non-linear adaptive noise reduction filter, resulting in three images per patient. Image quality was assessed using visual grading with three criteria from the European guidelines for assessment of image quality and two additional criteria regarding the left main artery and the overall image quality. Also, the HU value and its standard deviation were measured in the ascending and descending aorta. Data were analyzed using Visual Grading Regression and paired t-test. Result For all five criteria, there was a significant ( P < 0.01 or better) improvement in perceived image quality when comparing postprocessed low-dose images with low-dose images without noise reduction. Comparing full dose images with postprocessed low-dose images resulted in a considerably larger, significant ( P < 0.001) difference. Also, there was a significant reduction of the standard deviation of the HU values in the ascending and descending aorta when comparing postprocessed low-dose images with low-dose images without postprocessing. Conclusion Even with an 80% dose reduction, there was a significant improvement in the perceived image quality when using a 2D noise-reduction filter, though not approaching the quality of full-dose images. This indicates that cardiac CT examinations could benefit from noise-reducing postprocessing with 2D non-linear adaptive filters.

Background. The diagnostic value of non-invasive methods for diagnosing renal artery stenosis in patients with renal insufficiency is incompletely known. Methods. Forty-seven consecutive patients with moderately impaired renal function and a clinical suspicion of renal artery stenosis were investigated with computed tomography angiography (CTA), gadolinium-enhanced magnetic resonance angiography (MRA), contrast-enhanced Doppler ultrasound and captopril renography. The primary reference standard was stenosis reducing the vessel diameter by at least 50% on CTA, and an alternative reference standard (‘morphological and functional stenosis’) was defined as at least 50% diameter reduction on CTA or MRA, combined with a positive finding from ultrasound or captopril renography. Results. The frequency of positive findings, calculated on the basis of individual patients, was 70% for CTA, 60% for MRA, 53% for ultrasound and 30% for captopril renography. Counting kidneys rather than patients, corresponding frequencies were 53%, 41%, 29% and 15%, respectively. In relation to the CTA standard, the sensitivity (and specificity) at the patient level was 0.81 (0.79) for MRA, 0.70 (0.89) for ultrasound and 0.42 (1.00) for captopril renography, and at the kidney level 0.76 (0.82), 0.53 (0.81) and 0.30 (0.86), respectively. Relative to the alternative reference standard, corresponding values at the patient level were 1.00 (0.62) for CTA, 0.90 (0.69) for MRA, 0.91 (1.00) for ultrasound and 0.67 (1.00) for captopril renography, and at the kidney level 0.96 (0.76), 0.85 (0.79), 0.71 (0.97) and 0.50 (0.97), respectively. Conclusions. CTA and MRA are superior to ultrasound and captopril renography at diagnosing morphological stenosis, but ultrasound may be useful as a screening method and captopril renography for verifying renin-dependent hypertension. Original Publication:Per Eriksson, Ahmed Abdulilah Mohammed, Jakob De Geer, Johan Kihlberg, Anders Persson, Göran Granerus, Fredrik Nyström and Örjan Smedby, Non-invasive investigations of potential renal artery stenosis in renal insufficiency, 2010, Nephrology, Dialysis and Transplantation.http://dx.doi.org/10.1093/ndt/gfq259Copyright: Oxford University Presshttp://www.oxfordjournals.org/