Rationale and objectives
We sought to explore the impact of intracycle motion correction algorithms (MCA) in the interpretability and diagnostic accuracy of computed tomography coronary angiography (CTCA) performed in patients suspected of coronary artery disease (CAD) referred to invasive coronary angiography.
Materials and Methods
Patients with suspected CAD referred to invasive coronary angiography previously underwent CTCA. Patients under rate-control medications were advised to withhold for the previous 24 hours. The primary end point of the study was to evaluate image interpretability and diagnostic performance of MCA compared to conventional reconstructions in patients referred to invasive angiography because of suspected CAD.
Results
Thirty-five patients were prospectively included in the study protocol. The mean age was 61.4 ± 9.4 years. Twenty-seven (77%) patients were men. A total of 533 coronary segments were evaluated using conventional and MCA reconstructions. MCA reconstructions were associated to higher interpretability rates (525 of 533, 98.5% vs. 515 of 533, 96.6 %; P < .001) and image quality scores (3.88 ± 0.54 vs. 3.78 ± 0.76; P < .0001) compared to conventional reconstructions. Although only mild, a significant difference was observed regarding the diagnostic performance between reconstruction modes, with an area under the curve of 0.90 (0.87–0.92) versus 0.89 (0.86–0.92), respectively, for MCA and conventional reconstructions ( P = .0447).
Conclusions
In this pilot investigation, MCA reconstructions performed in patients with suspected CAD were associated to higher interpretability rates and image quality scores compared to conventional reconstructions, although only mild differences were observed regarding the diagnostic performance between reconstruction modes.
During the past decade, computed tomography coronary angiography (CTCA) has gained a role in a number of diagnostic algorithms as a validated noninvasive diagnostic tool aimed at evaluating symptomatic patients at intermediate risk of coronary artery disease (CAD). This position has been obtained mainly on the basis of a high sensitivity and an excellent negative predictive value . Nevertheless, the positive predictive value of CTCA has yielded considerably lower results, particularly in patients with intermediate-to-high probability of CAD, driven by a larger prevalence of false-positive findings in such populations. Indeed, although CTCA has shown a high diagnostic accuracy in most clinical scenarios, it does not provide a significant incremental value over functional tests in patients with high pretest probability . Most false-positive findings in CTCA are associated to diffuse coronary calcification and/or motion artifacts . So far, the development of newer generations of CT scanners has failed to provide major improvements in the evaluation of diffusely calcified lesions. In turn, several hardware- and software-based approaches have demonstrated, with different success rates, to improve temporal resolution to diminish motion artifacts associated to high or irregular heart rates . Recently, intracycle motion correction algorithms (MCA) that use information from adjacent cardiac phases to compensate for coronary motion have been proposed as a potential means to scan patients with high or irregular heart rates without using rate-control medications . We therefore sought to explore the impact of MCA in the interpretability and diagnostic accuracy of CTCA performed in patients suspected of CAD referred to invasive coronary angiography.
Methods
Study Population
The present was a single-center, investigator-driven, prospective study that involved patients with suspected CAD referred to invasive coronary angiography. All patients included were aged >18 years, in sinus rhythm, able to maintain a breath-hold for 15 seconds, without a history of contrast-related allergy, renal failure, or hemodynamic instability. Additional exclusion criteria comprised a history of previous myocardial infarction within the previous 30 days, previous percutaneous coronary revascularization or coronary bypass graft surgery, or chronic heart failure. Patients under rate-control medications were advised to withhold for the previous 24 hours. Coronary risk factors and clinical status were recorded at the time of the CT scan, and clinical variables were defined as indicated by the Framingham risk score assessment. No rate-control medications were administrated before the scan.
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Image Acquisition
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Image Analyses
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Invasive Angiography Acquisition and Analyses
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Statistical Analysis
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Results
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Table 1
Demographical Characteristics ( n = 35)
Age (years ± standard deviation) 61.4 ± 9.4 Male (%) 27 (77) BMI (kg/m 2 ) 28.0 ± 2.4 Heart rate (median; interquartile range) 62.0 (50.0–68.0) Diabetes (%) 7 (20%) Hypertension (%) 32 (91.4%) Hypercholesterolemia (%) 21 (60.0%) Previous smoking (%) 15 (42.9%) Current smoking (%) 2 (5.7%)
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Table 2
Image Quality Scores with Motion Correction Algorithm (MCA) and Conventional Reconstructions According to Vessel Territories
Reconstruction Mode MCA Conventional_P_ Value Per segment 3.88 ± 0.54 3.78 ± 0.76 <.0001 Per territory Right coronary artery (RCA) 3.81 ± 0.69 3.60 ± 1.08 <.001 Left main coronary artery (LMCA) 3.97 ± 0.17 3.97 ± 0.17 1.0 Left anterior descending (LAD) 3.88 ± 0.57 3.85 ± 0.61 .058 Left circumflex (LCx) 3.94 ± 0.26 3.87 ± 0.48 .007
Analysis of variance for differences between territories among the same group was nonsignificant for MCA reconstructions ( P = .11) and significant for conventional reconstructions ( P = .001). All post hoc comparisons (Bonferroni) were nonsignificant among the MCA group and significant among the conventional reconstruction group (RCA vs. LAD, P = .009; RCA vs. LCx P = .010; RCA vs. LMCA P = .046).
Table 3
Image Quality Scores with Motion Correction Algorithm (MCA) and Conventional Reconstructions According to Heart Rate Tertiles
Reconstruction / Heart rate tertile Tertile 1 ( n = 153) Tertile 2 ( n = 254) Tertile 3 ( n = 170)P (Analysis of Variance) MCA 3.93 ± 0.47 3.91 ± 0.50 3.79 ± 0.64 .052 Conventional 3.90 ± 0.51 3.86 ± 0.59 3.53 ± 1.09 <.0001P value .045 .041 <.0001
All post hoc comparisons (Bonferroni) among the MCA group were nonsignificant.
The following post hoc comparisons among the conventional reconstruction group were significant: tertile 1 versus tertile 3, P < .001; tertile 2 versus tertile 3 P < .001.
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Table 4
Diagnostic Accuracy of Motion Correction Algorithm (MCA) and Conventional Reconstructions for Detection of Stenosis ≥50% Based on Invasive Angiography
Reconstruction Per Segment Per Patient MCA Conventional MCA Conventional Sensitivity 88.8 (81.0–93.8) 88.8 (80.9–93.8) 100 (84.0–100) 100 (84.0–100) Specificity 90.3 (87.0–92.9) 89.3 (86.0–92.0) 66.7 (30.9–91.0) 66.7 (30.9–91.0) Positive predictive value 70.0 (61.3–77.3) 67.9 (59.4–75.4) 82.9 (65.7–92.8) 82.9 (65.7–92.8) Negative predictive value 96.9 (94.6–98.3) 96.9 (94.5–98.3) 100 (51.7–100) 100 (51.7–100) Positive likelihood ratio 9.1 (6.8–12.3) 8.3 (6.3–11.1) 8.7 (2.9–25.5) 8.7 (2.9–25.5) Negative likelihood ratio 0.12 (0.07–0.21) 0.13 (0.07–0.21) 0 (0–NA) 0 (0–NA) Area under the curve (ROC) 0.90 (0.87–0.92) 0.89 (0.86–0.92) ∗ 0.83 (0.64–1.0) 0.83 (0.64–1.0)
NA, not applicable; ROC, receiver operating characteristic.
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Table 5
Diagnostic Accuracy of Motion Correction Algorithm (MCA) and Conventional Reconstructions for Detection of Lesions ≥50% According to Heart Rate Tertiles
Reconstruction Tertile 1 Tertile 2 Tertile 3 MCA Conventional MCA Conventional MCA Conventional Sensitivity 88.9 (73.0–96.4) 88.9 (73.0–96.4) 88.9 (73.0–96.4) 89.1 (75.6–95.9) 88.0 (67.7–96.8) 88.0 (67.7–96.8) Specificity 93.0 (86.3–96.7) 93.0 (86.3–96.7) 93.3 (86.8–96.8) 84.2 (77.9–89.0) 96.0 (90.4–98.5) 93.5 (87.3–97.0) PPV 80.0 (63.8–90.4) 80.0 (63.8–90.4) 80.0 (63.8–90.4) 58.6 (46.2–70.0) 80.0 (63.9–90.4) 73.3 (53.8–87.0) NPV 96.4 (90.5–98.8) 96.4 (90.5–98.8) 96.5 (90.8–98.9) 96.9 (92.4–98.8) 97.5 (92.4–99.4) 97.5 (92.3–99.3) LR+ 12.8 (6.5–25.2) 12.8 (6.5–25.2) 13.2 (6.7–26.1) 5.6 (4.0–8.0) 21.8 (9.1–52.1) 13.6 (6.9–27.1) LR− 0.12 (0.05–0.30) 0.12 (0.05–0.30) 0.12 (0.05–0.30) 0.13 (0.06–0.30) 0.13 (0.04–0.36) 0.13 (0.04–0.37) AUC (ROC) 0.91 (0.84–0.98) 0.91 (0.84–0.98) 0.87 (0.81–0.93) 0.87 (0.81–0.93) 0.92 (0.84–0.99) 0.91 (0.83–0.99)
AUC, area under the ROC; LR+, positive likelihood ratio; LR−, negative likelihood ratio; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic.
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Discussion
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Conclusions
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