Four‐dimensional computed tomography of the left ventricle, Part I: Motion artifact reduction

PurposeStandard four-dimensional computed tomography (4DCT) cardiac reconstructions typically include spiraling artifacts that depend not only on the motion of the heart but also on the gantry angle range over which the data was acquired. We seek to reduce these motion artifacts and, thereby, improve the accuracy of left ventricular wall positions in 4DCT image series.MethodsWe use a motion artifact reduction approach (ResyncCT) that is based largely on conjugate pairs of partial angle reconstruction (PAR) images. After identifying the key locations where motion artifacts exist in the uncorrected images, paired subvolumes within the PAR images are analyzed with a modified cross-correlation function in order to estimate 3D velocity and acceleration vectors at these locations. A subsequent motion compensation process (also based on PAR images) includes the creation of a dense motion field, followed by a backproject-and-warp style compensation. The algorithm was tested on a 3D printed phantom, which represents the left ventricle (LV) and on challenging clinical cases corrupted by severe artifacts.ResultsThe results from our preliminary phantom test as well as from clinical cardiac scans show crisp endocardial edges and resolved double-wall artifacts. When viewed as a temporal series, the corrected images exhibit a much smoother motion of the LV endocardial boundary as compared to the uncorrected images. In addition, quantitative results from our phantom studies show that ResyncCT processing reduces endocardial surface distance errors from 0.9 ± 0.8 to 0.2 ± 0.1 mm.ConclusionsThe ResyncCT algorithm was shown to be effective in reducing motion artifacts and restoring accurate wall positions. Some perspectives on the use of conjugate-PAR images and on techniques for CT motion artifact reduction more generally are also given

High-Speed Onsite Deep-Learning Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as Reference Standard

Background: Estimation of fractional flow reserve (FFR) from coronary CTA (FFR-CT) is an established method to assess coronary lesions' hemodynamic significance. However, clinical implementation has progressed slowly, partly related to offsite data transfer with long turnaround times while awaiting results. Objectives: We aimed to evaluate the diagnostic performance of FFR-CT computed onsite with a high-speed deep-learning based algorithm, using invasive hemodynamic indices as reference standard. Methods: This retrospective study included 59 patients (46 men, 13 women; mean age 66.5±10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive FFR and/or instantaneous wave-free ratio (iwFR) measurements from December 2014 to October 2021. Coronary artery lesions were considered to show hemodynamically significant stenosis in presence of invasive FFR ≤0.80 and/or iwFR ≤0.89. A single cardiologist evaluated CTA images using an onsite deep-learning based semiautomated algorithm employing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected by invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations, and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. Results: Invasive angiography identified 74 lesions. FFR-CT and invasive FFR showed strong correlation (r=0.81), and, in Bland-Altman analysis, showed bias of 0.01 and 95% limits of agreement of -0.13 to +0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At cutoff of ≤0.80, FFR-CT had accuracy of 95.9%, sensitivity of 93.5%, and specificity of 97.7%. In 39 lesions with severe calcifications (≥400 Agatston units), FFR-CT had AUC of 0.991, with cutoff of ≤0.80 yielding sensitivity of 94.7%, specificity of 95.0%, and accuracy of 94.9%. Mean analysis time per patient was 7 minutes 54 seconds. Interobserver and intraobserver agreement were good-to-excellent (intraclass correlation coefficient, 0.944 and 0.854; bias -0.01 and -0.01; 95% limits of agreement, -0.08 to +0.07, and -0.12 and +0.10, respectively). Conclusion: A high-speed onsite deep-learning based FFR-CT algorithm showed excellent diagnostic performance for hemodynamically significant stenosis, with high reproducibility. Clinical Impact: The algorithm should facilitate the FFR-CT technology's implementation into routine clinical practice