In vivo comparison of arterial lumen dimensions assessed by co-registered three-dimensional (3D) quantitative coronary angiography, intravascular ultrasound and optical coherence tomography

This study sought to compare lumen dimensions as assessed by 3D quantitative coronary angiography (QCA) and by intravascular ultrasound (IVUS) or optical coherence tomography (OCT), and to assess the association of the discrepancy with vessel curvature. Coronary lumen dimensions often show discrepancies when assessed by X-ray angiography and by IVUS or OCT. One source of error concerns a possible mismatch in the selection of corresponding regions for the comparison. Therefore, we developed a novel, real-time co-registration approach to guarantee the point-to-point correspondence between the X-ray, IVUS and OCT images. A total of 74 patients with indication for cardiac catheterization were retrospectively included. Lumen morphometry was performed by 3D QCA and IVUS or OCT. For quantitative analysis, a novel, dedicated approach for co-registration and lumen detection was employed allowing for assessment of lumen size at multiple positions along the vessel. Vessel curvature was automatically calculated from the 3D arterial vessel centerline. Comparison of 3D QCA and IVUS was performed in 519 distinct positions in 40 vessels. Correlations were r = 0.761, r = 0.790, and r = 0.799 for short diameter (SD), long diameter (LD), and area, respectively. Lumen sizes were larger by IVUS (P < 0.001): SD, 2.51 ± 0.58 mm versus 2.34 ± 0.56 mm; LD, 3.02 ± 0.62 mm versus 2.63 ± 0.58 mm; Area, 6.29 ± 2.77 mm2versus 5.08 ± 2.34 mm2. Comparison of 3D QCA and OCT was performed in 541 distinct positions in 40 vessels. Correlations were r = 0.880, r = 0.881, and r = 0.897 for SD, LD, and area, respectively. Lumen sizes were larger by OCT (P < 0.001): SD, 2.70 ± 0.65 mm versus 2.57 ± 0.61 mm; LD, 3.11 ± 0.72 mm versus 2.80 ± 0.62 mm; Area 7.01 ± 3.28 mm2versus 5.93 ± 2.66 mm2. The vessel-based discrepancy between 3D QCA and IVUS or OCT long diameters increased with increasing vessel curvature. In conclusion, our comparison of co-registered 3D QCA and invasive imaging data suggests a bias towards larger lume

Fusion of 3D QCA and IVUS/OCT

The combination/fusion of quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS)/optical coherence tomography (OCT) depends to a great extend on the co-registration of X-ray angiography (XA) and IVUS/OCT. In this work a new and robust three-dimensional (3D) segmentation and registration approach is presented and validated. The approach starts with standard QCA of the vessel of interest in the two angiographic views (either biplane or two monoplane views). Next, the vessel of interest is reconstructed in 3D and registered with the corresponding IVUS/OCT pullback series by a distance mapping algorithm. The accuracy of the registration was retrospectively evaluated on 12 silicone phantoms with coronary stents implanted, and on 24 patients who underwent both coronary angiography and IVUS examinations of the left anterior descending artery. Stent borders or sidebranches were used as markers for the validation. While the most proximal marker was set as the baseline position for the distance mapping algorithm, the subsequent markers were used to evaluate the registration error. The correlation between the registration error and the distance from the evaluated marker to the baseline position was analyzed. The XA-IVUS registration error for the 12 phantoms was 0.03 ± 0.32 mm (P = 0.75). One OCT pullback series was excluded from the phantom study, since it did not cover the distal stent border. The XA-OCT registration error for the remaining 11 phantoms was 0.05 ± 0.25 mm (P = 0.49). For the in vivo validation, two patients were excluded due to insufficient image quality for the analysis. In total 78 sidebranches were identified from the remaining 22 patients and the registration error was evaluated on 56 markers. The registration error was 0.03 ± 0.45 mm (P = 0.67). The error was not correlated to the distance between the evaluated marker and the baseline position (P = 0.73). In conclusion, the new XA-IVUS/OCT co-registration approach is a straightforward and reliable solution to combine X-ray angiography and IVUS/OCT imaging for the assessment of the extent of coronary artery disease. It provides the interventional cardiologist with detailed information about vessel size and plaque size at every position along the vessel of interest, making this a suitable tool during the actual intervention

Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.

AIMS: Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). METHODS AND RESULTS: Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s. CONCLUSIONS: The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644)

Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach

Cardiovascular imaging 2011 in the International Journal of Cardiovascular Imaging

Cardiovascular Aspects of Radiolog