Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography - comparison and registration with IVUS

Background: The aim of this study is to present a new methodology for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography Angiography (CTA). Methods: The methodology is summarized in six stages: 1) pre-processing of the initial raw images, 2) rough estimation of the lumen and outer vessel wall borders and approximation of the vessel's centerline, 3) manual adaptation of plaque parameters, 4) accurate extraction of the luminal centerline, 5) detection of the lumen - outer vessel wall borders and calcium plaque region, and 6) finally 3D surface construction. Results: The methodology was compared to the estimations of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method. The correlation coefficients for calcium volume, surface area, length and angle vessel were 0.79, 0.86, 0.95 and 0.88, respectively. Additionally, when comparing the inner and outer vessel wall volumes of the reconstructed arteries produced by IVUS and CTA the observed correlation was 0.87 and 0.83, respectively. Conclusions: The results indicated that the proposed methodology is fast and accurate and thus it is likely in the future to have applications in research and clinical arena

Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography - comparison and registration with IVUS

BACKGROUND: The aim of this study is to present a new methodology for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography Angiography (CTA). METHODS: The methodology is summarized in six stages: 1) pre-processing of the initial raw images, 2) rough estimation of the lumen and outer vessel wall borders and approximation of the vessel’s centerline, 3) manual adaptation of plaque parameters, 4) accurate extraction of the luminal centerline, 5) detection of the lumen - outer vessel wall borders and calcium plaque region, and 6) finally 3D surface construction. RESULTS: The methodology was compared to the estimations of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method. The correlation coefficients for calcium volume, surface area, length and angle vessel were 0.79, 0.86, 0.95 and 0.88, respectively. Additionally, when comparing the inner and outer vessel wall volumes of the reconstructed arteries produced by IVUS and CTA the observed correlation was 0.87 and 0.83, respectively. CONCLUSIONS: The results indicated that the proposed methodology is fast and accurate and thus it is likely in the future to have applications in research and clinical arena

Coronary Artery Calcium Quantification in Contrast-enhanced Computed Tomography Angiography

Coronary arteries are the blood vessels supplying oxygen-rich blood to the heart muscles. Coronary artery calcium (CAC), which is the total amount of calcium deposited in these arteries, indicates the presence or the future risk of coronary artery diseases. Quantification of CAC is done by using computed tomography (CT) scan which uses attenuation of x-ray by different tissues in the body to generate three-dimensional images. Calcium can be easily spotted in the CT images because of its higher opacity to x-ray compared to that of the surrounding tissue. However, the arteries cannot be identified easily in the CT images. Therefore, a second scan is done after injecting a patient with an x-ray opaque dye known as contrast material which makes different chambers of the heart and the coronary arteries visible in the CT scan. This procedure is known as computed tomography angiography (CTA) and is performed to assess the morphology of the arteries in order to rule out any blockage in the arteries. The CT scan done without the use of contrast material (non-contrast-enhanced CT) can be eliminated if the calcium can be quantified accurately from the CTA images. However, identification of calcium in CTA images is difficult because of the proximity of the calcium and the contrast material and their overlapping intensity range. In this dissertation first we compare the calcium quantification by using a state-of-the-art non-contrast-enhanced CT scan method to conventional methods suggesting optimal quantification parameters. Then we develop methods to accurately quantify calcium from the CTA images. The methods include novel algorithms for extracting centerline of an artery, calculating the threshold of calcium adaptively based on the intensity of contrast along the artery, calculating the amount of calcium in mixed intensity range, and segmenting the artery and the outer wall. The accuracy of the calcium quantification from CTA by using our methods is higher than the non-contrast-enhanced CT thus potentially eliminating the need of the non-contrast-enhanced CT scan. The implications are that the total time required for the CT scan procedure, and the patient\u27s exposure to x-ray radiation are reduced

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)

The Evolution of Data Fusion Methodologies Developed to Reconstruct Coronary Artery Geometry From Intravascular Imaging and Coronary Angiography Data: A Comprehensive Review

Understanding the mechanisms that regulate atherosclerotic plaque formation and evolution is a crucial step for developing treatment strategies that will prevent plaque progression and reduce cardiovascular events. Advances in signal processing and the miniaturization of medical devices have enabled the design of multimodality intravascular imaging catheters that allow complete and detailed assessment of plaque morphology and biology. However, a significant limitation of these novel imaging catheters is that they provide two-dimensional (2D) visualization of the lumen and vessel wall and thus they cannot portray vessel geometry and 3D lesion architecture. To address this limitation computer-based methodologies and user-friendly software have been developed. These are able to off-line process and fuse intravascular imaging data with X-ray or computed tomography coronary angiography (CTCA) to reconstruct coronary artery anatomy. The aim of this review article is to summarize the evolution in the field of coronary artery modeling; we thus present the first methodologies that were developed to model vessel geometry, highlight the modifications introduced in revised methods to overcome the limitations of the first approaches and discuss the challenges that need to be addressed, so these techniques can have broad application in clinical practice and research