Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography

The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist’s reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases

Automated quantification and evaluation of motion artifact on coronary CT angiography images

Abstract Purpose This study developed and validated a Motion Artifact Quantification algorithm to automatically quantify the severity of motion artifacts on coronary computed tomography angiography (CCTA) images. The algorithm was then used to develop a Motion IQ Decision method to automatically identify whether a CCTA dataset is of sufficient diagnostic image quality or requires further correction. Method The developed Motion Artifact Quantification algorithm includes steps to identify the right coronary artery (RCA) regions of interest (ROIs), segment vessel and shading artifacts, and to calculate the motion artifact score (MAS) metric. The segmentation algorithms were verified against ground‐truth manual segmentations. The segmentation algorithms were also verified by comparing and analyzing the MAS calculated from ground‐truth segmentations and the algorithm‐generated segmentations. The Motion IQ Decision algorithm first identifies slices with unsatisfactory image quality using a MAS threshold. The algorithm then uses an artifact‐length threshold to determine whether the degraded vessel segment is large enough to cause the dataset to be nondiagnostic. An observer study on 30 clinical CCTA datasets was performed to obtain the ground‐truth decisions of whether the datasets were of sufficient image quality. A five‐fold cross‐validation was used to identify the thresholds and to evaluate the Motion IQ Decision algorithm. Results The automated segmentation algorithms in the Motion Artifact Quantification algorithm resulted in Dice coefficients of 0.84 for the segmented vessel regions and 0.75 for the segmented shading artifact regions. The MAS calculated using the automated algorithm was within 10% of the values obtained using ground‐truth segmentations. The MAS threshold and artifact‐length thresholds were determined by the ROC analysis to be 0.6 and 6.25 mm by all folds. The Motion IQ Decision algorithm demonstrated 100% sensitivity, 66.7% ± 27.9% specificity, and a total accuracy of 86.7% ± 12.5% for identifying datasets in which the RCA required correction. The Motion IQ Decision algorithm demonstrated 91.3% sensitivity, 71.4% specificity, and a total accuracy of 86.7% for identifying CCTA datasets that need correction for any of the three main vessels. Conclusion The Motion Artifact Quantification algorithm calculated accurate

Evaluation of Motion Artifact Metrics for Coronary CT Angiography

Purpose This study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best‐phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground‐truth motion artifact scores from a series of pairwise comparisons. Method Five motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low‐Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine‐filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground‐truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground‐truth reader score. The Kendall\u27s Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed. Results On phantom images, the Kendall\u27s Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall\u27s Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall\u27s Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall\u27s Tau coefficient of 0.65. Conclusion The metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images

Automated Selection of the Optimal Cardiac Phase for Single-Beat Coronary CT Angiography Reconstruction

This thesis investigates an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. Reconstructing a low-motion cardiac phase improves coronary artery visualization in coronary CT angiography (CCTA) exams. Currently, standard end-systole and/or mid-diastole default phases are prescribed or alternatively, quiescent phases are determined by the user. As manual selection may be time-consuming and standard locations may be suboptimal due to patient variability, an automated method is investigated. An automated algorithm was developed to select the optimal phase based on quantitative image quality (IQ) metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. A binary metric based on the edge strength of in-plane vessels was calculated to determine if IQ of in-plane vessels was acceptable. The algorithm performance was evaluated using two observer studies. Fourteen single-beat CCTA exams (Revolution CT, GE Healthcare) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Inter-reader (RR) and reader-algorithm (RA) agreement was calculated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC). A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three readers using a 5pt Likert scale. There was no significant difference between RR and RA agreement for either MAD or CCC metrics (p\u3e0.2). The algorithm phase was within 2% of the consensus phase in 71% of cases. There was no significant difference (p\u3e0.2) between the IQ of the algorithm phase (4.06±0.73) and the consensus phase (4.11±0.76). The proposed algorithm was statistically equivalent to a reader in selecting an optimal cardiac phase for CCTA exams. When reader and algorithm phases differed by \u3e2%, IQ was statistically equivalent

Visualization of coronary arteries in paediatric patients using whole-heart coronary magnetic resonance angiography: comparison of image-navigation and the standard approach for respiratory motion compensation

Aims: To investigate the use of respiratory motion compensation using image-based navigation (iNAV) with constant respiratory efficiency using single end-expiratory thresholding (CRUISE) for coronary magnetic resonance angiography (CMRA), and compare it to the conventional diaphragmatic navigator (dNAV) in paediatric patients with congenital or suspected heart disease. Methods: iNAV allowed direct tracking of the respiratory heart motion and was generated using balanced steady state free precession startup echoes. Respiratory gating was achieved using CRUISE with a fixed 50% efficiency. Whole-heart CMRA was acquired with 1.3mm isotropic resolution. For comparison, CMRA with identical imaging parameters were acquired using dNAV. Scan time, visualization of coronary artery origins and mid-course, imaging quality and sharpness was compared between the two sequences. Results: Forty patients (13 females; median weight: 44 kg; median age: 12.6, range: 3 months–17 years) were enrolled. 25 scans were performed in awake patients. A contrast agent was used in 22 patients. The scan time was significantly reduced using iNAV for awake patients (iNAV 7:48 ± 1:26 vs dNAV 9:48 ± 3:11, P = 0.01) but not for patients under general anaesthesia (iNAV = 6:55 ± 1:50 versus dNAV = 6:32 ± 2:16; P = 0.32). In 98% of the cases, iNAV image quality had an equal or higher score than dNAV. The visual score analysis showed a clear difference, favouring iNAV (P = 0.002). The right coronary artery and the left anterior descending vessel sharpness was significantly improved (iNAV: 56.8% ± 10.1% vs dNAV: 53.7% ± 9.9%, P < 0.002 and iNAV: 55.8% ± 8.6% vs dNAV: 53% ± 9.2%, P = 0.001, respectively). Conclusion: iNAV allows for a higher success-rate and clearer depiction of the mid-course of coronary arteries in paediatric patients. Its acquisition time is shorter in awake patients and image quality score is equal or superior to the conventional method in most cases.Medical Engineering at King’s College London WT 088641/Z/09/ZBHF Centre of Excellence RE/08/0

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Role of computed tomography and magnetic resonance imaging in patients with cardiovascular disease

Background: Although there have been recent advances, cardiovascular disease remains the commonest cause of premature death in the United Kingdom. There is a need to develop safe non-invasive techniques to aid the diagnosis and treatment of patients with cardiovascular disease.Objectives: The aims of this thesis are: (i) to establish whether coronary artery calcification can be measured reproducibly by helical computed tomography; (ii) to establish the effect of lipid lowering therapy on the progression of coronary calcification; (iii) to determine whether multidetector computed tomography can predict graft patency in patients who have undergone coronary artery bypass grafting; and (iv), to investigate the role of magnetic resonance imaging to assess plaque characteristics following acute carotid plaque rupture.Methods: In 16 patients, coronary artery calcification was assessed twice within 4 weeks by helical computed tomography. As part of a randomised controlled trial, patients received atorvastatin 80 mg daily or matching placebo, and had coronary calcification assessed annually. Fifty patients with previous coronary artery bypass surgery who were listed for diagnostic coronary angiography underwent contrast enhanced computed tomography angiography using a 16-slice multidetector computed tomography scanner. Finally, 15 patients with recent symptoms and signs of an acute transient ischaemic attack, amaurosis fugax or stroke underwent magnetic resonance angiography of the carotid arteries using dedicated surface coils. Plaque volume, regional plaque densities and neovascularisation were determined before and after gadolinium enhancement.Results: Quantification of coronary artery calcification demonstrated good reproducibility in patients with scores > 100 AU. Despite reducing systemic inflammation and halving serum low-density lipoprotein cholesterol concentrations, atorvastatin therapy did not affect the rate of progression of coronary artery calcification. Computed tomography angiography was found to be highly specific for the detection of graft patency. Assessment of plaque characteristics by magnetic resonance scanning in patients with recent acute carotid plaque was feasible and reproducible.Conclusions: Coronary artery calcium scores can be determined in a reproducible manner. Although they correlate well with the presence of atherosclerosis and predict future coronary risk. there is little role for monitoring progression of coronary artery calcification in order to assess the response to lipid lowering therapy. Computed tomography can be used reliably to predict graft patency in patients who have undergone coronary artery bypass grafting, and is an acceptable non-invasive alternative to invasive coronary angiography. Magnetic resonance imaging techniques ' can be employed in a feasible, timely and reproducible manner to detect plaque characteristics associated with acute atherothrombotic disease

Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topological soft gradient prescreening method and luminal analysis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135084/1/mp5958.pd

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated