The left atrial appendage (LAA) is a finger-like extension originating from the main body of the left atrium. Atrial fibrillation (AF) is the most common clinically important cardiac arrhythmia, occurring in approximately 0.4% to 1% of the general population and increasing with age to >8% in those >80 years of age. In the presence of AF thrombus, formation often occurs within the LAA because of reduced contractility and stasis; thus, attention should be given to the LAA when evaluating and assessing patients with AF to determine the risk for cardioembolic complications. It is clinically important to understand LAA anatomy and function. It is also critical to choose the optimal imaging techniques to identify or exclude LAA thrombi in the setting of AF, before cardioversion, and with current and emerging transcatheter therapies, which include mitral balloon valvuloplasty, pulmonary vein isolation, MitraClip (Abbott Laboratories, Abbott Park, Illinois) valve repair, and the implantation of LAA occlusion and exclusion devices. In this review, we present the current data regarding LAA anatomy, LAA function, and LAA imaging using the currently available noninvasive imaging modalities.

Purpose To develop a cardiac and respiratory self-gated 4D coronary MRA technique for simultaneous cardiac anatomy and function visualization. Methods A contrast-enhanced, ungated spoiled gradient echo sequence with self-gating (SG) and 3DPR trajectory was used for image acquisition. Data was retrospectively binned into different cardiac and respiratory phases based on information extracted from SG projections using principal component analysis. Each cardiac phase was reconstructed using a respiratory motion-corrected self-calibrating SENSE framework, and those belong to the quiescent period were retrospectively combined for coronary visualization. Healthy volunteer studies were conducted to evaluate the efficacy of the SG method, the accuracy of the left ventricle (LV) function parameters and the quality of coronary artery visualization. Results SG performed reliably for all subjects including one with poor ECG. The LV function parameters showed excellent agreement with those from a conventional cine protocol. For coronary imaging, the proposed method yielded comparable apparent SNR and coronary sharpness and lower apparent CNR on three subjects compared with an ECG and navigator-gated Cartesian protocol and an ECG-gated, respiratory motion-corrected 3DPR protocol. Conclusion A fully self-gated 4D whole-heart imaging technique was developed, potentially allowing cardiac anatomy and function assessment from a single measurement.

We compared the performance of a fully automated quantification of attenuation-corrected (AC) and non-corrected (NC) myocardial perfusion single photon emission computed tomography (MPS) with the corresponding performance of experienced readers for the detection coronary artery disease (CAD). Methods 995 rest/stress 99mTc-sestamibi MPS studies, [650 consecutive cases with coronary angiography and 345 with likelihood of CAD < 5% (LLk)] were obtained by MPS with AC. Total perfusion deficit (TPD) for AC and NC data were compared to the visual summed stress and rest scores of 2 experienced readers. Visual reads were performed in 4 consecutive steps with the following information progressively revealed: NC data, AC+NC data, computer results, all clinical information. Results The diagnostic accuracy of TPD for detection of CAD was similar to both readers (NC: 82% vs. 84%, AC: 86% vs. 85–87% p = NS) with the exception of second reader when using clinical information (89%, p < 0.05). The Receiver-Operator-Characteristics Areas-Under-Curve (ROC-AUC) for TPD were significantly better than visual reads for NC (0.91 vs. 0.87 and 0.89, p < 0.01) and AC (0.92 vs. 0.90, p < 0.01), and it was comparable to visual reads incorporating all clinical information. Per-vessel accuracy of TPD was superior to one reader for NC (81% vs. 77%, p < 0.05) and AC (83% vs. 78%, p < 0.05) and equivalent to second reader [NC (79%) and AC (81%)]. Per-vessel ROC-AUC for NC (0.83) and AC (0.84) for TPD were better than (0.78–0.80 p < 0.01), and comparable to second reader (0.82–0.84, p = NS), for all steps. Conclusion For the detection of ≥ 70% stenosis based on angiographic criteria, a fully automated computer analysis of NC and AC MPS data is equivalent for per-patient and can be superior for per-vessel analysis, when compared to expert analysis.

Objective We aimed to investigate if early revascularization in patients with suspected coronary artery disease (CAD) can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine learning (ML) approach. Methods 713 rest 201Thallium/stress 99mTechnetium MPS studies with correlating invasive angiography (372 revascularization events (275 PCI / 97 CABG) within 90 days after MPS (91% within 30 days) were considered. Transient ischemic dilation (TID), stress combined supine/prone total perfusion deficit (TPD), quantitative rest and stress TPD, exercise ejection fraction, and end-systolic volume along with clinical parameters including patient gender, history of hypertension and diabetes mellitus, ST-depression on baseline ECG, ECG and clinical response during stress, and post-ECG probability by boosted ensemble ML algorithm (LogitBoost) to predict revascularization events. These features were selected using an automated feature selection algorithm from all available clinical and quantitative data (33 parameters). 10-fold cross-validation was utilized to train and test the prediction model. The prediction of revascularization by ML algorithm was compared to standalone measures of perfusion and visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data. Results The sensitivity of machine learning (73.6±4.3%) for prediction of revascularization was similar to one reader (73.9±4.6%) and standalone measures of perfusion (75.5±4.5%). The specificity of machine learning (74.7±4.2%) was also better than both expert readers (67.2±4.9% and 66.0±5.0%, P < 0.05), but was similar to ischemic TPD (68.3±4.9%, P < 0.05). The Receiver-Operator-Characteristics areas-under-curve for machine learning (0.81±0.02) was similar to reader 1 (0.81±0.02) but superior to reader 2 (0.72±0.02, P < 0.01) and standalone measure of perfusion (0.77±0.02, P < 0.01). Conclusion ML approach is comparable or better than experienced reader in prediction of the early revascularization after MPS and is significantly better than standalone measures of perfusion derived from MPS.

Objective We aimed to improve the diagnostic accuracy of myocardial perfusion SPECT (MPS) by integrating clinical data and quantitative image features with machine learning (ML) algorithms. Methods 1,181 rest 201Tl/stress 99mTc-sestamibi dual-isotope MPS studies [713 consecutive cases with correlating invasive coronary angiography (ICA) and suspected coronary artery disease (CAD) and 468 with low likelihood (LLk) of CAD <5%] were considered. Cases with stenosis <70% by ICA and LLk of CAD were considered normal. Total stress perfusion deficit (TPD) for supine/prone data, stress/rest perfusion change, and transient ischemic dilatation were derived by automated perfusion quantification software and were combined with age, sex, and post-electrocardiogram CAD probability by a boosted ensemble ML algorithm (Logit-Boost). The diagnostic accuracy of the model for prediction of obstructive CAD ≥70% was compared to standard prone/supine quantification and to visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data. Tenfold stratified cross-validation was performed. Results The diagnostic accuracy of ML (87.3% ± 2.1%) was similar to Expert 1 (86.0% ± 2.1%), but superior to combined supine/prone TPD (82.8% ± 2.2%) and Expert 2 (82.1% ± 2.2%) (P <.01). The receiver operator characteristic areas under curve for ML algorithm (0.94 ± 0.01) were higher than those for TPD and both visual readers (P <.001). The sensitivity of ML algorithm (78.9% ± 4.2%) was similar to TPD (75.6% ± 4.4%) and Expert 1 (76.3% ± 4.3%), but higher than that of Expert 2 (71.1% ± 4.6%), (P <.01). The specificity of ML algorithm (92.1% ± 2.2%) was similar to Expert 1 (91.4% ± 2.2%) and Expert 2 (88.3% ± 2.5%), but higher than TPD (86.8% ± 2.6%), (P <.01). Conclusion ML significantly improves diagnostic performance of MPS by computational integration of quantitative perfusion and clinical data to the level rivaling expert analysis.

Among individuals without known CAD, non-obstructive, and obstructive CAD are associated with higher MACE rates, with different risk profiles based on age.

• Coronary CTA enables the assessment of coronary atherosclerotic plaque. • High-risk plaque characteristics and overall plaque burden can predict future cardiac events. • Coronary atherosclerotic plaque quantification is currently unfeasible in practice. • Quantitative computed tomography coronary plaque analysis software (QCT) enables feasible plaque quantification. • Fully automatic QCT analysis shows excellent performance.

Abstract. Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.