Deep learning-based fast kV-switching CT (DL-FKSCT) generates complete sinograms for fast kV-switching dual-energy CT (DECT) scans by using a trained neural network to restore missing views. Such restoration significantly enhances the image quality of coronary CT angiography (CCTA), and the allowable heart rate (HR) may vary between DECT and single-energy CT (SECT). This study aimed to examine HR's effect onCCTA using DL-FKSCT. We scanned stenotic coronary artery phantoms attached to a pulsating cardiac phantom with DECT and SECT modes on a DL-FKSCT scanner. The phantom unit was operated with simulated HRs ranging from 0 (static) to 50-70 beats per minute (bpm). The sharpness and stenosis ratio of the coronary model were quantitatively compared between DECT and SECT, stratified by simulated HR settings using the paired t-test (significance was set at p < 0.01 with a Bonferroni adjustment for multiple comparisons). Regarding image sharpness, DECT showed significant superiority over SECT. In terms of the stenosis ratio compared to a static image reference, 70 keV virtual monochromatic image in DECT exhibited errors exceeding 10 % at HRs surpassing 65 bpm (p < 0.01), whereas 120 kVp SECT registered errors below 10 % across all HR settings, with no significant differences observed. In DL-FKSCT, DECT exhibited a lower upper limit of HR than SECT. Therefore, HR control is important for DECT scans in DL-FKSCT.

BackgroundPositron Emission Tomography (PET) myocardial perfusion imaging (MPI) is a powerful tool for predicting coronary artery disease (CAD). Coronary artery calcium (CAC) provides incremental risk stratification to PET-MPI and enhances diagnostic accuracy. We assessed additive value of CAC score, derived from PET/CT attenuation maps to stress TPD results using the novel 18F-flurpiridaz tracer in detecting significant CAD. Methods and ResultsPatients from 18F-flurpiridaz phase III clinical trial who underwent PET/CT MPI with 18F-flurpiridaz tracer, had available CT attenuation correction (CTAC) scans for CAC scoring, and underwent invasive coronary angiography (ICA) within a 6-month period between 2011 and 2013, were included. Total perfusion deficit (TPD) was quantified automatically, and CAC scores from CTAC scans were assessed using artificial intelligence (AI)-derived segmentation and manual scoring. Obstructive CAD was defined as [&ge;]50% stenosis in Left Main (LM) artery, or 70% or more stenosis in any of the other major epicardial vessels. Prediction performance for CAD was assessed by comparing the area under receiver operating characteristic curve (AUC) for stress TPD alone and in combination with CAC score. Among 498 patients (72% males, median age 63 years) 30.1% had CAD. Incorporating CAC score resulted in a greater AUC: manual scoring (AUC=0.87, 95% Confidence Interval [CI] 0.34-0.90; p=0.015) and AI-based scoring (AUC=0.88, 95%CI 0.85-0.90; p=0.002) compared to stress TPD alone (AUC 0.84, 95% CI 0.80-0.92). ConclusionsCombining automatically derived TPD and CAC score enhances 18F-flurpiridaz PET MPI accuracy in detecting significant CAD, offering a method that can be routinely used with PET/CT scanners without additional scanning or technologist time. CONDENSED ABSTRACTO_ST_ABSBackgroundC_ST_ABSWe assessed the added value of CAC score from hybrid PET/CT CTAC scans combined with stress TPD for detecting significant CAD using novel 18F-flurpiridaz tracer Methods and resultsPatients from the 18F-flurpiridaz phase III clinical trial (n=498, 72% male, median age 63) who underwent PET/CT MPI and ICA within 6-months were included. TPD was quantified automatically, and CAC scores were assessed by AI and manual methods. Adding CAC score to TPD improved AUC for manual (0.87) and AI-based (0.88) scoring versus TPD alone (0.84). ConclusionsCombining TPD and CAC score enhances 18F-flurpiridaz PET MPI accuracy for CAD detection O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/25330013v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): [email protected]@ba93d1org.highwire.dtl.DTLVardef@13eabd9org.highwire.dtl.DTLVardef@1845505_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract.C_FLOATNO Overview of the study design. C_FIG

Robust differentiation between infarcted and normal myocardial tissue is essential for improving diagnostic accuracy and personalizing treatment in myocardial infarction (MI). This study proposes a hybrid framework combining radiomic texture analysis with deep learning-based segmentation to enhance MI detection on non-contrast cine cardiac magnetic resonance (CMR) imaging.The approach incorporates radiomic features derived from the Gray-Level Co-Occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) methods into a modified U-Net segmentation network. A three-stage feature selection pipeline was employed, followed by classification using multiple machine learning models. Early and intermediate fusion strategies were integrated into the hybrid architecture. The model was validated on cine-CMR data from the SCD and Kaggle datasets.Joint Entropy, Max Probability, and RLNU emerged as the most discriminative features, with Joint Entropy achieving the highest AUC (0.948). The hybrid model outperformed standalone U-Net in segmentation (Dice = 0.887, IoU = 0.803, HD95 = 4.48 mm) and classification (accuracy = 96.30%, AUC = 0.97, precision = 0.96, recall = 0.94, F1-score = 0.96). Dimensionality reduction via PCA and t-SNE confirmed distinct class separability. Correlation coefficients (r = 0.95-0.98) and Bland-Altman plots demonstrated high agreement between predicted and reference infarct sizes.Integrating radiomic features into a deep learning segmentation pipeline improves MI detection and interpretability in cine-CMR. This scalable and explainable hybrid framework holds potential for broader applications in multimodal cardiac imaging and automated myocardial tissue characterization.

Manually analyzing a series of MRI images to obtain information about the heart's motion is a time-consuming and labor-intensive task. Recently, many AI-driven tools have been used to automatically analyze cardiac MRI. However, it is still unknown whether the results generated by these tools are consistent. The aim of the present study was to investigate the agreement of AI-powered automated tools for measuring cine MRI-derived cardiac function and motion indices. Cine MRI datasets of 23 healthy volunteers (10 males, 32.7 ± 11.3 years) were processed using heart deformation analysis (HDA, Trufistrain) and Circle CVI 42. The left and right ventricular (LV/RV) end-diastolic volume (LVEDV and RVEDV), end-systolic volume (LVESV and RVESV), stroke volume (LVSV and RVSV), cardiac output (LVCO and RVCO), ejection fraction (LVEF and RVEF), LV mass (LVM), LV global strain, strain rate, displacement, and velocity were calculated without interventions. Agreements and discrepancies of indices acquired with the two tools were evaluated from various aspects using t-tests, Pearson correlation coefficient (r), interclass correlation coefficient (ICC), and coefficient of variation (CoV). Systematic biases for measuring cardiac function and motion indices were observed. In global cardiac function indices, LVEF (56.9% ± 6.4 vs. 57.8% ± 5.7, p = 0.433, r = 0.609, ICC = 0.757, CoV = 6.7%) and LVM (82.7 g ± 21.6 vs. 82.6 g ± 18.7, p = 0.988, r = 0.923, ICC = 0.956, CoV = 11.7%) acquired with HDA and Circle seemed to be exchangeable. Among cardiac motion indices, circumferential strain rate demonstrated good agreements between two tools (97 ± 14.6 vs. 97.8 ± 13.6, p = 0.598, r = 0.89, ICC = 0.943, CoV = 5.1%). Cine MRI-derived cardiac function and motion indices obtained using different AI-powered image processing tools are related but may also differ. Such variations should be considered when evaluating results sourced from different studies.

Aims: To develop a deep-learning (DL) framework that will allow fully automated longitudinal and circumferential co-registration of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images. Methods and results: Data from 230 patients (714 vessels) with acute coronary syndrome that underwent near-infrared spectroscopy (NIRS)-IVUS and OCT imaging in their non-culprit vessels were included in the present analysis. The lumen borders annotated by expert analysts in 61,655 NIRS-IVUS and 62,334 OCT frames, and the side branches and calcific tissue identified in 10,000 NIRS-IVUS frames and 10,000 OCT frames, were used to train DL solutions for the automated extraction of these features. The trained DL solutions were used to process NIRS-IVUS and OCT images and their output was used by a dynamic time warping algorithm to co-register longitudinally the NIRS-IVUS and OCT images, while the circumferential registration of the IVUS and OCT was optimized through dynamic programming. On a test set of 77 vessels from 22 patients, the DL method showed high concordance with the expert analysts for the longitudinal and circumferential co-registration of the two imaging sets (concordance correlation coefficient >0.99 for the longitudinal and >0.90 for the circumferential co-registration). The Williams Index was 0.96 for longitudinal and 0.97 for circumferential co-registration, indicating a comparable performance to the analysts. The time needed for the DL pipeline to process imaging data from a vessel was <90s. Conclusion: The fully automated, DL-based framework introduced in this study for the co-registration of IVUS and OCT is fast and provides estimations that compare favorably to the expert analysts. These features renders it useful in research in the analysis of large-scale data collected in studies that incorporate multimodality imaging to characterize plaque composition.

Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its clinical presentation. It is associated with significant clinical complications such as high-degree atrioventricular block, ventricular tachycardia, heart failure and sudden cardiac death. It is challenging to diagnose clinically, and its increasing detection rate may represent increasing awareness of the disease by clinicians as well as a rising incidence. Prompt diagnosis and risk stratification reduces morbidity and mortality from cardiac sarcoidosis. Noninvasive diagnostic modalities such as ECG, echocardiography, PET/computed tomography (PET/CT) and cardiac MRI (cMRI) are increasingly playing important roles in cardiac sarcoidosis diagnosis. Artificial intelligence driven applications are increasingly being applied to these diagnostic modalities to improve the detection of cardiac sarcoidosis. Review of the recent literature suggests artificial intelligence based algorithms in PET/CT and cMRIs can predict cardiac sarcoidosis as accurately as trained experts, however, there are few published studies on artificial intelligence based algorithms in ECG and echocardiography. The impressive advances in artificial intelligence have the potential to transform patient screening in cardiac sarcoidosis, aid prompt diagnosis and appropriate risk stratification and change clinical practice.

Incidental pulmonary embolism (PE) is detected in 1% of cardiac CT angiography (CCTA) scans, despite the targeted aortic opacification and limited field of view. While artificial intelligence (AI) algorithms have proven effective in detecting PE in CT pulmonary angiography (CTPA), their use in CCTA remains unexplored. This study aimed to evaluate the feasibility of an AI algorithm for detecting incidental PE in CCTA scans. A dedicated AI algorithm was retrospectively applied to CCTA scans to detect PE. Radiology reports were reviewed using a natural language processing (NLP) tool to detect mentions of PE. Discrepancies between the AI and radiology reports triggered a blinded review by a cardiothoracic radiologist. All scans identified as positive for PE were thoroughly assessed for radiographic features, including the location of emboli and right ventricular (RV) strain. The performance of the AI algorithm for PE detection was compared to the original radiology report. Between 2021 and 2023, 1534 CCTA scans were analyzed. The AI algorithm identified 27 positive PE scans, with a subsequent review confirming PE in 22/27 cases. Of these, 10 (45.5%) were missed in the initial radiology report, all involving segmental or subsegmental arteries (P < 0.05) with no evidence of RV strain. This study demonstrates the feasibility of using an AI algorithm to detect incidental PE in CCTA scans. A notable radiology report miss rate (45.5%) of segmental and subsegmental emboli was documented. While these findings emphasize the potential value of AI for PE detection in the daily radiology workflow, further research is needed to fully determine its clinical impact.

Coronary magnetic resonance angiography (CMRA) scans are generally time-consuming. CMRA with compressed sensing (CS) and artificial intelligence (AI) (CSAI CMRA) is expected to shorten the imaging time while maintaining image quality. This study aimed to evaluate the usefulness of CS and AI for non-contrast CMRA. Twenty volunteers underwent both CS and conventional CMRA. Conventional CMRA employed parallel imaging (PI) with an acceleration factor of 2. CS CMRA employed a combination of PI and CS with an acceleration factor of 3. Deep learning reconstruction was performed offline on the CS CMRA data after scanning, which was defined as CSAI CMRA. We compared the imaging time, image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and vessel sharpness for each CMRA scan. The CS CMRA scan time was significantly shorter than that of conventional CMRA (460 s [343,753 s] vs. 727 s [567,939 s], p < 0.001). The image quality scores of the left anterior descending artery (LAD) and left circumflex artery (LCX) were significantly higher in conventional CMRA (LAD: 3.3 ± 0.7, LCX: 3.3 ± 0.7) and CSAI CMRA (LAD: 3.7 ± 0.6, LCX: 3.5 ± 0.7) than the CS CMRA (LAD: 2.9 ± 0.6, LCX: 2.9 ± 0.6) (p < 0.05). The right coronary artery scores did not vary among the three groups (p = 0.087). The SNR and CNR were significantly higher in CSAI CMRA (SNR: 12.3 [9.7, 13.7], CNR: 12.3 [10.5, 14.5]) and CS CMRA (SNR: 10.5 [8.2, 12.6], CNR: 9.5 [7.9, 12.6]) than conventional CMRA (SNR: 9.0 [7.8, 11.1], CNR: 7.7 [6.0, 10.1]) (p < 0.01). The vessel sharpness was significantly higher in CSAI CMRA (LAD: 0.87 [0.78, 0.91]) (p < 0.05), with no significant difference between the CS CMRA (LAD: 0.77 [0.71, 0.83]) and conventional CMRA (LAD: 0.77 [0.71, 0.86]). CSAI CMRA can shorten the imaging time while maintaining good image quality.

The integration of artificial intelligence (AI) with point-of-care ultrasound (POCUS) is transforming cardiovascular diagnostics by enhancing image acquisition, interpretation, and workflow efficiency. These advancements hold promise in expanding access to cardiovascular imaging in resource-limited settings and enabling early disease detection through screening applications. This review explores the opportunities and challenges of AI-enabled POCUS as it reshapes the landscape of cardiovascular imaging. AI-enabled systems can reduce operator dependency, improve image quality, and support clinicians-both novice and experienced-in capturing diagnostically valuable images, ultimately promoting consistency across diverse clinical environments. However, widespread adoption faces significant challenges, including concerns around algorithm generalizability, bias, explainability, clinician trust, and data privacy. Addressing these issues through standardized development, ethical oversight, and clinician-AI collaboration will be critical to safe and effective implementation. Looking ahead, emerging innovations-such as autonomous scanning, real-time predictive analytics, tele-ultrasound, and patient-performed imaging-underscore the transformative potential of AI-enabled POCUS in reshaping cardiovascular care and advancing equitable healthcare delivery worldwide.

Myocardial infarction (MI) is a leading cause of death worldwide. Late gadolinium enhancement (LGE) and T2-weighted cardiac magnetic resonance (CMR) imaging can respectively identify scarring and edema areas, both of which are essential for MI risk stratification and prognosis assessment. Although combining complementary information from multi-sequence CMR is useful, acquiring these sequences can be time-consuming and prohibitive, e.g., due to the administration of contrast agents. Cine CMR is a rapid and contrast-free imaging technique that can visualize both motion and structural abnormalities of the myocardium induced by acute MI. Therefore, we present a new end-to-end deep neural network, referred to as CineMyoPS, to segment myocardial pathologies, i.e., scars and edema, solely from cine CMR images. Specifically, CineMyoPS extracts both motion and anatomy features associated with MI. Given the interdependence between these features, we design a consistency loss (resembling the co-training strategy) to facilitate their joint learning. Furthermore, we propose a time-series aggregation strategy to integrate MI-related features across the cardiac cycle, thereby enhancing segmentation accuracy for myocardial pathologies. Experimental results on a multi-center dataset demonstrate that CineMyoPS achieves promising performance in myocardial pathology segmentation, motion estimation, and anatomy segmentation.