Background and AimsIn cardiovascular magnetic resonance (CMR), myocardial native T1 mapping enables quantitative, non-invasive tissue characterization and is sensitive to subclinical changes in myocardial structure and composition. We investigated how age, sex, and cardiometabolic risk factors are associated with myocardial T1 in a population-based analysis within the German National Cohort (NAKO). MethodsThis cross-sectional study included 29,573 prospectively enrolled participants who underwent CMR-based midventricular T1 mapping at 3.0 T, alongside clinical phenotyping. After artificial intelligence-assisted myocardial segmentation, a subset of 9,162 outliers was subjected to manual quality control according to clinical evaluation standards. Associations with cardiometabolic risk factors, identified through self-reported medical history, clinical chemistry, and blood pressure measurements, were evaluated using adjusted linear regression models. ResultsWomen had higher T1 values than men, with sex differences progressively declining with age. T1 was significantly elevated in individuals with diabetes ({beta}=3.91 ms; p<0.001), kidney disease ({beta}=3.44 ms; p<0.001), and current smoking ({beta}=6.67 ms; p<0.001). Conversely, hyperlipidaemia was significantly associated with lower T1 ({beta}=-4.41 ms; p<0.001). Associations with hypertension showed a sex-specific pattern: T1 was lower in women but increased with hypertension severity in men. ConclusionsMyocardial native T1 varies by sex and age and shows associations with major cardiometabolic risk factors. Notably, lower T1 times in participants with hyperlipidaemia may indicate a direct effect of blood lipids on the heart. Our findings support the utility of T1 mapping as a sensitive marker of early myocardial changes and highlight the sex-specific interplay between cardiometabolic health and myocardial tissue composition. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/25331651v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): [email protected]@131514borg.highwire.dtl.DTLVardef@d03877org.highwire.dtl.DTLVardef@2b2fec_HPS_FORMAT_FIGEXP M_FIG C_FIG Key QuestionHow are age, sex, and cardiometabolic risk factors associated with myocardial native T1, a quantitative magnetic resonance imaging marker of myocardial tissue composition, in a large-scale population-based evaluation within the German National Cohort (NAKO)? Key FindingT1 relaxation times were higher in women and gradually converged between sexes with age. Diabetes, kidney disease, smoking, and hypertension in men were associated with prolonged T1 times. Unexpectedly, hyperlipidaemia and hypertension in women showed a negative association with T1. Take-Home MessageNative T1 mapping is sensitive to subclinical myocardial changes and reflects a close interplay between metabolic and myocardial health. It reveals marked age-dependent sex differences and sex-specific responses in myocardial tissue composition to cardiometabolic risk factors.

Coronary artery calcium scoring (CACS), i.e. quantification of Agatston (AS) or volume score (VS), can be time consuming. The aim of this study was to compare automated, artificial intelligence (AI)-based CACS to manual scoring, in cardiac and chest CT for lung cancer screening. We selected 684 participants (59 ± 4.8 years; 48.8 % men) who underwent cardiac and non-ECG-triggered chest CT, including 484 participants with AS > 0 on cardiac CT. AI-based results were compared to manual AS and VS, by assessing sensitivity and accuracy, intraclass correlation coefficient (ICC), Bland-Altman analysis and Cohen's kappa for classification in AS strata (0;1-99;100-299;≥300). AI showed high CAC detection rate: 98.1% in cardiac CT (accuracy 97.1%) and 92.4% in chest CT (accuracy 92.1%). AI showed excellent agreement with manual AS (ICC:0.997 and 0.992) and manual VS (ICC:0.997 and 0.991), in cardiac CT and chest CT, respectively. In Bland-Altman analysis, there was a mean difference of 2.3 (limits of agreement (LoA):-42.7, 47.4) for AS on cardiac CT; 1.9 (LoA:-36.4, 40.2) for VS on cardiac CT; -0.3 (LoA:-74.8, 74.2) for AS on chest CT; and -0.6 (LoA:-65.7, 64.5) for VS on chest CT. Cohen's kappa was 0.952 (95%CI:0.934-0.970) for cardiac CT and 0.901 (95%CI:0.875-0.926) for chest CT, with concordance in 95.9 and 91.4% of cases, respectively. AI-based CACS shows high detection rate and strong correlation compared to manual CACS, with excellent risk classification agreement. AI may reduce evaluation time and enable opportunistic screening for CAC on low-dose chest CT.

Coronary Artery Disease-Reporting and Data System (CAD-RADS), a standardized reporting system of stenosis severity from coronary computed tomography angiography (CCTA), is performed manually by expert radiologists, being time-consuming and prone to interobserver variability. While deep learning methods automating CAD-RADS scoring have been proposed, radiomics-based machine-learning approaches are lacking, despite their improved interpretability. This study aims to introduce a novel radiomics-based machine-learning approach for automating CAD-RADS scoring from CCTA images with multiplanar reconstruction. This retrospective monocentric study included 251 patients (male 70 %; mean age 60.5 ± 12.7) who underwent CCTA in 2016-2018 for clinical evaluation of CAD. Images were automatically segmented, and radiomic features were extracted. Clinical characteristics were collected. The image dataset was partitioned into training and test sets (90 %-10 %). The training phase encompassed feature scaling and selection, data balancing and model training within a 5-fold cross-validation. A cascade pipeline was implemented for both 6-class CAD-RADS scoring and 4-class therapy-oriented classification (0-1, 2, 3-4, 5), through consecutive sub-tasks. For each classification task the cascade pipeline was applied to develop clinical, radiomic, and combined models. The radiomic, combined and clinical models yielded AUC = 0.88 [0.86-0.88], AUC = 0.90 [0.88-0.90], and AUC = 0.66 [0.66-0.67] for the CAD-RADS scoring, and AUC = 0.93 [0.91-0.93], AUC = 0.97 [0.96-0.97], and AUC = 79 [0.78-0.79] for the therapy-oriented classification. The radiomic and combined models significantly outperformed (DeLong p-value < 0.05) the clinical one in class 1 and 2 (CAD-RADS cascade) and class 2 (therapy-oriented cascade). This study represents the first CAD-RADS classification radiomic model, guaranteeing higher explainability and providing a promising support system in coronary artery stenosis assessment.

This study assesses the effectiveness of super-resolution deep learning reconstruction (SR-DLR), conventional deep learning reconstruction (C-DLR), and hybrid iterative reconstruction (HIR) in enhancing image quality and diagnostic performance for pediatric congenital heart disease (CHD) in CT angiography (CCTA). A total of 91 pediatric patients aged 1-10 years, suspected of having CHD, were consecutively enrolled for CCTA under free-breathing conditions. Reconstructions were performed using SR-DLR, C-DLR, and HIR algorithms. Objective metrics-standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)-were quantified. Two radiologists provided blinded subjective image quality evaluations. The full width at half maximum of lesions was significantly larger on SR-DLR (9.50 ± 6.44 mm) than on C-DLR (9.08 ± 6.23 mm; p < 0.001) and HIR (8.98 ± 6.37 mm; p < 0.001). SR-DLR exhibited superior performance with significantly reduced SD and increased SNR and CNR, particularly in the left ventricle, left atrium, and right ventricle regions (p < 0.05). Subjective evaluations favored SR-DLR over C-DLR and HIR (p < 0.05). The accuracy (99.12%), sensitivity (99.07%), and negative predictive value (85.71%) of SR-DLR were the highest, significantly exceeding those of C-DLR (+7.01%, +7.40%, and +45.71%) and HIR (+20.17%, +21.29%, and +65.71%), with statistically significant differences (p < 0.05 and p < 0.001). In the detection of atrial septal defects (ASDs) and ventricular septal defects (VSDs), SR-DLR demonstrated significantly higher sensitivity compared to C-DLR (+8.96% and +9.09%) and HIR (+20.90% and +36.36%). For multi-perforated ASDs and VSDs, SR-DLR's sensitivity reached 85.71% and 100%, far surpassing C-DLR and HIR. SR-DLR significantly reduces image noise and enhances resolution, improving the diagnostic visualization of CHD structures in pediatric patients. It outperforms existing algorithms in detecting small lesions, achieving diagnostic accuracy close to that of ultrasound. Question Pediatric cardiac computed tomography angiography (CCTA) often fails to adequately visualize intracardiac structures, creating diagnostic challenges for CHD, particularly complex multi-perforated atrioventricular defects. Findings SR-DLR markedly improves image quality and diagnostic accuracy, enabling detailed visualization and precise detection of small congenital lesions. Clinical relevance SR-DLR enhances the diagnostic confidence and accuracy of CCTA in pediatric CHD, reducing missed diagnoses and improving the characterization of complex intracardiac anomalies, thus supporting better clinical decision-making.

The prediction and management of sudden cardiac death risk continue to pose significant challenges in cardiovascular care despite advances in therapies over the last two decades. Late gadolinium enhancement (LGE) on cardiac magnetic resonance-a marker of myocardial fibrosis-is a powerful non-invasive tool with the potential to aid the prediction of sudden death and direct the use of preventative therapies in several cardiovascular conditions. In this state-of-the-art review, we provide a critical appraisal of the current evidence base underpinning the utility of LGE in both ischaemic and non-ischaemic cardiomyopathies together with a focus on future perspectives and the role for machine learning and digital twin technologies.

Epicardial Adipose Tissue (EAT) is a recognized risk factor for cardiovascular diseases and plays a pivotal role in the pathophysiology of Atrial Fibrillation (AF). Accurate automatic segmentation of the EAT around the Left Atrium (LA) from Magnetic Resonance Imaging (MRI) data remains challenging. While Convolutional Neural Networks excel at multi-scale feature extraction using stacked convolutions, they struggle to capture long-range self-similarity and hierarchical relationships, which are essential in medical image segmentation. In this study, we present and validate PoinUNet, a deep learning model that integrates a Poincaré embedding layer into a 3D UNet to enhance LA wall and fat segmentation from Dixon MRI data. By using hyperbolic space learning, PoinUNet captures complex LA and EAT relationships and addresses class imbalance and fat geometry challenges using a new loss function. Sixty-six participants, including forty-eight AF patients, were scanned at 1.5T. The first network identified fat regions, while the second utilized Poincaré embeddings and convolutional layers for precise segmentation, enhanced by fat fraction maps. PoinUNet achieved a Dice Similarity Coefficient of 0.87 and a Hausdorff distance of 9.42 on the test set. This performance surpasses state-of-the-art methods, providing accurate quantification of the LA wall and LA EAT.

Phase-sensitive inversion recovery late gadolinium enhancement (LGE) improves tissue contrast, however it is challenging to combine with a free-breathing acquisition. Deep-learning (DL) algorithms have growing applications in cardiac magnetic resonance imaging (CMR) to improve image quality. We compared a novel combination of a free-breathing single-shot phase-sensitive LGE with respiratory triggering (FB-PS) sequence with DL noise reduction reconstruction algorithm to a conventional segmented phase-sensitive LGE acquired during breath holding (BH-PS). 61 adult subjects (29 male, age 51 ± 15) underwent clinical CMR (1.5 T) with the FB-PS sequence and the conventional BH-PS sequence. DL noise reduction was incorporated into the image reconstruction pipeline. Qualitative metrics included image quality, artifact severity, diagnostic confidence. Quantitative metrics included septal-blood border sharpness, LGE sharpness, blood-myocardium apparent contrast-to-noise ratio (CNR), LGE-myocardium CNR, LGE apparent signal-to-noise ratio (SNR), and LGE burden. The sequences were compared via paired t-tests. 27 subjects had positive LGE. Average time to acquire a slice for FB-PS was 4-12 s versus ~32-38 s for BH-PS (including breath instructions and break time in between breath hold). FB-PS with medium DL noise reduction had better image quality (FB-PS 3.0 ± 0.7 vs. BH-PS 1.5 ± 0.6, p < 0.0001), less artifact (4.8 ± 0.5 vs. 3.4 ± 1.1, p < 0.0001), and higher diagnostic confidence (4.0 ± 0.6 vs. 2.6 ± 0.8, p < 0.0001). Septum sharpness in FB-PS with DL reconstruction versus BH-PS was not significantly different. There was no significant difference in LGE sharpness or LGE burden. FB-PS had superior blood-myocardium CNR (17.2 ± 6.9 vs. 16.4 ± 6.0, p = 0.040), LGE-myocardium CNR (12.1 ± 7.2 vs. 10.4 ± 6.6, p = 0.054), and LGE SNR (59.8 ± 26.8 vs. 31.2 ± 24.1, p < 0.001); these metrics further improved with DL noise reduction. A FB-PS sequence shortens scan time by over 5-fold and reduces motion artifact. Combined with a DL noise reduction algorithm, FB-PS provides better or similar image quality compared to BH-PS. This is a promising solution for patients who cannot hold their breath.

This study aims to present the Segmentation-based Myocardial Advanced Refinement Tracking (SMART) system, a novel artificial intelligence (AI)-based framework for transthoracic echocardiography (TTE) that incorporates motion tracking and left ventricular (LV) myocardial segmentation for automated LV mass (LVM) and global longitudinal strain (LVGLS) assessment. The SMART system demonstrates LV speckle tracking based on motion vector estimation, refined by structural information using endocardial and epicardial segmentation throughout the cardiac cycle. This approach enables automated measurement of LVM_SMART and LVGLS_SMART. The feasibility of SMART is validated in 111 hypertrophic cardiomyopathy (HCM) patients (median age: 58 years, 69% male) who underwent TTE and cardiac magnetic resonance imaging (CMR). LVGLS_SMART showed a strong correlation with conventional manual LVGLS measurements (Pearson's correlation coefficient [PCC] 0.851; mean difference 0 [-2-0]). When compared to CMR as the reference standard for LVM, the conventional dimension-based TTE method overestimated LVM (PCC 0.652; mean difference: 106 [90-123]), whereas LVM_SMART demonstrated excellent agreement with CMR (PCC 0.843; mean difference: 1 [-11-13]). For predicting extensive myocardial fibrosis, LVGLS_SMART and LVM_SMART exhibited performance comparable to conventional LVGLS and CMR (AUC: 0.72 and 0.66, respectively). Patients identified as high risk for extensive fibrosis by LVGLS_SMART and LVM_SMART had significantly higher rates of adverse outcomes, including heart failure hospitalization, new-onset atrial fibrillation, and defibrillator implantation. The SMART technique provides a comparable LVGLS evaluation and a more accurate LVM assessment than conventional TTE, with predictive values for myocardial fibrosis and adverse outcomes. These findings support its utility in HCM management.

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

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.