Deformable image registration (DIR) is a crucial and challenging technique for aligning anatomical structures in medical images and is widely applied in diverse clinical applications. However, existing approaches often struggle to capture fine-grained local deformations and large-scale global deformations simultaneously within a unified framework. We present FractMorph, a novel 3D dual-parallel transformer-based architecture that enhances cross-image feature matching through multi-domain fractional Fourier transform (FrFT) branches. Each Fractional Cross-Attention (FCA) block applies parallel FrFTs at fractional angles of 0{\deg}, 45{\deg}, 90{\deg}, along with a log-magnitude branch, to effectively extract local, semi-global, and global features at the same time. These features are fused via cross-attention between the fixed and moving image streams. A lightweight U-Net style network then predicts a dense deformation field from the transformer-enriched features. On the ACDC cardiac MRI dataset, FractMorph achieves state-of-the-art performance with an overall Dice Similarity Coefficient (DSC) of 86.45%, an average per-structure DSC of 75.15%, and a 95th-percentile Hausdorff distance (HD95) of 1.54 mm on our data split. We also introduce FractMorph-Light, a lightweight variant of our model with only 29.6M parameters, which maintains the superior accuracy of the main model while using approximately half the memory. Our results demonstrate that multi-domain spectral-spatial attention in transformers can robustly and efficiently model complex non-rigid deformations in medical images using a single end-to-end network, without the need for scenario-specific tuning or hierarchical multi-scale networks. The source code of our implementation is available at https://github.com/shayankebriti/FractMorph.

Fractional flow reserve (FFR) - a physiological indicator of coronary stenosis significance - has now become a widely used parameter also in the guidance of percutaneous coronary intervention (PCI). Several studies have shown the superiority of FFR compared to visual assessment, contributing to the reduction in clinical endpoints. However, the current approach to FFR assessment requires coronary instrumentation with a dedicated pressure wire and thus increasing invasiveness, cost, and duration of the procedure. Alternative, noninvasive methods of FFR assessment based on computational fluid dynamics are being widely tested; these approaches are generally not fully automated and may sometimes require substantial computational power. Nowadays, one of the most rapidly expanding fields in medicine is the use of artificial intelligence (AI) in therapy optimization, diagnosis, treatment, and risk stratification. AI usage contributes to the development of more sophisticated methods of imaging analysis and allows for the derivation of clinically important parameters in a faster and more accurate way. Over the recent years, AI utility in deriving FFR in a noninvasive manner has been increasingly reported. In this review, we critically summarize current knowledge in the field of AI-derived FFR based on data from computed tomography angiography, invasive angiography, optical coherence tomography, and intravascular ultrasound. Available solutions, possible future directions in optimizing cathlab performance, including the use of mixed reality, as well as current limitations standing behind the wide adoption of these techniques, are overviewed.

Magnetic susceptibility differences at the heart-lung interface introduce B₀-field inhomogeneities that challenge cardiac MRI at high field strengths (≥ 3 T). Although hardware-based shimming has advanced, conventional approaches often neglect dynamic variations in thoracic anatomy caused by cardiac and respiratory motion, leading to residual off-resonance artifacts. This study aims to characterize motion-induced B₀-field fluctuations in the heart and evaluate a deep learning-enabled motion-adaptive B₀ shimming pipeline to mitigate them. A motion-resolved B₀ mapping sequence was implemented at 3 T to quantify cardiac and respiratory-induced B₀ variations. A motion-adaptive shimming framework was then developed and validated through numerical simulations and human imaging studies. B₀-field homogeneity and T₂* mapping accuracy were assessed in multiple breath-hold positions using standard and motion-adaptive shimming. Respiratory motion significantly altered myocardial B₀ fields (p < 0.01), whereas cardiac motion had minimal impact (p = 0.49). Compared with conventional scanner shimming, motion-adaptive B₀ shimming yielded significantly improved field uniformity across both inspiratory (post-shim SD_ratio: 0.68 ± 0.10 vs. 0.89 ± 0.11; p < 0.05) and expiratory (0.65 ± 0.16 vs. 0.84 ± 0.20; p < 0.05) breath-hold states. Corresponding improvements in myocardial T₂* map homogeneity were observed, with reduced coefficient of variation (0.44 ± 0.19 vs. 0.39 ± 0.22; 0.59 ± 0.30 vs. 0.46 ± 0.21; both p < 0.01). The proposed motion-adaptive B₀ shimming approach effectively compensates for respiration-induced B₀ fluctuations, enhancing field homogeneity and reducing off-resonance artifacts. This strategy improves the robustness and reproducibility of T₂* mapping, enabling more reliable high-field cardiac MRI.

This study aimed to develop and validate a deep learning (DL) model to detect obstructive coronary artery disease (CAD, ≥ 50% stenosis) in coronary CT angiography (CCTA) among patients presenting to the emergency department (ED) with acute chest pain. The training dataset included 378 patients with acute chest pain who underwent CCTA (10,060 curved multiplanar reconstruction [MPR] images) from a single-center ED between January 2015 and December 2022. The external validation dataset included 298 patients from 3 ED centers between January 2021 and December 2022. A DL model based on You Only Look Once v4, requires manual preprocessing for curved MPR extraction and was developed using 15 manually preprocessed MPR images per major coronary artery. Model performance was evaluated per artery and per patient. The training dataset included 378 patients (mean age 61.3 ± 12.2 years, 58.2% men); the external dataset included 298 patients (mean age 58.3 ± 13.8 years, 54.6% men). Obstructive CAD prevalence in the external dataset was 27.5% (82/298). The DL model achieved per-artery sensitivity, specificity, positive predictive value, negative predictive value (NPV), and area under the curve (AUC) of 92.7%, 89.9%, 62.6%, 98.5%, and 0.919, respectively; and per-patient values of 93.3%, 80.7%, 67.7%, 96.6%, and 0.871, respectively. The DL model demonstrated high sensitivity and NPV for identifying obstructive CAD in patients with acute chest pain undergoing CCTA, indicating its potential utility in aiding ED physicians in CAD detection.

Acute myocardial infarction (MI) alters cardiomyocyte geometry and architecture, leading to changes in the acoustic properties of the myocardium. This study examines ultrasomics-a novel cardiac ultrasound-based radiomics technique to extract high-throughput pixel-level information from images-for identifying ultrasonic texture and morphologic changes associated with infarcted myocardium. We included 684 participants from multisource data: a) a retrospective single-center matched case-control dataset, b) a prospective multicenter matched clinical trial dataset, and c) an open-source international and multivendor dataset. Handcrafted and deep transfer learning-based ultrasomics features from 2- and 4-chamber echocardiographic views were used to train machine learning (ML) models with the use of leave-one-source-out cross-validation for external validation. The ML model showed a higher AUC than transfer learning-based deep features in identifying MI [AUCs: 0.87 [95% CI: 0.84-0.89] vs 0.74 [95% CI: 0.70-0.77]; P < 0.0001]. ML probability was an independent predictor of MI even after adjusting for conventional echocardiographic parameters [adjusted OR: 1.03 [95% CI: 1.01-1.05]; P < 0.0001]. ML probability showed diagnostic value in differentiating acute MI, even in the presence of myocardial dysfunction (averaged longitudinal strain [LS] <16%) (AUC: 0.84 [95% CI: 0.77-0.89]). In addition, combining averaged LS with ML probability significantly improved predictive performance compared with LS alone (AUCs: 0.86 [95% CI: 0.80-0.91] vs 0.80 [95% CI: 0.72-0.87]; P = 0.02). Visualization of ultrasomics features with the use of a Manhattan plot discriminated infarcted and noninfarcted segments (P < 0.001) and facilitated parametric visualization of infarcted myocardium. This study demonstrates the potential of cardiac ultrasomics to distinguish healthy from infarcted myocardium and highlights the need for validation in diverse populations to define its role and incremental value in myocardial tissue characterization beyond conventional echocardiography.

Atrial fibrillation, a common cardiac arrhythmia with rapid and irregular atrial electrical activity, requires accurate left atrial segmentation for effective treatment planning. Recently, deep learning methods have gained encouraging success in left atrial segmentation. However, current methodologies critically depend on the assumption of consistently complete centered left atrium as input, which neglects the structural incompleteness and boundary discontinuities arising from random-crop operations during inference. In this paper, we propose BSA-Net, which exploits an adaptive adjustment strategy in both feature position and loss optimization to establish long-range feature relationships and strengthen robust intermediate feature representations in boundary regions. Specifically, we propose a Spatial-adaptive Convolution (SConv) that employs a shuffle operation combined with lightweight convolution to directly establish cross-positional relationships within regions of potential relevance. Moreover, we develop the dual Boundary Prioritized loss, which enhances boundary precision by differentially weighting foreground and background boundaries, thus optimizing complex boundary regions. With the above technologies, the proposed method enjoys a better speed-accuracy trade-off compared to current methods. BSA-Net attains Dice scores of 92.55%, 91.42%, and 84.67% on the LA, Utah, and Waikato datasets, respectively, with a mere 2.16 M parameters-approximately 80% fewer than other contemporary state-of-the-art models. Extensive experimental results on three benchmark datasets have demonstrated that BSA-Net, consistently and significantly outperforms existing state-of-the-art methods.

BackgroundKawasaki disease (KD) is an acute, pediatric vasculitis associated with coronary artery abnormality (CAA) development. Echocardiography at month 1 post-diagnosis remains the standard for CAA surveillance despite limitations, including patient distress and increased healthcare burden. With declining CAA incidence due to improved treatment, the need for routine follow-up imaging is being reconsidered. This study aimed to develop and externally validate models for predicting CAA development and guide the need for echocardiography. MethodsThis study used two prospective multicenter Japanese registries: PEACOCK for model development and internal validation, and Post-RAISE for external validation. The primary outcome was CAA at the month 1 follow-up, defined as a maximum coronary artery Z score (Zmax) [&ge;] 2. Twenty-nine clinical, laboratory, echocardiographic, and treatment-related variables obtained within one week of diagnosis were selected as predictors. The models included simple models using the previous Zmax as a single predictor, logistic regression models, and machine learning models (LightGBM and XGBoost). Their discrimination, calibration, and clinical utility were assessed. ResultsAfter excluding patients without outcome data, 4,973 and 2,438 patients from PEACOCK and Post-RAISE, respectively, were included. The CAA incidence at month 1 was 5.5% and 6.8% for the respective group. For external validation, a simple model using the Zmax at week 1 produced an area under the curve of 0.79, which failed to improve by more than 0.02 after other variables were added or more complex models were used. Even the best-performing models with a highly sensitive threshold failed to reduce the need for echocardiography at month 1 by more than 30% while maintaining the number of undiagnosed CAA cases to less than ten. The predictive performance declined considerably when the Zmax was omitted from the multivariable models. ConclusionsThe Zmax at week 1 was the strongest predictor of CAA at month 1 post-diagnosis. Even advanced models incorporating additional variables failed to achieve a clinically acceptable trade-off between reducing the need for echocardiography and reducing the number of undiagnosed CAA cases. Until superior predictors are identified, echocardiography at month 1 should remain the standard practice. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIThe maximum Z score on echocardiography one week after diagnosis was the strongest of 29 variables for predicting coronary artery abnormalities (CAA) in patients with Kawasaki disease. C_LIO_LIEven the most sensitive models had a suboptimal ability to predict CAA development and reduce the need for imaging studies, suggesting they have limited utility in clinical decision-making. C_LI What Are the Clinical Implications?O_LIUntil more accurate predictors are found or imaging strategies are optimized, performing echocardiography at one-month follow-up should remain the standard of care. C_LI

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated with catheter ablation procedures, but procedural outcomes are highly variable. Evaluating and improving ablation efficacy is challenging due to the complex interaction between patient-specific tissue and procedural factors. This paper asks two questions: Can AF recurrence be predicted by simulating the effects of procedural parameters? How should we ablate to reduce AF recurrence? We propose SOFA (Simulating and Optimizing Atrial Fibrillation Ablation), a novel deep-learning framework that addresses these questions. SOFA first simulates the outcome of an ablation strategy by generating a post-ablation image depicting scar formation, conditioned on a patient's pre-ablation LGE-MRI and the specific procedural parameters used (e.g., ablation locations, duration, temperature, power, and force). During this simulation, it predicts AF recurrence risk. Critically, SOFA then introduces an optimization scheme that refines these procedural parameters to minimize the predicted risk. Our method leverages a multi-modal, multi-view generator that processes 2.5D representations of the atrium. Quantitative evaluations show that SOFA accurately synthesizes post-ablation images and that our optimization scheme leads to a 22.18\% reduction in the model-predicted recurrence risk. To the best of our knowledge, SOFA is the first framework to integrate the simulation of procedural effects, recurrence prediction, and parameter optimization, offering a novel tool for personalizing AF ablation.

It is unclear whether coronary artery stenosis, plaque burden, and composition differ between major epicardial arteries supplying ischemic myocardial territories. We studied 837 symptomatic patients undergoing coronary computed tomography angiography (CTA) and ¹⁵O-water PET myocardial perfusion imaging for suspected obstructive coronary artery disease. Coronary CTA was analyzed using Artificial Intelligence-Guided Quantitative Computed Tomography (AI-QCT) to assess stenosis and atherosclerotic plaque characteristics. Myocardial ischemia was defined by regional PET perfusion in the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) territories. Among arteries supplying ischemic territories, the LAD exhibited significantly higher stenosis and both absolute and normalized plaque volumes compared to LCX and RCA (p<0.001 for all). Multivariable logistic regression showed diameter stenosis (p=0.001-0.015), percent atheroma volume (PAV; p<0.001), and percent non-calcified plaque volume (p=0.001-0.017) were associated with ischemia across all three arteries. Percent calcified plaque volume was associated with ischemia only in the RCA (p=0.001). The degree of stenosis and atherosclerotic burden are significantly higher in LAD as compared to LCX and RCA, both in epicardial coronary arteries supplying non-ischemic or ischemic myocardial territories. In all the three main coronary arteries both luminal narrowing and plaque burden are independent predictors of ischemia, where the plaque burden is mainly driven by non-calcified plaque. However, many vessels supplying ischemic territories have relatively low stenosis degree and plaque burden, especially in the LCx and RCA, limiting the ability of diameter stenosis and PAV to predict myocardial ischemia.

Coronary artery disease (CAD) remains the leading cause of death globally, with computed tomography coronary angiography (CTCA) serving as a key diagnostic tool. However, coronary arterial analysis using CTCA, such as identifying artery-specific features from computational modelling, is labour-intensive and time-consuming. Automated anatomical labelling of coronary arteries offers a potential solution, yet the inherent anatomical variability of coronary trees presents a significant challenge. Traditional knowledge-based labelling methods fall short in leveraging data-driven insights, while recent deep-learning approaches often demand substantial computational resources and overlook critical clinical knowledge. To address these limitations, we propose a lightweight method that integrates anatomical knowledge with rule-based topology constraints for effective coronary artery labelling. Our approach achieves state-of-the-art performance on benchmark datasets, providing a promising alternative for automated coronary artery labelling.