Machine-learning surrogate model for estimating pulsatile hemodynamic fields (velocity, pressure) in coronary arteries from a steady-state computational fluid dynamics (CFD) prior, avoiding the high computational cost of full pulsatile CFD. The model, a neural field conditioned on hemodynamic boundary conditions, is discretisation-independent and can be parametrised with message-passing or self-attention layers by relaxing point-wise action to permutation-equivariance. Evaluated on 74 stenotic coronary arteries from coronary CT angiography (CCTA) with patient-specific pulsatile CFD as ground truth, the model produced accurate, discretisation-independent estimates of pulsatile velocity and pressure fields.

Automatic coronary artery segmentation pipeline for coronary CT angiography (CCTA). A 2D DenseNet classifier first screens out CT slices that don't contain coronary artery, then a 3D-UNet -- enhanced with dense blocks in the encoder for richer feature extraction and residual, feature-rectifying blocks in the decoder -- segments the coronary artery tree in the remaining slices. A Gaussian-weighted merging scheme combines overlapping 3D patch predictions, up-weighting the more reliable predictions near each patch's center. On the authors' in-house CCTA dataset, the method achieved a Dice similarity coefficient of 0.826.

Coronary artery calcium (CAC) scoring model that transfers a CNN trained for calcium scoring on non-contrast CT (NCCT) to coronary CT angiography (CCTA), where iodinated contrast otherwise confounds calcium detection and large annotated CCTA training sets are scarce. The CAC-scoring CNN is split into a feature generator and a classifier; the feature generator is trained on the NCCT source domain and adapted to the CCTA target domain via adversarial learning combined with a maximum-mean-discrepancy loss, while the source-domain classifier is reused unchanged for the target domain. Builds directly on the authors' earlier non-contrast CT calcium-scoring network.

Multi-instance-learning (MIL) model for detecting >=50% coronary stenosis directly from curved multiplanar reformation (CMR) images generated during routine coronary CT angiography (CCTA) reads, without requiring slice-level annotations. A multi-range Hounsfield-unit preprocessing pipeline (Sobel edge detection across five attenuation windows) highlights plaque and vessel-wall structures, which a VGG16-based encoder with positional encoding and multi-head attention aggregates across each patient's 'bag' of up to 36 CMR slices per artery to give an interpretable, attention-weighted patient-level prediction. Trained and five-fold cross-validated on 900 real-world CCTA cases (776 LAD / 694 RCA / 600 LCX) from Sahlgrenska University Hospital, reaching AUCs of 0.91-0.92 across the three major coronary arteries. Code (preprocessing + MIL training pipeline) is public; the clinical CMR dataset and trained weights are not released.