Albert Hsiao

A deep learning model demonstrated robust performance for detecting cardiac chamber enlargement on anteroposterior chest radiographs, outperforming both conventional measurements and radiologist assessment.

A fully automated deep learning–based system can be trained to measure systemic and pulmonary blood flow and shunt fraction using four-dimensional flow MRI with accuracy and reliability similar to that of expert physicians.

In this review, we highlight how artificial intelligence, specifically deep learning, is reshaping every aspect of cardiovascular magnetic resonance imaging: from planning and acquisition to reconstruction, analysis, and clinical report generation. We first introduce core machine learning paradigms and concepts, then survey recent deep learning advances to automate and enhance multiple aspects of MRI. We highlight the range of recent advances to provide a conceptual understanding of how the field has rapidly evolved in the last 10 years, enabling improvements in acquisition speed, spatial resolution, suppression of artifacts, and correction for motion. Automation of postprocessing is providing us a deeper look into detailed analysis of regional cardiac function and measurement of hemodynamics, and a greater ability to automatically integrate interpretation with nonimaging clinical data to support prognostication and management. Advances in artificial intelligence will continue to shape our practice of clinical cardiovascular MRI to provide greater efficiency and enrich our ability to guide the management of patients with cardiovascular disease.

A convolutional neural network for R-wave detection in MRI-acquired electrocardiograms achieved higher F1 scores and lower false-positive rates than standard algorithms, particularly at 3.0 T, supporting its potential to improve electrocardiographic gating and cardiac MRI scan quality.

Foundation models can streamline imaging workflows, improve diagnostic accuracy, enable personalized treatment, and, via integrated generative artificial intelligence, create synthetic images to augment scarce real-world data; however, successful adoption requires explainability, trust, and workflow integration.

Tracheal morphology and lung volume change between inspiratory and expiratory phases at chest CT can aid quality assurance and improve detection of chronic lung allograft dysfunction and decline in forced expiratory volume in 1 second in patients after lung transplantation.

Cardiac CT plays an important role in diagnosing heart diseases but is conventionally limited by its complex workflow that requires dedicated phase and bolus tracking devices [e.g., electrocardiogram (ECG) gating]. This work reports first progress towards robust and autonomous cardiac CT exams through joint deep learning (DL) and analytical analysis of pulsed-mode projections (PMPs). To this end, cardiac phase and its uncertainty were simultaneously estimated using a novel projection domain cardiac phase estimation network (PhaseNet), which utilizes sliding-window multi-channel feature extraction strategy and a long short-term memory (LSTM) block to extract temporal correlation between time-distributed PMPs. An uncertainty-driven Viterbi (UDV) regularizer was developed to refine the DL estimations at each time point through dynamic programming. Stronger regularization was performed at time points where DL estimations have higher uncertainty. The performance of the proposed phase estimation pipeline was evaluated using accurate physics-based emulated data. PhaseNet achieved improved phase estimation accuracy compared to the competing methods in terms of RMSE (~50% improvement vs. standard CNN-LSTM; ~24% improvement vs. multi-channel residual network). The added UDV regularizer resulted in an additional ~14% improvement in RMSE, achieving accurate phase estimation with <6% RMSE in cardiac phase (phase ranges from 0-100%). To our knowledge, this is the first publication of prospective cardiac phase estimation in the projection domain. Combined with our previous work on PMP-based bolus curve estimation, the proposed method could potentially be used to achieve autonomous cardiac scanning without ECG device and expert-in-the-loop bolus timing.

The diagnosis of a common cause of chronic pelvic pain can be made by visualizing reflux in the ovarian veins. Fluoroscopic venography is the gold standard for diagnosing ovarian vein reflux, but it is an invasive technique that exposes patients to ionizing radiation. MRI, with its lack of ionizing radiation and capability of high-temporal and spatial-resolution vascular imaging, has the potential to provide similar diagnostic information. This retrospective report describes and assesses the utility of a dynamic contrast-enhanced MRI technique based on Differential Subsampling with Cartesian Ordering (DISCO)–MRI in 30 patients with chronic pelvic pain. Among the 14 patients who underwent both DISCO–MRI and fluoroscopic venograms, 11 (78.6%) exhibited concordant results, while 3 patients (21.4%) had discordant findings. These results suggest the potential of multiphasic contrast-enhanced DISCO–MRI as a non-invasive diagnostic tool for evaluating chronic pelvic pain.