Browsing by Author "Young, Alistair"

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    A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis
    (2024) Machado, Ines; Puyol-Anton, Esther; Hammernik, Kerstin; Cruz, Gastao; Ugurlu, Devran; Olakorede, Ihsane; Oksuz, Ilkay; Ruijsink, Bram; Castelo-Branco, Miguel; Young, Alistair; Prieto, Claudia; Schnabel, Julia; King, Andrew
    Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this article, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework produces high quality reconstructions and segmentations, leading to undersampling factors that are optimised on a scan-by-scan basis. This results in reduced scan times and automated analysis, enabling robust and accurate estimation of functional biomarkers. To demonstrate the feasibility of the proposed approach, we perform simulations of radial k-space acquisitions using in-vivo cine CMR data from 270 subjects from the UK Biobank (with synthetic phase) and in-vivo cine CMR data from 16 healthy subjects (with real phase). The results demonstrate that the optimal undersampling factor varies for different subjects by approximately 1 to 2 seconds per slice. We show that our method can produce quality-controlled images in a mean scan time reduced from 12 to 4 seconds per slice, and that image quality is sufficient to allow clinically relevant parameters to be automatically estimated to lie within 5% mean absolute difference.
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    Site-specific analysis of thoracic aortic aneurysm and cardiovascular mortality: Insights from the National Echo Database Australia
    (2025) Nadel, James; Suinesiaputra, Avan; Paratz, Elizabeth D.; Humphries, Juile; Young, Alistair; Botnar, René Michael; Celermajer, David S.; Strange, Geoff; Playford, David
    Background Aortic diameter remains the most utilised criterion for considering surgical correction. In uncomplicated cases guidelines do not differentiate between the size of aneurysms at the root and ascending aorta. In order to improve practice, greater understanding of site-specific TAA is needed. A nationwide echocardiographic dataset linked to mortality outcomes was examined to determine how TAA affects cardiovascular (CVD) mortality.Methods The National Echo Database Australia (NEDA) was examined for aortic dimensions at the sinuses of Valsalva (SoV), sinotubular junction (STJ) and ascending aorta (AscAo). Patients were stratified according to absolute aortic diameters and grouped as normal (<4cm), mild (4.0-4.5cm), moderate (4.5-5cm) and severely (>5cm) dilated at the prescribed thoracic aortic sites. Mortality data was linked from the National Death Index.Results 477,501 echocardiographs from 175,158 patients with 2,897,357 patient-years of follow-up were included. Severe TAA at any site increased likelihood of 10-year CVD mortality compared to normal aortic diameters (31% vs. 14%, p<0.0001), with incremental increase in probability of CVD death when moving from the proximal to distal ascending aorta; CVD mortality at SoV 30% (HR 1.79; CI 1.2-2.67; p=0.004), STJ 41% (HR 1.91; CI 1.11-3.29; p=0.002) and AscAo 45% (HR 3.96; CI 2.06-7.64; p<0.001).Conclusions Severe TAA increases the probability of cardiovascular mortality. Given the low event rate of aortic death (0.2%) this is not solely explained by increased dissection risk. Interestingly, there is a doubling of CVD mortality likelihood when moving from the proximal to distal ascending aorta. These results suggest those with severe AscAo dilatation may be at higher CVD risk compared to those with aortic root aneurysms, identifying new considerations for risk stratification and surgical management.