arXiv subjects

Rita G Nunes

Publications and source records attributed to Rita G Nunes.

3 recordsLinked to original sources

arXiv2026

T2 estimation from MOLLI acquisitions

Cardiac T1 and T2 quantitative magnetic resonance imaging (MRI) is a technique that provides characterization of a multitude of myocardial pathologies. However, each parameter requires its own specialized acquisition sequence, which may be time-consuming. In this work, we demonstrate that MOLLI, an acquisition sequence commonly used to perform T1 mapping, is also sensitive to T2, and can also be used for myocardial T2 mapping. Although MOLLI is not designed for T2 mapping, its signal evolution inherently depends on both T1 and T2 relaxation. Here, we demonstrate that this dependence is sufficiently strong to allow T2 estimation directly from MOLLI T1-weighted images, using appropriate signal modelling. This finding opens the possibility of obtaining simultaneously T1 and T2 maps from a single MOLLI acquisition, reducing total scan time and potentially improving patient comfort and clinical throughput.

physics.med-ph↗

arXiv2025

Quantitative First-Pass Perfusion CMR: from technical principles to clinical practice

Myocardial perfusion cardiovascular magnetic resonance (pCMR) using first-pass contrast-enhanced imaging could play an important role in the detection of epicardial and microvascular coronary artery disease. Recently, the emergence of quantitative pCMR has provided a more reliable and observer-independent analysis compared to visual interpretation of dynamic images. This review aims to cover the basics of quantitative pCMR, from acquisition protocols, its use in preclinical and clinical studies, image reconstruction and motion handling, to automated quantitative pCMR pipelines. It also offers an overview of emerging tools in the field, including artificial intelligence-based methods.

q-bio.QM↗

arXiv2023

Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI

First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternatives for speeding up reconstructions. However, these approaches require fully sampled data for training, which is not possible to obtain, particularly high-resolution FPP-CMR images. Here, we propose a physics-informed self-supervised deep learning FPP-CMR reconstruction approach for accelerating FPP-CMR scans and hence facilitate high spatial resolution imaging. The proposed method provides high-quality FPP-CMR images from 10x undersampled data without using fully sampled reference data.

eess.IV↗