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Leo I, Nakou E, Artico J, Androulakis E, Wong J, Moon JC, Indolfi C, Bucciarelli-Ducci C. Strengths and weaknesses of alternative noninvasive imaging approaches for microvascular ischemia. J Nucl Cardiol 2023; 30:227-238. [PMID: 35918590 DOI: 10.1007/s12350-022-03066-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 06/19/2022] [Indexed: 11/26/2022]
Abstract
Structural and functional abnormalities of coronary microvasculature are highly prevalent in several clinical settings and often associated with worse clinical outcomes. Therefore, there is a growing interest in the detection and treatment of this, often overlooked, disease. Coronary angiography allows the assessment of the Coronary flow reserve (CFR) and the index of microcirculatory resistance (IMR). However, the measurement of these parameters is not always feasible because of limited technical availability and the need for a cardiac catheterization with a small but real risk of potential complications. Recent advances in non-invasive imaging techniques allow the assessment of coronary microvascular function with good accuracy and reproducibility. The objective of this review is to discuss the strengths and weaknesses of alternative non-invasive approaches used in the diagnosis of coronary microvascular dysfunction (CMD), highlighting the most recent advances for each imaging modality.
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Affiliation(s)
- Isabella Leo
- Royal Brompton and Harefield Hospitals, Guys's and St Thomas' NHS Foundation Trust, London, UK
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Eleni Nakou
- Royal Brompton and Harefield Hospitals, Guys's and St Thomas' NHS Foundation Trust, London, UK
| | - Jessica Artico
- Institute of Cardiovascular Science, University College London, Gower Street, London, UK
- St Bartholomew's Hospital, Barts Heart Centre, West Smithfield, London, UK
| | - Emmanouil Androulakis
- Royal Brompton and Harefield Hospitals, Guys's and St Thomas' NHS Foundation Trust, London, UK
| | - Joyce Wong
- Royal Brompton and Harefield Hospitals, Guys's and St Thomas' NHS Foundation Trust, London, UK
| | - James C Moon
- Institute of Cardiovascular Science, University College London, Gower Street, London, UK
- St Bartholomew's Hospital, Barts Heart Centre, West Smithfield, London, UK
| | - Ciro Indolfi
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
- Mediterranea Cardiocentro, Naples, Italy
| | - Chiara Bucciarelli-Ducci
- Royal Brompton and Harefield Hospitals, Guys's and St Thomas' NHS Foundation Trust, London, UK.
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College University, London, UK.
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Wang L, Dong D, Tian FB. Fast prediction of blood flow in stenosed arteries using machine learning and immersed boundary-lattice Boltzmann method. Front Physiol 2022; 13:953702. [PMID: 36091404 PMCID: PMC9459013 DOI: 10.3389/fphys.2022.953702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/25/2022] [Indexed: 11/23/2022] Open
Abstract
A fast prediction of blood flow in stenosed arteries with a hybrid framework of machine learning and immersed boundary-lattice Boltzmann method (IB–LBM) is presented. The integrated framework incorporates the immersed boundary method for its excellent capability in handling complex boundaries, the multi-relaxation-time LBM for its efficient modelling for unsteady flows and the deep neural network (DNN) for its high efficiency in artificial learning. Specifically, the stenosed artery is modelled by a channel for two-dimensional (2D) cases or a tube for three-dimensional (3D) cases with a stenosis approximated by a fifth-order polynomial. An IB–LBM is adopted to obtain the training data for the DNN which is constructed to generate an approximate model for the fast flow prediction. In the DNN, the inputs are the characteristic parameters of the stenosis and fluid node coordinates, and the outputs are the mean velocity and pressure at each node. To characterise complex stenosis, a convolutional neural network (CNN) is built to extract the stenosis properties by using the data generated by the aforementioned polynomial. Both 2D and 3D cases (including 3D asymmetrical case) are constructed and examined to demonstrate the effectiveness of the proposed method. Once the DNN model is trained, the prediction efficiency of blood flow in stenosed arteries is much higher compared with the direct computational fluid dynamics simulations. The proposed method has a potential for applications in clinical diagnosis and treatment where the real-time modelling results are desired.
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