PURPOSE: To develop and evaluate an image reconstruction technique for cardiac MRI (CMR) perfusion that uses localized spatio-temporal constraints. METHODS: CMR perfusion plays an important role in detecting myocardial ischemia in patients with coronary artery disease. Breath-hold k-t-based image acceleration techniques are typically used in CMR perfusion for superior spatial/temporal resolution and improved coverage. In this study, we propose a novel compressed sensing-based image reconstruction technique for CMR perfusion, with applicability to free-breathing examinations. This technique uses local spatio-temporal constraints by regularizing image patches across a small number of dynamics. The technique was compared with conventional dynamic-by-dynamic reconstruction, and sparsity regularization using a temporal principal-component (pc) basis, as well as zero-filled data in multislice two-dimensional (2D) and three-dimensional (3D) CMR perfusion. Qualitative image scores were used (1 = poor, 4 = excellent) to evaluate the technique in 3D perfusion in 10 patients and five healthy subjects. On four healthy subjects, the proposed technique was also compared with a breath-hold multislice 2D acquisition with parallel imaging in terms of signal intensity curves. RESULTS: The proposed technique produced images that were superior in terms of spatial and temporal blurring compared with the other techniques, even in free-breathing datasets. The image scores indicated a significant improvement compared with other techniques in 3D perfusion (x-pc regularization, 2.8 ± 0.5 versus 2.3 ± 0.5; dynamic-by-dynamic, 1.7 ± 0.5; zero-filled, 1.1 ± 0.2). Signal intensity curves indicate similar dynamics of uptake between the proposed method with 3D acquisition and the breath-hold multislice 2D acquisition with parallel imaging. CONCLUSION: The proposed reconstruction uses sparsity regularization based on localized information in both spatial and temporal domains for highly accelerated CMR perfusion with potential use in free-breathing 3D acquisitions.
PURPOSE: To develop and evaluate an image reconstruction technique for cardiac MRI (CMR) perfusion that uses localized spatio-temporal constraints. METHODS: CMR perfusion plays an important role in detecting myocardial ischemia in patients with coronary artery disease. Breath-hold k-t-based image acceleration techniques are typically used in CMR perfusion for superior spatial/temporal resolution and improved coverage. In this study, we propose a novel compressed sensing-based image reconstruction technique for CMR perfusion, with applicability to free-breathing examinations. This technique uses local spatio-temporal constraints by regularizing image patches across a small number of dynamics. The technique was compared with conventional dynamic-by-dynamic reconstruction, and sparsity regularization using a temporal principal-component (pc) basis, as well as zero-filled data in multislice two-dimensional (2D) and three-dimensional (3D) CMR perfusion. Qualitative image scores were used (1 = poor, 4 = excellent) to evaluate the technique in 3D perfusion in 10 patients and five healthy subjects. On four healthy subjects, the proposed technique was also compared with a breath-hold multislice 2D acquisition with parallel imaging in terms of signal intensity curves. RESULTS: The proposed technique produced images that were superior in terms of spatial and temporal blurring compared with the other techniques, even in free-breathing datasets. The image scores indicated a significant improvement compared with other techniques in 3D perfusion (x-pc regularization, 2.8 ± 0.5 versus 2.3 ± 0.5; dynamic-by-dynamic, 1.7 ± 0.5; zero-filled, 1.1 ± 0.2). Signal intensity curves indicate similar dynamics of uptake between the proposed method with 3D acquisition and the breath-hold multislice 2D acquisition with parallel imaging. CONCLUSION: The proposed reconstruction uses sparsity regularization based on localized information in both spatial and temporal domains for highly accelerated CMR perfusion with potential use in free-breathing 3D acquisitions.
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Authors: Sarah McElroy; Giulio Ferrazzi; Muhummad Sohaib Nazir; Carl Evans; Joana Ferreira; Filippo Bosio; Nabila Mughal; Karl P Kunze; Radhouene Neji; Peter Speier; Daniel Stäb; Tevfik F Ismail; Pier Giorgio Masci; Adriana D M Villa; Reza Razavi; Amedeo Chiribiri; Sébastien Roujol Journal: Magn Reson Med Date: 2022-03-28 Impact factor: 3.737