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- W3029797921 abstract "The application of compressed sensing (CS) in magnetic resonance imaging (MRI) demonstrates that it is possible to reconstruct MR images from a few undersampled k-space measurements and thereby reducing the clinical MRI scan time drastically. Generally, in clinical practice radiologists prefer to use sedation for paediatric patients, and those having emergency conditions. MRI scan time reduction due to the CS can prevent frequent application of anaesthesia, offer better patient comfort, and bring down the per seating cost of a scan, significantly. Development of fast CS reconstruction techniques for clinical applications is an active research problem in MRI as the computational cost of nonlinear reconstruction algorithms still remains the major bottleneck. Clinical MRI scanners are capable of parallel data acquisition, like in the multi-channel or parallel MRI (pMRI) scanners. Recently, calibrationless pMRI reconstruction methods attract the interest of CS-MRI research community as it does not require any coil specific sensitivity information either explicitly or implicitly during data reconstruction. This chapter first proposes a brief review of the state-of-the-art CS-based pMRI reconstruction algorithms. Next, we propose a novel calibrationless CS-based parallel MRI reconstruction for rapid MRI reconstruction and its implementation using parallel hardware. Simulations are extensively carried out on complex parallel MRI data and results are compared with the state-of-the-art in terms of both MRI specific objective evaluation metrics and visual analysis." @default.
- W3029797921 created "2020-06-05" @default.
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- W3029797921 date "2020-01-01" @default.
- W3029797921 modified "2023-09-24" @default.
- W3029797921 title "Calibrationless parallel compressed sensing reconstruction for rapid magnetic resonance imaging" @default.
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- W3029797921 doi "https://doi.org/10.1016/b978-0-12-821247-9.00019-6" @default.
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