Accelerating GRAPPA reconstruction using SoC design for real-time cardiac MRI
Tài liệu tham khảo
McRobbie, 2017
Hashemi, 2012
Jhamb, 2015, A review on image reconstruction through MRI k-space data, Int. J. Image Graph. Signal Process., 7, 42, 10.5815/ijigsp.2015.07.06
Moratal, 2008, k-Space tutorial: an MRI educational tool for a better understanding of k-space, Biomed. Imaging Interv. J., 4, 1, 10.2349/biij.4.1.e15
Shannon, 1949, Communication in the presence of noise, 37, 10
Tsao, 2012, MRI temporal acceleration techniques, J. Magn. Reson. Imag., 36, 543, 10.1002/jmri.23640
Hamilton, 2017, Recent advances in parallel imaging for MRI, Prog. Nucl. Magn. Reson. Spectrosc., 101, 71, 10.1016/j.pnmrs.2017.04.002
Zhang, 2018, Comparison of parallel MRI reconstruction algorithms: analysis of image quality and clinical utility, Magn. Reson. Imag., 47, 103
Larkman, 2007, Parallel magnetic resonance imaging, Phys. Med. Biol., 52, 7, 10.1088/0031-9155/52/7/R01
Blaimer, 2004, SMASH, SENSE, PILS, GRAPPA: how to choose the optimal method, Top. Magn. Reson. Imag., 15, 223, 10.1097/01.rmr.0000136558.09801.dd
Nayak, 2022, Real‐time magnetic resonance imaging, J. Magn. Reson. Imag., 55, 81, 10.1002/jmri.27411
Guo, 2022, Emerging techniques in cardiac magnetic resonance imaging, J. Magn. Reson. Imag., 55, 1043, 10.1002/jmri.27848
Wang, 2021, Fast real-time cardiac MRI: a review of current techniques and future directions, Invest. Magn. Reson. Imag., 25, 252, 10.13104/imri.2021.25.4.252
Seraphim, 2020, Quantitative cardiac MRI, J. Magn. Reson. Imag., 51, 693, 10.1002/jmri.26789
Qayyum, 2015, Measuring myocardial perfusion: the role of PET, MRI and CT, Clin. Radiol., 70, 576, 10.1016/j.crad.2014.12.017
Mukherjee, 2019, Advances in real-time MRI–guided electrophysiology, Curr. Cardiovasc. Imag. Rep., 12, 1
Backhaus, 2021, Defining the optimal temporal and spatial resolution for cardiovascular magnetic resonance imaging feature tracking, J. Cardiovasc. Magn. Reson., 23, 1, 10.1186/s12968-021-00740-5
Giese, 2014, Towards highly accelerated Cartesian time-resolved 3D flow cardiovascular magnetic resonance in the clinical setting, J. Cardiovasc. Magn. Reson., 16, 1, 10.1186/1532-429X-16-42
Allen, 2018, Accelerated real-time cardiac MRI using iterative sparse SENSE reconstruction: comparing performance in patients with sinus rhythm and atrial fibrillation, Eur. Radiol., 28, 3088, 10.1007/s00330-017-5283-0
Goldfarb, 2004, The SENSE ghost: field‐of‐view restrictions for SENSE imaging, J. Magn. Reson. Imag.: Off. J. Int. Soc. Magn. Reson. Med., 20, 1046, 10.1002/jmri.20204
Noël, 2009, Parallel imaging artifacts in body magnetic resonance imaging, Can. Assoc. Radiol. J., 60, 91, 10.1016/j.carj.2009.02.036
Axel, 2016, Accelerated MRI for the assessment of cardiac function, Br. J. Radiol., 89, 10.1259/bjr.20150655
Griswold, 2002, Generalized autocalibrating partially parallel acquisitions (GRAPPA), Magn. Reson. Med.: Off. J. Int. Soc. Magn. Reson. Med., 47, 1202, 10.1002/mrm.10171
Luo, 2019, A GRAPPA algorithm for arbitrary 2D/3D non‐Cartesian sampling trajectories with rapid calibration, Magn. Reson. Med., 82, 1101, 10.1002/mrm.27801
Jung, 2012, Kt-GRAPPA accelerated flow measurements, J. Cardiovasc. Magn. Reson., 14, 1
Gamper, 2008, Compressed sensing in dynamic MRI, Magn. Reson. Med.: Off. J. Int. Soc. Magn. Reson. Med., 59, 365, 10.1002/mrm.21477
Feng, 2017, Compressed sensing for body MRI, J. Magn. Reson. Imag., 45, 966, 10.1002/jmri.25547
Lustig, 2006, Kt SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity, 2420
Usman, 2011, k‐t group sparse: a method for accelerating dynamic MRI, Magn. Reson. Med., 66, 1163, 10.1002/mrm.22883
Pruessmann, 2006, Encoding and reconstruction in parallel MRI, NMR Biomed.: Int. J. Devoted Dev. Appl. Magn. Reson. In Vivo, 19, 288, 10.1002/nbm.1042
Huang, 2008, A software channel compression technique for faster reconstruction with many channels, Magn. Reson. Imag., 26, 133, 10.1016/j.mri.2007.04.010
Lyu, 2015, Fast GRAPPA reconstruction with random projection, Magn. Reson. Med., 74, 71, 10.1002/mrm.25373
Inam, 2017, Iterative schemes to solve low-dimensional calibration equations in parallel MR image reconstruction with GRAPPA, BioMed Res. Int., 2017
Wang, 2018, A survey of GPU-based acceleration techniques in MRI reconstructions, Quant. Imag. Med. Surg., 8, 196, 10.21037/qims.2018.03.07
Inam, 2017, GPU-accelerated self-calibrating GRAPPA operator gridding for rapid reconstruction of non-Cartesian MRI data, Appl. Magn. Reson., 48, 1055, 10.1007/s00723-017-0932-7
Inam, 2022, GPU accelerated Cartesian GRAPPA reconstruction using CUDA, J. Magn. Reson., 337, 10.1016/j.jmr.2022.107175
Ullah, 2018, QR-decomposition based SENSE reconstruction using parallel architecture, Comput. Biol. Med., 95, 1, 10.1016/j.compbiomed.2018.01.013
Chang, 2017, Compressed sensing MRI reconstruction from 3D multichannel data using GPUs, Magn. Reson. Med., 78, 2265, 10.1002/mrm.26636
Siddiqui, 2017, FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities, Magn. Reson. Imag., 44, 82, 10.1016/j.mri.2017.08.005
Inam, 2020, FPGA-based hardware accelerator for SENSE (a parallel MR image reconstruction method), Comput. Biol. Med., 117, 10.1016/j.compbiomed.2019.103598
Khan, 2020, FPGA-based pipelined architecture for real-time estimation of sensitivity maps using pre-scan method in parallel MRI, J. Circ. Syst. Comput., 29, 10.1142/S021812662050125X
Hansen, 2013, Gadgetron: an open source framework for medical image reconstruction, Magn. Reson. Med., 69, 1768, 10.1002/mrm.24389
Cong, 2022, FPGA HLS today: successes, challenges, and opportunities, ACM Trans. Reconfigurable Technol. Syst. (TRETS), 15, 1, 10.1145/3530775
Nane, 2015, A survey and evaluation of FPGA high-level synthesis tools, IEEE Trans. Comput. Aided Des. Integrated Circ. Syst., 35, 1591, 10.1109/TCAD.2015.2513673
Zeng, 2019, Combining high-level synthesis and handwritten RTL for FPGA implementation of deep neural networks, IEEE Transact. Neural Networks Learn. Syst., 30, 2916
Chen, 2020