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Background and Objective: GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisition) is an advanced parallel MRI reconstruction method (pMRI) that enables under-sampled data acquisition with multiple receiver coils to reduce the MRI scan time and reconstructs artifact free image from the acquired under-sampled data. However, the reduction in MRI scan time comes at the expense of long reconstruction time. It is because the GRAPPA reconstruction time shows exponential growth with increasing number of receiver coils. Consequently, the conventional CPU platforms may not adhere to the requirements of fast data processing for MR image reconstruction., Methods: Graphics Processing Units (GPUs) have recently emerged as a viable commodity hardware to reduce the reconstruction time of pMRI methods. This paper presents a novel GPU based implementation of GRAPPA using custom built CUDA kernels, to meet the rising demands of fast MRI processing. The proposed framework exploits intrinsic parallelism in the calibration and synthesis phases of GRAPPA reconstruction process, aiming to achieve high speed MR image reconstruction for various GRAPPA configuration settings using different number of receiver coils, auto-calibration signals (ACS), sizes of GRAPPA kernel and acceleration factors. In-vivo experiments (using 8, 12 and 30 receiver coils) are performed to compare the performance of the proposed GPU accelerated GRAPPA with the CPU based GRAPPA extensions and GPU counterpart., Results: The results indicate that the proposed method achieves up to ≈47.8× , ≈17× and ≈3.8× speed up gains over multicore CPU (single thread), multicore CPU (8 thread) and Gadgetron (GPU based GRAPPA) respectively, without compromising the reconstruction accuracy., Conclusions: The proposed method reduces the GRAPPA reconstruction time by employing the calibration phase (GRAPPA weights estimation) and synthesis phase (interpolation) on GPU. Our study shows that the proposed GPU based parallel framework for GRAPPA reconstruction provides a solution for high-speed image reconstruction while maintaining the quality of the reconstructed images., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Magnetic Resonance Imaging (MRI) uses non-ionizing radiations and is safer as compared to CT and X-ray imaging. MRI is broadly used around the globe for medical diagnostics. One main limitation of MRI is its long data acquisition time. Parallel MRI (pMRI) was introduced in late 1990's to reduce the MRI data acquisition time. In pMRI, data is acquired by under-sampling the Phase Encoding (PE) steps which introduces aliasing artefacts in the MR images. SENSitivity Encoding (SENSE) is a pMRI based method that reconstructs fully sampled MR image from the acquired under-sampled data using the sensitivity information of receiver coils. In SENSE, precise estimation of the receiver coil sensitivity maps is vital to obtain good quality images. Eigen-value method (a recently proposed method in literature for the estimation of receiver coil sensitivity information) does not require a pre-scan image unlike other conventional methods of sensitivity estimation. However, Eigen-value method is computationally intensive and takes a significant amount of time to estimate the receiver coil sensitivity maps. This work proposes a parallel framework for Eigen-value method of receiver coil sensitivity estimation that exploits its inherent parallelism using Graphics Processing Units (GPUs). We evaluated the performance of the proposed algorithm on in-vivo and simulated MRI datasets (i.e. human head and simulated phantom datasets) with Peak Signal-to-Noise Ratio (PSNR) and Artefact Power (AP) as evaluation metrics. The results show that the proposed GPU implementation reduces the execution time of Eigen-value method of receiver coil sensitivity estimation (providing up to 30 times speed up in our experiments) without degrading the quality of the reconstructed image., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Magnetic Resonance Imaging (MRI) is widely used in medical diagnostics and image reconstruction is a vital part of MRI systems. In Parallel MRI (pMRI), imaging process is accelerated by acquiring less data (undersampled) using multiple receiver coils and offline reconstruction algorithms are applied to reconstruct the fully sampled image. In this research, an Application Specific Integrated Circuits (ASIC) model of SENSE (a pMRI algorithm) is presented which reconstructs the image from the undersampled data right on the data acquisition module of the scanner. The proposed ASIC HDL architecture is compared with SENSE reconstruction model implemented on FPGAs, Multi-core CPU and Graphics Processing Units. The proposed architecture is validated using simulated brain data with 8-channel receiver coils and a human cardiac dataset with 20-channel receiver coils. The quality of the reconstructed images is analyzed using Artifact Power (0.0098), Peak Signal-to-Noise Ratio (53.4) and Structured Similarity Index (0.871) which validate the quality of the reconstructed images using the proposed design. The results show that the proposed ASIC HDL SENSE reconstruction model is ∼8000 times faster as compared to the multi-core CPU reconstruction, ∼700 times faster than the GPU implementation and ∼16 times faster as compared to the FPGA reconstruction model. The proposed architecture is suitable for image reconstruction right on the data acquisition system of the scanner and will open new ways for faster image reconstruction on portable MRI scanners., (Copyright © 2019. Published by Elsevier Ltd.)

Magnetic Resonance Imaging (MRI) is a powerful medical imaging technique that provides essential clinical information about the human body. One major limitation of MRI is its long scan time. Implementation of advance MRI algorithms on a parallel architecture (to exploit inherent parallelism) has a great potential to reduce the scan time. Sensitivity Encoding (SENSE) is a Parallel Magnetic Resonance Imaging (pMRI) algorithm that utilizes receiver coil sensitivities to reconstruct MR images from the acquired under-sampled k-space data. At the heart of SENSE lies inversion of a rectangular encoding matrix. This work presents a novel implementation of GPU based SENSE algorithm, which employs QR decomposition for the inversion of the rectangular encoding matrix. For a fair comparison, the performance of the proposed GPU based SENSE reconstruction is evaluated against single and multicore CPU using openMP. Several experiments against various acceleration factors (AFs) are performed using multichannel (8, 12 and 30) phantom and in-vivo human head and cardiac datasets. Experimental results show that GPU significantly reduces the computation time of SENSE reconstruction as compared to multi-core CPU (approximately 12x speedup) and single-core CPU (approximately 53x speedup) without any degradation in the quality of the reconstructed images., (Copyright © 2018 Elsevier Ltd. All rights reserved.)