Compressed SVD-based L + S model to reconstruct undersampled dynamic MRI data using parallel architecture.

BACKGROUND: Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to capture the dynamic and functional information of body organs e.g. cardiac MRI is employed to assess cardiac structure and evaluate blood flow dynamics through the cardiac valves. Long scan time is the main drawback of MRI, which makes it difficult for the patients to remain still during the scanning process. OBJECTIVE: By collecting fewer measurements, MRI scan time can be shortened, but this undersampling causes aliasing artifacts in the reconstructed images. Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts. These algorithms are computationally expensive and require a long time for reconstruction which makes them infeasible for real-time clinical applications e.g. cardiac MRI. However, exploiting the inherent parallelism in these algorithms can help to reduce their computation time. METHODS: Low-rank plus sparse (L+S) matrix decomposition model is a technique used in literature to reconstruct the highly undersampled dynamic MRI (dMRI) data at the expense of long reconstruction time. In this paper, Compressed Singular Value Decomposition (cSVD) model is used in L+S decomposition model (instead of conventional SVD) to reduce the reconstruction time. The results provide improved quality of the reconstructed images. Furthermore, it has been observed that cSVD and other parts of the L+S model possess highly parallel operations; therefore, a customized GPU based parallel architecture of the modified L+S model has been presented to further reduce the reconstruction time. RESULTS: Four cardiac MRI datasets (three different cardiac perfusion acquired from different patients and one cardiac cine data), each with different acceleration factors of 2, 6 and 8 are used for experiments in this paper. Experimental results demonstrate that using the proposed parallel architecture for the reconstruction of cardiac perfusion data provides a speed-up factor up to 19.15× (with memory latency) and 70.55× (without memory latency) in comparison to the conventional CPU reconstruction with no compromise on image quality. CONCLUSION: The proposed method is well-suited for real-time clinical applications, offering a substantial reduction in reconstruction time.

Accelerating GRAPPA reconstruction using SoC design for real-time cardiac MRI.

Real-time cardiac MRI is a rapidly developing area of research that has the potential to improve the diagnosis and treatment of cardiovascular diseases. However, the acquisition of high-quality real-time cardiac MR (CMR) images is challenging as it requires a high frame rate and temporal resolution. To overcome this challenge, there have been recent efforts on several approaches including hardware-based improvements and image reconstruction techniques such as compressed sensing and parallel MRI. The use of parallel MRI techniques such as GRAPPA (Generalized Autocalibrating Partial Parallel Acquisition) is a promising approach for improving the temporal resolution of MRI and expanding its applications in clinical practice. However, the GRAPPA algorithm involves a significant amount of computation, particularly for high acceleration factors and large datasets. This can result in long reconstruction times, which can limit the ability to achieve real-time imaging or high frame rates. One solution to this challenge is to use specialized hardware i.e. field-programmable gate arrays (FPGAs). In this work, a novel 32-bit floating-point FPGA-based GRAPPA accelerator is proposed with an aim to reconstruct high-quality cardiac MR images at higher frame rates, making it well suited for real-time clinical applications. The proposed FPGA-based accelerator consists of custom-designed data processing units named as dedicated computational engines (DCEs) that allow for a continuous flow of data between the calibration and synthesis stages of GRAPPA reconstruction process. This greatly increases the throughput and reduces the latency of the overall proposed system. Moreover, a high-speed memory module (DDR4-SDRAM) is integrated with the proposed architecture to store the multi-coil MR data. An on-chip quad-core ARM Cortex-A53 processor is used to manage access control information required for data transfer between the DCEs and DDR4-SDRAM. The proposed accelerator is implemented on Xilinx Zynq UltraScale + MPSoC using high-level synthesis (HLS) and hardware descriptive language (HDL) with an aim to explore the trade-offs between the reconstruction time, resource utilization and design effort. Several experiments have been performed using in-vivo cardiac datasets i.e. 18-receiver coil and 30-receiver coil to evaluate the performance of the proposed accelerator. A comparison is performed with the contemporary CPU and GPU-based GRAPPA reconstruction methods in terms of reconstruction time, frames-per-second and reconstruction accuracy (RMSE and SNR). The results show that the proposed accelerator achieves speed-up factors up to 121× and 9× as compared to the contemporary CPU-based and GPU-based GRAPPA reconstruction methods, respectively. Moreover, it has been demonstrated that the proposed accelerator can achieve reconstruction rates of up to ∼27 frames-per-second while maintaining the visual quality of the reconstructed images.

GPU accelerated grouped magnetic resonance fingerprinting using clustering techniques.

Magnetic Resonance Fingerprinting (MRF) is a new quantitative technique of Magnetic Resonance Imaging (MRI). Conventionally, MRF requires sequential correlation of the acquired MRF signals with all the signals of (a large sized) MRF dictionary. This is a computationally intensive matching process and is a major challenge in MRF image reconstruction. This paper introduces the use of clustering techniques (to reduce the effective size of MRF dictionary) by splitting MRF dictionary into multiple small sized MRF dictionary components called MRF signal groups. The proposed method has been further optimized for parallel processing to reduce the computation time of MRF pattern matching. A multi-core GPU based parallel framework has been developed that enables the MRF algorithm to process multiple MRF signals simultaneously. Experiments have been performed on human head and phantom datasets. The results show that the proposed method accelerates the conventional MRF (MATLAB based) reconstruction time up to 25× with single-core CPU implementation, 300× with multi- core CPU implementation and 1035× with the proposed multi-core GPU based framework by keeping the SNR of the resulting images in a clinically acceptable range. Furthermore, experimental results show that the memory requirements of MRF dictionary get significantly reduced (due to efficient memory utilization) in the proposed method.

Accelerated Diffusion-Weighted MR Image Reconstruction Using Deep Neural Networks.

Under-sampling in diffusion-weighted imaging (DWI) decreases the scan time that helps to reduce off-resonance effects, geometric distortions, and susceptibility artifacts; however, it leads to under-sampling artifacts. In this paper, diffusion-weighted MR image (DWI-MR) reconstruction using deep learning (DWI U-Net) is proposed to recover artifact-free DW images from variable density highly under-sampled k-space data. Additionally, different optimizers, i.e., RMSProp, Adam, Adagrad, and Adadelta, have been investigated to choose the best optimizers for DWI U-Net. The reconstruction results are compared with the conventional Compressed Sensing (CS) reconstruction. The quality of the recovered images is assessed using mean artifact power (AP), mean root mean square error (RMSE), mean structural similarity index measure (SSIM), and mean apparent diffusion coefficient (ADC). The proposed method provides up to 61.1%, 60.0%, 30.4%, and 28.7% improvements in the mean AP value of the reconstructed images in our experiments with different optimizers, i.e., RMSProp, Adam, Adagrad, and Adadelta, respectively, as compared to the conventional CS at an acceleration factor of 6 (i.e., AF = 6). The results of DWI U-Net with the RMSProp, Adam, Adagrad, and Adadelta optimizers show 13.6%, 10.0%, 8.7%, and 8.74% improvements, respectively, in terms of mean SSIM with respect to the conventional CS at AF = 6. Also, the proposed technique shows 51.4%, 29.5%, 24.04%, and 18.0% improvements in terms of mean RMSE using the RMSProp, Adam, Adagrad, and Adadelta optimizers, respectively, with reference to the conventional CS at AF = 6. The results confirm that DWI U-Net performs better than the conventional CS reconstruction. Also, when comparing the different optimizers in DWI U-Net, RMSProp provides better results than the other optimizers.

Real-time, single breath-hold, multi-slice, 2D cine radial MR image reconstruction using sc-GROG k-t ESPIRiT.

Background:Multi-slice, multiple breath-hold ECG-gated 2D cine MRI is a standard technique for evaluating heart function and restricted to one or two images per breath-hold. Therefore, the standard cine MRI requires long scan time and can result in slice-misalignments because of various breath-hold locations in the multiple acquisitions.Methods:This work proposes the sc-GROG based k-t ESPIRiT with Total Variation (TV) constraint (sc-GROG k-t ESPIRiT) to reconstruct unaliased cardiac real-time cine MR images from highly accelerated whole heart multi-slice, single breath-hold, real-time 2D cine radial data acquired using the balanced steady-state free precession (trueFISP) sequence in 8 patients. The proposed method quality is assessed via Artifact Power (AP), Root-Mean Square Error (RMSE), Structure Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), blood-pool to myocardial Contrast-to-Noise-Ratio (CNR), Signal-to-Noise-Ratio (SNR) and spatial-temporal intensity plots through the blood-myocardium boundary. The proposed method quantitative results are compared with the NUFFT based k-t ESPIRiT with Total Variation (TV) constraint (NUFFT k-t ESPIRiT) approach. Furthermore, clinical analysis and function quantification are assessed by Bland-Altman (BA) analyses.Results:As supported by the visual assessment and evaluation parameters, the reconstruction results of the sc-GROG k-t ESPIRiT approach provide an average 21%, 12%, 1% and 47% improvement in AP, RMSE, SSIM and PSNR, respectively in comparison to the NUFFT k-t ESPIRiT approach. Furthermore, the proposed method gives on average 45% and 58% improved blood-pool to myocardial CNR and SNR than the NUFFT k-t ESPIRiT approach. Also, from the BA plot, the proposed method gives better left ventricular and right ventricular function measurements as compared to the NUFFT k-t ESPIRiT scheme.Conclusions:The sc-GROG k-t ESPIRiT (Proposed Method) improves the spatio-temporal quality of the whole heart multi-slice, single breath-hold, real-time 2D cine radial MR and semi-automated analysis using standard clinical software, as compared to the NUFFT k-t ESPIRiT approach.

GPU accelerated Cartesian GRAPPA reconstruction using CUDA.

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.

GPU based parallel framework for receiver coil sensitivity estimation in SENSE reconstruction.

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.

Transfer learning in deep neural network-based receiver coil sensitivity map estimation.

INTRODUCTION: The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming. MATERIALS AND METHODS: A transfer learning approach, i.e., end-to-end fine-tuning is proposed to address the data scarcity and generalization problems of deep learning-based receiver coil sensitivity map estimation. First, generalization capabilities of a pre-trained U-Net (initially trained on 1.5T receiver coil sensitivity maps) are thoroughly assessed for 3T receiver coil sensitivity map estimation. Later, end-to-end fine-tuning is performed on the pre-trained U-Net to estimate the 3T receiver coil sensitivity maps. RESULT AND CONCLUSION: Peak Signal-to-Noise Ratio, Root Mean Square Error and central line profiles (of the SENSE reconstructed images) show a successful SENSE reconstruction by utilizing the receiver coil sensitivity maps estimated by the proposed method.

Transfer learning in deep neural network based under-sampled MR image reconstruction.

In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data. In this research, a transfer learning approach i.e. end-to-end fine tuning is proposed for U-Net to address the data scarcity and generalization problems of deep learning-based MR image reconstruction. First the generalization capabilities of a pre-trained U-Net (initially trained on the human brain images of 1.5 T scanner) are assessed for: (a) MR images acquired from MRI scanners of different magnetic field strengths, (b) MR images of different anatomies and (c) MR images under-sampled by different acceleration factors. Later, end-to-end fine tuning of the pre-trained U-Net is proposed for the reconstruction of the above-mentioned MR images (i.e. (a), (b) and (c)). The results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images.

Parallel implementation of L + S signal recovery in dynamic MRI.

Dynamic MRI is useful to diagnose different diseases, e.g. cardiac ailments, by monitoring the structure and function of the heart and blood flow through the valves. Faster data acquisition is highly desirable in dynamic MRI, but this may lead to aliasing artifacts due to under-sampling. Advanced image reconstruction algorithms are required to obtain aliasing-free MR images from the acquired under-sampled data. One major limitation of using the advanced reconstruction algorithms is their computationally expensive and time-consuming nature, which make them infeasible for clinical use, especially for applications like cardiac MRI. L + S decomposition model is an approach provided in literature which separates the sparse and low-rank information in dynamic MRI. However, L + S decomposition model is a computationally complex process demanding significant computation time. In this paper, a parallel framework is proposed to accelerate the image reconstruction process of L + S decomposition model using GPU. Experiments are performed on cardiac perfusion dataset ([Formula: see text]) and cardiac cine dataset ([Formula: see text]) using NVIDIA's GeForce GTX780 GPU and Core-i7 CPU. The results show that the proposed method provides up to 18 × speed-up including the memory transfer time (i.e. data transfer between the CPU and GPU) and ~ 46 × speed-up without memory transfer for the cardiac perfusion dataset in our experiments. This level of improvement in the reconstruction time will increase the usefulness of L + S reconstruction by making it feasible for clinical applications.

Respiratory motion compensation using data binning in dynamic contrast enhanced golden-angle radial MRI.

GRASP (Golden-Angle Radial Sparse Parallel MRI) is a data acquisition and reconstruction technique that combines parallel imaging and golden-angle radial sampling. The continuously acquired free breathing Dynamic Contrast Enhanced (DCE) golden-angle radial MRI data of liver and abdomen has artifacts due to respiratory motion, resulting in low vessel-tissue contrast that makes GRASP reconstructed images less suitable for diagnosis. In this paper, DCE golden-angle radial MRI data of abdomen and liver perfusion is sorted into different motion states using the self-gating property of radial acquisition and then reconstructed using GRASP. Three methods of amplitude-based data binning namely uniform binning, adaptive binning and optimal binning are applied on the DCE golden-angle radial data to extract different motion states and a comparison is performed with the conventional GRASP reconstruction. Also, a comparison among the amplitude-based data binning techniques is performed and benefits of each of these binning techniques are discussed from a clinical perspective. The image quality assessment in terms of hepatic vessel clarity, liver edge sharpness, contrast enhancement clarity and streaking artifacts is performed by a certified radiologist. The results show that DCE golden-angle radial trajectories benefit from all the three types of amplitude-based data binning methods providing improved reconstruction results. The choice of binning technique depends upon the clinical application e.g. uniform and adaptive binning are helpful for a detailed analysis of lesion characteristic and contrast enhancement in different motion states while optimal binning can be used when clinical analysis requires a single image per contrast enhancement phase with no motion blurring artifacts.

Association between scripture memorization and brain atrophy using magnetic resonance imaging.

We investigated the association between scripture memorization and brain tissue using magnetic resonance imaging techniques. Participants comprised 63 healthy adults between the ages of 35 and 80 years old with no neurological or psychological disorders. Of these, 19 had completely memorized the Quran, 28 had partially memorized parts of Quran while 16, the control group, had not committed the Quran into their memory. White matter, grey matter and cerebrospinal fluid volumes were calculated. The brain tissue volumes of those who memorized the entire Quran and those who memorized only a small portion were compared with the control group using one­way ANOVA implemented in SPSS. There was no significant effect of age between the three groups (p>0.50). The group who completely memorized the Quran had larger grey matter and white matter volumes than the control group. Our results showed that those who memorized scripture had more brain tissues preserved compared with those who had not memorized scripture. These findings suggest that engaging our brains by memorizing scripture may increase brain health.

FPGA-based hardware accelerator for SENSE (a parallel MR image reconstruction method).

SENSE (Sensitivity Encoding) is a parallel MRI (pMRI) technique that allows accelerated data acquisition using multiple receiver coils and reconstructs the artifact-free images from the acquired under-sampled data. However, an increasing number of receiver coils has raised the computational demands of pMRI techniques to an extent where the reconstruction time on general purpose computers becomes impractically long for real-time MRI. Field Programmable Gate Arrays (FPGAs) have recently emerged as a viable hardware platform for accelerating pMRI algorithms (e.g. SENSE). However, recent efforts to accelerate SENSE using FPGAs have been focused on a fixed number of receiver coils (L=8) and acceleration factor (Af=2). This paper presents a novel 32-bit floating-point FPGA-based hardware accelerator for SENSE (HW-ACC-SENSE); having an ability to work in coordination with an on-chip ARM processor performing reconstructions for different values of L and Af. Moreover, the proposed design provides flexibility to integrate multiple units of HW-ACC-SENSE with an on-chip ARM processor, for low-latency image reconstruction. The VIVADO High-Level-Synthesis (HLS) tool has been used to design and implement the HW-ACC-SENSE on the Xilinx FPGA development board (ZCU102). A series of experiments has been performed on in-vivo datasets acquired using 8, 12 and 30 receiver coil elements. The performance of the proposed architecture is compared with the single thread and multi-thread CPU-based implementations of SENSE. The results show that the proposed design withstands the reconstruction quality of the SENSE algorithm while demonstrating a maximum speed-gain up to 298× over the CPU counterparts in our experiments.

Respiratory motion-corrected, compressively sampled dynamic MR image reconstruction by exploiting multiple sparsity constraints and phase correlation-based data binning.

INTRODUCTION: Cardiac magnetic resonance imaging (cMRI) is a standard method that is clinically used to evaluate the function of the human heart. Respiratory motion during a cMRI scan causes blurring artefacts in the reconstructed images. In conventional MRI, breath holding is used to avoid respiratory motion artefacts, which may be difficult for cardiac patients. MATERIALS AND METHODS: This paper proposes a method in which phase correlation-based binning, followed by image registration-based sparsity along with spatio-temporal sparsity, is incorporated into the standard low rank + sparse (L+S) reconstruction for free-breathing cardiac cine MRI. The proposed method is validated on clinical data and simulated free-breathing cardiac cine data for different acceleration factors (AFs). The reconstructed images are analysed using visual assessment, artefact power (AP) and root-mean-square error (RMSE). The results of the proposed method are compared with the contemporary motion-corrected compressed sensing (MC-CS) method given in the literature. RESULTS: Our results show that the proposed method successfully reconstructs the motion-corrected images from respiratory motion-corrupted, compressively sampled cardiac cine MR data, e.g., there is 26% and 24% improvement in terms of AP and RMSE values, respectively, at AF = 4 and 20% and 16.04% improvement in terms of AP and RMSE values, respectively, at AF = 8 in the reconstruction results from the proposed method for the cardiac phantom cine data. CONCLUSION: The proposed method achieves significant improvement in the AP and RMSE values at different AFs for both the phantom and in vivo data.

ASIC modelling of SENSE for parallel MRI.

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.

QR-decomposition based SENSE reconstruction using parallel architecture.

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.

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm.

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k-space. This paper introduces an improved iterative algorithm based on p-thresholding technique for CS-MRI image reconstruction. The use of p-thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p-thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p-thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Iterative Schemes to Solve Low-Dimensional Calibration Equations in Parallel MR Image Reconstruction with GRAPPA.

GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition) is a widely used parallel MRI reconstruction technique. The processing of data from multichannel receiver coils may increase the storage and computational requirements of GRAPPA reconstruction. Random projection on GRAPPA (RP-GRAPPA) uses random projection (RP) method to overcome the computational overheads of solving large linear equations in the calibration phase of GRAPPA, saving reconstruction time. However, RP-GRAPPA compromises the reconstruction accuracy in case of large reductions in the dimensions of calibration equations. In this paper, we present the implementation of GRAPPA reconstruction method using potential iterative solvers to estimate the reconstruction coefficients from the randomly projected calibration equations. Experimental results show that the proposed methods withstand the reconstruction accuracy (visually and quantitatively) against large reductions in the dimension of linear equations, when compared with RP-GRAPPA reconstruction. Particularly, the proposed method using conjugate gradient for least squares (CGLS) demonstrates more savings in the computational time of GRAPPA, without significant loss in the reconstruction accuracy, when compared with RP-GRAPPA. It is also demonstrated that the proposed method using CGLS complements the channel compression method for reducing the computational complexities associated with higher channel count, thereby resulting in additional memory savings and speedup.

FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities.

Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.