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

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

In parallel magnetic resonance imaging (MRI), the quality of image reconstruction is very important. The signal to noise ratio (SNR) of reconstruction image would be obviously reduced with the increase of the acceleration factors because of the ill-posed problem in the sensitivity encoding (SENSE) reconstruction. Through in-depth analysis of the total variation (TV) regularized SENSE reconstruction model, an efficient Bregman iteration algorithm was introduced to obtain the optimal solution and improve the image reconstruction quality. In this paper, the simulation experiments were performed on the real cardiac data and brain data of MRI, respectively. The experimental results demonstrated that the proposed algorithm can alleviate the aliasing artifacts and has a better image reconstruction effect. Compared with the conventional TV regularized SENSE reconstruction algorithm, under the condition of the higher acceleration factors, the SNR has also been improved and the normalized mean squared error (NMSE) of reconstruction image is also decreased. This shows that the proposed iteration TV SENSE image reconstruction algorithm is effective and feasible.

The application of partially parallel imaging techniques to regular clinical MRI studies has brought about the benefit of significantly faster acquisitions but at the cost of amplified and non-uniformly distributed noise, especially, for high acceleration factors. In this work, denoising of the images reconstructed by the sensitivity- encoding (SENSE) algorithm is presented. To efficiently remove noise and simultaneously preserve the image details, weighted total variation smoothing with weighted edge restoration has been developed. Denoising is guided by the g-factor of the SENSE reconstruction and image gradient that is representative of edge information. An automatic iteration termination scheme is proposed to balance denoising and edge preservation, and reduce the difficulty of parameter decision in conventional approaches. The proposed g-factor and gradient weighted denoising with edge restoration (g-DENOISER) method retains the image details better than the denoising techniques guided by only the g-factor and simultaneously suppresses the noise stronger than the techniques where g-factor was not used to adaptively adjust the denoiser parameters according to local noise characteristics. It can be effectively used to reduce noise in images acquired using high acceleration factors and reconstructed using SENSE.

Parallel magnetic resonance imaging (pMRI) reduces MRI acquisition time, with Sensitivity Encoding (SENSE) being a widely used method that exploits coil sensitivity maps for efficient image reconstruction. However, SENSE can introduce aliasing and noise artifacts, especially at high acceleration factors. To address this limitation, we propose a deep learning‐based postprocessing framework that enhances SENSE‐reconstructed images using a Convolutional Autoencoder (CAE). The CAE is applied after the SENSE reconstruction to reduce artifacts and improve image quality, without modifying the original reconstruction pipeline. A dataset of 842 fully sampled anatomical images is used to simulate 8‐channel coil data, with both uniform and variable‐density (VD) undersampling applied at different acceleration factors. The CAE is trained on paired inputs (SENSE‐reconstructed images) and targets (fully sampled references) to learn the mapping from degraded to high‐quality images. Quantitative evaluation using Peak Signal‐to‐Noise Ratio (PSNR) and Normalized Mean Squared Error (NMSE), along with qualitative visual assessment, shows that the proposed CAE‐SENSE framework significantly improves image fidelity, particularly with variable‐density undersampling. These results demonstrate the potential of deep learning as a complementary tool to enhance conventional parallel imaging methods in accelerated MRI.

Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data in the k-space. So far, first attempts to combine sensitivity encoding (SENSE) imaging in pMRI with CS have been proposed in the context of Cartesian trajectories. Here, we extend these approaches to non-Cartesian trajectories by jointly formulating the CS and SENSE recovery in a hybrid Fourier/wavelet framework and optimizing a convex but nons-mooth criterion. On anatomical MRI data, we show that HYR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PICS outperforms wavelet-based regularized SENSE reconstruction. Our results are also in agreement with the Transform Point Spread Function (TPSF) criterion that measures the degree of incoherence of k-space undersampling schemes.

Parallel MRI techniques reconstruct full-FOV images from undersampled k-space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal-to-noise ratio (SNR) is degraded because of the reduced data samples and the spatially correlated nature of multiple RF receivers. Regularization has been proposed to mitigate the SNR loss originating due to the latter reason. Since it is necessary to utilize static prior to regularization, the dynamic contrast-to-noise ratio (CNR) in parallel MRI will be affected. In this paper we investigate the CNR of regularized sensitivity encoding (SENSE) acquisitions. We propose to implement regularized parallel MRI acquisitions in functional MRI (fMRI) experiments by incorporating the prior from combined segmented echo-planar imaging (EPI) acquisition into SENSE reconstructions. We investigated the impact of regularization on the CNR by performing parametric simulations at various BOLD contrasts, acceleration rates, and sizes of the active brain areas. As quantified by receiver operating characteristic (ROC) analysis, the simulations suggest that the detection power of SENSE fMRI can be improved by regularized reconstructions, compared to unregularized reconstructions. Human motor and visual fMRI data acquired at different field strengths and array coils also demonstrate that regularized SENSE improves the detection of functionally active brain regions.

The application of parallel magnetic resonance (MR) imaging is increasing as clinicians continue to strive for improved spatial and temporal resolution, benefits that arise from the use of fewer phase encodings during imaging. To reconstruct images, extra information is needed to map the spatial sensitivity of each coil element, which may be accomplished by acquiring a calibration image in one common implementation of parallel MR imaging. Although obtaining a quick calibration image is an efficient method for gathering this information, corruption of the image or disharmony with subsequent images may lead to errors in reconstruction. Although conventional MR imaging sequences may be employed with parallel MR imaging, the altered image reconstruction introduces several new artifacts and changes the appearance of conventional artifacts. The altered appearance of traditional artifacts may obscure the source of the problem, and, in some cases, the severity of artifacts associated with parallel MR imaging may be exacerbated, hindering image interpretation. Several artifacts arise in the context of parallel MR imaging, including both traditional artifacts and those associated with parallel MR imaging.

Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.

Dual double arterial phase dynamic MR imaging with sensitivity encoding (SENSE): which is better for diagnosing hypervascular hepatocellular carcinomas, in-phase or opposed-phase imaging?

Fast acquisition in magnetic resonance imaging (MRI) is considered in this paper. Often, fast acquisition is achieved using parallel imaging (pMRI) techniques. It has been shown recently that the combination of pMRI and compressed sensing (CS), which enables exact reconstruction of sparse or compressible signals from a small number of random measurements, can accelerate the speed of MRI acquisition because the number of measurements are much smaller than that by pMRI per se. Also recently in CS, chaos filters were designed to obtain chaotic measurements. This chaotic CS approach potentially offers simpler hardware implementation. In this paper, we combine chaotic CS and pMRI. However, instead of using chaos filters, the measurements are obtained by chaotically undersampling the k-space. MRI image reconstruction is then performed by using nonlinear conjugate gradient optimization. For pMRI, we use the well-known approach SENSE - sensitivity encoding -, which requires an estimation of the sensitivity maps. The performance of the proposed method is analyzed using the point spread function, the transform point spread function, and the reconstruction error measure.

High-resolution MRI using compressed sensing-sensitivity encoding (CS-SENSE) for patients with suspected neurovascular compression syndrome: comparison with the conventional SENSE parallel acquisition technique

In this paper, we present a fast iterative magnetic resonance imaging (MRI) reconstruction algorithm taking advantage of the prevailing GPGPU programming paradigm. In clinical environment, MRI reconstruction is usually performed via fast Fourier transform (FFT). However, imaging artifacts (i.e. signal loss) resulting from susceptibility-induced magnetic field inhomogeneities degrade the quality of reconstructed images. These artifacts must be addressed using accurate modeling of the physics of the system coupled with iterative reconstruction. We have developed a reconstruction algorithm with improved image quality at the expense of computation time and hence an implementation on GPUs achieving significant speedup. In this work, we extend our previous work on GPU implementation by adding several new features. First, we enable Sensitivity Encoding for Fast MRI (SENSE) reconstruction (from data acquired using a multi-receiver coil array) which can reduce the acquisition time. Besides, we have implemented a GPU-based total variation regularization in our SENSE reconstruction framework. In this paper, we describe the different optimizations employed from levels of algorithm, program code structures, and specific architecture performance tuning, featuring both our MRI reconstruction algorithm and GPU hardware specifics. Results show that the current GPU implementation produces accurate image estimates while significantly accelerating the reconstruction.

Compressive sensing magnetic resonance (MR) imaging is aimed at achieving high-quality MR image reconstruction by undersampling K-space data. It is crucial to explore prior information since compressive sensing MR imaging relies heavily on some prior assumptions, such as signal's sparse property. In this study, in order to explore the prior information fully, an improved MR image reconstruction model based on compressive sensing theory is proposed, named reference image MR imaging with adaptive tight frame. In the proposed model, an adaptive tight frame is involved to explore the sparse prior information adapt to MR images and the similarity prior information to the target image. Meanwhile, improved adaptive weighting parameters are used to trade off the sparsity between the regions with much similarity and that of little similarity. In addition, the smoothing-based fast iterative shrinkage-threshold algorithm is utilised to tackle the optimisation problem so as to speed up imaging. The experimental results demonstrate that the proposed MR image reconstruction method outperforms some state-of-the-art methods in terms of quantitative results.

In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of 0.4–1.0. The L1TV-LAD with a regularization strength of 0.4–0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner.

SENSitivity Encoding (SENSE) is an effective mathematical formulation for reconstructing under-sampled MRI data obtained in Parallel Magnetic Resonance Imaging (Parallel MRI). The functional model includes a regularization term and a data fidelity term which need to be minimized to obtain a high quality MRI result. The proper choice of regularization is essential for image quality. In this paper, we show that Euler’s elastica is an effective regularization for Parallel MRI data reconstruction, and has advantages over the Total Variation (TV) regularization in improving image signal to noise ratio and image relative error. The Euler’s elastica functional is however complex to minimize as it is non-convex, non-smooth, and highly nonlinear. In this paper, we propose a new numerical method to solve Euler’s elastica regularized SENSE efficiently. This algorithm is based on a variable splitting approach and proper relaxation of the functional. Numerical examples are presented to show the effectiveness of the proposed Euler’s elastica algorithm in comparison to Bregman Operator Splitting with Variable Stepsize, a TV based algorithm.