Combined Parallel Imaging Research Articles

Speed and efficiency: evaluating pulmonary nodule detection with AI-enhanced 3D gradient echo imaging.

Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing. In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC). Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3-107 mm] part-solid n = 6 [ground glass/solid 8 mm/4-28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size. The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules. Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality. Lung cancer screening by MRI may be possible but would benefit from scan time optimisation. Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors. Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.

Conventional and Deep-Learning-Based Image Reconstructions of Undersampled K-Space Data of the Lumbar Spine Using Compressed Sensing in MRI: A Comparative Study on 20 Subjects.

Compressed sensing accelerates magnetic resonance imaging (MRI) acquisition by undersampling of the k-space. Yet, excessive undersampling impairs image quality when using conventional reconstruction techniques. Deep-learning-based reconstruction methods might allow for stronger undersampling and thus faster MRI scans without loss of crucial image quality. We compared imaging approaches using parallel imaging (SENSE), a combination of parallel imaging and compressed sensing (COMPRESSED SENSE, CS), and a combination of CS and a deep-learning-based reconstruction (CS AI) on raw k-space data acquired at different undersampling factors. 3D T2-weighted images of the lumbar spine were obtained from 20 volunteers, including a 3D sequence (standard SENSE), as provided by the manufacturer, as well as accelerated 3D sequences (undersampling factors 4.5, 8, and 11) reconstructed with CS and CS AI. Subjective rating was performed using a 5-point Likert scale to evaluate anatomical structures and overall image impression. Objective rating was performed using apparent signal-to-noise and contrast-to-noise ratio (aSNR and aCNR) as well as root mean square error (RMSE) and structural-similarity index (SSIM). The CS AI 4.5 sequence was subjectively rated better than the standard in several categories and deep-learning-based reconstructions were subjectively rated better than conventional reconstructions in several categories for acceleration factors 8 and 11. In the objective rating, only aSNR of the bone showed a significant tendency towards better results of the deep-learning-based reconstructions. We conclude that CS in combination with deep-learning-based image reconstruction allows for stronger undersampling of k-space data without loss of image quality, and thus has potential for further scan time reduction.

Analysis of accelerated 4D flow MRI in the murine aorta by radial acquisition and compressed sensing reconstruction.

Preclinical 4D flow MRI remains challenging and is restricted for parallel imaging acceleration due to the limited number of available receive channels. A radial acquisition with combined parallel imaging and temporal compressed sensing reconstruction was implemented to achieve accelerated preclinical 4D flow MRI. In order to increase the accuracy of the measured velocities, a quantitative evaluation of different temporal regularization weights for the compressed sensing reconstruction based on velocity instead of magnitude data is performed. A 3D radial retrospectively triggered phase contrast sequence with a combined parallel imaging and compressed sensing reconstruction with temporal regularization was developed. It was validated in a phantom and in vivo (C57BL/6 J mice), against an established fully sampled Cartesian sequence. Different undersampling factors (USFs [12, 15, 20, 30, 60]) were evaluated, and the effect of undersampling was analyzed in detail for magnitude and velocity data. Temporal regularization weights λ were evaluated for different USFs. Acceleration factors of up to 20 compared with full Nyquist sampling were achieved. The peak flow differences compared with the Cartesian measurement were the following: USF 12, 3.38%; USF 15, 4.68%; USF 20, 0.95%. The combination of 3D radial center-out trajectories and compressed sensing reconstruction is robust against motion and flow artifacts and can significantly reduce measurement time to 30 min at a resolution of 180 μm3 . Concisely, radial acquisition with combined compressed sensing and parallel imaging proved to be an excellent method for analyzing complex flow patterns in mice.

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.

To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data. We propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art parallel imaging and compressed sensing method known as -ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets (R=12) with respect to standard image quality metrics as well as automatic deep learning-based segmentations of left ventricular volumes. Feasibility of DL-ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice. The (2+1)D DL-ESPIRiT method produces higher fidelity image reconstructions when compared to -ESPIRiT reconstructions with respect to standard image quality metrics (P < .001). As a result of improved image quality, segmentations made from (2+1)D DL-ESPIRiT images are also more accurate than segmentations from -ESPIRiT images. DL-ESPIRiT synergistically combines a robust parallel imaging model and deep learning-based priors to produce high-fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof-of-concept is shown, further experiments are necessary to determine the efficacy of DL-ESPIRiT in prospectively undersampled data.

Direct measurement of atrioventricular valve regurgitant jets using 4D flow cardiovascular magnetic resonance is accurate and reliable for children with congenital heart disease: a retrospective cohort study

Background3D-time resolved flow (4DF) cardiovascular magnetic resonance (CMR) with retrospective analysis of atrioventricular valve regurgitation (AVVR) allows for internal validation by multiple direct and indirect methods. Limited data exist on direct measurement of AVVR by 4DF CMR in pediatric congenital heart disease (CHD). We aimed to validate direct measurement of the AVVR jet as accurate and reliable compared to the volumetric method (clinical standard by 2D CMR) and as a superior method of internal validation than the annular inflow method.MethodsWe identified 44 consecutive patients with diverse CHD referred for evaluation of AVVR by CMR. 1.5 T or 3 T scanners, intravenous contrast, and a combination of parallel imaging and compressed sensing were used. Four methods of measuring AVVR volume (RVol) were used: volumetric method (VOL; the clinical standard) = stroke volume by 2D balanced steady-state free precession – semilunar valve forward flow (SLFF); annular inflow method (AIM) = atrioventricular valve forward flow [AVFF] – semilunar valve net flow (SLNF); and direct measurement (JET). AVFF was measured using static and retrospective valve tracking planes. SLFF, SLNF, AVFF, and JET were measured by 4DF phase contrast. Regurgitant fraction was calculated as [RVol/(RVol+SLNF)]× 100. Statistical methods included Spearman, Wilcoxon rank sum test/Student paired t-test, Bland Altman analysis, and intra-class coefficient (ICC), where appropriate.ResultsRegurgitant fraction by JET strongly correlated with the indirect methods (VOL and AIM) (ρ = 0.73–0.80, p < 0.001) and was similar to VOL with a median difference (interquartile range) of − 1.5% (− 8.3–7.2%; p = 0.624). VOL had weaker correlations with AIM and JET (ρ = 0.69–0.73, p < 0.001). AIM underestimated RF by 3.6–6.9% compared to VOL and JET, p < 0.03. Intra- and inter- observer reliability were excellent for all methods (ICC 0.94–0.99). The mean (±standard deviation) inter-observer difference for VOL was 2.4% (±5.1%), p < 0.05.ConclusionsIn a diverse cohort of pediatric CHD, measurement of AVVR using JET is accurate and reliable to VOL and is a superior method of internal validation compared to AIM. This study supports use of 4DF CMR for measurement of AVVR, obviating need for expert prospective prescription during image acquisition by 2D CMR.

Acceleration of chemical shift encoding-based water fat MRI for liver proton density fat fraction and T2* mapping using compressed sensing.

ObjectivesTo evaluate proton density fat fraction (PDFF) and T2* measurements of the liver with combined parallel imaging (sensitivity encoding, SENSE) and compressed sensing (CS) accelerated chemical shift encoding-based water-fat separation.MethodsSix-echo Dixon imaging was performed in the liver of 89 subjects. The first acquisition variant used acceleration based on SENSE with a total acceleration factor equal to 2.64 (acquisition labeled as SENSE). The second acquisition variant used acceleration based on a combination of CS with SENSE with a total acceleration factor equal to 4 (acquisition labeled as CS+SENSE). Acquisition times were compared between acquisitions and proton density fat fraction (PDFF) and T2*-values were measured and compared separately for each liver segment.ResultsTotal scan duration was 14.5 sec for the SENSE accelerated image acquisition and 9.3 sec for the CS+SENSE accelerated image acquisition. PDFF and T2* values did not differ significantly between the two acquisitions (paired Mann-Whitney and paired t-test P>0.05 in all cases). CS+SENSE accelerated acquisition showed reduced motion artifacts (1.1%) compared to SENSE acquisition (12.3%).ConclusionCS+SENSE accelerates liver PDFF and T2*mapping while retaining the same quantitative values as an acquisition using only SENSE and reduces motion artifacts.

Magnetic resonance cholangiopancreatography using optimized integrated combination with parallel imaging and compressed sensing technique.

To assess the combined parallel imaging (PI) and optimized integrated compressed sensing technique (prototype Compressed SENSE) for magnetic resonance cholangiopancreatography (MRCP) compared with conventional MRCP. This prospective study was approved by our Institutional Review Board, and all patients provided written informed consent. A total of 56 consecutive patients (27 men and 29 women; mean age 67.2years) underwent breath-hold three-dimensional (3D) MRCP with PI alone (BH-MRCP; acquisition time, 23s), respiratory-triggered 3D MRCP with PI alone (RT-MRCP; 201s) and respiratory-triggered 3D MRCP with Compressed SENSE (RT-MRCPcs; 45s). Relative duct-to-periductal contrast ratios (RCs) of the pancreaticobiliary ducts were calculated for quantitative image analyses. Two radiologists graded the visibility of the pancreaticobiliary ducts, pancreatic cystic lesion, motion artifact, and overall image quality using a five-point rating scale for qualitative image analyses. Theses qualitative and quantitative measurements were then compared among the three sequences. RCs of the common bile duct, right hepatic duct (RHD), left hepatic duct (LHD), and main pancreatic duct at the pancreatic head, body, and tail segments, were significantly higher RT-MRCP, followed by RT-MRCPcs and BH-MRCP (P < 0.001). The visibility of the peripheral RHD and LHD was slightly better in RT-MRCP than in RT-MRCPcs and BH-MRCP (P < 0.001). The visibility of other pancreaticobiliary ducts, pancreatic cystic lesion, motion artifact, and overall image quality were almost comparable among three sequences. The acquisition time was markedly reduced in RT-MRCPcs compared with conventional RT-MRCP while there were significant differences in both quantitative and qualitative analyses, the differences were small enough that the reduced acquisition time makes up for it.

Assessment of velopharyngeal function with dual-planar high-resolution real-time spiral dynamic MRI.

To develop a real-time dynamic MRI method for comprehensive evaluation of velum movement during speech. Dynamic MRI has been used to study velopharyngeal insufficiency (VPI) by imaging the movement of the velum during speech, because it can provide good anatomic details with no exposed radiation. To be able to comprehensively evaluate dynamic velum movement, a real-time spiral non-balanced SSFP sequence was developed with simultaneous dual-planar coverage and improved spatial and temporal resolution using a combination of parallel imaging and spatial and temporal compressed sensing to achieve 6 × acceleration. New off-resonance correction and post-processing methods were also developed to reduce blurring and slice crosstalk. The method demonstrated good image quality for visualizing dynamic velum movement with reduced blurring and improved image homogeneity. Spatial resolution of 1.2*1.2 mm2 with 150 mm FOV and temporal resolution of 20 frames-per-second with simultaneous dual-planar coverage was achieved. This work describes a new technique for studying speech disorders using dual-planar accelerated spiral dynamic MRI.

Parallel compressive sensing in a hybrid space: Application in interventional MRI.

Recently, susceptibility based positive contrast MRI technique emerged as an effective method of visualizing the small MR compatible devices, such as brachytherapy seeds. One of the challenges associated with this method is the long scan time. In this work, we present an accelerated susceptibility based positive contrast MR imaging method, in which the susceptibility map can be generated from an under-sampled data. We use a combination of parallel imaging (GRAPPA) and compressive sensing (CS) technique in a hybrid k-space. The results in brachytherapy seeds imaging show that 3-D high-quality images can be achieved at an acceleration factor up to 4, which can decrease the scan time from 4 minutes to 1.4 minutes.

Feasibility of 3D navigator-triggered magnetic resonance cholangiopancreatography with combined parallel imaging and compressed sensing reconstruction at 3T.

To assess the feasibility of 3D navigator-triggered magnetic resonance cholangiopancreatography (MRCP) with combined parallel imaging (PI) and compressed sensing (CS). With Institutional Review Board approval, 30 consecutive patients who underwent MRCP for suspected pancreaticobiliary disease were prospectively recruited. All patients underwent 3D navigator-triggered MRCP with conventional PI alone, and with combined PI and CS using a 3T machine. The acquisition time and relative duct-to-periductal contrast ratios (RCs) at three biliary segments were quantitatively compared between the two MRCP methods. Qualitative image parameters were independently evaluated by two blinded radiologists, and were compared between two methods using the Wilcoxon signed-rank test. The mean acquisition time of MRCP with combined PI and CS (131.87 ± 33.60 sec) was significantly shorter compared with that of MRCP with PI (253.63 ± 56.08 sec; P < 0.001). The RC obtained using MRCP with combined PI and CS at two segments was slightly lower compared to that obtained using MRCP with PI (P = 0.007 and 0.002). Both reviewers found no significant differences in duct visualization, overall image quality, and degree of artifacts between the two methods (P ≥ 0.063; P = 0.637; and P = 0.752, respectively). Lesion conspicuity and confidence in duct abnormalities were comparable between two MRCP methods in both readers (P = 0.564 and P > 0.999). Combined PI and CS reconstruction is feasible for 3D navigator-triggered MRCP, providing image quality comparable to that of MRCP with PI alone, in about half the acquisition time. 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1289-1297.

Combining a reduced field of excitation with SENSE-based parallel imaging for maximum imaging efficiency.

PurposeTo show that a combination of parallel imaging using sensitivity encoding (SENSE) and inner volume imaging (IVI) combines the known benefits of both techniques. SENSE with a reduced field of excitation (rFOX) is termed rSENSE.Theory and MethodsThe noise level in SENSE reconstructions is reduced by removing voxels from the unfolding process that are rendered silent by using rFOX. The silent voxels need to be identified beforehand, this is done by using rFOX in the coil sensitivity maps. In vivo experiments were performed at 7 Tesla using a 32‐channel receive coil.ResultsGood image quality could be obtained in vivo with rSENSE at acceleration factors that are higher than could be obtained using SENSE or IVI alone. With rSENSE we were also able to accelerate scans using an rFOX that was purposely designed to be imperfect or incompatible at all with IVI.ConclusionrSENSE has been demonstrated in vivo with two‐dimensionally selective radiofrequency pulses. Besides allowing additional scan acceleration, it offers a greater robustness and flexibility than IVI. The proposed method can be used with other field strengths, anatomies and other rFOX techniques. Magn Reson Med 78:88–96, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution Non Commercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

LORAKS makes better SENSE: Phase-constrained partial fourier SENSE reconstruction without phase calibration.

Parallel imaging and partial Fourier acquisition are two classical approaches for accelerated MRI. Methods that combine these approaches often rely on prior knowledge of the image phase, but the need to obtain this prior information can place practical restrictions on the data acquisition strategy. In this work, we propose and evaluate SENSE-LORAKS, which enables combined parallel imaging and partial Fourier reconstruction without requiring prior phase information. The proposed formulation is based on combining the classical SENSE model for parallel imaging data with the more recent LORAKS framework for MR image reconstruction using low-rank matrix modeling. Previous LORAKS-based methods have successfully enabled calibrationless partial Fourier parallel MRI reconstruction, but have been most successful with nonuniform sampling strategies that may be hard to implement for certain applications. By combining LORAKS with SENSE, we enable highly accelerated partial Fourier MRI reconstruction for a broader range of sampling trajectories, including widely used calibrationless uniformly undersampled trajectories. Our empirical results with retrospectively undersampled datasets indicate that when SENSE-LORAKS reconstruction is combined with an appropriate k-space sampling trajectory, it can provide substantially better image quality at high-acceleration rates relative to existing state-of-the-art reconstruction approaches. The SENSE-LORAKS framework provides promising new opportunities for highly accelerated MRI. Magn Reson Med 77:1021-1035, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction

Dynamic arterial spin labeling (ASL) MRI measures the perfusion bolus at multiple observation times and yields accurate estimates of cerebral blood flow in the presence of variations in arterial transit time. ASL has intrinsically low signal-to-noise ratio (SNR) and is sensitive to motion, so that extensive signal averaging is typically required, leading to long scan times for dynamic ASL. The goal of this study was to develop an accelerated dynamic ASL method with improved SNR and robustness to motion using a model-based image reconstruction that exploits the inherent sparsity of dynamic ASL data. The first component of this method is a single-shot 3D turbo spin echo spiral pulse sequence accelerated using a combination of parallel imaging and compressed sensing. This pulse sequence was then incorporated into a dynamic pseudo continuous ASL acquisition acquired at multiple observation times, and the resulting images were jointly reconstructed enforcing a model of potential perfusion time courses. Performance of the technique was verified using a numerical phantom and it was validated on normal volunteers on a 3-Tesla scanner. In simulation, a spatial sparsity constraint improved SNR and reduced estimation errors. Combined with a model-based sparsity constraint, the proposed method further improved SNR, reduced estimation error and suppressed motion artifacts. Experimentally, the proposed method resulted in significant improvements, with scan times as short as 20s per time point. These results suggest that the model-based image reconstruction enables rapid dynamic ASL with improved accuracy and robustness.

A model-based reconstruction for undersampled radial spin-echo DTI with variational penalties on the diffusion tensor.

Radial spin-echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging, due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled diffusion-tensor imaging (DTI). A model-based reconstruction implicitly exploits redundancies in the diffusion-weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a total variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (three and two volunteers, respectively). Evaluation of the new approach was conducted by comparing the results with reconstructions performed with gridding, combined parallel imaging and compressed sensing and a recently proposed model-based approach. The experiments demonstrated improvements in terms of reduction of noise and streaking artifacts in the quantitative parameter maps, as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin-echo diffusion-tensor imaging without degrading parameter quantification and/or SNR.

Bilateral diffusion-weighted MR imaging of breast tumors with submillimeter resolution using readout-segmented echo-planar imaging at 7 T.

To evaluate the image quality, robustness, and diagnostic performance of submillimeter in-plane resolution diffusion-weighted ( DW diffusion-weighted ) magnetic resonance (MR) imaging at 7 T in the assessment of breast tumors. Institutional review board approval and written informed consent of five volunteers and 33 patients with 33 breast lesions (31 with histopathologic confirmation, two with confirmation at follow-up) were obtained. Image quality optimization and comparisons of readout-segmented echo-planar imaging ( rs-EPI readout-segmented echo-planar imaging ) and single-shot echo-planar imaging ( ss-EPI single-shot echo-planar imaging ) with or without parallel imaging were performed in volunteers. In all patients, bilateral DW diffusion-weighted imaging was performed in 3 minutes 35 seconds by using combined rs-EPI readout-segmented echo-planar imaging and parallel imaging with 0.9 × 0.9 mm in-plane resolution with a 7-T whole-body MR imager. Image quality, lesion conspicuity, and image properties (ie, signal-to-noise ratio, contrast-to-noise ratio) were assessed. Regions of interest were drawn in the largest lesion in each patient (23 malignant lesions, 10 benign lesions) by two independent readers. Apparent diffusion coefficient ( ADC apparent diffusion coefficient ) values were used to differentiate between benign and malignant breast tumors. DW diffusion-weighted imaging with combined parallel imaging and rs-EPI readout-segmented echo-planar imaging reduced artifacts (ie, blurring and geometric distortions) by a calculated factor of seven when compared with DW diffusion-weighted imaging with ss-EPI single-shot echo-planar imaging , and it improved image quality from a score of 1 of 10 to a score of 8 of 10. The rs-EPI readout-segmented echo-planar imaging sequence with a b value of 0 sec/mm(2) yielded high-spatial-resolution T2-weighted MR images. An ADC apparent diffusion coefficient threshold of 1.275 × 10(-3) mm(2)/sec enabled differentiation between benign and malignant breast lesions, with sensitivity and specificity of 96% and 100%, respectively, for both independent readers. At 7 T, one DW diffusion-weighted imaging examination of less than 4 minutes yielded high-quality ADC apparent diffusion coefficient maps and high-spatial-resolution T2-weighted MR images that were used to assess tumor and breast morphology. ADC apparent diffusion coefficient quantification alone enabled excellent differentiation of benign and malignant breast lesions.

Accelerating parameter mapping with a locally low rank constraint

To accelerate MR parameter mapping using a locally low rank (LLR) constraint, and the combination of parallel imaging and the LLR constraint. An LLR method is developed for MR parameter mapping and compared with a globally low rank method in a multiecho spin-echo T2 mapping experiment. For acquisition with coil arrays, a combined LLR and parallel imaging method is proposed. The proposed method is evaluated in a variable flip angle T1 mapping experiment and compared with the LLR method and parallel imaging alone. In the multiecho spin-echo T2 mapping experiment, the LLR method is more accurate than the globally low rank method for acceleration factors 2 and 3, especially for tissues with high T2 values. Variable flip angle T1 mapping is achieved by acquiring datasets with 10 flip angles, each dataset accelerated by a factor of 6, and reconstructed by the proposed method with a small normalized root mean square error of 0.025. The LLR method is likely superior to the globally low rank method for MR parameter mapping. The proposed combined LLR and parallel imaging method has better performance than the two methods alone, especially with highly accelerated acquisition.

Clinical performance of contrast enhanced abdominal pediatric MRI with fast combined parallel imaging compressed sensing reconstruction

To deploy clinically, a combined parallel imaging compressed sensing method with coil compression that achieves a rapid image reconstruction, and assess its clinical performance in contrast-enhanced abdominal pediatric MRI. With Institutional Review Board approval and informed patient consent/assent, 29 consecutive pediatric patients were recruited. Dynamic contrast-enhanced MRI was acquired on a 3 Tesla scanner using a dedicated 32-channel pediatric coil and a three-dimensional SPGR sequence, with pseudo-random undersampling at a high acceleration (R = 7.2). Undersampled data were reconstructed with three methods: a traditional parallel imaging method and a combined parallel imaging compressed sensing method with and without coil compression. The three sets of images were evaluated independently and blindly by two radiologists at one siting, for overall image quality and delineation of anatomical structures. Wilcoxon tests were performed to test the hypothesis that there was no significant difference in the evaluations, and interobserver agreement was analyzed. Fast reconstruction with coil compression did not deteriorate image quality. The mean score of structural delineation of the fast reconstruction was 4.1 on a 5-point scale, significantly better (P < 0.05) than traditional parallel imaging (mean score 3.1). Fair to substantial interobserver agreement was reached in structural delineation assessment. A fast combined parallel imaging compressed sensing method is feasible in a pediatric clinical setting. Preliminary results suggest it may improve structural delineation over parallel imaging.

Combined parallel and partial fourier MR reconstruction for accelerated 8-channel hyperpolarized carbon-13 in vivo magnetic resonance Spectroscopic imaging (MRSI).

To implement and evaluate combined parallel magnetic resonance imaging (MRI) and partial Fourier acquisition and reconstruction for rapid hyperpolarized carbon-13 ((13) C) spectroscopic imaging. Short acquisition times mitigate hyperpolarized signal losses that occur due to T1 decay, metabolism, and radiofrequency (RF) saturation. Human applications additionally require rapid imaging to permit breath-holding and to minimize the effects of physiologic motion. Numerical simulations were employed to validate and characterize the reconstruction. In vivo MR spectroscopic images were obtained from a rat following injection of hyperpolarized (13) C pyruvate using an 8-channel array of carbon-tuned receive elements. For small spectroscopic matrix sizes, combined parallel imaging and partial Fourier undersampling resulted primarily in decreased spatial resolution, with relatively less visible spatial aliasing. Parallel reconstruction qualitatively restored lost image detail, although some pixel spectra had persistent numerical error. With this technique, a 30 × 10 × 16 matrix of 4800 3D MR spectroscopy imaging voxels from a whole rat with isotropic 8 mm(3) resolution was acquired within 11 seconds. Parallel MRI and partial Fourier acquisitions can provide the shorter imaging times and wider spatial coverage that will be necessary as hyperpolarized (13) C techniques move toward human clinical applications.

High‐resolution human diffusion tensor imaging using 2‐D navigated multishot SENSE EPI at 7 T

The combination of parallel imaging with partial Fourier acquisition has greatly improved the performance of diffusion-weighted single-shot EPI and is the preferred method for acquisitions at low to medium magnetic field strength such as 1.5 or 3 T. Increased off-resonance effects and reduced transverse relaxation times at 7 T, however, generate more significant artifacts than at lower magnetic field strength and limit data acquisition. Additional acceleration of k-space traversal using a multishot approach, which acquires a subset of k-space data after each excitation, reduces these artifacts relative to conventional single-shot acquisitions. However, corrections for motion-induced phase errors are not straightforward in accelerated, diffusion-weighted multishot EPI because of phase aliasing. In this study, we introduce a simple acquisition and corresponding reconstruction method for diffusion-weighted multishot EPI with parallel imaging suitable for use at high field. The reconstruction uses a simple modification of the standard sensitivity-encoding (SENSE) algorithm to account for shot-to-shot phase errors; the method is called image reconstruction using image-space sampling function (IRIS). Using this approach, reconstruction from highly aliased in vivo image data using 2-D navigator phase information is demonstrated for human diffusion-weighted imaging studies at 7 T. The final reconstructed images show submillimeter in-plane resolution with no ghosts and much reduced blurring and off-resonance artifacts.

Chemical shift encoded water-fat separation using parallel imaging and compressed sensing.

Chemical shift encoded techniques have received considerable attention recently because they can reliably separate water and fat in the presence of off-resonance. The insensitivity to off-resonance requires that data be acquired at multiple echo times, which increases the scan time as compared to a single echo acquisition. The increased scan time often requires that a compromise be made between the spatial resolution, the volume coverage, and the tolerance to artifacts from subject motion. This work describes a combined parallel imaging and compressed sensing approach for accelerated water-fat separation. In addition, the use of multiscale cubic B-splines for B(0) field map estimation is introduced. The water and fat images and the B(0) field map are estimated via an alternating minimization. Coil sensitivity information is derived from a calculated k-space convolution kernel and l(1)-regularization is imposed on the coil-combined water and fat image estimates. Uniform water-fat separation is demonstrated from retrospectively undersampled data in the liver, brachial plexus, ankle, and knee as well as from a prospectively undersampled acquisition of the knee at 8.6x acceleration.