JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions.

PURPOSE: To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions. THEORY AND METHODS: A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method. RESULTS: With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2 w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system. CONCLUSION: A subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

Rapid whole-brain myelin imaging with selective inversion recovery and compressed SENSE.

PURPOSE: Quantitative magnetization transfer (QMT) using selective inversion recovery (SIR) can quantify the macromolecular-to-free proton pool size ratio (PSR), which has been shown to relate closely with myelin content. Currently clinical applications of SIR have been hampered by long scan times. In this work, the acceleration of SIR-QMT using CS-SENSE (compressed sensing SENSE) was systematically studied. THEORY AND METHODS: Phantoms of varied concentrations of bovine serum albumin and human scans were first conducted to evaluate the SNR, precision of SIR-QMT parameters, and scan time. Based on these results, an optimized CS-SENSE factor of 8 was determined and the test-retest repeatability was further investigated. RESULTS: A whole-brain SIR imaging of 6 min can be achieved. Bland-Altman analyses indicated excellent agreement between the test and retest sessions with a difference in mean PSR of 0.06% (and a difference in mean R1f of -0.001 s-1 ). In addition, the assessment of the intraclass correlation coefficient (ICC) revealed high reliability in nearly all the white matter and gray matter regions. In white matter regions, the ICC was 0.93 (95% confidence interval [CI]: 0.88-0.96, p < 0.001) for PSR, and 0.90 (95% CI: 0.83-0.94, p < 0.001) for R1f . In gray matter, ICC was 0.84 (95% CI: 0.66-0.93, p < 0.001) in PSR, and 0.98 (95% CI: 0.95-0.99, p < 0.001) for R1f . The method also showed excellent capability to detect focal lesions in multiple sclerosis. CONCLUSION: Rapid, reliable, and sensitive whole-brain SIR imaging can be achieved using CS-SENSE, which is expected to significantly promote widespread clinical translation.

DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning.

PURPOSE: To improve the estimation of coil sensitivity functions from limited auto-calibration signals (ACS) in SENSE-based reconstruction for brain imaging. METHODS: We propose to use deep learning to estimate coil sensitivity functions by leveraging information from previous scans obtained using the same RF receiver system. Specifically, deep convolutional neural networks were designed to learn an end-to-end mapping from the initial sensitivity to the high-resolution counterpart. Sensitivity alignment was further proposed to reduce the geometric variation caused by different subject positions and imaging FOVs. Cross-validation with a small set of datasets was performed to validate the learned neural network. Iterative SENSE reconstruction was adopted to evaluate the utility of the sensitivity functions from the proposed and conventional methods. RESULTS: The proposed method produced improved sensitivity estimates and SENSE reconstructions compared to the conventional methods in terms of aliasing and noise suppression with very limited ACS data. Cross-validation with a small set of data demonstrated the feasibility of learning coil sensitivity functions for brain imaging. The network learned on the spoiled GRE data can be applied to predict sensitivity functions for spin-echo and MPRAGE datasets. CONCLUSION: A deep learning-based method has been proposed for improving the estimation of coil sensitivity functions. Experimental results have demonstrated the feasibility and potential of the proposed method for improving SENSE-based reconstructions especially when the ACS data are limited.

A joint linear reconstruction for multishot diffusion weighted non-Carr-Purcell-Meiboom-Gill fast spin echo with full signal.

PURPOSE: Diffusion weighted Fast Spin Echo (DW-FSE) is a promising approach for distortionless DW imaging that is robust to system imperfections such as eddy currents and off-resonance. Due to non-Carr-Purcell-Meiboom-Gill (CPMG) magnetization, most DW-FSE sequences discard a large fraction of the signal ( 2 - 2 × $$ \sqrt{2}-2\times $$ ), reducing signal-to-noise ratio (SNR) efficiency compared to DW-EPI. The full FSE signal can be preserved by quadratically incrementing the transmit phase of the refocusing pulses, but this method of resolving non-CPMG magnetization has only been applied to single-shot DW-FSE due to challenges associated with image reconstruction. We present a joint linear reconstruction for multishot quadratic phase increment data that addresses these challenges and corrects ghosting from both shot-to-shot phase and intrashot signal oscillations. Multishot imaging reduces T2 blur and joint reconstruction of shots improves g-factor performance. A thorough analysis on the condition number of the proposed linear system is described. METHODS: A joint multishot reconstruction is derived from the non-CPMG signal model. Multishot quadratic phase increment DW-FSE was tested in a standardized diffusion phantom and compared to single-shot DW-FSE and DW-EPI in vivo in the brain, cervical spine, and prostate. The pseudo multiple replica technique was applied to generate g-factor and SNR maps. RESULTS: The proposed joint shot reconstruction eliminates ghosting from shot-to-shot phase and intrashot oscillations. g-factor performance is improved compared to previously proposed reconstructions, permitting efficient multishot imaging. apparent diffusion coefficient estimates in phantom experiments and in vivo are comparable to those obtained with conventional methods. CONCLUSION: Multi-shot non-CPMG DW-FSE data with full signal can be jointly reconstructed using a linear model.

Multi-shot Diffusion-Weighted MRI With Multiplexed Sensitivity Encoding (MUSE) in the Assessment of Active Inflammation in Crohn's Disease.

BACKGROUND: Single-shot diffusion-weighted imaging (ssDWI) has been shown useful for detecting active bowel inflammation in Crohn's disease (CD) without MRI contrast. However, ssDWI suffers from geometric distortion and low spatial resolution. PURPOSE: To compare conventional ssDWI with higher-resolution ssDWI (HR-ssDWI) and multi-shot DWI based on multiplexed sensitivity encoding (MUSE-DWI) for evaluating bowel inflammation in CD, using contrast-enhanced MR imaging (CE-MRI) as the reference standard. STUDY TYPE: Prospective. SUBJECTS: Eighty nine patients with histological diagnosis of CD from previous endoscopy (55 male/34 female, age: 17-69 years). FIELD STRENGTH/SEQUENCES: ssDWI (2.7 mm × 2.7 mm), HR-ssDWI (1.8 mm × 1.8 mm), MUSE-DWI (1.8 mm × 1.8 mm) based on echo-planar imaging, T2-weighted imaging, and CE-MRI sequences, all at 1.5 T. ASSESSMENT: Five raters independently evaluated the tissue texture conspicuity, geometry accuracy, minimization of artifacts, diagnostic confidence, and overall image quality using 5-point Likert scales. The diagnostic performance (sensitivity, specificity and accuracy) of each DWI sequences was assessed on per-bowel-segment basis. STATISTICAL TESTS: Inter-rater agreement for qualitative evaluation of each parameter was measured by the intra-class correlation coefficient (ICC). Paired Wilcoxon signed-rank tests were performed to evaluate the statistical significance of differences in qualitative scoring between DWI sequences. A P value <0.05 was considered to be statistically significant. RESULTS: Tissue texture conspicuity, geometric distortions, and overall image quality were significantly better for MUSE-DWI than for ssDWI and HR-ssDWI with good agreement among five raters (ICC: 0.70-0.89). HR-ssDWI showed significantly poorer performance to ssDWI and MUSE-DWI for all qualitative scores and had the worst diagnostic performance (sensitivity of 57.0% and accuracy of 87.3%, with 36 undiagnosable cases due to severe artifacts). MUSE-DWI showed significantly higher sensitivity (97.5% vs. 86.1%) and accuracy (98.9% vs. 95.1%) than ssDWI for detecting bowel inflammation. DATA CONCLUSION: MUSE-DWI was advantageous in assessing bowel inflammation in CD, resulting in improved spatial resolution and image quality. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Image quality and acquisition time assessments for phase oversampling in compressed sensing sensitivity encoding: Comparison with conventional SENSE.

This study compared sensitivity encoding (SENSE) and compressed sensing sensitivity encoding (CS-SENSE) for phase oversampling distance and assessed its impact on image quality and image acquisition time. The experiment was performed with a large diameter phantom using 16-channel anterior body coils. All imaging data were divided into three groups according to the parallel imaging technique and oversampling distances: groups A (SENSE with phase oversampling distance of 150 mm), B (CS-SENSE with phase oversampling distance of 100 mm), and C (CS-SENSE with phase oversampling distance of 75 mm). No statistically significant differences were observed among groups A, B, and C regarding both T2 and T1 turbo spin-echo (TSE) sequences using an acceleration factor (AF) of 2 (p = 0.301 and 0.289, respectively). In comparison with AF 2 of group A, the scan time of AF 2 of groups B and C was reduced by 11.2% and 23.5% (T2 TSE) and 15.8% and 22.7% (T1 TSE), respectively, while providing comparable image quality. Significant image noise and aliasing artifact were more evident at AF ≥ $ \ge $ 2 in group A compared with groups B and C. CS-SENSE with a less phase oversampling distance can reduce image acquisition time without image quality degradation compared with that of SENSE, despite the increase in aliasing artifact as the AF increased in both CS-SENSE and SENSE.

Estimation and removal of spurious echo artifacts in single-voxel MRS using sensitivity encoding.

PURPOSE: In localized MRS, spurious echo artifacts commonly occur when unsuppressed signal outside the volume of interest is excited and refocused. In the spectral domain, these signals often overlap with metabolite resonances and hinder accurate quantification. Because the artifacts originate from regions separate from the target MRS voxel, this work proposes that sensitivity encoding based on receive-coil sensitivity profiles may be used to separate these signal contributions. METHODS: Numerical simulations were performed to explore the effect of sensitivity-encoded separation for unknown artifact regions. An imaging-based approach was developed to identify regions that may contribute to spurious echo artifacts, and tested for sensitivity-based unfolding of signal on six data sets from three brain regions. Spectral data reconstructed using the proposed method ("ERASE") were compared with the standard coil combination. RESULTS: The method was able to fully unfold artifact signals if regions were known a priori. Mismatch between estimated and true artifact regions reduced the efficiency of removal, yet metabolite signals were unaffected. Water suppression imaging was able to identify regions of unsuppressed signal, and ERASE (from up to eight regions) led to visible removal of artifacts relative to standard reconstruction. Fitting errors across major metabolites were also lower; for example, Cramér-Rao lower bounds of myo-inositol were 13.7% versus 17.5% for ERASE versus standard reconstruction, respectively. CONCLUSION: The ERASE reconstruction tool was demonstrated to reduce spurious echo artifacts in single-voxel MRS. This tool may be incorporated into standard workflows to improve spectral quality when hardware limitations or other factors result in out-of-voxel signal contamination.

Elimination of residual aliasing artifact that resembles brain lesion on multi-oblique diffusion-weighted echo-planar imaging with parallel imaging using virtual coil acquisition.

BACKGROUND: Single-shot diffusion-weighted echo-planar imaging (ssDW-EPI) acquired with parallel imaging and a multi-oblique scan plane may suffer from residual aliasing artifacts, resembling lesions on the calculated apparent diffusion coefficient (ADC) map. PURPOSE: To combine ssDW-EPI and virtual coil acquisition and develop a self-reference reconstruction method to eliminate the residual aliasing artifact on multi-oblique ssDW-EPI sequence with parallel imaging and multiple signal averaging. STUDY TYPE: Prospective. SUBJECTS: Three healthy subjects and 50 stroke patients. FIELD STRENGTH/SEQUENCE: Conventional ssDW-EPI with parallel imaging, and proposed ssDW-EPI with virtual coil acquisition at 1.5T. ASSESSMENT: The efficacy of the proposed method was evaluated in 50 stroke patients by comparing the ssDW-EPI with conventional parallel imaging reconstructions. The extent of residual aliasing artifacts were rated on a 5-point Likert scale by three independent raters. Only the data without residual aliasing artifacts on conventional ssDW-EPI were included for the assessment of signal-to-noise ratio (SNR), ghost-to-signal ratio (GSR), and ADC. STATISTICAL TESTS: The interobserver agreements for examining residual aliasing artifacts were measured by the intraclass correlation coefficient (ICC). A two-sample t-test was performed for comparing SNR, GSR, and ADC. RESULTS: There was a perfect agreement (ICC = 1.00) in the examination of residual aliasing artifacts on images obtained using the proposed method. The incidence rates of the residual aliasing artifact on the ADC maps obtained from the scanner console and proposed method were 60% (ie, 30 out of 50) and 0%, respectively. The proposed method offers significantly lower GSR than conventional parallel imaging reconstruction (P < 0.001). There was no significant difference in SNR (P = 0.20-0.51) and ADC values (P = 0.20-0.94) between conventional parallel imaging reconstructions and the proposed method. DATA CONCLUSION: It appears that our method could effectively eliminate artifacts and significantly improve the GSR of b = 0 T2 WI and b > 0 DWI, as well as permit ADC measurement consistent with conventional techniques. Our method may be beneficial to clinical assessment of the brain that utilizes multi-oblique ssDW-EPI. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1442-1453.

Fast 3D chemical exchange saturation transfer imaging with variably-accelerated sensitivity encoding (vSENSE).

PURPOSE: To extend the variably-accelerated sensitivity encoding (vSENSE) method from 2D to 3D for fast chemical exchange saturation transfer (CEST) imaging, and prospectively implement it for clinical MRI. METHODS: The CEST scans were acquired from 7 normal volunteers and 15 brain tumor patients using a 3T clinical scanner. The 2D and 3D "artifact suppression" (AS) vSENSE algorithms were applied to generate sensitivity maps from a first scan acquired with conventional SENSE-accelerated 2D and 3D CEST data. The AS sensitivity maps were then applied to reconstruct the other CEST frames at higher acceleration factors. Both retrospective and prospective acceleration in phase-encoding and slice-encoding dimensions were implemented. RESULTS: Applying the 2D AS vSENSE algorithm to a 2-fold undersampled 3.5-ppm CEST frame halved the scan time of conventional SENSE, while generating essentially identical reconstruction errors (p ≈ 1.0). The 3D AS vSENSE algorithm permitted prospective acceleration by up to 8-fold, in total, from phase-encoding and slice-encoding directions for individual source CEST images, and an overall speed-up in scan time of 5-fold. The resulting vSENSE-accelerated amide proton transfer-weighted images agreed with conventional 2-fold-accelerated SENSE CEST results in brain tumor patients and healthy volunteers. Importantly, the vSENSE method eliminated unfolding artifacts in the slice-encoding direction that compromised conventional SENSE CEST scans. CONCLUSION: The vSENSE method can be extended to 3D CEST imaging to provide higher acceleration factors than conventional SENSE without compromising accuracy.

Fast data acquisition techniques in magnetic resonance spectroscopic imaging.

Magnetic resonance spectroscopic imaging (MRSI) is an important technique for assessing the spatial variation of metabolites in vivo. The long scan times in MRSI limit clinical applicability due to patient discomfort, increased costs, motion artifacts, and limited protocol flexibility. Faster acquisition strategies can address these limitations and could potentially facilitate increased adoption of MRSI into routine clinical protocols with minimal addition to the current anatomical and functional acquisition protocols in terms of imaging time. Not surprisingly, a lot of effort has been devoted to the development of faster MRSI techniques that aim to capture the same underlying metabolic information (relative metabolite peak areas and spatial distribution) as obtained by conventional MRSI, in greatly reduced time. The gain in imaging time results, in some cases, in a loss of signal-to-noise ratio and/or in spatial and spectral blurring. This review examines the current techniques and advances in fast MRSI in two and three spatial dimensions and their applications. This review categorizes the acceleration techniques according to their strategy for acquisition of the k-space. Techniques such as fast/turbo-spin echo MRSI, echo-planar spectroscopic imaging, and non-Cartesian MRSI effectively cover the full k-space in a more efficient manner per TR . On the other hand, techniques such as parallel imaging and compressed sensing acquire fewer k-space points and employ advanced reconstruction algorithms to recreate the spatial-spectral information, which maintains statistical fidelity in test conditions (ie no statistically significant differences on voxel-wise comparisions) with the fully sampled data. The advantages and limitations of each state-of-the-art technique are reviewed in detail, concluding with a note on future directions and challenges in the field of fast spectroscopic imaging.

Phase correction for three-dimensional (3D) diffusion-weighted interleaved EPI using 3D multiplexed sensitivity encoding and reconstruction (3D-MUSER).

PURPOSE: Three-dimensional (3D) multiplexed sensitivity encoding and reconstruction (3D-MUSER) algorithm is proposed to reduce aliasing artifacts and signal corruption caused by inter-shot 3D phase variations in 3D diffusion-weighted echo planar imaging (DW-EPI). THEORY AND METHODS: 3D-MUSER extends the original framework of multiplexed sensitivity encoding (MUSE) to a hybrid k-space-based reconstruction, thereby enabling the correction of inter-shot 3D phase variations. A 3D single-shot EPI navigator echo was used to measure inter-shot 3D phase variations. The performance of 3D-MUSER was evaluated by analyses of point-spread function (PSF), signal-to-noise ratio (SNR), and artifact levels. The efficacy of phase correction using 3D-MUSER for different slab thicknesses and b-values were investigated. RESULTS: Simulations showed that 3D-MUSER could eliminate artifacts because of through-slab phase variation and reduce noise amplification because of SENSE reconstruction. All aliasing artifacts and signal corruption in 3D interleaved DW-EPI acquired with different slab thicknesses and b-values were reduced by our new algorithm. A near-whole brain single-slab 3D DTI with 1.3-mm isotropic voxel acquired at 1.5T was successfully demonstrated. CONCLUSION: 3D phase correction for 3D interleaved DW-EPI data is made possible by 3D-MUSER, thereby improving feasible slab thickness and maximum feasible b-value. Magn Reson Med 79:2702-2712, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Chemical exchange saturation transfer (CEST) imaging with fast variably-accelerated sensitivity encoding (vSENSE).

PURPOSE: The widespread clinical use of chemical exchange saturation transfer (CEST) imaging is hampered by relatively long scan times due to its requirement that multiple saturation-offset image frames be acquired. Here, a novel variably-accelerated sensitivity encoding (vSENSE) method is proposed that provides faster CEST acquisition than conventional SENSE. THEORY AND METHODS: The vSENSE method fully samples one CEST saturation frame, then undersamples the other frames variably. The fully-sampled frame, in conjunction with newly proposed incoherence absorption and artifact suppression strategies, improves the accuracy of sensitivity maps and permits higher acceleration factors for the other undersampled frames than regular SENSE. vSENSE is validated in a phantom, a normal volunteer and eight brain tumor patients at 3 Tesla. RESULTS: vSENSE with an acceleration factor of four generated a 3-6 times smaller error on average than conventional SENSE (P ≤ 0.02), with acceleration factors of 2-4, as compared with full k-space reconstruction. vSENSE permitted four-fold acceleration for amide proton transfer-weighted images, while regular SENSE could only provide a factor of two. When conventional SENSE is used with vSENSE's variable undersampling pattern, erroneous (∼9%) z-spectra result. CONCLUSION: The vSENSE method enabled twice the acceleration and generated more accurate images than conventional SENSE. Magn Reson Med 77:2225-2238, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Self-gated MRI of multiple beat morphologies in the presence of arrhythmias.

PURPOSE: Develop self-gated MRI for distinct heartbeat morphologies in subjects with arrhythmias. METHODS: Golden angle radial data was obtained in seven sinus and eight arrhythmias subjects. An image-based cardiac navigator was derived from single-shot images, distinct beat types were identified, and images were reconstructed for repeated morphologies. Image sharpness, contrast, and volume variation were quantified and compared with self-gated MRI. Images were scored for image quality and artifacts. Hemodynamic parameters were computed for each distinct beat morphology in bigeminy and trigeminy subjects and for sinus beats in patients with infrequent premature ventricular contractions. RESULTS: Images of distinct beat types were reconstructed except for two patients with infrequent premature ventricular contractions. Image contrast and sharpness were similar to sinus self-gated images (contrast = 0.45 ± 0.13 and 0.43 ± 0.15; sharpness = 0.21 ± 0.11 and 0.20 ± 0.05). Visual scoring was highest in self-gated images (4.1 ± 0.3) compared with real-time (3.9 ± 0.4) and ECG-gated cine (3.4 ± 1.5). ECG-gated cine had less artifacts than self-gating (2.3 ± 0.7 and 2.1 ± 0.2), but was affected by misgating in two subjects. Among arrhythmia subjects, post-extrasystole/sinus (58.1 ± 8.6 mL) and interrupted sinus (61.4 ± 5.9 mL) stroke volume was higher than extrasystole (32.0 ± 16.5 mL; P < 0.02). CONCLUSION: Self-gated imaging can reconstruct images during ectopy and allowed for quantification of hemodynamic function of different beat morphologies. Magn Reson Med 78:678-688, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Motion immune diffusion imaging using augmented MUSE for high-resolution multi-shot EPI.

PURPOSE: To develop new techniques for reducing the effects of microscopic and macroscopic patient motion in diffusion imaging acquired with high-resolution multishot echo-planar imaging. THEORY: The previously reported multiplexed sensitivity encoding (MUSE) algorithm is extended to account for macroscopic pixel misregistrations, as well as motion-induced phase errors in a technique called augmented MUSE (AMUSE). Furthermore, to obtain more accurate quantitative diffusion-tensor imaging measures in the presence of subject motion, we also account for the altered diffusion encoding among shots arising from macroscopic motion. METHODS: MUSE and AMUSE were evaluated on simulated and in vivo motion-corrupted multishot diffusion data. Evaluations were made both on the resulting imaging quality and estimated diffusion tensor metrics. RESULTS: AMUSE was found to reduce image blurring resulting from macroscopic subject motion compared to MUSE but yielded inaccurate tensor estimations when neglecting the altered diffusion encoding. Including the altered diffusion encoding in AMUSE produced better estimations of diffusion tensors. CONCLUSION: The use of AMUSE allows for improved image quality and diffusion tensor accuracy in the presence of macroscopic subject motion during multishot diffusion imaging. These techniques should facilitate future high-resolution diffusion imaging.

Self-feeding MUSE: a robust method for high resolution diffusion imaging using interleaved EPI.

Single-shot echo planar imaging (EPI) with parallel imaging techniques has been well established as the most popular method for clinical diffusion imaging, due to its fast acquisition and motion insensitivity. However, this approach is limited by the relatively low spatial resolution and image distortion. Interleaved EPI is able to break the limitations but the phase variations among different shots must be considered for artifact suppression. The introduction of multiplexed sensitivity-encoding (MUSE) can address the phase issue using sensitivity encoding (SENSE) for self-navigation of each interleave. However, MUSE has suboptimal results when the number of shots is high. To achieve higher spatial resolution and lower geometric distortion, we introduce two new schemes into the MUSE framework: 1) a self-feeding mechanism is adopted by using prior information regularized SENSE in order to obtain reliable phase estimation; and 2) retrospective motion detection and data rejection strategies are performed to exclude unusable data corrupted by severe pulsatile motions. The proposed method is named self-feeding MUSE (SF-MUSE). Experiments on healthy volunteers demonstrate that this new SF-MUSE approach provides more accurate motion-induced phase estimation and fewer artifacts caused by data corruption when compared with the original MUSE method. SF-MUSE is a robust method for high resolution diffusion imaging and suitable for practical applications with reasonable scan time.

Accelerated whole-heart coronary MRA using motion-corrected sensitivity encoding with three-dimensional projection reconstruction.

PURPOSE: To achieve whole-heart coronary magnetic resonance angiography (MRA) with (1.0 mm)(3) spatial resolution and 5 min of free-breathing scan time. METHODS: We used an electrocardiograph-gated, T2-prepared and fat-saturated balanced steady state free precession sequence with 3DPR trajectory for free-breathing data acquisition with 100% gating efficiency. For image reconstruction, we used a self-calibrating iterative SENSE scheme with integrated retrospective motion correction. We performed healthy volunteer study to compare the proposed method with motion-corrected gridding at different retrospective undersampling levels on apparent signal-to-noise ratio (aSNR) and subjective coronary artery (CA) visualization scores. RESULTS: Compared with gridding, the proposed method significantly improved both image quality metrics for undersampled datasets with 6000, 8000, and 10,000 projections. With as few as 10,000 projections, the proposed method yielded good CA visualization scores (3.02 of 4) and aSNR values comparable to those with 20,000 projections. CONCLUSION: Using the proposed method, good image quality was observed for free breathing whole-heart coronary MRA at (1.0 mm)(3) resolution with an achievable scan time of 5 min.

Reduction of voxel bleeding in highly accelerated parallel (1) H MRSI by direct control of the spatial response function.

PURPOSE: To substantially improve spatial localization in magnetic resonance spectroscopic imaging (MRSI) accelerated by parallel imaging. This is important in order to make MRSI more reliable as a tool for clinical applications. METHODS: The sensitivity encoding acceleration technique with spatial overdiscretization is applied for the reconstruction of parallel MRSI. In addition, the spatial response function is optimized by minimizing its deviation from a previously chosen target function. This modified minimum-norm sensitivity encoding-MRSI reconstruction approach is applied in this article for in vivo pulse-acquire MRSI of human brain at 7T with simulated acceleration factors of 2, 4, and 9 as well as actual 4-fold accelerated MRSI. RESULTS: The sidelobes of the spatial response function are significantly suppressed, which reduces far-reaching voxel bleeding. At the same time, the major enlargement of the effective voxel size, which would be introduced by conventional k-space apodization methods, is largely avoided. Regularization allows for a practical trade-off between noise minimization, effective voxel size, and unaliasing. Although not aiming at increasing the nominal spatial resolution, a better spatial specificity is achieved. CONCLUSION: Simultaneous suppression of short- and far-reaching voxel bleeding in MRSI is analyzed and reconstruction of highly accelerated parallel in vivo MRSI is demonstrated.

Interleaved diffusion-weighted improved by adaptive partial-Fourier and multiband multiplexed sensitivity-encoding reconstruction.

PURPOSE: We report a series of techniques to reliably eliminate artifacts in interleaved echo-planar imaging (EPI) based diffusion-weighted imaging (DWI). METHODS: First, we integrate the previously reported multiplexed sensitivity encoding (MUSE) algorithm with a new adaptive Homodyne partial-Fourier reconstruction algorithm, so that images reconstructed from interleaved partial-Fourier DWI data are free from artifacts even in the presence of either (a) motion-induced k-space energy peak displacement, or (b) susceptibility field gradient induced fast phase changes. Second, we generalize the previously reported single-band MUSE framework to multiband MUSE, so that both through-plane and in-plane aliasing artifacts in multiband multishot interleaved DWI data can be effectively eliminated. RESULTS: The new adaptive Homodyne-MUSE reconstruction algorithm reliably produces high-quality and high-resolution DWI, eliminating residual artifacts in images reconstructed with previously reported methods. Furthermore, the generalized MUSE algorithm is compatible with multiband and high-throughput DWI. CONCLUSION: The integration of the multiband and adaptive Homodyne-MUSE algorithms significantly improves the spatial-resolution, image quality, and scan throughput of interleaved DWI. We expect that the reported reconstruction framework will play an important role in enabling high-resolution DWI for both neuroscience research and clinical uses.

POCS-based reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE): A general algorithm for reducing motion-related artifacts.

PURPOSE: A projection onto convex sets reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE) is developed to reduce motion-related artifacts, including respiration artifacts in abdominal imaging and aliasing artifacts in interleaved diffusion-weighted imaging. THEORY: Images with reduced artifacts are reconstructed with an iterative projection onto convex sets (POCS) procedure that uses the coil sensitivity profile as a constraint. This method can be applied to data obtained with different pulse sequences and k-space trajectories. In addition, various constraints can be incorporated to stabilize the reconstruction of ill-conditioned matrices. METHODS: The POCSMUSE technique was applied to abdominal fast spin-echo imaging data, and its effectiveness in respiratory-triggered scans was evaluated. The POCSMUSE method was also applied to reduce aliasing artifacts due to shot-to-shot phase variations in interleaved diffusion-weighted imaging data corresponding to different k-space trajectories and matrix condition numbers. RESULTS: Experimental results show that the POCSMUSE technique can effectively reduce motion-related artifacts in data obtained with different pulse sequences, k-space trajectories and contrasts. CONCLUSION: POCSMUSE is a general post-processing algorithm for reduction of motion-related artifacts. It is compatible with different pulse sequences, and can also be used to further reduce residual artifacts in data produced by existing motion artifact reduction methods.

Prospective motion correction of segmented diffusion weighted EPI.

PURPOSE: Recently, a new algorithm was introduced to combine segments of under-sampled diffusion weighted data using multiplexed sensitivity encoding. While the algorithm provides good results in cooperative volunteers, motion during the data acquisition is not accounted for. In this work, the continuous prospective motion correction of a segmented diffusion weighted acquisition is combined with multiplexed sensitivity encoding. METHODS: Simulations investigate the influence of motion on the reconstruction. Additionally, the change in coil sensitivities due to patient motion is taken into consideration. Finally, in vivo experiments display the effects of motion and its prospective correction on high resolution diffusion weighted imaging. RESULTS: Inconsistencies of the imaging plane lead to artifacts and blurring in the reconstructed dataset. Additionally, motion during the diffusion weighting period can lead to substantial image artifacts and signal dropouts. The change in coil sensitivities shows minor effect for the simulated range of motion (5°). Prospective motion correction is shown to improve image quality in the case of large motion (5°) and to reliably correct for small motion (1°). CONCLUSION: The combination of prospective motion correction and multiplexed sensitivity encoding allows for high resolution diffusion weighted imaging even in the presence of substantial head motion.