Search

Purpose: To present and assess an outlier mitigation method that makes free‐running volumetric cardiovascular MRI (CMR) more robust to motion. Methods: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics‐guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three‐dimensional (3D) cine, 12 rest four‐dimensional (4D) flow, and eight stress 4D flow imaging datasets. Results: Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. Conclusion: An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free‐running CMR applications. [ABSTRACT FROM AUTHOR]

Purpose: To improve image quality, mitigate quantification biases and variations for free‐breathing liver proton density fat fraction (PDFF) and R2*$$ {\mathrm{R}}_2^{\ast } $$ quantification accelerated by radial k‐space undersampling. Methods: A free‐breathing multi‐echo stack‐of‐radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath‐hold Cartesian acquisitions. Images, PDFF, and R2*$$ {\mathrm{R}}_2^{\ast } $$ maps were reconstructed using different radial view k‐space sampling factors and reconstruction settings. Results were compared with reference‐standard results using Bland–Altman analysis. Using linear mixed‐effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF and R2*$$ {\mathrm{R}}_2^{\ast } $$, respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality. Results: Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [−0.5; −33.6, 32.7] s−1 for R2*$$ {\mathrm{R}}_2^{\ast } $$ and [−1.0%; −5.8%, 3.8%] for PDFF, close to those of a previous self‐gating method using fully sampled radial views: [−0.1; −27.1, 27.0] s−1 for R2*$$ {\mathrm{R}}_2^{\ast } $$ and [−0.4%; −4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0. Conclusion: This proposed method may allow accelerated free‐breathing liver PDFF and R2*$$ {\mathrm{R}}_2^{\ast } $$ mapping with reduced biases and variations. [ABSTRACT FROM AUTHOR]

Purpose: To develop a highly accelerated multi‐echo spin‐echo method, TEMPURA, for reducing the acquisition time and/or increasing spatial resolution for kidney T2 mapping. Methods: TEMPURA merges several adjacent echoes into one k‐space by either combining independent echoes or sharing one echo between k‐spaces. The combined k‐space is reconstructed based on compressed sensing theory. Reduced flip angles are used for the refocusing pulses, and the extended phase graph algorithm is used to correct the effects of indirect echoes. Two sequences were developed: a fast breath‐hold sequence; and a high‐resolution sequence. The performance was evaluated prospectively on a phantom, 16 healthy subjects, and two patients with different types of renal tumors. Results: The fast TEMPURA method reduced the acquisition time from 3–5 min to one breath‐hold (18 s). Phantom measurements showed that fast TEMPURA had a mean absolute percentage error (MAPE) of 8.2%, which was comparable to a standardized respiratory‐triggered sequence (7.4%), but much lower than a sequence accelerated by purely k‐t undersampling (21.8%). High‐resolution TEMPURA reduced the in‐plane voxel size from 3 × 3 to 1 × 1 mm2, resulting in improved visualization of the detailed anatomical structure. In vivo T2 measurements demonstrated good agreement (fast: MAPE = 1.3%–2.5%; high‐resolution: MAPE = 2.8%–3.3%) and high correlation coefficients (fast: R = 0.85–0.98; high‐resolution: 0.82–0.96) with the standardized method, outperforming k‐t undersampling alone (MAPE = 3.3–4.5%, R = 0.57–0.59). Conclusion: TEMPURA provides fast and high‐resolution renal T2 measurements. It has the potential to improve clinical throughput and delineate intratumoral heterogeneity and tissue habitats at unprecedented spatial resolution. [ABSTRACT FROM AUTHOR]

Purpose: We present SCAMPI (Sparsity Constrained Application of deep Magnetic resonance Priors for Image reconstruction), an untrained deep Neural Network for MRI reconstruction without previous training on datasets. It expands the Deep Image Prior approach with a multidomain, sparsity‐enforcing loss function to achieve higher image quality at a faster convergence speed than previously reported methods. Methods: Two‐dimensional MRI data from the FastMRI dataset with Cartesian undersampling in phase‐encoding direction were reconstructed for different acceleration rates for single coil and multicoil data. Results: The performance of our architecture was compared to state‐of‐the‐art Compressed Sensing methods and ConvDecoder, another untrained Neural Network for two‐dimensional MRI reconstruction. SCAMPI outperforms these by better reducing undersampling artifacts and yielding lower error metrics in multicoil imaging. In comparison to ConvDecoder, the U‐Net architecture combined with an elaborated loss‐function allows for much faster convergence at higher image quality. SCAMPI can reconstruct multicoil data without explicit knowledge of coil sensitivity profiles. Moreover, it is a novel tool for reconstructing undersampled single coil k‐space data. Conclusion: Our approach avoids overfitting to dataset features, that can occur in Neural Networks trained on databases, because the network parameters are tuned only on the reconstruction data. It allows better results and faster reconstruction than the baseline untrained Neural Network approach. [ABSTRACT FROM AUTHOR]

Purpose: To assess the potential for accelerating continuous‐wave (CW) T1ρ dispersion measurement with compressed sensing approach via studying the effect that the data reduction has on the ability to detect differences between intact and degenerated articular cartilage with different spin‐lock amplitudes and to assess quantitative bias due to acceleration. Methods: Osteochondral plugs (n = 27, 4 mm diameter) from femur (n = 14) and tibia (n = 13) regions from human cadaver knee joints were obtained from commercial biobank (Science Care, USA) under Ethical permission 134/2015. MRI of specimens was performed at 9.4T with magnetization prepared radial balanced SSFP (bSSFP) readout sequence, and the CWT1ρ relaxation time maps were computed from the measured data. The relaxation time maps were evaluated in the cartilage zones for different acceleration factors. For reference, Osteoarthritis Research Society International (OARSI) grading and biomechanical measurements were performed and correlated with the MRI findings. Results: Four‐fold acceleration of CWT1ρ dispersion measurement by compressed sensing approach was feasible without meaningful loss in the sensitivity to osteoarthritic (OA) changes within the articular cartilage. Differences were significant between intact and OA groups in the superficial and transitional zones, and CWT1ρ correlated moderately with the reference measurements (0.3 < r < 0.7). Conclusion: CWT1ρ was able to differentiate between intact and OA cartilage even with four‐fold acceleration. This indicates that acceleration of CWT1ρ dispersion measurement by compressed sensing approach is feasible with negligible loss in the sensitivity to osteoarthritic changes in articular cartilage. [ABSTRACT FROM AUTHOR]

Purpose: Hyperpolarized 129Xe MRI benefits from non‐Cartesian acquisitions that sample k‐space efficiently and rapidly. However, their reconstructions are complex and burdened by decay processes unique to hyperpolarized gas. Currently used gridded reconstructions are prone to artifacts caused by magnetization decay and are ill‐suited for undersampling. We present a compressed sensing (CS) reconstruction approach that incorporates magnetization decay in the forward model, thereby producing images with increased sharpness and contrast, even in undersampled data. Methods: Radio‐frequency, T1, and T2*$$ {\mathrm{T}}_2^{\ast } $$ decay processes were incorporated into the forward model and solved using iterative methods including CS. The decay‐modeled reconstruction was validated in simulations and then tested in 2D/3D‐spiral ventilation and 3D‐radial gas‐exchange MRI. Quantitative metrics including apparent‐SNR and sharpness were compared between gridded, CS, and twofold undersampled CS reconstructions. Observations were validated in gas‐exchange data collected from 15 healthy and 25 post‐hematopoietic‐stem‐cell‐transplant participants. Results: CS reconstructions in simulations yielded images with threefold increases in accuracy. CS increased sharpness and contrast for ventilation in vivo imaging and showed greater accuracy for undersampled acquisitions. CS improved gas‐exchange imaging, particularly in the dissolved‐phase where apparent‐SNR improved, and structure was made discernable. Finally, CS showed repeatability in important global gas‐exchange metrics including median dissolved‐gas signal ratio and median angle between real/imaginary components. Conclusion: A non‐Cartesian CS reconstruction approach that incorporates hyperpolarized 129Xe decay processes is presented. This approach enables improved image sharpness, contrast, and overall image quality in addition to up‐to threefold undersampling. This contribution benefits all hyperpolarized gas MRI through improved accuracy and decreased scan durations. [ABSTRACT FROM AUTHOR]

Purpose: To accelerate whole‐brain quantitative T2$$ {\mathrm{T}}_2 $$ mapping in preclinical imaging setting. Methods: A three‐dimensional (3D) multi‐echo spin echo sequence was highly undersampled with a variable density Poisson distribution to reduce the acquisition time. Advanced iterative reconstruction based on linear subspace constraints was employed to recover high‐quality raw images. Different subspaces, generated using exponential or extended‐phase graph (EPG) simulations or from low‐resolution calibration images, were compared. The subspace dimension was investigated in terms of T2$$ {\mathrm{T}}_2 $$ precision. The method was validated on a phantom containing a wide range of T2$$ {\mathrm{T}}_2 $$ and was then applied to monitor metastasis growth in the mouse brain at 4.7T. Image quality and T2$$ {\mathrm{T}}_2 $$ estimation were assessed for 3 acceleration factors (6/8/10). Results: The EPG‐based dictionary gave robust estimations of a large range of T2$$ {\mathrm{T}}_2 $$. A subspace dimension of 6 was the best compromise between T2$$ {\mathrm{T}}_2 $$ precision and image quality. Combining the subspace constrained reconstruction with a highly undersampled dataset enabled the acquisition of whole‐brain T2$$ {\mathrm{T}}_2 $$ maps, the detection and the monitoring of metastasis growth of less than 500 μm3$$ \mu {\mathrm{m}}^3 $$. Conclusion: Subspace‐based reconstruction is suitable for 3D T2$$ {\mathrm{T}}_2 $$ mapping. This method can be used to reach an acceleration factor up to 8, corresponding to an acquisition time of 25 min for an isotropic 3D acquisition of 156 μ$$ \mu $$m on the mouse brain, used here for monitoring metastases growth. [ABSTRACT FROM AUTHOR]

Purpose: To develop a new sequence to simultaneously acquire Cartesian sodium (23Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low‐rank reconstruction frameworks. Theory and Methods: The Double Half‐Echo technique enables short echo time Cartesian 23Na MRI and acquires two k‐space halves, reconstructed by a low‐rank coupling constraint. Additionally, three‐dimensional (3D) 23Na Multi‐Quantum Coherences (MQC) MRI requires multi‐echo sampling paired with phase‐cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k‐Space Estimation (SAKE) were used to reconstruct highly undersampled 23Na MQC MRI. Reconstruction performance was assessed against five‐dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal‐to‐noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo. Results: The proposed sequence enabled the simultaneous acquisition of fully sampled 23Na MRI while leveraging prospective undersampling for 23Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23Na MQC MRI was enhanced from 8×8×15$$ 8\times 8\times 15 $$ mm3 to 8×8×8$$ 8\times 8\times 8 $$ mm3 while reducing acquisition time from 2×31$$ 2\times 31 $$ min to 2×23$$ 2\times 23 $$ min. Conclusion: The proposed sequence, coupled with low‐rank reconstructions, provides an efficient framework for comprehensive whole‐brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low‐rank matrix completion enables the reconstruction of highly undersampled 23Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether parallel imaging‐imposed geometric coil constraints can be relaxed when using a deep learning (DL)‐based image reconstruction method as opposed to a traditional non‐DL method. Theory and Methods: Traditional and DL‐based MR image reconstruction approaches operate in fundamentally different ways: Traditional methods solve a system of equations derived from the image data whereas DL methods use data/target pairs to learn a generalizable reconstruction model. Two sets of head coil profiles were evaluated: (1) 8‐channel and (2) 32‐channel geometries. A DL model was compared to conjugate gradient SENSE (CG‐SENSE) and L1‐wavelet compressed sensing (CS) through quantitative metrics and visual assessment as coil overlap was increased. Results: Results were generally consistent between experiments. As coil overlap increased, there was a significant (p < 0.001) decrease in performance in most cases for all methods. The decrease was most pronounced for CG‐SENSE, and the DL models significantly outperformed (p < 0.001) their non‐DL counterparts in all scenarios. CS showed improved robustness to coil overlap and signal‐to‐noise ratio (SNR) versus CG‐SENSE, but had quantitatively and visually poorer reconstructions characterized by blurriness as compared to DL. DL showed virtually no change in performance across SNR and very small changes across coil overlap. Conclusion: The DL image reconstruction method produced images that were robust to coil overlap and of higher quality than CG‐SENSE and CS. This suggests that geometric coil design constraints can be relaxed when using DL reconstruction methods. [ABSTRACT FROM AUTHOR]

Purpose: To develop a highly‐accelerated, real‐time phase contrast (rtPC) MRI pulse sequence with 40 fps frame rate (25 ms effective temporal resolution). Methods: Highly‐accelerated golden‐angle radial sparse parallel (GRASP) with over regularization may result in temporal blurring, which in turn causes underestimation of peak velocity. Thus, we amplified GRASP performance by synergistically combining view‐sharing (VS) and k‐space weighted image contrast (KWIC) filtering. In 17 pediatric patients with congenital heart disease (CHD), the conventional GRASP and the proposed GRASP amplified by VS and KWIC (or GRASP + VS + KWIC) reconstruction for rtPC MRI were compared with respect to clinical standard PC MRI in measuring hemodynamic parameters (peak velocity, forward volume, backward volume, regurgitant fraction) at four locations (aortic valve, pulmonary valve, left and right pulmonary arteries). Results: The proposed reconstruction method (GRASP + VS + KWIC) achieved better effective spatial resolution (i.e., image sharpness) compared with conventional GRASP, ultimately reducing the underestimation of peak velocity from 17.4% to 6.4%. The hemodynamic metrics (peak velocity, volumes) were not significantly (p > 0.99) different between GRASP + VS + KWIC and clinical PC, whereas peak velocity was significantly (p < 0.007) lower for conventional GRASP. RtPC with GRASP + VS + KWIC also showed the ability to assess beat‐to‐beat variation and detect the highest peak among peaks. Conclusion: The synergistic combination of GRASP, VS, and KWIC achieves 25 ms effective temporal resolution (40 fps frame rate), while minimizing the underestimation of peak velocity compared with conventional GRASP. [ABSTRACT FROM AUTHOR]

Purpose: Time‐lapse MRI enables tracking of single iron‐labeled cells. Yet, due to temporal blurring, only slowly moving cells can be resolved. To study faster cells for example during inflammatory processes, accelerated acquisition is needed. Methods: A rotating phantom system was developed to quantitatively measure the current maximum detectable speed of cells in time‐lapse MRI. For accelerated cell tracking, an interleaved radial acquisition scheme was applied to phantom and murine brain in vivo time‐lapse MRI experiments at 9.4 T. Detection of iron‐labeled cells was evaluated in fully sampled and undersampled reconstructions with and without compressed sensing. Results: The rotating phantom system enabled ultra‐slow rotation of phantoms and a velocity detection limit of full‐brain Cartesian time‐lapse MRI of up to 172 μm/min was determined. Both phantom and in vivo measurements showed that single cells can be followed dynamically using radial time‐lapse MRI. Higher temporal resolution of undersampled reconstructions reduced geometric distortion, the velocity detection limit was increased to 1.1 mm/min in vitro, and previously hidden fast‐moving cells were recovered. In the mouse brain after in vivo labeling, a total of 42 ± 4 cells were counted in fully sampled, but only 7 ± 1 in undersampled images due to streaking artifacts. Using compressed sensing 33 ± 4 cells were detected. Conclusion: Interleaved radial time‐lapse MRI permits retrospective reconstruction of both fully sampled and accelerated images, enables single cell tracking at higher temporal resolution and recovers cells hidden before due to blurring. The velocity detection limit as determined with the rotating phantom system increased two‐ to three‐fold compared to previous results. [ABSTRACT FROM AUTHOR]

PURPOSE: To improve image quality and resolution of dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) by developing acquisition and reconstruction methods exploiting the temporal regularity property of DSC-PWI signal. THEORY AND METHODS: A novel regularized reconstruction is proposed that recovers DSC-PWI series from interleaved segmented spiral k-space acquisition using higher order temporal smoothness (HOTS) properties of the DSC-PWI signal. The HOTS regularization is designed to tackle representational insufficiency of the standard first-order temporal regularizations for supporting higher accelerations. The higher accelerations allow for k-space coverage with shorter spiral interleaves resulting in improved acquisition point spread function, and acquisition of images at multiple TEs for more accurate DSC-PWI analysis. RESULTS: The methods were evaluated in simulated and in-vivo studies. HOTS regularization provided increasingly more accurate models for DSC-PWI than the standard first-order methods with either quadratic or robust norms at the expense of increased noise. HOTS DSC-PWI optimized for noise and accuracy demonstrated significant advantages over both spiral DSC-PWI without temporal regularization and traditional echo-planar DSC-PWI, improving resolution and mitigating image artifacts associated with long readout, including blurring and geometric distortions. In context of multi-echo DSC-PWI, the novel methods allowed ∼4.3× decrease of voxel volume, providing 2× number of TEs compared to the previously published results. CONCLUSIONS: Proposed HOTS reconstruction combined with dynamic spiral sampling represents a valid mechanism for improving image quality and resolution of DSC-PWI significantly beyond those available with established fast imaging techniques.

Purpose: To develop a model‐based deep neural network for high‐quality image reconstruction of undersampled multi‐coil CEST data. Theory and Methods: Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST‐VN) with a k‐space data‐sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial–frequential convolution kernels that exploit correlations in the x‐ω domain. Additionally, a new pipeline based on multiple‐pool Bloch–McConnell simulations is devised to synthesize multi‐coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST‐specific loss function that jointly measures the structural and CEST contrast. The performance of CEST‐VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state‐of‐the‐art reconstruction methods. Results: The proposed CEST‐VN method generated high‐quality CEST source images and amide proton transfer‐weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST‐VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST‐specific loss function and data‐sharing block used. Conclusions: The proposed CEST‐VN method can offer high‐quality CEST source images and amide proton transfer‐weighted maps from highly undersampled multi‐coil data by integrating the deep learning prior and multi‐coil sensitivity encoding model. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel deep learning approach for 4D‐MRI reconstruction, named Movienet, which exploits space–time‐coil correlations and motion preservation instead of k‐space data consistency, to accelerate the acquisition of golden‐angle radial data and enable subsecond reconstruction times in dynamic MRI. Methods: Movienet uses a U‐net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion‐resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD‐GRASP image used for training. Movienet is demonstrated for motion‐resolved 4D MRI and motion‐resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR‐Linac (1.5‐fold acquisition acceleration) and diagnostic 3T MRI scanners (2‐fold and 2.25‐fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers. Results: The reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD‐GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD‐GRASP with similar overall image quality and improved suppression of streaking artifacts. Conclusion: Movienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion‐resistant 3D anatomical imaging or motion‐resolved 4D imaging. [ABSTRACT FROM AUTHOR]

Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for three‐dimensional (3D) non‐Cartesian acquisitions. One common approach is to reduce the number of coil channels actively used during reconstruction as in coil compression. While effective for Cartesian imaging, coil compression inherently loses signal energy, producing shading artifacts that compromise image quality for 3D non‐Cartesian imaging. We propose coil sketching, a general and versatile method for computationally‐efficient iterative MR image reconstruction. Theory and Methods: We based our method on randomized sketching algorithms, a type of large‐scale optimization algorithms well established in the fields of machine learning and big data analysis. We adapt the sketching theory to the MRI reconstruction problem via a structured sketching matrix that, similar to coil compression, considers high‐energy virtual coils obtained from principal component analysis. But, unlike coil compression, it also considers random linear combinations of the remaining low‐energy coils, effectively leveraging information from all coils. Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non‐Cartesian datasets. The resulting design yielded both improved computatioanal efficiency and preserved signal‐to‐noise ratio (SNR) as measured by the inverse g‐factor. Then, we verified the efficacy of our approach on high‐dimensional non‐Cartesian 3D cones datasets, where coil sketching yielded up to three‐fold faster reconstructions with equivalent image quality. Conclusion: Coil sketching is a general and versatile reconstruction framework for computationally fast and memory‐efficient reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: Sodium MRI can be used to quantify tissue sodium concentration (TSC) in vivo; however, UTE sequences are required to capture the rapidly decaying signal. 2D MRI enables high in‐plane resolution but typically has long TEs. Half‐sinc excitation may enable UTE; however, twice as many readouts are necessary. Scan time can be minimized by reducing the number of signal averages (NSAs), but at a cost to SNR. We propose using compressed sensing (CS) to accelerate 2D half‐sinc acquisitions while maintaining SNR and TSC. Methods: Ex vivo and in vivo TSC were compared between 2D spiral sequences with full‐sinc (TE = 0.73 ms, scan time ≈ 5 min) and half‐sinc excitation (TE = 0.23 ms, scan time ≈ 10 min), with 150 NSAs. Ex vivo, these were compared to a reference 3D sequence (TE = 0.22 ms, scan time ≈ 24 min). To investigate shortening 2D scan times, half‐sinc data was retrospectively reconstructed with fewer NSAs, comparing a nonuniform fast Fourier transform to CS. Resultant TSC and image quality were compared to reference 150 NSAs nonuniform fast Fourier transform images. Results: TSC was significantly higher from half‐sinc than from full‐sinc acquisitions, ex vivo and in vivo. Ex vivo, half‐sinc data more closely matched the reference 3D sequence, indicating improved accuracy. In silico modeling confirmed this was due to shorter TEs minimizing bias caused by relaxation differences between phantoms and tissue. CS was successfully applied to in vivo, half‐sinc data, maintaining TSC and image quality (estimated SNR, edge sharpness, and qualitative metrics) with ≥50 NSAs. Conclusion: 2D sodium MRI with half‐sinc excitation and CS was validated, enabling TSC quantification with 2.25 × 2.25 mm2 resolution and scan times of ≤5 mins. [ABSTRACT FROM AUTHOR]

Purpose: Temporal resolution of time‐lapse MRI to track individual iron‐labeled cells is limited by the required data‐acquisition time to fill k‐space and to reach sufficient SNR. Although motion of slowly patrolling monocytes can be resolved, detection of fast‐moving immune cells requires improved acquisition and reconstruction strategies. Theory and Methods: For accelerated MRI cell tracking, a Cartesian sampling scheme was designed, in which the fully sampled and undersampled k‐space data for different acceleration factors were acquired simultaneously, and multiple undersampling ratios could be chosen retrospectively. Compressed‐sensing reconstruction was applied using dictionary learning and low‐rank constraints. Detection of iron‐labeled monocytes was evaluated with simulations, rotating phantom experiments and in vivo mouse brain measurements at 9.4 T. Results: Fully sampled and 2.4‐times and 4.8‐times accelerated images were reconstructed and had sufficient contrast‐to‐noise ratio (CNR) for single cells to be resolved and followed dynamically. The phantom experiments showed an improvement in CNR of 6.1% per μm/s in the 4.8‐times undersampled images. Geometric distortion of cells caused by motion was visibly reduced in the accelerated images, which enabled detection of moving cells with velocities of up to 7.0 μm/s. In vivo, additional cells were resolved in the accelerated images due to the improved temporal resolution. Conclusion: The easy‐to‐implement flexible Cartesian sampling scheme with compressed‐sensing reconstruction permits simultaneous acquisition of both fully sampled and high temporal resolution images. The CNR of moving cells is effectively improved, enabling the recovery of high velocity cells with sufficient contrast at virtually no cost. [ABSTRACT FROM AUTHOR]

Purpose: Non‐Cartesian MRI with long arbitrary readout directions are susceptible to off‐resonance artifacts due to patient induced B0$$ {B}_0 $$ inhomogeneities. This results in degraded image quality with strong signal losses and blurring. Current solutions to address this issue involve correcting the off‐resonance artifacts during image reconstruction or reducing inhomogeneities through improved shimming. Theory: The recently developed SPARKLING algorithm is extended to drastically diminish off‐resonance artifacts by generating temporally smooth k‐space sampling patterns. For doing so, the cost function which is optimized in SPARKLING is modified using a temporal weighting factor. Additionally, oversampling of the center of k‐space beyond the Nyquist criteria is prevented through the use of gridded sampling in the region, enforced with affine constraints. Methods: Prospective k‐space data was acquired at 3 T on new trajectories, and we show robustness to B0$$ {\mathrm{B}}_0 $$ inhomogeneities through in silico experiments by adding ΔB0$$ \Delta {\mathrm{B}}_0 $$ through artificial degradation of system B0$$ {\mathrm{B}}_0 $$ shimming. Later on, in vivo experiments were carried out to optimize parameters of the new improvements and benchmark the gain in performance. Results: The improved trajectories allowed for the recovery of signal dropouts observed on original SPARKLING acquisitions at larger B0$$ {\mathrm{B}}_0 $$ field inhomogeneities. Furthermore, imposing gridded sampling at the center of k‐space provided improved reconstructed image quality with limited artifacts. Conclusion: These advancements allowed us for nearly 4.62×$$ 4.62\times $$ shorter scan time compared to GRAPPA‐p4x1, allowing us to reach 600 µm isotropic resolution in 3D T2∗$$ {\mathrm{T}}_2^{\ast } $$‐w imaging in just 3.3 min at 3 T with negligible degradation in image quality. [ABSTRACT FROM AUTHOR]

Purpose: Knee cartilage experiences repetitive loading during physical activities, which is altered during the pathogenesis of diseases like osteoarthritis. Analyzing the biomechanics during motion provides a clear understanding of the dynamics of cartilage deformation and may establish essential imaging biomarkers of early‐stage disease. However, in vivo biomechanical analysis of cartilage during rapid motion is not well established. Methods: We used spiral displacement encoding with stimulated echoes (DENSE) MRI on in vivo human tibiofemoral cartilage during cyclic varus loading (0.5 Hz) and used compressed sensing on the k‐space data. The applied compressive load was set for each participant at 0.5 times body weight on the medial condyle. Relaxometry methods were measured on the cartilage before (T1ρ, T2) and after (T1ρ) varus load. Results: Displacement and strain maps showed a gradual shift of displacement and strain in time. Compressive strain was observed in the medial condyle cartilage and shear strain was roughly half of the compressive strain. Male participants had more displacement in the loading direction compared to females, and T1ρ values did not change after cyclic varus load. Compressed sensing reduced the scanning time up to 25% to 40% when comparing the displacement maps and substantially lowered the noise levels. Conclusion: These results demonstrated the ease of which spiral DENSE MRI could be applied to clinical studies because of the shortened imaging time, while quantifying realistic cartilage deformations that occur through daily activities and that could serve as biomarkers of early osteoarthritis. [ABSTRACT FROM AUTHOR]

Purpose: MRI is increasingly utilized for image‐guided radiotherapy due to its outstanding soft‐tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning‐based method that rapidly reconstructs distortion‐corrected images from raw k‐space data for MR‐guided radiotherapy applications. Methods: We leverage recent advances in interpretable unrolling networks to develop a Distortion‐Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL‐encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI‐Linac. The DCReconNet, CS‐, PI‐and UNet‐based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported. Results: Imaging results demonstrated that DCReconNet better preserves image structures compared to CS‐ and PI‐based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 10‐times faster than iterative, regularized reconstruction methods. Conclusions: DCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI‐guided radiotherapy applications. [ABSTRACT FROM AUTHOR]

Purpose: The expanded encoding model incorporates spatially‐ and time‐varying field perturbations for correction during reconstruction. To date, these reconstructions have used the conjugate gradient method with early stopping used as implicit regularization. However, this approach is likely suboptimal for low‐SNR cases like diffusion or high‐resolution MRI. Here, we investigate the extent that ℓ1$$ {\ell}_1 $$‐wavelet regularization, or equivalently compressed sensing (CS), combined with expanded encoding improves trade‐offs between spatial resolution, readout time and SNR for single‐shot spiral DWI at 7T. The reconstructions were performed using our open‐source graphics processing unit‐enabled reconstruction toolbox, "MatMRI," that allows inclusion of the different components of the expanded encoding model, with or without CS. Methods: In vivo accelerated single‐shot spirals were acquired with five acceleration factors (R) (2×–6×) and three in‐plane spatial resolutions (1.5, 1.3, and 1.1 mm). From the in vivo reconstructions, we estimated diffusion tensors and computed fractional anisotropy maps. Then, simulations were used to quantitatively investigate and validate the impact of CS‐based regularization on image quality when compared to a known ground truth. Results: In vivo reconstructions revealed improved image quality with retainment of small features when CS was used. Simulations showed that the joint use of the expanded encoding model and CS improves accuracy of image reconstructions (reduced mean‐squared error) over the range of R investigated. Conclusion: The expanded encoding model and CS regularization are complementary tools for single‐shot spiral diffusion MRI, which enables both higher spatial resolutions and higher R. [ABSTRACT FROM AUTHOR]

Purpose: To develop an accelerated 3D intracranial time‐of‐flight (TOF) magnetic resonance angiography (MRA) sequence with wave‐encoding (referred to as 3D wave‐TOF) and to evaluate two variants: wave–controlled aliasing in parallel imaging (CAIPI) and compressed‐sensing wave (CS‐wave). Methods: A wave‐TOF sequence was implemented on a 3 T clinical scanner. Wave‐encoded and Cartesian k‐space datasets from six healthy volunteers were retrospectively and prospectively undersampled with 2D‐CAIPI sampling and variable‐density Poisson disk sampling. 2D‐CAIPI, wave‐CAIPI, standard CS, and CS‐wave schemes were compared at various acceleration factors. Flow‐related artifacts in wave‐TOF were investigated, and a set of practicable wave parameters was developed. Quantitative analysis of wave‐TOF and traditional Cartesian TOF MRA was performed by comparing the contrast‐to‐background ratio between the vessel and background tissue in source images, and the structural similarity index measure (SSIM) between the maximum intensity projection images from accelerated acquisitions and their respective fully sampled references. Results: Flow‐related artifacts caused by the wave‐encoding gradients in wave‐TOF were eliminated by properly chosen parameters. Images from wave‐CAIPI and CS‐wave acquisitions had a higher SNR and better‐preserved contrast than traditional parallel imaging (PI) and CS methods. Maximum intensity projection images from wave‐CAIPI and CS‐wave acquisitions had a cleaner background, with vessels that were better depicted. Quantitative analyses indicated that wave‐CAIPI had the highest contrast‐to‐background ratio, SSIM, and vessel‐masked SSIM among the sampling schemes studied, followed by the CS‐wave acquisition. Conclusion: 3D wave‐TOF improves the capability of accelerated MRA and provides better image quality at higher acceleration factors compared to traditional PI‐ or CS‐accelerated TOF, suggesting the potential use of wave‐TOF in cerebrovascular disease. [ABSTRACT FROM AUTHOR]

PURPOSE: Structure-guided total variation is a recently introduced prior that allows reconstruction of images using knowledge of the location and orientation of edges in a reference image. In this work, we demonstrate the advantages of a variant of structure-guided total variation known as directional total variation (DTV), over traditional total variation (TV), in the context of compressed-sensing reconstruction and super-resolution. METHODS: We compared TV and DTV in retrospectively undersampled ex vivo diffusion tensor imaging and diffusion spectrum imaging data from healthy, sham, and hypertrophic rat hearts. RESULTS: In compressed sensing at an undersampling factor of 8, the RMS error of mean diffusivity and fractional anisotropy relative to the fully sampled ground truth were 44% and 20% lower in DTV compared with TV. In super-resolution, these values were 29% and 14%, respectively. Similarly, we observed improvements in helix angle, transverse angle, sheetlet elevation, and sheetlet azimuth. The RMS error of the diffusion kurtosis in the undersampled data relative to the ground truth was uniformly lower (22% on average) with DTV compared to TV. CONCLUSION: Acquiring one fully sampled non-diffusion-weighted image and 10 diffusion-weighted images at 8× undersampling would result in an 80% net reduction in data needed. We demonstrate efficacy of the DTV algorithm over TV in reducing data sampling requirements, which can be translated into higher apparent resolution and potentially shorter scan times. This method would be equally applicable in diffusion MRI applications outside the heart.

Purpose: The aim of this paper is to show that geometrical criteria for designing multishell q$$ q $$‐space sampling procedures do not necessarily translate into reconstruction matrices with high figures of merit commonly used in the compressed sensing theory. In addition, we show that a well‐known method for visiting k‐space in radial three‐dimensional acquisitions, namely, the Spiral Phyllotaxis, is a competitive initialization for the optimization of our nonconvex objective function. Theory and Methods: We propose the gradient design method WISH (WeIghting SHells) which uses an objective function that accounts for weighted distances between gradients within M‐tuples of consecutive shells, with M$$ M $$ ranging between 1 and the maximum number of shells S$$ S $$. All the M$$ M $$‐tuples share the same weight ωM$$ {\omega}_M $$. The objective function is optimized for a sample of these weights, using Spiral Phyllotaxis as initialization. State‐of‐the‐art General Electrostatic Energy Minimization (GEEM) and Spherical Codes (SC) were used for comparison. For the three methods, reconstruction matrices of the attenuation signal using MAP‐MRI were tested using figures of merit borrowed from the Compressed Sensing theory (namely, Restricted Isometry Property —RIP— and Coherence); we also tested the gradient design using a geometric criterion based on Voronoi cells. Results: For RIP and Coherence, WISH got better results in at least one combination of weights, whilst the criterion based on Voronoi cells showed an unrelated pattern. Conclusion: The versatility provided by WISH is supported by better results. Optimization in the weight parameter space is likely to provide additional improvements. For a practical design with an intermediate number of gradients, our results recommend to carry out the methodology here used to determine the appropriate gradient table. [ABSTRACT FROM AUTHOR]

Purpose: To develop and test compressed sensing–based multiframe 3D MRI of grid‐tagged hyperpolarized gas in the lung. Theory and Methods: Applying grid‐tagging RF pulses to inhaled hyperpolarized gas results in images in which signal intensity is predictably and sparsely distributed. In the present work, this phenomenon was used to produce a sampling pattern in which k‐space is undersampled by a factor of approximately seven, yet regions of high k‐space energy remain densely sampled. Three healthy subjects received multiframe 3D 3He tagging MRI using this undersampling method. Images were collected during a single exhalation at eight timepoints spanning the breathing cycle from end‐of‐inhalation to end‐of‐exhalation. Grid‐tagged images were used to generate 3D displacement maps of the lung during exhalation, and time‐resolved maps of principal strains and fractional volume change were generated from these displacement maps using finite‐element analysis. Results: Tags remained clearly resolvable for 4–6 timepoints (5–8 s) in each subject. Displacement maps revealed noteworthy temporal and spatial nonlinearities in lung motion during exhalation. Compressive normal strains occurred along all three principal directions but were primarily oriented in the head–foot direction. Fractional volume changes displayed clear bilateral symmetry, but with the lower lobes displaying slightly higher change than the upper lobes in 2 of the 3 subjects. Conclusion: We developed a compressed sensing–based method for multiframe 3D MRI of grid‐tagged hyperpolarized gas in the lung during exhalation. This method successfully overcomes previous challenges for 3D dynamic grid‐tagging, allowing time‐resolved biomechanical readouts of lung function to be generated. [ABSTRACT FROM AUTHOR]

Purpose: To improve the MRI reconstruction performance of model‐based networks and to alleviate their large demand for GPU memory. Methods: A model‐based neural network with enhanced deep learned regularizers (MEDL‐Net) was proposed. The MEDL‐Net is separated into several submodules, each of which consists of several cascades to mimic the optimization steps in conventional MRI reconstruction algorithms. Information from shallow cascades is densely connected to latter ones to enrich their inputs in each submodule, and additional revising blocks (RB) are stacked at the end of the submodules to bring more flexibility. Moreover, a composition loss function was designed to explicitly supervise RBs. Results: Network performance was evaluated on a publicly available dataset. The MEDL‐Net quantitatively outperforms the state‐of‐the‐art methods on different MR image sequences with different acceleration rates (four‐fold and six‐fold). Moreover, the reconstructed images showed that the detailed textures are better preserved. In addition, fewer cascades are required when achieving the same reconstruction results compared with other model‐based networks. Conclusion: In this study, a more efficient model‐based deep network was proposed to reconstruct MR images. The experimental results indicate that the proposed method improves reconstruction performance with fewer cascades, which alleviates the large demand for GPU memory. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To explore a fetal 3D cardiovascular cine acquisition using a radial image acquisition and compressed‐sensing reconstruction and compare image quality and scan time with conventional multislice 2D imaging. Methods: Volumetric fetal cardiac data were acquired in 26 volunteers using a radial 3D balanced SSFP pulse sequence. Cardiac gating was performed using a Doppler ultrasound device. Images were reconstructed using a parallel‐imaging and compressed‐sensing algorithm. Multiplanar reformatting to standard cardiac views was performed before image analysis. Clinical 2D images were used for comparison. Qualitative and quantitative image evaluation were performed by two experienced observers (scale: 1–4). Volumes, mass, and function were assessed. Results: Average scan time for the 3D imaging was 6 min, including one localizer. A 2D imaging stack covering the entire heart including localizer sequences took at least 6.5 min, depending on planning complexity. The 3D acquisition was successful in 7 of 26 subjects (27%). Overall image contrast and perceived resolution were lower in the 3D images. Nonetheless, the 3D images had, on average, a moderate cardiac diagnostic quality (median [range]: 3 [1–4]). Standard clinical 2D acquisitions had a high cardiac diagnostic quality (median [range]: 4 [3, 4]). Cardiac measurements were not different between 2D and 3D images (all p > 0.16). Conclusion: The presented free‐breathing whole‐heart fetal 3D radial cine MRI acquisition and reconstruction method enables retrospective visualization of all cardiac views while keeping examination times short. This proof‐of‐concept work produced images with diagnostic quality, while at the same time reducing the planning complexity to a single localizer. [ABSTRACT FROM AUTHOR]

Purpose: The goal of this study was to apply a fast data‐driven optimization algorithm, called bias‐accelerated subset selection, for MR brain T1ρ mapping to generate optimized sampling patterns (SPs) for compressed sensing reconstruction of brain 3D‐T1ρ MRI. Methods: Five healthy volunteers were recruited, and fully sampled Cartesian 3D‐T1ρ MRIs were obtained. Variable density (VD) and Poisson disc (PD) undersampling was used as the input to SP optimization process. The reconstruction used 3 compressed sensing methods: spatiotemporal finite differences, low‐rank plus sparse with spatial finite differences, and low rank. The performance of images and T1ρ maps using PD‐SP and VD‐SP and their optimized sampling patterns (PD‐OSP and VD‐OSP) were compared to the fully sampled reference using normalized root mean square error (NRMSE). Results: The VD‐OSP with spatiotemporal finite differences reconstruction (NRMSE = 0.078) and the PD‐OSP with spatiotemporal finite differences reconstruction (NRMSE = 0.079) at the highest acceleration factors (AF = 30) showed the largest improvement compared to the respective nonoptimized SPs (VD NRMSE = 0.087 and PD NRMSE = 0.149). Prospective undersampling was tested at AF = 4, with VD‐OSP NRMSE = 0.057 versus PD‐OSP NRMSE = 0.060, with optimized sampling performing better that input PD or VD sampling. For brain T1ρ mapping, the VD‐OSP with low rank reconstruction for AFs <10 and VD‐OSP with spatiotemporal finite differences for AFs >10 perform better. Conclusions: The study demonstrated that the appropriate use of data‐driven optimized sampling and suitable compressed sensing reconstruction technique can be employed to potentially accelerate 3D T1ρ mapping for brain imaging applications. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate hemodynamic markers obtained by accelerated GRAPPA (R = 2, 3, 4) and compressed sensing (R = 7.6) 4D flow MRI sequences under complex flow conditions. Methods: The accelerated 4D flow MRI scans were performed on a pulsatile flow phantom, along with a nonaccelerated fully sampled k‐space acquisition. Computational fluid dynamics simulations based on the experimentally measured flow fields were conducted for additional comparison. Voxel‐wise comparisons (Bland–Altman analysis, L2$$ {L}_2 $$‐norm metric), as well as nonderived quantities (velocity profiles, flow rates, and peak velocities), were used to compare the velocity fields obtained from the different modalities. Results: 4D flow acquisitions and computational fluid dynamics depicted similar hemodynamic patterns. Voxel‐wise comparisons between the MRI scans highlighted larger discrepancies at the voxels located near the phantom's boundary walls. A trend for all MR scans to overestimate velocity profiles and peak velocities as compared to computational fluid dynamics was noticed in regions associated with high velocity or acceleration. However, good agreement for the flow rates was observed, and eddy‐current correction appeared essential for consistency of the flow rates measurements with respect to the principle of mass conservation. Conclusion: GRAPPA (R = 2, 3) and highly accelerated compressed sensing showed good agreement with the fully sampled acquisition. Yet, all 4D flow MRI scans were hampered by artifacts inherent to the phase‐contrast acquisition procedure. Computational fluid dynamics simulations are an interesting tool to assess these differences but are sensitive to modeling parameters. [ABSTRACT FROM AUTHOR]

Purpose: To develop an accelerated MRI method to quantify the epicardial adipose tissue (EAT) fatty acid composition (FAC) and test the hypothesis that eplerenone (EPL) shifts the EAT FAC toward unsaturation in obese mice. Methods: Undersampled multi‐echo gradient echo imaging employing a dictionary‐based compressed‐sensing reconstruction and iterative decomposition with echo asymmetry and least‐squares–based mapping (IDEAL) was developed, validated, and used to study EAT in obese mice scanned at 7T. Fully sampled and rate 2, 2.5, 3, and 3.5 undersampled image data were acquired, reconstructed, and assessed using RMSE and structural similarity (SSIM). Two groups of mice were studied: untreated (control, n = 10) and EPL‐treated (n = 10) mice fed a high‐fat high‐sucrose diet. MRI included imaging of EAT FAC, EAT volume, and myocardial perfusion reserve. Results: Rate 3 acceleration provided RMSE <5% and structural similarity >0.85 for FAC MRI. After 6 weeks of diet, EPL‐treated compared to untreated mice had a reduced EAT saturated fatty acid fraction (0.27 ± 0.09 vs. 0.39 ± 0.07, P < 0.05) and increased EAT unsaturation degree (4.37 ± 0.32 vs. 3.69 ± 0.58, P < 0.05). Also, EAT volume in EPL‐treated compared to untreated mice was reduced (8.1 ± 0.6 mg vs. 11.4 ± 0.7 mg, P < 0.01), and myocardial perfusion reserve was improved (1.83 ± 0.15 vs. 1.61 ± 0.17, P < 0.05). Conclusion: Rate 3 accelerated FAC MRI enabled accurate quantification of EAT FAC in mice. EPL treatment shifted the EAT FAC toward increased unsaturation and was associated with improvement of coronary microvascular function. [ABSTRACT FROM AUTHOR]

Purpose: To accelerate chemical shift encoded (CSE) water–fat imaging by applying a model‐guided deep learning water–fat separation (MGDL‐WF) framework to the undersampled k‐space data. Methods: A model‐guided deep learning water–fat separation framework is proposed for the acceleration using Cartesian/radial undersampling data. The proposed MGDL‐WF combines the power of CSE water–fat imaging model and data‐driven deep learning by jointly using a multi‐peak fat model and a modified residual U‐net network. The model is used to guide the image reconstruction, and the network is used to capture the artifacts induced by the undersampling. A data consistency layer is used in MGDL‐WF to ensure the output images to be consistent with the k‐space measurements. A Gauss‐Newton iteration algorithm is adapted for the gradient updating of the networks. Results: Compared with the compressed sensing water–fat separation (CS‐WF) algorithm/2‐step procedure algorithm, the MGDL‐WF increased peak signal‐to‐noise ratio (PSNR) by 5.31/5.23, 6.11/4.54, and 4.75 dB/1.88 dB with Cartesian sampling, and by 4.13/6.53, 2.90/4.68, and 1.68 dB/3.48 dB with radial sampling, at acceleration rates (R) of 4, 6, and 8, respectively. By using MGDL‐WF, radial sampling increased the PSNR by 2.07 dB at R = 8, compared with Cartesian sampling. Conclusions: The proposed MGDL‐WF enables exploiting features of the water images and fat images from the undersampled multi‐echo data, leading to improved performance in the accelerated CSE water–fat imaging. By using MGDL‐WF, radial sampling can further improve the image quality with comparable scan time in comparison with Cartesian sampling. [ABSTRACT FROM AUTHOR]

Purpose: Patient‐induced inhomogeneities in the magnetic field cause distortions and blurring during acquisitions with long readouts such as in susceptibility‐weighted imaging (SWI). Most correction methods require collecting an additional ΔB0$$ \Delta {\mathrm{B}}_0 $$ field map to remove these artifacts. Theory: The static ΔB0$$ \Delta {\mathrm{B}}_0 $$ field map can be approximated with an acceptable error directly from a single echo acquisition in SWI. The main component of the observed phase is linearly related to ΔB0$$ \Delta {\mathrm{B}}_0 $$ and the echo time (TE), and the relative impact of non‐ ΔB0$$ \Delta {\mathrm{B}}_0 $$ terms becomes insignificant with TE$$ \mathrm{TE} $$ >20 ms at 3 T for a well‐tuned system. Methods: The main step is to combine and unfold the multi‐channel phase maps wrapped many times, and several competing algorithms are compared for this purpose. Four in vivo brain data sets collected using the recently proposed 3D spreading projection algorithm for rapid k‐space sampling (SPARKLING) readouts are used to assess the proposed method. Results: The estimated 3D field maps generated with a 0.6 mm isotropic spatial resolution provide overall similar off‐resonance corrections compared to reference corrections based on an external ΔB0$$ \Delta {\mathrm{B}}_0 $$ acquisitions, and even improved for 2 of 4 individuals. Although a small estimation error is expected, no aftermath was observed in the proposed corrections, whereas degradations were observed in the references. Conclusion: A static ΔB0$$ \Delta {\mathrm{B}}_0 $$ field map estimation method was proposed to take advantage of acquisitions with long echo times, and outperformed the reference technique based on an external field map. The difference can be attributed to an inherent robustness to mismatches between volumes and external ΔB0$$ \Delta {\mathrm{B}}_0 $$ maps, and diverse other sources investigated. [ABSTRACT FROM AUTHOR]

Purpose: To propose respiratory motion‐informed locally low‐rank reconstruction (MI‐LLR) for robust free‐breathing single‐bolus quantitative 3D myocardial perfusion CMR imaging. Simulation and in‐vivo results are compared to locally low‐rank (LLR) and compressed sensing reconstructions (CS) for reference. Methods: Data were acquired using a 3D Cartesian pseudo‐spiral in‐out k‐t undersampling scheme (R = 10) and reconstructed using MI‐LLR, which encompasses two stages. In the first stage, approximate displacement fields are derived from an initial LLR reconstruction to feed a motion‐compensated reference system to a second reconstruction stage, which reduces the rank of the inverse problem. For comparison, data were also reconstructed with LLR and frame‐by‐frame CS using wavelets as sparsifying transform (ℓ1$$ {\ell}_1 $$‐wavelet). Reconstruction accuracy relative to ground truth was assessed using synthetic data for realistic ranges of breathing motion, heart rates, and SNRs. In‐vivo experiments were conducted in healthy subjects at rest and during adenosine stress. Myocardial blood flow (MBF) maps were derived using a Fermi model. Results: Improved uniformity of MBF maps with reduced local variations was achieved with MI‐LLR. For rest and stress, intra‐volunteer variation of absolute and relative MBF was lower in MI‐LLR (±0.17 mL/g/min [26%] and ±1.07 mL/g/min [33%]) versus LLR (±0.19 mL/g/min [28%] and ±1.22 mL/g/min [36%]) and versus ℓ1$$ {\ell}_1 $$‐wavelet (±1.17 mL/g/min [113%] and ±6.87 mL/g/min [115%]). At rest, intra‐subject MBF variation was reduced significantly with MI‐LLR. Conclusion: The combination of pseudo‐spiral Cartesian undersampling and dual‐stage MI‐LLR reconstruction improves free‐breathing quantitative 3D myocardial perfusion CMR imaging under rest and stress condition. [ABSTRACT FROM AUTHOR]

Purpose: The synergistic use of k‐t undersampling and multiband (MB) imaging has the potential to provide extended slice coverage and high spatial resolution for first‐pass perfusion MRI. The low‐rank plus sparse (L + S) model has shown excellent performance for accelerating single‐band (SB) perfusion MRI. Methods: A MB data consistency method employing ESPIRiT maps and through‐plane coil information was developed. This data consistency method was combined with the temporal L + S constraint to form the slice‐L + S method. Slice‐L + S was compared to SB L + S and the sequential operations of split slice‐GRAPPA and SB L + S (seq‐SG‐L + S) using synthetic data formed from multislice SB images. Prospectively k‐t undersampled MB data were also acquired and reconstructed using seq‐SG‐L + S and slice‐L + S. Results: Using synthetic data with total acceleration rates of 6–12, slice‐L + S outperformed SB L + S and seq‐SG‐L + S (N = 7 subjects) with respect to normalized RMSE and the structural similarity index (P < 0.05 for both). For the specific case with MB factor = 3 and rate 3 undersampling, or for SB imaging with rate 9 undersampling (N = 7 subjects), the normalized RMSE values were 0.037 ± 0.007, 0.042 ± 0.005, and 0.031 ± 0.004; and the structural similarity index values were 0.88 ± 0.03, 0.85 ± 0.03, and 0.89 ± 0.02 for SB L + S, seq‐SG‐L + S, and slice‐L + S, respectively (P < 0.05 for both). For prospectively undersampled MB data, slice‐L + S provided better image quality than seq‐SG‐L + S for rate 6 (N = 7) and rate 9 acceleration (N = 7) as scored by blinded experts. Conclusion: Slice‐L + S outperformed SB‐L + S and seq‐SG‐L + S and provides 9 slice coverage of the left ventricle with a spatial resolution of 1.5 mm × 1.5 mm with good image quality. [ABSTRACT FROM AUTHOR]

Purpose: Respiration‐related CSF flow through the cerebral aqueduct may be useful for elucidating physiology and pathophysiology of the glymphatic system, which has been proposed as a mechanism of brain waste clearance. Therefore, we aimed to (1) develop a real‐time (CSF) flow imaging method with high spatial and sufficient temporal resolution to capture respiratory effects, (2) validate the method in a phantom setup and numerical simulations, and (3) apply the method in vivo and quantify its repeatability and correlation with different respiratory conditions. Methods: A golden‐angle radial flow sequence (reconstructed temporal resolution 168 ms, spatial resolution 0.6 mm) was implemented on a 7T MRI scanner and reconstructed using compressed sensing. A phantom setup mimicked simultaneous cardiac and respiratory flow oscillations. The effect of temporal resolution and vessel diameter was investigated numerically. Healthy volunteers (n = 10) were scanned at four different respiratory conditions, including repeat scans. Results: Phantom data show that the developed sequence accurately quantifies respiratory oscillations (ratio real‐time/reference QR = 0.96 ± 0.02), but underestimates the rapid cardiac oscillations (ratio QC = 0.46 ± 0.14). Simulations suggest that QC can be improved by increasing temporal resolution. In vivo repeatability was moderate to very strong for cranial and caudal flow (intraclass correlation coefficient range: 0.55–0.99) and weak to strong for net flow (intraclass correlation coefficient range: 0.48–0.90). Net flow was influenced by respiratory condition (p < 0.01). Conclusions: The presented real‐time flow MRI method can quantify respiratory‐related variations of CSF flow in the cerebral aqueduct, but it underestimates rapid cardiac oscillations. In vivo, the method showed good repeatability and a relationship between flow and respiration. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to determine an optimal saturation‐recovery time (TS) for minimizing the underestimation of arterial input function (AIF) in quantitative cardiac perfusion MRI without multiple gadolinium injections per subject. Methods: We scanned 18 subjects (mean age = 59 ± 14 years, 9/9 males/females) to acquire resting perfusion data and 1 additional subject (age = 38 years, male) to obtain stress‐rest perfusion data using a 5‐fold accelerated pulse sequence with radial k‐space sampling and applied k‐space weighted image contrast (KWIC) filters on the same k‐space data to retrospectively reconstruct five AIF images with effective TS ranging from 10 to 21.2 ms (2.8 ms steps). Undersampled images were reconstructed using a compressed sensing framework with temporal‐total‐variation and temporal‐principal‐component as 2 orthogonal sparsifying transforms. The image processing steps included, same motion correction across five different AIF images, signal normalization by the proton‐density‐weighted‐image, signal‐to‐T1 conversion using a Bloch equation, T1‐to‐gadolinium‐concentration conversion assuming fast water exchange, T2* correction to the AIF, and gadolinium‐concentration to myocardial blood flow (MBF) conversion based on a Fermi model. Results: Among five TS values, the shortest TS (10 ms) produced significantly (P < 0.05) higher peak AIF and lower resting MBF (13.73 mM, 0.73 mL g−1 min−1) than 12.8 ms (11.24 mM, 0.89 mL g−1 min−1), 15.6 ms (9.56 mM, 1.05 mL g−1 min−1), 18.4 ms (8.55 mM, 1.17 mL g−1 min−1), and 21.2 ms (7.95 mM, 1.27 mL g−1 min−1). Similarly, shorter TS reduced underestimation of AIF (or overestimation of MBF) for both during stress and at rest, but this effect was canceled in myocardial‐perfusion‐reserve (MPR). Conclusion: This study demonstrates that TS of 10 ms reduces the underestimation of AIF and, hence, the overestimation of MBF compared with longer TS values (12.8‐21.2 ms). [ABSTRACT FROM AUTHOR]

Purpose: 3D time‐of‐flight MRA can accurately visualize the intracranial vasculature but is limited by long acquisition times. Compressed sensing reconstruction can be used to substantially accelerate acquisitions. The quality of those reconstructions depends on the undersampling patterns used. In this work, we optimize sets of undersampling parameters for various acceleration factors of Cartesian 3D time‐of‐flight MRA. Methods: Fully sampled datasets, acquired at 7 Tesla, were retrospectively undersampled using variable‐density Poisson disk sampling with various autocalibration region sizes, polynomial orders, and acceleration factors. The accuracy of reconstructions from the different undersampled datasets was assessed using the vessel‐masked structural similarity index. Identified optimal undersampling parameters were then evaluated in additional prospectively undersampled datasets. Compressed sensing reconstruction parameters were chosen based on a preliminary reconstruction parameter optimization. Results: For all acceleration factors, using a fully sampled calibration area of 12 × 12 k‐space lines and a polynomial order of 2 resulted in the highest image quality. The importance of parameter optimization of the sampling was found to increase for higher acceleration factors. The results were consistent across resolutions and regions of interest with vessels of varying sizes and tortuosity. The number of visible small vessels increased by 7.0% and 14.2% when compared to standard parameters for acceleration factors of 7.2 and 15, respectively. Conclusion: The image quality of compressed sensing time‐of‐flight MRA can be improved by appropriate choice of undersampling parameters. The optimized sets of parameters are independent of the acceleration factor and enable a larger number of vessels to be visualized. [ABSTRACT FROM AUTHOR]

Purpose: To implement and evaluate a simultaneous multi‐slice balanced SSFP (SMS‐bSSFP) perfusion sequence and compressed sensing reconstruction for cardiac MR perfusion imaging with full left ventricular (LV) coverage (nine slices/heartbeat) and high spatial resolution (1.4 × 1.4 mm2) at 1.5T. Methods: A preliminary study was performed to evaluate the performance of blipped controlled aliasing in parallel imaging (CAIPI) and RF‐CAIPI with gradient‐controlled local Larmor adjustment (GC‐LOLA) in the presence of fat. A nine‐slice SMS‐bSSFP sequence using RF‐CAIPI with GC‐LOLA with high spatial resolution (1.4 × 1.4 mm2) and a conventional three‐slice sequence with conventional spatial resolution (1.9 × 1.9 mm2) were then acquired in 10 patients under rest conditions. Qualitative assessment was performed to assess image quality and perceived signal‐to‐noise ratio (SNR) on a 4‐point scale (0: poor image quality/low SNR; 3: excellent image quality/high SNR), and the number of myocardial segments with diagnostic image quality was recorded. Quantitative measurements of myocardial sharpness and upslope index were performed. Results: Fat signal leakage was significantly higher for blipped CAIPI than for RF‐CAIPI with GC‐LOLA (7.9% vs. 1.2%, p = 0.010). All 10 SMS‐bSSFP perfusion datasets resulted in 16/16 diagnostic myocardial segments. There were no significant differences between the SMS and conventional acquisitions in terms of image quality (2.6 ± 0.6 vs. 2.7 ± 0.2, p = 0.8) or perceived SNR (2.8 ± 0.3 vs. 2.7 ± 0.3, p = 0.3). Inter‐reader variability was good for both image quality (ICC = 0.84) and perceived SNR (ICC = 0.70). Myocardial sharpness was improved using the SMS sequence compared to the conventional sequence (0.37 ± 0.08 vs 0.32 ± 0.05, p < 0.001). There was no significant difference between measurements of upslope index for the SMS and conventional sequences (0.11 ± 0.04 vs. 0.11 ± 0.03, p = 0.84). Conclusion: SMS‐bSSFP with multiband factor 3 and compressed sensing reconstruction enables cardiac MR perfusion imaging with three‐fold increased spatial coverage and improved myocardial sharpness compared to a conventional sequence, without compromising perceived SNR, image quality, upslope index or number of diagnostic segments. [ABSTRACT FROM AUTHOR]

Purpose: To improve reconstruction fidelity of fine structures and textures in deep learning‐ (DL) based reconstructions. Methods: A novel patch‐based Unsupervised Feature Loss (UFLoss) is proposed and incorporated into the training of DL‐based reconstruction frameworks in order to preserve perceptual similarity and high‐order statistics. The UFLoss provides instance‐level discrimination by mapping similar instances to similar low‐dimensional feature vectors and is trained without any human annotation. By adding an additional loss function on the low‐dimensional feature space during training, the reconstruction frameworks from under‐sampled or corrupted data can reproduce more realistic images that are closer to the original with finer textures, sharper edges, and improved overall image quality. The performance of the proposed UFLoss is demonstrated on unrolled networks for accelerated two‐ (2D) and three‐dimensional (3D) knee MRI reconstruction with retrospective under‐sampling. Quantitative metrics including normalized root mean squared error (NRMSE), structural similarity index (SSIM), and our proposed UFLoss were used to evaluate the performance of the proposed method and compare it with others. Results: In vivo experiments indicate that adding the UFLoss encourages sharper edges and more faithful contrasts compared to traditional and learning‐based methods with pure ℓ2$$ {\ell}_2 $$ loss. More detailed textures can be seen in both 2D and 3D knee MR images. Quantitative results indicate that reconstruction with UFLoss can provide comparable NRMSE and a higher SSIM while achieving a much lower UFLoss value. Conclusion: We present UFLoss, a patch‐based unsupervised learned feature loss, which allows the training of DL‐based reconstruction to obtain more detailed texture, finer features, and sharper edges with higher overall image quality under DL‐based reconstruction frameworks. (Code available at: https://github.com/mikgroup/UFLoss) [ABSTRACT FROM AUTHOR]

Purpose: To develop a highly accelerated multi-echo spin-echo method, TEMPURA, for reducing the acquisition time and/or increasing spatial resolution for kidney T 2 mapping., Methods: TEMPURA merges several adjacent echoes into one k-space by either combining independent echoes or sharing one echo between k-spaces. The combined k-space is reconstructed based on compressed sensing theory. Reduced flip angles are used for the refocusing pulses, and the extended phase graph algorithm is used to correct the effects of indirect echoes. Two sequences were developed: a fast breath-hold sequence; and a high-resolution sequence. The performance was evaluated prospectively on a phantom, 16 healthy subjects, and two patients with different types of renal tumors., Results: The fast TEMPURA method reduced the acquisition time from 3-5 min to one breath-hold (18 s). Phantom measurements showed that fast TEMPURA had a mean absolute percentage error (MAPE) of 8.2%, which was comparable to a standardized respiratory-triggered sequence (7.4%), but much lower than a sequence accelerated by purely k-t undersampling (21.8%). High-resolution TEMPURA reduced the in-plane voxel size from 3 × 3 to 1 × 1 mm 2 , resulting in improved visualization of the detailed anatomical structure. In vivo T 2 measurements demonstrated good agreement (fast: MAPE = 1.3%-2.5%; high-resolution: MAPE = 2.8%-3.3%) and high correlation coefficients (fast: R = 0.85-0.98; high-resolution: 0.82-0.96) with the standardized method, outperforming k-t undersampling alone (MAPE = 3.3-4.5%, R = 0.57-0.59)., Conclusion: TEMPURA provides fast and high-resolution renal T 2 measurements. It has the potential to improve clinical throughput and delineate intratumoral heterogeneity and tissue habitats at unprecedented spatial resolution., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To implement rosette readout trajectories with compressed sensing reconstruction for fast and motion-robust CEST and magnetization transfer contrast imaging with inherent correction of B 0 inhomogeneity., Methods: A pulse sequence was developed for fast saturation transfer imaging using a stack of rosette trajectories with a higher sampling density near the k-space center. Each rosette lobe was segmented into two halves to generate dual-echo images. B 0 inhomogeneities were estimated using the phase difference between the images and corrected subsequently. The rosette-based imaging was evaluated in comparison to a fully sampled Cartesian trajectory and demonstrated on CEST phantoms (creatine solutions and egg white) and healthy volunteers at 3 T., Results: Compared with the conventional Cartesian acquisition, compressed sensing reconstructed rosette images provided image quality with overall higher contrast-to-noise ratio and significantly faster readout time. Accurate B 0 map estimation was achieved from the rosette acquisition with a negligible bias of 0.01 Hz between the rosette and dual-echo Cartesian gradient echo B 0 maps, using the latter as ground truth. The water-saturation spectra (Z-spectra) and amide proton transfer weighted signals obtained from the rosette-based sequence were well preserved compared with the fully sampled data, both in the phantom and human studies., Conclusions: Fast, motion-robust, and inherent B 0 -corrected CEST and magnetization transfer contrast imaging using rosette trajectories could improve subject comfort and compliance, contrast-to-noise ratio, and provide inherent B 0 homogeneity information. This work is expected to significantly accelerate the translation of CEST-MRI into a robust, clinically viable approach., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To accelerate the Pointwise Encoding Time Reduction with Radial Acquisition (PETRA) sequence using compressed sensing while preserving the image quality for high‐resolution MRI of tissue with ultra‐short T2∗values. Methods: Compressed sensing was introduced in the PETRA sequence (csPETRA) to accelerate the time‐consuming single point acquisition of the k‐space center data. Random undersampling was applied to achieve acceleration factors up to Acc = 32. Phantom and in vivo images of the knee joint of six volunteers were measured at 3T using csPETRA sequence with Acc = 4, 8, 12, 16, 24, and 32. Images were compared against fully sampled PETRA data (Acc = 1) for structural similarity and normalized‐mean‐square‐error. Qualitative and semi‐quantitative analyses were performed to assess the effect of the acceleration on image artifacts, image quality, and delineation of anatomical structures at the knee. Results: Even at high acceleration factors of Acc = 16 no aliasing artifacts were observed, and the anatomical details were preserved compared with the fully sampled data. The normalized‐mean‐square‐error was less than 1% for Acc = 16, in which single point imaging acquisition time was reduced from 165 to 10 s, reducing the total scan time from 7.8 to 5.2 min. Semi‐quantitative analyses suggest that Acc = 16 yields comparable diagnostic quality as the fully sampled data for knee imaging at a scan time of 5.2 min. Conclusion: csPETRA allows for ultra‐short T2∗ imaging of the knee joint in clinically acceptable scan times while maintaining the image quality of original non‐accelerated PETRA sequence. [ABSTRACT FROM AUTHOR]

Purpose: To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. Methods: A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI‐CS preconditioner for varying undersampling factors, number of coil elements and anatomies. Results: The learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies. Conclusion: It is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state‐of‐the‐art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks. [ABSTRACT FROM AUTHOR]

PurposeA new acquisition and reconstruction method called T2 Shuffling is presented for volumetric fast spin-echo (three-dimensional [3D] FSE) imaging. T2 Shuffling reduces blurring and recovers many images at multiple T2 contrasts from a single acquisition at clinically feasible scan times (6-7 min).Theory and methodsThe parallel imaging forward model is modified to account for temporal signal relaxation during the echo train. Scan efficiency is improved by acquiring data during the transient signal decay and by increasing echo train lengths without loss in signal-to-noise ratio (SNR). By (1) randomly shuffling the phase encode view ordering, (2) constraining the temporal signal evolution to a low-dimensional subspace, and (3) promoting spatio-temporal correlations through locally low rank regularization, a time series of virtual echo time images is recovered from a single scan. A convex formulation is presented that is robust to partial voluming and radiofrequency field inhomogeneity.ResultsRetrospective undersampling and in vivo scans confirm the increase in sharpness afforded by T2 Shuffling. Multiple image contrasts are recovered and used to highlight pathology in pediatric patients. A proof-of-principle method is integrated into a clinical musculoskeletal imaging workflow.ConclusionThe proposed T2 Shuffling method improves the diagnostic utility of 3D FSE by reducing blurring and producing multiple image contrasts from a single scan. Magn Reson Med 77:180-195, 2017. © 2016 Wiley Periodicals, Inc.

Purpose: To reconstruct aliasing‐free full field‐of‐view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods. Theory and Methods: The sensitivity encoding method (SENSE) was extended to the Soft‐SENSE models supporting multiple‐set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple‐set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft‐SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction. Results: Soft‐SENSE using multiple‐set full FOV CSM and PSF successfully reconstruct aliasing‐free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high‐resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem. Conclusion: The proposed Soft‐SENSE using full FOV CSM and PSF could reconstruct aliasing‐free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: To develop a new sequence to simultaneously acquire Cartesian sodium ( 23 Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low-rank reconstruction frameworks., Theory and Methods: The Double Half-Echo technique enables short echo time Cartesian 23 Na MRI and acquires two k-space halves, reconstructed by a low-rank coupling constraint. Additionally, three-dimensional (3D) 23 Na Multi-Quantum Coherences (MQC) MRI requires multi-echo sampling paired with phase-cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k-Space Estimation (SAKE) were used to reconstruct highly undersampled 23 Na MQC MRI. Reconstruction performance was assessed against five-dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo., Results: The proposed sequence enabled the simultaneous acquisition of fully sampled 23 Na MRI while leveraging prospective undersampling for 23 Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23 Na MQC MRI was enhanced from 8 × 8 × 15 $$ 8\times 8\times 15 $$ mm 3 to 8 × 8 × 8 $$ 8\times 8\times 8 $$ mm 3 while reducing acquisition time from 2 × 31 $$ 2\times 31 $$ min to 2 × 23 $$ 2\times 23 $$ min., Conclusion: The proposed sequence, coupled with low-rank reconstructions, provides an efficient framework for comprehensive whole-brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low-rank matrix completion enables the reconstruction of highly undersampled 23 Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

PurposeTo develop a compressed sensing (CS) acceleration method with a high spectral bandwidth exploiting the spatial-spectral sparsity of MR spectroscopic imaging (MRSI).MethodsAccelerations were achieved using blip gradients during the readout to perform nonoverlapped and stochastically delayed random walks in kx -ky -t space, combined with block-Hankel matrix completion for efficient reconstruction. Both retrospective and prospective CS accelerations were applied to (13) C MRSI experiments, including in vivo rodent brain and liver studies with administrations of hyperpolarized [1-(13) C] pyruvate at 7.0 Tesla (T) and [2-(13) C] dihydroxyacetone at 3.0 T, respectively.ResultsIn retrospective undersampling experiments using in vivo 7.0 T data, the proposed method preserved spectral, spatial, and dynamic fidelities with R(2) ≥ 0.96 and ≥ 0.87 for pyruvate and lactate signals, respectively, 750-Hz spectral separation, and up to 6.6-fold accelerations. In prospective in vivo experiments, with 3.8-fold acceleration, the proposed method exhibited excellent spatial localization of metabolites and peak recovery for pyruvate and lactate at 7.0 T as well as for dihydroxyacetone and its metabolic products with a 4.5-kHz spectral span (140 ppm at 3.0 T).ConclusionsThis study demonstrated the feasibility of a new CS approach to accelerate high spectral bandwidth MRSI experiments. Magn Reson Med 76:369-379, 2016. © 2016 Wiley Periodicals, Inc.

PurposeTo implement an accelerated five-dimensional (5D) echo-planar J-resolved spectroscopic imaging sequence combining 3 spatial and 2 spectral encoding dimensions and to apply the sequence in human brain.MethodsAn echo planar readout was used to acquire a single spatial and a single spectral dimension during one readout. Nonuniform sampling was applied to the two phase-encoded spatial directions and the indirect spectral dimension. Nonlinear reconstruction was used to minimize the ℓ1-norm or the total variation and included a spectral mask to enhance sparsity. Retrospective reconstructions at multiple undersamplings were performed in phantom. Ten healthy volunteers were scanned with 8× undersampling and compared to a fully sampled single slice scan.ResultsRetrospective reconstruction of fully sampled phantom data showed excellent quality at 4×, 8×, 12×, and 16× undersampling using either reconstruction method. Reconstruction of prospectively acquired in vivo scans with 8× undersampling showed excellent quality in the occipito-parietal lobes and good quality in the frontal lobe, consistent with the fully sampled single slice scan.ConclusionBy utilizing nonuniform sampling with nonlinear reconstruction, 2D J-resolved spectra can be acquired over a 3D spatial volume with a total scan time of 20 min, which is reasonable for in vivo studies.

PurposeTo implement a 5D (three spatial + two spectral) correlated spectroscopic imaging sequence for application to human calf.Theory and methodsNonuniform sampling was applied across the two phase encoded dimensions and the indirect spectral dimension of an echo planar-correlated spectroscopic imaging sequence. Reconstruction was applied that minimized the group sparse mixed ℓ2,1-norm of the data. Multichannel data were compressed using a sensitivity map-based approach with a spatially dependent transform matrix and utilized the self-sparsity of the individual coil images to simplify the reconstruction.ResultsSingle channel data with 8× and 16× undersampling are shown in the calf of a diabetic patient. A 15-channel scan with 12× undersampling of a healthy volunteer was reconstructed using 5 virtual channels and compared to a fully sampled single slice scan. Group sparse reconstruction faithfully reconstructs the lipid cross peaks much better than ℓ1 minimization.ConclusionCOSY spectra can be acquired over a 3D spatial volume with scan time under 15 min using echo planar readout with highly undersampled data and group sparse reconstruction.