SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation. METHODS: SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data. RESULTS: We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10 × $$ 10\times $$ ). Our results also highlight the importance of different modules of SPICER-including the DMBA, the CSM estimation, and the SPICER training loss-on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited. CONCLUSION: Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully sampled ground-truth data.

A flexible 16-channel custom coil array for accelerated imaging of upper and infraglottic airway at 3 T.

PURPOSE: To develop a custom coil and evaluate its utility for accelerated upper and infraglottic airway MRI at 3 T. METHODS: A 16-channel flexible and anatomy-conforming coil was developed to provide localized sensitivity over upper and infraglottic airway regions of interest. Parallel-imaging capabilities were compared against existing head and head-neck coils. SENSE geometry factor losses were quantified for retrospectively accelerating 3D MRI. Blinded image-quality ratings from two experts were performed. Spiral GRAPPA reconstructions were evaluated for a speaking task at a time resolution of 40 ms. Contrast-to-noise ratios between air and tissue at key landmarks along the vocal tract were compared. SENSE imaging with the custom coil in the lateral recumbent posture was evaluated. Multislice imaging was performed to image swallowing at 17 ms/frame via constrained reconstruction. RESULTS: The custom coil showed improved SENSE imaging up to 3-fold acceleration when accelerated along either the anterior-posterior or the superior-inferior direction and a net 4-fold acceleration when accelerated along both directions. Spiral GRAPPA reconstructions with the custom coil showed higher contrast-to-noise ratio when compared with existing coils. In the lateral posture, robust SENSE imaging was achieved at up to 2-fold and 3-fold acceleration levels in the superior-inferior and anterior-posterior directions, respectively. Key events of swallowing in the multislice dynamic images were identified by an otolaryngologist. CONCLUSION: The coil provided improved parallel imaging of upper and infraglottic airway in both supine and lateral recumbent postures. It enabled efficient accelerated dynamic imaging of speaking and swallowing.

Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL.

PURPOSE: The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths. METHODS: A single unrolled architecture, which offers good reconstructions for multiple acquisition settings, is introduced. The proposed scheme adapts the model to each setting by scaling the convolutional neural network (CNN) features and the regularization parameter with appropriate weights. The scaling weights and regularization parameter are derived using a multilayer perceptron model from conditional vectors, which represents the specific acquisition setting. The perceptron parameters and the CNN weights are jointly trained using data from multiple acquisition settings, including differences in field strengths, acceleration, and contrasts. The conditional network is validated using datasets acquired with different acquisition settings. RESULTS: The comparison of the adaptive framework, which trains a single model using the data from all the settings, shows that it can offer consistently improved performance for each acquisition condition. The comparison of the proposed scheme with networks that are trained independently for each acquisition setting shows that it requires less training data per acquisition setting to offer good performance. CONCLUSION: The Ada-MoDL framework enables the use of a single model-based unrolled network for multiple acquisition settings. In addition to eliminating the need to train and store multiple networks for different acquisition settings, this approach reduces the training data needed for each acquisition setting.

Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters.

The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [Ktrans ], fractional volume of the extravascular extracellular space [ve ], and blood plasma volume fraction [vp ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of Ktrans and ve , and less than 11% in the estimation of vp . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.

Virtual slice concept for improved simultaneous multi-slice MRI employing an extended leakage constraint.

PURPOSE: To develop a novel, simultaneous multi-slice (SMS) reconstruction that extends an inter-slice leakage constraint to intra-slice aliasing with a virtual slice concept for artifact reduction. METHODS: Inter-slice leakage constraint has been used for SMS reconstruction that mitigates leakage artifacts from the adjacent slices. In this work, the leakage constraint is extended to more general framework that includes SMS and parallel MRI as special cases by viewing intra-slice aliasing artifacts from undersampling as virtual slices while imposing data fidelity to ensure the measurement consistency. In this way, the reconstruction makes it feasible to directly estimate the individual slices from the undersampled SMS acquisition as a one-step method. The performance of the extended method is evaluated with data acquired using 2D GRE and EPI sequences. RESULTS: Compared to a two-step method that performs slice unaliasing followed by inplane unaliasing, the proposed one-step method reduces aliasing artifacts by employing the extended leakage constraint while lowering the noise amplification by improving the conditioning for the inverse problem. CONCLUSIONS: The proposed one-step method takes advantage of virtual slices as additional encoding power for improved image quality. We successfully demonstrated that the proposed one-step method minimizes a trade-off between aliasing artifacts and amplified noises over the two-step method.

Full utilization of conjugate symmetry: combining virtual conjugate coil reconstruction with partial Fourier imaging for g-factor reduction in accelerated MRI.

PURPOSE: In this study we propose a method to combine the parallel virtual conjugate coil (VCC) reconstruction with partial Fourier (PF) acquisition to improve reconstruction conditioning and reduce noise amplification in accelerated MRI where PF is used. METHODS: Accelerated measurements are reconstructed in k-space by GRAPPA, with a VCC reconstruction kernel trained and applied in the central, symmetrically sampled part of k-space, while standard reconstruction is performed on the asymmetrically sampled periphery. The two reconstructed regions are merged to form a full reconstructed dataset, followed by PF reconstruction. The method is tested in vivo using T1-weighted spin-echo and T2*-weighted gradient-echo echo planar imaging (EPI) sequences, using both in-plane and simultaneous multislice (SMS) acceleration, at 1.5T and 3T field strengths. Noise amplification is estimated with theoretical calculations and pseudo-multiple-replica computations, for different PF factors, using zero-filling, homodyne, and projection onto convex sets (POCS) PF reconstruction. RESULTS: Depending on the PF algorithm and the inherent benefit of VCC reconstruction without PF, approximately 35% to 80%, 15% to 60%, and 5% to 30% of that intrinsic SNR gain can be retained for PF factors 7/8, 6/8, and 5/8, respectively, by including the VCC signals in the reconstruction. Compared with VCC-reconstructed acquisitions of higher acceleration, without PF, but having the same net acceleration, the combined method can provide a higher SNR if the inherent benefit of VCC is low or moderate. CONCLUSION: The proposed technique enables the partial application of VCC reconstruction to measurements with PF using either in-plane or SMS acceleration, and therefore can reduce the noise amplification of such acquisitions.

Controlling the object phase for g-factor reduction in phase-Constrained parallel MRI using spatially selective RF pulses.

PURPOSE: Parallel imaging generally entails a reduction in the signal-to-noise ratio of the final image. Phase-constrained methods aim to improve reconstruction quality by using symmetry properties of k-space. Noise amplification in phase-constrained reconstruction depends heavily on the object background phase. The purpose of this work is to present a new approach of using tailored radiofrequency pulses to optimize the object phase distribution in order to maximize the benefit of phase-constrained reconstruction, and to minimize the noise amplification. METHODS: Intrinsic object phase and coil sensitivity profiles are measured in a prescan. Optimal phase distribution is computed to maximize signal-to-noise ratio in the given setup. Tailored radiofrequency pulses are designed to introduce the optimal phase map in the following accelerated acquisitions, subsequently reconstructed by phase-constrained methods. The potential of the method is demonstrated in vivo with in-plane accelerated (8x) and simultaneous multislice (3x) acquisitions. RESULTS: Mean g-factors are reduced by up to a factor of 2 compared with conventional techniques when an appropriate phase-constrained reconstruction is applied to phase-optimized acquisitions, enhancing the signal-to-noise ratio of the final images and the visibility of small details. CONCLUSIONS: Combining phase-constrained reconstruction and phase optimization by tailored radiofrequency pulses can provide notable improvement in the signal-to-noise ratio and reconstruction quality of accelerated MRI. Magn Reson Med 79:2113-2125, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Multichannel compressed sensing MR image reconstruction using statistically optimized nonlinear diffusion.

PURPOSE: Eliminate the need for parametric tuning in total variation (TV) based multichannel compressed-sensing image reconstruction using statistically optimized nonlinear diffusion without compromising image quality. THEORY AND METHODS: Nonlinear diffusion controls the denoising process using a contrast parameter that separates the gradients corresponding to noise and true edges in the image. This parameter is statistically estimated from the variance of combined image gradient to yield minimum steady-state reconstruction error. In addition, it uses acquired k-space data to bias the diffusion process toward an optimal solution. RESULTS: The proposed method is compared with TV using a set of noisy spine and brain data sets for three, four, and five-fold undersampling. It is observed that the choice of regularization parameter (step size) of TV-based methods requires prior tuning based on an extensive search procedure. In contrast, statistical estimation of contrast parameter removes this need for tuning by adapting to the changes in data sets and undersampling levels. CONCLUSIONS: Although an a-priori tuned TV-based reconstruction can provide a comparable image quality to that of controlled nonlinear diffusion, there are practical limitations with regard to its time complexity for ad-hoc applications to multicoil compressed-sensing reconstruction. Magn Reson Med 78:754-762, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

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

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

SENSE reconstruction for multiband EPI including slice-dependent N/2 ghost correction.

PURPOSE: Sensitivity encoding (SENSE) reconstruction of multiband echo planar imaging (EPI) may cause artifacts when simultaneously excited slices require different phase correction to remove the EPI-specific ghost shifted by half of the matrix size (N). We propose a simplified solution of this problem that combines SENSE unfolding with the EPI phase correction in the image domain. THEORY AND METHODS: Slice-dependent phase correction was included in equations linking folded slice images reconstructed separately from even and odd echoes of all receivers with the true images of each slice. Compared with the previously proposed combination of ghost suppression with SENSE based on a direct image fit to echo data, our method reduces the problem complexity by N(2) /4. It was applied to reconstruct images of phantoms and human brain. RESULTS: The proposed method tolerates high differences of phase correction between slices, which may result, e.g., from anisotropic gradient delay. It suppresses artifacts better than standard SENSE even when the latter is repeated with the ghost correction targeting each of the slices and works significantly faster than the direct fit version of ghost-correcting SENSE. CONCLUSION: With the proposed modification SENSE allows a rapid separation of slices simultaneously acquired with EPI even when the phase correction needed for each slice is different. Magn Reson Med 76:873-879, 2016. © 2015 Wiley Periodicals, Inc.

P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data.

PURPOSE: To propose and evaluate P-LORAKS a new calibrationless parallel imaging reconstruction framework. THEORY AND METHODS: LORAKS is a flexible and powerful framework that was recently proposed for constrained MRI reconstruction. LORAKS was based on the observation that certain matrices constructed from fully sampled k-space data should have low rank whenever the image has limited support or smooth phase, and made it possible to accurately reconstruct images from undersampled or noisy data using low-rank regularization. This paper introduces P-LORAKS, which extends LORAKS to the context of parallel imaging. This is achieved by combining the LORAKS matrices from different channels to yield a larger but more parsimonious low-rank matrix model of parallel imaging data. This new model can be used to regularize the reconstruction of undersampled parallel imaging data, and implicitly imposes phase, support, and parallel imaging constraints without needing to calibrate phase, support, or sensitivity profiles. RESULTS: The capabilities of P-LORAKS are evaluated with retrospectively undersampled data and compared against existing parallel MRI reconstruction methods. Results show that P-LORAKS can improve parallel imaging reconstruction quality, and can enable the use of new k-space trajectories that are not compatible with existing reconstruction methods. CONCLUSION: The P-LORAKS framewok provides a new and effective way to regularize parallel imaging reconstruction.

Comparison of phase-constrained parallel MRI approaches: Analogies and differences.

PURPOSE: Phase-constrained parallel MRI approaches have the potential for significantly improving the image quality of accelerated MRI scans. The purpose of this study was to investigate the properties of two different phase-constrained parallel MRI formulations, namely the standard phase-constrained approach and the virtual conjugate coil (VCC) concept utilizing conjugate k-space symmetry. METHODS: Both formulations were combined with image-domain algorithms (SENSE) and a mathematical analysis was performed. Furthermore, the VCC concept was combined with k-space algorithms (GRAPPA and ESPIRiT) for image reconstruction. In vivo experiments were conducted to illustrate analogies and differences between the individual methods. Furthermore, a simple method of improving the signal-to-noise ratio by modifying the sampling scheme was implemented. RESULTS: For SENSE, the VCC concept was mathematically equivalent to the standard phase-constrained formulation and therefore yielded identical results. In conjunction with k-space algorithms, the VCC concept provided more robust results when only a limited amount of calibration data were available. Additionally, VCC-GRAPPA reconstructed images provided spatial phase information with full resolution. CONCLUSIONS: Although both phase-constrained parallel MRI formulations are very similar conceptually, there exist important differences between image-domain and k-space domain reconstructions regarding the calibration robustness and the availability of high-resolution phase information.

A subspace-based coil combination method for phased-array magnetic resonance imaging.

PURPOSE: Coil-by-coil reconstruction methods are followed by coil combination to obtain a single image representing a spin density map. Typical coil combination methods, such as square-root sum-of-squares and adaptive coil combining, yield images that exhibit spatially varying modulation of image intensity. Existing practice is to first combine coils according to a signal-to-noise criterion, then postprocess to correct intensity inhomogeneity. If inhomogeneity is severe, however, intensity correction methods can yield poor results. The purpose of this article is to present an alternative optimality criterion for coil combination; the resulting procedure yields reduced intensity inhomogeneity while preserving contrast. THEORY AND METHODS: A minimum mean squared error criterion is adopted for combining coils via a subspace decomposition. Techniques are compared using both simulated and in vivo data. RESULTS: Experimental results for simulated and in vivo data demonstrate lower bias, higher signal-to-noise ratio (about 7×) and contrast-to-noise ratio (about 2×), compared to existing coil combination techniques. CONCLUSION: The proposed coil combination method is noniterative and does not require estimation of coil sensitivity maps or image mask; the method is particularly suited to cases where intensity inhomogeneity is too severe for existing approaches.

Regularization method for phase-constrained parallel MRI.

PURPOSE: To implement a regularization method for the phase-constrained generalized partially parallel acquisitions (GRAPPA) algorithm to reduce image artifacts caused by data inconsistencies. METHODS: Phase-constrained GRAPPA reconstructions are implemented through the use of virtual coils. To that end, synthetic virtual coils are generated by using complex conjugate symmetric signals from the actual coils. Regularization is achieved by applying coefficient-based penalty factors during the GRAPPA calibration procedure. Different penalizing factors are applied for the actual and virtual coils. The method is tested in vivo using T2-weighted turbo spin echo (TSE) images. RESULTS: T2 signal decay perturbs conjugate k-space symmetry and produces artifacts in phase-constrained parallel MRI reconstructions of T2-weighted TSE images. Using the proposed regularization method, artifacts are suppressed at the cost of noise amplification. However, there is still a significant SNR gain compared with conventional GRAPPA. CONCLUSION: The proposed regularization method is an efficient approach for artifact suppression and maintains the SNR benefit of phase-constrained parallel MRI over conventional parallel MRI.

Constrained alternating minimization for parameter mapping (CAMP).

OBJECTIVE: To improve image quality in highly accelerated parameter mapping by incorporating a linear constraint that relates consecutive images. APPROACH: In multi-echo T1 or T2 mapping, scan time is often shortened by acquiring undersampled but complementary measures of k-space at each TE or TI. However, residual undersampling artifacts from the individual images can then degrade the quality of the final parameter maps. In this work, a new reconstruction method, dubbed Constrained Alternating Minimization for Parameter mapping (CAMP), is introduced. This method simultaneously extracts T2 or T1* maps in addition to an image for each TE or TI from accelerated datasets, leveraging the constraints of the decay to improve the reconstructed image quality. The model enforces exponential decay through a linear constraint, resulting in a biconvex objective function that lends itself to alternating minimization. The method was tested in four in vivo volunteer experiments and validated in phantom studies and healthy subjects, using T2 and T1 mapping, with accelerations of up to 12. MAIN RESULTS: CAMP is demonstrated for accelerated radial and Cartesian acquisitions in T2 and T1 mapping. The method is even applied to generate an entire T2 weighted image series from a single TSE dataset, despite the blockwise k-space sampling at each echo time. Experimental undersampled phantom and in vivo results processed with CAMP exhibit reduced artifacts without introducing bias. SIGNIFICANCE: For a wide array of applications, CAMP linearizes the model cost function without sacrificing model accuracy so that the well-conditioned and highly efficient reconstruction algorithm improves the image quality of accelerated parameter maps.

Noise amplification in parallel whole-head ultra-low-field magnetic resonance imaging using 306 detectors.

In ultra-low-field magnetic resonance imaging, arrays of up to hundreds of highly sensitive superconducting quantum interference devices (SQUIDs) can be used to detect the weak magnetic fields emitted by the precessing magnetization. Here, we investigate the noise amplification in sensitivity-encoded ultra-low-field MRI at various acceleration rates using a SQUID array consisting of 102 magnetometers, 102 gradiometers, or 306 magnetometers and gradiometers, to cover the whole head. Our results suggest that SQUID arrays consisting of 102 magnetometers and 102 gradiometers are similar in g-factor distribution. A SQUID array of 306 sensors (102 magnetometers and 204 gradiometers) only marginally improves the g-factor. Corroborating with previous studies, the g-factor in 2D sensitivity-encoded ultra-low-field MRI with 9 to 16-fold 2D accelerations using the SQUID array studied here may be acceptable.

Accelerating MR parameter mapping using sparsity-promoting regularization in parametric dimension.

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several-fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which uses smoothness of signal evolution in the parametric dimension within compressed sensing framework (p-CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T1 mapping and compared favorably to two representative reconstruction approaches, image space-based total variation regularization and an analytical model-based reconstruction. The proposed p-CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model-based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist.

Accelerated fractional ventilation imaging with hyperpolarized Gas MRI.

PURPOSE: To investigate the utility of accelerated imaging to enhance multibreath fractional ventilation (r) measurement accuracy using hyperpolarized gas MRI. Undersampling shortens the breath-hold time, thereby reducing the O2 -induced signal decay and allows subjects to maintain a more physiologically relevant breathing pattern. Additionally, it may improve r estimation accuracy by reducing radiofrequency destruction of hyperpolarized gas. METHODS: Image acceleration was achieved using an eight-channel phased array coil. Undersampled image acquisition was simulated in a series of ventilation images and data was reconstructed for various matrix sizes (48-128) using generalized auto-calibrating partially parallel acquisition. Parallel accelerated r imaging was also performed on five mechanically ventilated pigs. RESULTS: Optimal acceleration factor was fairly invariable (2.0-2.2×) over the range of simulated resolutions. Estimation accuracy progressively improved with higher resolutions (39-51% error reduction). In vivo r values were not significantly different between the two methods: 0.27 ± 0.09, 0.35 ± 0.06, 0.40 ± 0.04 (standard) versus 0.23 ± 0.05, 0.34 ± 0.03, 0.37 ± 0.02 (accelerated); for anterior, medial, and posterior slices, respectively, whereas the corresponding vertical r gradients were significant (P < 0.001): 0.021 ± 0.007 (standard) versus 0.019 ± 0.005 (accelerated) (cm(-1) ). CONCLUSION: Quadruple phased array coil simulations resulted in an optimal acceleration factor of ∼2× independent of imaging resolution. Results advocate undersampled image acceleration to improve accuracy of fractional ventilation measurement with hyperpolarized gas MRI.

Double cross magnetic wall decoupling for quadrature transceiver RF array coils using common-mode differential-mode resonators.

In contrast to linearly polarized RF coil arrays, quadrature transceiver coil arrays are capable of improving signal-to-noise ratio (SNR), spatial resolution, and parallel imaging performance. Owing to a reduced excitation power, a low specific absorption rate can also be obtained using quadrature RF coils. However, due to the complex nature of their structure and their electromagnetic properties, it is challenging to achieve sufficient electromagnetic decoupling while designing multichannel quadrature RF coil arrays, particularly in ultra-high fields. In this work, we proposed a double-cross magnetic wall decoupling for quadrature transceiver RF arrays and implemented the decoupling method on common-mode differential mode quadrature (CMDM) quadrature transceiver arrays at an ultrahigh field of 7 T. The proposed magnetic decoupling wall, comprised of two intrinsically decoupled loops, is used to reduce the mutual coupling between all the multi-mode currents present in the quadrature CMDM array. The decoupling network has no physical connection with the CMDMs' resonators, which provides less design constraint over size-adjustable RF arrays. To validate the feasibility of the proposed cross-magnetic decoupling wall, systematic studies on the decoupling performance based on the impedance of two intrinsic loops are numerically performed. A pair of quadrature transceiver CMDMs is constructed along with the proposed decoupling network, and their scattering matrix is characterized using a network analyzer. The measured results indicate that all the current modes from coupling are simultaneously suppressed using the proposed cross-magnetic wall. Moreover, field distribution and local specific absorption rate (SAR) are numerically obtained for a well-decoupled 8-channel quadrature knee-coil array.

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.