Estimating absolute-phase maps using ESPIRiT and virtual conjugate coils

DPP: deep phase prior for parallel imaging with wave encoding

Objective.In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.Approach.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness.Main results.Ablation experiments were conducted to ascertain the proposed method's capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%.Significance.The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at:https://github.com/sober235/DPP.

High angular resolution susceptibility imaging and estimation of fiber orientation distribution functions in primate brain

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution.

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

PURPOSE

Diffusion weighted Fast Spin Echo (DW-FSE) is a promising approach for distortionless DW imaging that is robust to system imperfections such as eddy currents and off-resonance. Due to non-Carr-Purcell-Meiboom-Gill (CPMG) magnetization, most DW-FSE sequences discard a large fraction of the signal ( 2 - 2 × $$ \sqrt{2}-2\times $$ ), reducing signal-to-noise ratio (SNR) efficiency compared to DW-EPI. The full FSE signal can be preserved by quadratically incrementing the transmit phase of the refocusing pulses, but this method of resolving non-CPMG magnetization has only been applied to single-shot DW-FSE due to challenges associated with image reconstruction. We present a joint linear reconstruction for multishot quadratic phase increment data that addresses these challenges and corrects ghosting from both shot-to-shot phase and intrashot signal oscillations. Multishot imaging reduces T2 blur and joint reconstruction of shots improves g-factor performance. A thorough analysis on the condition number of the proposed linear system is described.

METHODS

A joint multishot reconstruction is derived from the non-CPMG signal model. Multishot quadratic phase increment DW-FSE was tested in a standardized diffusion phantom and compared to single-shot DW-FSE and DW-EPI in vivo in the brain, cervical spine, and prostate. The pseudo multiple replica technique was applied to generate g-factor and SNR maps.

RESULTS

The proposed joint shot reconstruction eliminates ghosting from shot-to-shot phase and intrashot oscillations. g-factor performance is improved compared to previously proposed reconstructions, permitting efficient multishot imaging. apparent diffusion coefficient estimates in phantom experiments and in vivo are comparable to those obtained with conventional methods.

CONCLUSION

Multi-shot non-CPMG DW-FSE data with full signal can be jointly reconstructed using a linear model.

Virtual Conjugate Coil for Improving KerNL Reconstruction

Ker NL is a general kernel-based framework for auto calibrated reconstruction method, which does not need any explicit formulas of the kernel function for characterizing nonlinear relationships between acquired and unacquired k-space data. It is non-iterative without requiring a large amount of computational costs. Since the limited autocalibration signals (ACS) are acquired to perform KerNL calibration and the calibration suffers from the overfitting problem, more training data can improve the kernel model accuracy. In this work, virtual conjugate coil data are incorporated into the KerNL calibration and estimation process for enhancing reconstruction performance. Experimental results show that the proposed method can further suppress noise and aliasing artifacts with fewer ACS data and higher acceleration factors. Computation efficiency is still retained to keep fast reconstruction with the random projection.

Volumetric coronary endothelial function assessment: a feasibility study exploiting stack-of-stars 3D cine MRI and image-based respiratory self-gating

Abnormal coronary endothelial function (CEF), manifesting as depressed vasoreactive responses to endothelial-specific stressors, occurs early in atherosclerosis, independently predicts cardiovascular events, and responds to cardioprotective interventions. CEF is spatially heterogeneous along a coronary artery in patients with atherosclerosis, and thus recently developed and tested non-invasive 2D MRI techniques to measure CEF may not capture the extent of changes in CEF in a given coronary artery. The purpose of this study was to develop and test the first volumetric coronary 3D MRI cine method for assessing CEF along the proximal and mid-coronary arteries with isotropic spatial resolution and in free-breathing. This approach, called 3D-Stars, combines a 6 min continuous, untriggered golden-angle stack-of-stars acquisition with a novel image-based respiratory self-gating method and cardiac and respiratory motion-resolved reconstruction. The proposed respiratory self-gating method agreed well with respiratory bellows and center-of-k-space methods. In healthy subjects, 3D-Stars vessel sharpness was non-significantly different from that by conventional 2D radial in proximal segments, albeit lower in mid-portions. Importantly, 3D-Stars detected normal vasodilatation of the right coronary artery in response to endothelial-dependent isometric handgrip stress in healthy subjects. Coronary artery cross-sectional areas measured using 3D-Stars were similar to those from 2D radial MRI when similar thresholding was used. In conclusion, 3D-Stars offers good image quality and shows feasibility for non-invasively studying vasoreactivity-related lumen area changes along the proximal coronary artery in 3D during free-breathing.

Highly accelerated parallel MRI using wave encoding and virtual conjugate coils

PURPOSE

To propose a novel model incorporating virtual conjugate coil (VCC) reconstruction and wave encoding (Wave) for improved parallel MRI.

THEORY AND METHODS

A novel model (VCC-Wave) incorporating VCC and Wave is proposed. The correlation matrix of the encoding operator is introduced to analyze the encoding capability. In addition, simulation experiments are conducted to gain insights into VCC-Wave. In vivo experiments are performed to compare VCC-Wave with alternative methods.

RESULTS

The correlation matrix and the simulation experiments show that the proposed VCC-Wave can utilize more priors of Wave under the VCC framework. In vivo experiments show that the proposed VCC-Wave can achieve good image quality at a 6-fold acceleration in high-resolution and high-bandwidth cases, indicating an improvement over the original Wave technique.

CONCLUSION

The proposed VCC-Wave can not only combine the advantages of both the VCC and Wave but also exploit more priors of Wave under the VCC framework. The improvement in VCC-Wave alleviates the limitation of Wave in high-resolution and high-bandwidth cases.

Maxwell parallel imaging

PURPOSE

To develop a general framework for parallel imaging (PI) with the use of Maxwell regularization for the estimation of the sensitivity maps (SMs) and constrained optimization for the parameter-free image reconstruction.

THEORY AND METHODS

Certain characteristics of both the SMs and the images are routinely used to regularize the otherwise ill-posed optimization-based joint reconstruction from highly accelerated PI data. In this paper, we rely on a fundamental property of SMs-they are solutions of Maxwell equations-we construct the subspace of all possible SM distributions supported in a given field-of-view, and we promote solutions of SMs that belong in this subspace. In addition, we propose a constrained optimization scheme for the image reconstruction, as a second step, once an accurate estimation of the SMs is available. The resulting method, dubbed Maxwell parallel imaging (MPI), works for both 2D and 3D, with Cartesian and radial trajectories, and minimal calibration signals.

RESULTS

The effectiveness of MPI is illustrated for various undersampling schemes, including radial, variable-density Poisson-disc, and Cartesian, and is compared against the state-of-the-art PI methods. Finally, we include some numerical experiments that demonstrate the memory footprint reduction of the constructed Maxwell basis with the help of tensor decomposition, thus allowing the use of MPI for full 3D image reconstructions.

CONCLUSION

The MPI framework provides a physics-inspired optimization method for the accurate and efficient image reconstruction from arbitrary accelerated scans.

Improved real-time tagged MRI using REALTAG

OBJECTIVES

To evaluate a novel method for real-time tagged MRI with increased tag persistence using phase sensitive tagging (REALTAG), demonstrated for speech imaging.

METHODS

Tagging is applied as a brief interruption to a continuous real-time spiral acquisition. REALTAG is implemented using a total tagging flip angle of 180° and a novel frame-by-frame phase sensitive reconstruction to remove smooth background phase while preserving the sign of the tag lines. Tag contrast-to-noise ratio of REALTAG and conventional tagging (total flip angle of 90°) is simulated and evaluated in vivo. The ability to extend tag persistence is tested during the production of vowel-to-vowel transitions by American English speakers.

RESULTS

REALTAG resulted in a doubling of contrast-to-noise ratio at each time point and increased tag persistence by more than 1.9-fold. The tag persistence was 1150 ms with contrast-to-noise ratio >6 at 1.5T, providing 2 mm in-plane resolution, 179 frames/s, with 72.6 ms temporal window width, and phase sensitive reconstruction. The new imaging window is able to capture internal tongue deformation over word-to-word transitions in natural speech production.

CONCLUSION

Tag persistence is substantially increased in intermittently tagged real-time MRI by using the improved REALTAG method. This makes it possible to capture longer motion patterns in the tongue, such as cross-word vowel-to-vowel transitions, and provides a powerful new window to study tongue biomechanics.

Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils

To improve the reconstruction condition and alleviate the noise amplification of GRAPPA reconstruction by aggregating the phase conjugated and nonlinear kernel mapped coils with the original physical coil. Nonlinear GRAPPA (NL-GRAPPA) is a kernel-based non-iterative approach which can reduce noise-induced error in GRAPPA reconstruction. And virtual conjugate coil (VCC) embeds the conjugate symmetric property of k-space into GRAPPA data synthesis to improve reconstruction condition. This work proposed NL-VCC-GRAPPA to jointly utilize the nonlinear mapped virtual coil and phase conjugated virtual coil to further reduce noise amplification in parallel imaging. In vivo static and dynamic 2D imaging accelerated by uniform undersampling schemes were performed to evaluate the proposed method in terms of visual image quality, root-mean-square-error (RMSE), and geometry factor (g-factor). The effects of acceleration factors, calibration data size and kernel shape on the proposed model were also separately analyzed and discussed. The proposed method illustrated improved visual image quality evidenced by reduced retrospective RMSE and prospective g-factor comparing with conventional GRAPPA and the recently proposed iterative SENSE-LORAKS reconstructions. Although a larger amount of calibration data and smaller kernel size were required to stabilize the calibration of fourfold extended kernel for the proposed method, it was non-iterative and relatively insensitive to parameter adjustment in the applications. The proposed NL-VCC-extension to conventional GRAPPA brings visible improvements for imaging scenarios accelerated by the widely available uniform undersampling schemes in a practically efficient manner without iteration.

A variable flip angle golden-angle-ordered 3D stack-of-radial MRI technique for simultaneous proton resonant frequency shift and T1 -based thermometry

PURPOSE

To develop and evaluate a variable-flip-angle golden-angle-ordered 3D stack-of-radial MRI technique for simultaneous proton resonance frequency shift (PRF) and T₁ -based thermometry in aqueous and adipose tissues, respectively.

METHODS

The proposed technique acquires multiecho radial k-space data in segments with alternating flip angles to measure 3D temperature maps dynamically on the basis of PRF and T₁ . A sliding-window k-space weighted image contrast filter is used to increase temporal resolution. PRF is measured in aqueous tissues and T₁ in adipose tissues using fat/water masks. The accuracy for T₁ quantification was evaluated in a reference T₁ /T₂ phantom. In vivo nonheating experiments were conducted in healthy subjects to evaluate the stability of PRF and T₁ in the brain, prostate, and breast. The proposed technique was used to monitor high-intensity focused ultrasound (HIFU) ablation in ex vivo porcine fat/muscle tissues and compared to temperature probe readings.

RESULTS

The proposed technique achieved 3D coverage with 1.1-mm to 1.3-mm in-plane resolution and 2-s to 5-s temporal resolution. During 20 to 30 min of nonheating in vivo scans, the temporal coefficient of variation for T₁ was <5% in the brain, prostate, and breast fatty tissues, while the standard deviation of relative PRF temperature change was within 3°C in aqueous tissues. During ex vivo HIFU ablation, the temperatures measured by PRF and T₁ were consistent with temperature probe readings, with an absolute mean difference within 2°C.

CONCLUSION

The proposed technique achieves simultaneous PRF and T₁ -based dynamic 3D MR temperature mapping in aqueous and adipose tissues. It may be used to improve MRI-guided thermal procedures.

ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging

Robustness against data inconsistencies, imaging artifacts and acquisition speed are crucial factors limiting the possible range of applications for magnetic resonance imaging (MRI). Therefore, we report a novel calibrationless parallel imaging technique which simultaneously estimates coil profiles and image content in a relaxed forward model. Our method is robust against a wide class of data inconsistencies, minimizes imaging artifacts and is comparably fast, combining important advantages of many conceptually different state-of-the-art parallel imaging approaches. Depending on the experimental setting, data can be undersampled well below the Nyquist limit. Here, even high acceleration factors yield excellent imaging results while being robust to noise and the occurrence of phase singularities in the image domain, as we show on different data. Moreover, our method successfully reconstructs acquisitions with insufficient field-of-view. We further compare our approach to ESPIRiT and SAKE using spin-echo and gradient echo MRI data from the human head and knee. In addition, we show its applicability to non-Cartesian imaging on radial FLASH cardiac MRI data. Using theoretical considerations, we show that ENLIVE can be related to a low-rank formulation of blind multi-channel deconvolution, explaining why it inherently promotes low-rank solutions.

Eigenvector-based SPIRiT Parallel MR Imaging Reconstruction based on ℓp pseudo-norm Joint Total Variation

Parallel Magnetic Resonance (MR) imaging is a well-established acceleration technique based on the spatial sensitivities of array receivers. Eigenvector-based SPIRiT (ESPIRiT) is a new parallel MR imaging reconstruction method that combines the advantages of the SENSE and GRAPPA methods. It estimates multiple sets of the sensitivity maps from the calibration matrix that is constructed from the auto-calibration data. To improve the quality of the reconstructed image, we introduced the Total Variation (TV) and ℓ_p pseudo-norm Joint TV (ℓ_pJTV) regularization terms to the ESPIRiT model for parallel MR imaging reconstruction, which were solved by using the Operator Splitting (OS) method. The resulting denoising problems with the TV and ℓ_pJTV regularization terms were solved by exploiting the Majorization Minimization method. Simulation experiments on two in vivo data sets demonstrated that the proposed OS algorithm with the TV regularization term (OSTV) and OS algorithm with the ℓ_pJTV regularization term (OSℓ_pJTV) outperformed the conventional method with the ℓ₁ regularization term in terms of SNR and NRMSE. And the OSℓ_pJTV algorithm was slightly superior to the OSTV algorithm with the TV regularization term.

Wave-LORAKS: Combining wave encoding with structured low-rank matrix modeling for more highly accelerated 3D imaging

PURPOSE

Wave-CAIPI is a novel acquisition approach that enables highly accelerated 3D imaging. This paper investigates the combination of Wave-CAIPI with LORAKS-based reconstruction (Wave-LORAKS) to enable even further acceleration.

METHODS

LORAKS is a constrained image reconstruction framework that can impose spatial support, smooth phase, sparsity, and/or parallel imaging constraints. LORAKS requires minimal prior information, and instead uses the low-rank subspace structure of the raw data to automatically learn which constraints to impose and how to impose them. Previous LORAKS implementations addressed 2D image reconstruction problems. In this work, several recent advances in structured low-rank matrix recovery were combined to enable large-scale 3D Wave-LORAKS reconstruction with improved quality and computational efficiency. Wave-LORAKS was investigated by retrospective subsampling of two fully sampled Wave-encoded 3D MPRAGE datasets, and comparisons were made against existing Wave reconstruction approaches.

RESULTS

Results show that Wave-LORAKS can yield higher reconstruction quality with 16×-accelerated data than is obtained by traditional Wave-CAIPI with 9×-accerated data.

CONCLUSIONS

There are strong synergies between Wave encoding and LORAKS, which enables Wave-LORAKS to achieve higher acceleration and more flexible sampling compared to Wave-CAIPI.