Peng, Xi

AbstractPurposeTo develop a method that achieves simultaneous brain and neck time‐of‐flight (ToF) magnetic resonance angiography (MRA) within feasible scan timeframes.MethodsLocalized quadratic (LQ) encoding is efficient for both signal‐to‐noise ratio (SNR) and in‐flow enhancement. We proposed a spiral multiband LQ method to enable simultaneous intracranial and carotid ToF‐MRA within a single scan. To address the venous signal contamination that becomes a challenge with multiband (MB) ToF, tilt‐optimized non‐saturated excitation (TONE) and partial‐Fourier slice selection (PFSS) were further introduced in the LQ framework to mitigate the venous signal and improve artery contrast. A sequential spiral MB and LQ reconstruction pipeline was employed to obtain the brain‐and‐neck image volumes.ResultsThe proposed MB method was able to achieve simultaneous brain and neck ToF‐MRA within a 2:50‐min scan. The complementarily boosted SNR‐efficiency by MB and LQ acquisitions allows for the increased spatial coverage without increase in scan time or noticeable compromise in SNR. The incorporation of both TONE and PFSS effectively alleviated the venous contamination with improved small vessel sensitivity. Selection of scan parameters such as the LQ factor and flip angle reflected the trade‐off among SNR, blood contrast, and venous suppression.ConclusionsA novel MB spiral LQ approach was proposed to enable fast intracranial and carotid ToF‐MRA with minimized venous corruption. The method has shown promise in MRA applications where large spatial coverage is necessary.

Purpose The combination of SENSE and spiral imaging with fat/water separation enables high temporal efficiency. However, the corresponding computation increases due to the blurring/deblurring operation across the multi‐channel data. This study presents two alternative models to simplify computational complexity in the original full model (model 1). The performances of the models are evaluated in terms of the computation time and reconstruction error. Methods Two approximated spiral MRI reconstruction models were proposed: the comprehensive blurring before coil operation (model 2) and the regional blurring before coil operation (model 3), respectively, by altering the order of coil‐sensitivity encoding process to distribute signals among the multi‐channel coils. Four subjects were recruited for scanning both fully sampled T 1 ‐ and T 2 ‐weighted brain image data with simulated undersampling for testing the computational efficiency and accuracy on the approximation models. Results Based on the examples, the computation time can be reduced to 31%–47% using model 2, and to 39%–56% using model 3. The quality of the water image remains unchanged among the three models, whereas the primary difference in image quality is in the fat channel. The fat images from model 3 are consistent with those from model 1, but those from model 2 have higher normalized error, differing by up to 4.8%. Conclusion Model 2 provides the fastest computation but exhibits higher error in the fat channel, particularly in the high field and with long acquisition window. Model 3, an abridged alternative, is also faster than the full model and can maintain high accuracy in reconstruction.

PurposeTo improve calibrationless parallel imaging using pre‐learned subspaces of coil sensitivity functions.Theory and MethodsA subspace‐based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method.ResultsWith no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state‐of‐the‐art methods including JSENSE, DeepSENSE, P‐LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system.ConclusionA subspace‐based method named JSENSE‐Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

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

PurposeTo address the need for a method to acquire 3D data for MR elastography (MRE) of the whole brain with substantially improved spatial resolution, high SNR, and reduced acquisition time compared with conventional methods.MethodsWe combined a novel 3D spiral staircase data‐acquisition method with a spoiled gradient‐echo pulse sequence and MRE motion‐encoding gradients (MEGs). The spiral‐out acquisition permitted use of longer‐duration motion‐encoding gradients (ie, over two full oscillatory cycles) to enhance displacement SNR, while still maintaining a reasonably short TE for good phase‐SNR. Through‐plane parallel imaging with low noise penalties was implemented to accelerate acquisition along the slice direction. Shared anatomical information was exploited in the deblurring procedure to further boost SNR for stiffness inversion.ResultsIn vivo and phantom experiments demonstrated the feasibility of the proposed method in producing brain MRE results comparable to the spin‐echo–based approaches, both qualitatively and quantitatively. High‐resolution (2‐mm isotropic) brain MRE data were acquired in 5 minutes using our method with good SNR. Joint deblurring with shared anatomical information produced SNR‐enhanced images, leading to upward stiffness estimation.ConclusionA novel 3D gradient‐echo–based approach has been designed and implemented, and shown to have promising potential for fast and high‐resolution in vivo MRE of the whole brain.

PurposeTo develop a robust, accurate, and accelerated T1ρ quantification solution for submillimeter in vivo whole‐brain imaging.MethodsA multislice T1ρ mapping solution (MS‐T1ρ) was developed based on a two‐acquisition scheme using turbo spin echo with RF cycling to allow for whole‐brain coverage with 0.8‐mm in‐plane resolution. A compressed sensing–based fast imaging method, SCOPE, was used to accelerate the MS‐T1ρ acquisition time to a total scan time of 3 minutes 31 seconds. A phantom experiment was conducted to assess the accuracy of MS‐T1ρ by comparing the T1ρ value obtained using MS‐T1ρ with the reference value obtained using the standard single‐slice T1ρ mapping method. In vivo scans of 13 volunteers were acquired prospectively to validate the robustness of MS‐T1ρ.ResultsIn the phantom study, the T1ρ values obtained with MS‐T1ρ were in good agreement with the reference T1ρ values (R2 = 0.9991) and showed high consistency throughout all slices (coefficient of variation = 2.2 ± 2.43%). In the in vivo experiments, T1ρ maps were successfully acquired for all volunteers with no visually noticeable artifacts. There was no significant difference in T1ρ values between MS‐T1ρ acquisitions and fully sampled acquisitions for all brain tissues (p‐value > .05). In the intraclass correlation coefficient and Bland‐Altman analyses, the accelerated T1ρ measurements show moderate to good agreement to the fully sampled reference values.ConclusionThe proposed MS‐T1ρ solution allows for high‐resolution whole‐brain T1ρ mapping within 4 minutes and may provide a potential tool for investigating neural diseases.