Calibrationless reconstruction of uniformly-undersampled multi-channel MR data with deep learning estimated ESPIRiT maps.

PURPOSE: To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (ESPIRiT) maps from uniformly-undersampled multi-channel MR data by deep learning. METHODS: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. RESULTS: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. CONCLUSION: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from the coil and protocol-specific data.

Real-time, single breath-hold, multi-slice, 2D cine radial MR image reconstruction using sc-GROG k-t ESPIRiT.

Background:Multi-slice, multiple breath-hold ECG-gated 2D cine MRI is a standard technique for evaluating heart function and restricted to one or two images per breath-hold. Therefore, the standard cine MRI requires long scan time and can result in slice-misalignments because of various breath-hold locations in the multiple acquisitions.Methods:This work proposes the sc-GROG based k-t ESPIRiT with Total Variation (TV) constraint (sc-GROG k-t ESPIRiT) to reconstruct unaliased cardiac real-time cine MR images from highly accelerated whole heart multi-slice, single breath-hold, real-time 2D cine radial data acquired using the balanced steady-state free precession (trueFISP) sequence in 8 patients. The proposed method quality is assessed via Artifact Power (AP), Root-Mean Square Error (RMSE), Structure Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), blood-pool to myocardial Contrast-to-Noise-Ratio (CNR), Signal-to-Noise-Ratio (SNR) and spatial-temporal intensity plots through the blood-myocardium boundary. The proposed method quantitative results are compared with the NUFFT based k-t ESPIRiT with Total Variation (TV) constraint (NUFFT k-t ESPIRiT) approach. Furthermore, clinical analysis and function quantification are assessed by Bland-Altman (BA) analyses.Results:As supported by the visual assessment and evaluation parameters, the reconstruction results of the sc-GROG k-t ESPIRiT approach provide an average 21%, 12%, 1% and 47% improvement in AP, RMSE, SSIM and PSNR, respectively in comparison to the NUFFT k-t ESPIRiT approach. Furthermore, the proposed method gives on average 45% and 58% improved blood-pool to myocardial CNR and SNR than the NUFFT k-t ESPIRiT approach. Also, from the BA plot, the proposed method gives better left ventricular and right ventricular function measurements as compared to the NUFFT k-t ESPIRiT scheme.Conclusions:The sc-GROG k-t ESPIRiT (Proposed Method) improves the spatio-temporal quality of the whole heart multi-slice, single breath-hold, real-time 2D cine radial MR and semi-automated analysis using standard clinical software, as compared to the NUFFT k-t ESPIRiT approach.

SURE-based automatic parameter selection for ESPIRiT calibration.

PURPOSE: ESPIRiT is a parallel imaging method that estimates coil sensitivity maps from the auto-calibration region (ACS). This requires choosing several parameters for the optimal map estimation. While fairly robust to these parameter choices, occasionally, poor selection can result in reduced performance. The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams. METHODS: By viewing ESPIRiT as a denoiser, Stein's unbiased risk estimate (SURE) is leveraged to automatically optimize parameter selection in a data-driven manner. The optimum parameters corresponding to the minimum true squared error, minimum SURE as derived from densely sampled, high-resolution, and non-accelerated data and minimum SURE as derived from ACS are compared using simulation experiments. To avoid optimizing the rank of ESPIRiT's auto-calibrating matrix (one of the parameters), a heuristic derived from SURE-based singular value thresholding is also proposed. RESULTS: Simulations show SURE derived from the densely sampled, high-resolution, and non-accelerated data to be an accurate estimator of the true mean squared error, enabling automatic parameter selection. The parameters that minimize SURE as derived from ACS correspond well to the optimal parameters. The soft-threshold heuristic improves computational efficiency while providing similar results to an exhaustive search. In-vivo experiments verify the reliability of this method. CONCLUSIONS: Using SURE to determine ESPIRiT parameters allows for automatic parameter selections. In-vivo results are consistent with simulation and theoretical results.

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 ℓp pseudo-norm Joint TV (ℓpJTV) 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 ℓpJTV 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 ℓpJTV regularization term (OSℓpJTV) outperformed the conventional method with the ℓ1 regularization term in terms of SNR and NRMSE. And the OSℓpJTV algorithm was slightly superior to the OSTV algorithm with the TV regularization term.

eIRIS: Eigen-analysis approach for improved spine multi-shot diffusion MRI.

Image reconstruction using image-space sampling function (IRIS) corrects motion-induced inter-shot phase variations using phase maps from navigator-echo for multi-shot diffusion MRI. However, the bandwidth along the phase-encoding direction of navigator-echo is usually lower than that of image-echo, and thus their geometric distortions may be different. This geometric mismatch is corrected in IRIS by using the B0 map from an additional scan. In this paper, we present an enhanced IRIS (eIRIS) method that remove the requirement of B0 map. eIRIS treats shots as virtual coils, and then uses an eigen-analysis-based approach, which is insensitive to geometric mismatch, to estimates coil sensitivity maps containing the inter-shot phase variations. The final image is reconstructed under the framework of SENSE. Simulation, phantom, and cervical spine experiments were performed to evaluate the eIRIS method. The images generated by IRIS without B0 correction contain severe artifacts. eIRIS obtains results without noticeable artifacts and comparable to those of IRIS with B0 correction and GRAPPA with a compact kernel (GRAPPA-CK) method. eIRIS slightly outperforms GRAPPA-CK in the terms of normalized root-mean-square error and signal-to-noise ratio. eIRIS has the potential to obtain high-quality diffusion-weighted images and will benefit the research and clinical diagnosis of spinal cord.

Wave-CAIPI for highly accelerated MP-RAGE imaging.

PURPOSE: To introduce a highly accelerated T1-weighted magnetization-prepared rapid gradient echo (MP-RAGE) acquisition that uses wave-controlled aliasing in parallel imaging (wave-CAIPI) encoding to retain high image quality. METHODS: Significant acceleration of the MP-RAGE sequence is demonstrated using the wave-CAIPI technique. Here, sinusoidal waveforms are used to spread aliasing in all three directions to improve the g-factor. Combined with a rapid (2 s) coil sensitivity acquisition and data-driven trajectory calibration, we propose an online integrated acquisition-reconstruction pipeline for highly efficient MP-RAGE imaging. RESULTS: The 9-fold accelerated MP-RAGE acquisition can be performed in 71 s, with a maximum and average g-factor of gmax = 1.27 and gavg = 1.06 at 3T. Compared with the state-of-the-art method controlled aliasing in parallel imaging results in higher acceleration (2D-CAIPIRINHA), this is a factor of 4.6/1.4 improvement in gmax /gavg . In addition, we demonstrate a 57 s acquisition at 7T with 12-fold acceleration. This acquisition has a g-factor performance of gmax = 1.15 and gavg = 1.04. CONCLUSION: Wave encoding overcomes the g-factor noise amplification penalty and allows for an order of magnitude acceleration of MP-RAGE acquisitions. Magn Reson Med 79:401-406, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities.

Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.

Estimating absolute-phase maps using ESPIRiT and virtual conjugate coils.

PURPOSE: To develop an ESPIRiT-based method to estimate coil sensitivities with image phase as a building block for efficient and robust image reconstruction with phase constraints. THEORY AND METHODS: ESPIRiT is a new framework for calibration of the coil sensitivities and reconstruction in parallel magnetic resonance imaging. Applying ESPIRiT to a combined set of physical and virtual conjugate coils (VCC-ESPIRiT) implicitly exploits conjugate symmetry in k-space similar to VCC-GRAPPA. Based on this method, a new post-processing step is proposed for the explicit computation of coil sensitivities that include the absolute phase of the image. The accuracy of the computed maps is directly validated using a test based on projection onto fully sampled coil images and also indirectly in phase-constrained parallel-imaging reconstructions. RESULTS: The proposed method can estimate accurate sensitivities which include low-resolution image phase. In case of high-frequency phase variations VCC-ESPIRiT yields an additional set of maps that indicates the existence of a high-frequency phase component. Taking this additional set of maps into account can improve the robustness of phase-constrained parallel imaging. CONCLUSION: The extended VCC-ESPIRiT is a useful tool for phase-constrained imaging. Magn Reson Med 77:1201-1207, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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.