Dynamic parallel imaging at 9.4 T using reconfigurable receive coaxial dipoles

Parallel imaging is one of the key MRI technologies that allow reduction of image acquisition time. However, the parallel imaging reconstruction commonly leads to a signal-to-noise ratio (SNR) drop evaluated using a so-called geometrical factor (g-factor). The g-factor is minimized by increasing the number of array elements and their spatial diversity. At the same time, increasing the element count requires a decrease in their size. This may lead to insufficient coil loading, an increase in the relative noise contribution from the RF coil itself, and hence SNR reduction. Previously, instead of increasing the channel number, we introduced the concept of electronically switchable time-varying sensitivities, which was shown to improve parallel imaging performance. In this approach, each reconfigurable receive element supports two spatially distinct sensitivity profiles. In this work, we developed and evaluated a novel eight-element human head receive-only reconfigurable coaxial dipole array for human head imaging at 9.4 T. In contrast to the previously reported reconfigurable dipole array, the new design does not include direct current (DC) control wires connected directly to the dipoles. The coaxial cable itself is used to deliver DC voltage to the PIN diodes located at the ends of the antennas. Thus, the novel reconfigurable coaxial dipole design opens a way to scale the dynamic parallel imaging up to a realistic number of channels, that is, 32 and above. The novel array was optimized and tested experimentally, including in vivo studies. It was found that dynamic sensitivity switching provided an 8% lower mean and 33% lower maximum g-factor (for Ry  × Rz  = 2 × 2 acceleration) compared with conventional static sensitivities.

Utility of the New Faster Compressed SENSE MRCP at 3 Tesla MRI in Children with Pancreatitis

OBJECTIVE

To compare the acquisition time, diagnostic efficacy, and image quality of the newer compressed SENSE 3D MRCP (CS-3D MRCP) with conventional 3D MRCP (C-3D MRCP) in children with pancreatitis.

METHODS

A total of 24 children (2-17 y) diagnosed with pancreatitis were included in this study. The children underwent CS-3D MRCP and C-3D MRCP sequences. C-3D MRCP and CS-3D MRCP images were evaluated for the acquisition time duration, visualization of the pancreaticobiliary ducts, background suppression, image quality degradation by artifacts, and overall image quality by the two radiologists independently. Paired sample t-test was used to compare the acquisition time, the McNemar test for the image quality features, and the kappa coefficient was used for interobserver agreement.

RESULTS

A two-fold decrease in the acquisition time of CS-3D MRCP (~148 ± 61 s) was seen, compared to C-3D MRCP (~310 ± 98 s), p < 0.001. The median scores for overall image quality on CS-3D MRCP and C-3D MRCP, respectively, were 2.05 ± 0.52 and 2.21 ± 0.53 (p = 0.18) for both radiologists. No significant difference was seen for the visibility of ducts, background suppression, and artifacts between the two radiologists, with substantial to almost perfect agreement seen for the different findings.

CONCLUSION

The application of compressed SENSE 3D MRCP in children with pancreatitis results in a two-fold reduction in acquisition time with acceptable image quality. This may help in reducing the need for long sedation in children requiring anesthesia support for the MRCP and potentially help in reducing motion artifacts.

Reconfigurable dipole receive array for dynamic parallel imaging at ultra-high magnetic field

PURPOSE

To extend the concept of 3D dynamic parallel imaging, we developed a prototype of an electronically reconfigurable dipole array that provides sensitivity alteration along the dipole length.

METHODS

We developed a radiofrequency array coil consisting of eight reconfigurable elevated-end dipole antennas. The receive sensitivity profile of each dipole can be electronically shifted toward one or the other end by electrical shortening or lengthening the dipole arms using positive-intrinsic-negative-diode lump-element switching units. Based on the results of electromagnetic simulations, we built the prototype and tested it at 9.4 T on phantom and healthy volunteer. A modified 3D SENSE reconstruction was used, and geometry factor (g-factor) calculations were performed to assess the new array coil.

RESULTS

Electromagnetic simulations showed that the new array coil was capable of alteration of its receive sensitivity profile along the dipole length. Electromagnetic and g-factor simulations showed closely agreeing predictions when compared to the measurements. The new dynamically reconfigurable dipole array provided significant improvement in geometry factor compared to static dipoles. We obtained up to 220% improvement for 3 × 2 (R_y × R_z ) acceleration compared to the static configuration case in terms of maximum g-factor and up to 54% in terms of mean g-factor for the same acceleration.

CONCLUSION

We presented an 8-element prototype of a novel electronically reconfigurable dipole receive array that permits rapid sensitivity modulations along the dipole axes. Applying dynamic sensitivity modulation during image acquisition emulates two virtual rows of receive elements along the z-direction, and therefore improves parallel imaging performance for 3D acquisitions.

Supporting and Enhancing NICU Sensory Experiences (SENSE), 2nd Edition: An Update on Developmentally Appropriate Interventions for Preterm Infants

The Supporting and Enhancing NICU Sensory Experiences (SENSE) program promotes consistent, age-appropriate, responsive, and evidence-based positive sensory exposures for preterm infants each day of NICU hospitalization to optimize infant and parent outcomes. The initial development included an integrative review, stakeholder input (NICU parents and healthcare professionals), and feasibility focus groups. To keep the program updated and evidence-based, a review of the recent evidence and engagement with an advisory team will occur every 5 years to inform changes to the SENSE program. Prior to the launch of the 2nd edition of the SENSE program in 2022, information from a new integrative review of 57 articles, clinician feedback, and a survey identifying the barriers and facilitators to the SENSE program's implementation in a real-world context were combined to inform initial changes. Subsequently, 27 stakeholders (neonatologists, nurse practitioners, clinical nurse specialists, bedside nurses, occupational therapists, physical therapists, speech-language pathologists, and parents) carefully considered the suggested changes, and refinements were made until near consensus was achieved. While the 2nd edition is largely the same as the original SENSE program, the refinements include the following: more inclusive language, clarification on recommended minimum doses, adaptations to allow for variability in how hospitals achieve different levels of light, the addition of visual tracking in the visual domain, and the addition of position changes in the kinesthetic domain.

SENSE EPI reconstruction with 2D phase error correction and channel-wise noise removal

PURPOSE

To develop a robust reconstruction pipeline for EPI data that enables 2D Nyquist phase error correction using sensitivity encoding without incurring major noise artifacts in low SNR data.

METHODS

SENSE with 2D phase error correction (PEC-SENSE) was combined with channel-wise noise removal using Marcenko-Pastur principal component analysis (MPPCA) to simultaneously eliminate Nyquist ghost artifacts in EPI data and mitigate the noise amplification associated with phase correction using parallel imaging. The proposed pipeline (coined SPECTRE) was validated in phantom DW-EPI data using the accuracy and precision of diffusion metrics; ground truth values were obtained from data acquired with a spin echo readout. Results from the SPECTRE pipeline were compared against PEC-SENSE reconstructions with three alternate denoising strategies: (i) no denoising; (ii) denoising of magnitude data after image formation; (iii) denoising of complex data after image formation. SPECTRE was then tested using highb $$ b $$ -value (i.e., low SNR) diffusion data (up tob = 3000 $$ b=3000 $$  s/mm2 $$ {}^2 $$ ) in four healthy subjects.

RESULTS

Noise amplification associated with phase error correction incurred a 23% bias in phantom mean diffusivity (MD) measurements. Phantom MD estimates using the SPECTRE pipeline were within 8% of the ground truth value. In healthy volunteers, the SPECTRE pipeline visibly corrected Nyquist ghost artifacts and reduced associated noise amplification in highb $$ b $$ -value data.

CONCLUSION

The proposed reconstruction pipeline is effective in correcting low SNR data, and improves the accuracy and precision of derived diffusion metrics.

Evaluating the Quality of Optimal MRCP Image Using RT-2D-Compressed SENSE(CS)Turbo Spin Echo: Comparing Respiratory Triggering(RT)-2D-SENSE Turbo Spin Echo and Breath Hold-2D-Single-Shot Turbo Spin Echo

This study aimed to select the pulse sequence providing the optimal MRCP image quality by applying various reduction and denoising level parameters—which could improve image quality and shorten examination time—to BH-2D-SSh TSE, RT- 2D-SENSE TSE, and RT-2D-Compressed SENSE(CS) TSE and then comparing and analyzing the obtained images. This study was carried out using 30 subjects (15 men and 15 women with a mean age of 53 ± 8.76 years) who underwent an MRCP test using 3.0T MRI equipment. These 30 subjects were composed of 20 patients (CHDD: 7; LC: 6; and IPMN: 7) and 10 volunteers without a disease. When the CS technique was used, five reduction values (1.1, 1.2, 1.3, 1.4, and 1.5) were used and four denoising levels (No, Weak, Medium, and Strong) were used. The existing SENSE method was based on a reduction value of 1, and other parameters were set the same. The image data of BH-2D-SSh TSE, RT-2D-SENSE TSE, and RT-CS-2D TSE used for the analysis were acquired in the coronal plane, and the acquired data underwent MIP post-processing for analysis. To compare these techniques, SNR and CNR were measured for six biliary duct images for the purpose of quantitative analysis, and qualitative analysis was performed on the sharpness of the duct, the overall quality of the image, and the motion artifact. The results of the quantitative and standard analyses showed that the RT-2D-CS TSE technique had the highest results for all IPMN, LC, and CHDD diseases (p < 0.05). Moreover, SNR and CNR were the highest when the reduction value was set to 1.3 and the denoising level was set to medium as the CS setting values (p < 0.05). Compared with the conventional RT-2D-SENSE TSE, the test time decreased by 20% and SNR and CNR increased by 14% on average. When conducting RT-2D-CS TSE, we found that it shortened the examination time and improved the image quality compared to the existing RT-2D-SENSE TSE. Unlike previous studies, this study using the RT technique shows that it is a useful MRI Pulse Sequence technique able to replace the BH-2D-SSh TSE and BH-3D-SENSE GRASE techniques, which require the patient to hold their breath during the test.

Accelerated MRI at 9.4 T with electronically modulated time-varying receive sensitivities

PURPOSE

To investigate how electronically modulated time-varying receive sensitivities can improve parallel imaging reconstruction at ultra-high field.

METHODS

Receive sensitivity modulation was achieved by introducing PIN diodes in the receive loops, which allow rapid switching of capacitances in both arms of each loop coil and by that alter B₁ ^- profiles, resulting in two distinct receive sensitivity configurations. A prototype 8-channel reconfigurable receive coil for human head imaging at 9.4T was built, and MR measurements were performed in both phantom and human subject. A modified SENSE reconstruction for time-varying sensitivities was formulated, and g-factor calculations were performed to investigate how modulation of receive sensitivity profiles during image encoding can improve parallel imaging reconstruction. The optimized modulation pattern was realized experimentally, and reconstructions with the time-varying sensitivities were compared with conventional static SENSE reconstructions.

RESULTS

The g-factor calculations showed that fast modulation of receive sensitivities in the order of the ADC dwell time during k-space acquisition can improve parallel imaging performance, as this effectively makes spatial information of both configurations simultaneously available for image encoding. This was confirmed by in vivo measurements, for which lower reconstruction errors (SSIM = 0.81 for acceleration R = 4) and g-factors (max g = 2.4; R = 4) were observed for the case of rapidly switched sensitivities compared to conventional reconstruction with static sensitivities (SSIM = 0.74 and max g = 3.2; R = 4). As the method relies on the short RF wavelength at ultra-high field, it does not yield significant benefits at 3T and below.

CONCLUSIONS

Time-varying receive sensitivities can be achieved by inserting PIN diodes in the receive loop coils, which allow modulation of B₁ ^- patterns. This offers an additional degree of freedom for image encoding, with the potential for improved parallel imaging performance at ultra-high field.

Acceleration of pCASL-Based Cerebral 4D MR Angiography Using Compressed SENSE: A Comparison With SENSE

Objectives

The objectives of this study were to accelerate the non-contrast-enhanced four-dimensional magnetic resonance angiography (4D MRA) based on pseudocontinuous arterial spin labeling combined with the Keyhole and View-sharing (4D-PACK) procedure using the Compressed SENSE (C-SENSE) and to improve intracranial vasculopathy evaluations for clinical purposes.

Methods

4D-PACK acquisition with different C-SENSE and SENSE acceleration factors was performed on 29 healthy volunteers and six patients by means of a 3.0 T MR system. Two radiologists used a 4-grade scale to qualitatively assess the vessel visualization of the middle cerebral artery (MCA) and used a 5-grade scale to qualitatively examine the image quality of 4D-PACK axial source images. Interobserver agreement was assessed by determining the weighted kappa statistic. The contrast-to-noise ratio (CNR) and arterial transmit time (ATT) were calculated in four segments of the MCA. The repeated measures one-way ANOVA for CNR and the Friedman test for source images and vessel visualization were used to analyse the differences in five sequences.

Results

(1) At the M4 segment, C-SENSE5 acquisition (scores, 2.72 ± 0.53) and C-SENSE6.5 (scores, 2.55 ± 0.57) provided similar vessel visualization compared with SENSE4.5 (scores, 2.72 ± 0.46); however, C-SENSE8 (scores, 1.79 ± 0.49) and C-SENSE10 (scores, 1.52 ± 0.51) had lower scores (P < 0.050). (2) The source image quality of C-SENSE5 (scores, 4.55 ± 0.51), C-SENSE6.5 (scores, 4.03 ± 0.33), and C-SENSE8 (scores, 3.48 ± 0.51) acquisition was higher than that of SENSE4.5 (scores, 3.07 ± 0.26) (P < 0.001). (3) CNRs of different MCA segments for C-SENSE5 and C-SENSE6.5 acquisitions were not significantly different compared with that of SENSE4.5 acquisition. However, the CNRs were significantly lower for C-SENSE8 (M1: 45.85 ± 13.91, M2: 27.08 ± 9.92, M4: 7.93 ± 4.49) and C-SENSE10 (M1: 37.94 ± 9.92, M2: 23.51 ± 9.0, M4: 6.78 ± 4.12) than for SENSE4.5 (M1: 55.49 ± 13.39, M2: 36.94 ± 11.02, M4: 10.18 ± 5.15) in each corresponding segment (P < 0.050). ATTs in all MCA segments within different accelerating C-SENSE factors were obviously correlated with SENSE4.5.

Conclusion

C-SENSE6.5 acquisition could be used to evaluate both the intracranial macrovascular and distal arteries, which could reduce the acquisition time by 18% (5 min 5 s) compared with SENSE4.5. Moreover, C-SENSE8 acquisition (37% acceleration, 3 min 54 s) could be used for routine screening and clinical diagnosis of intracranial macrovascular disease with balanced image quality.

Whole-brain high-resolution metabolite mapping with 3D compressed-sensing SENSE low-rank 1 H FID-MRSI

There is a growing interest in the neuroscience community to map the distribution of brain metabolites in vivo. Magnetic resonance spectroscopic imaging (MRSI) is often limited by either a poor spatial resolution and/or a long acquisition time, which severely restricts its applications for clinical and research purposes. Building on a recently developed technique of acquisition-reconstruction for 2D MRSI, we combined a fast Cartesian ¹ H-FID-MRSI acquisition sequence, compressed-sensing acceleration, and low-rank total-generalized-variation constrained reconstruction to produce 3D high-resolution whole-brain MRSI with a significant acquisition time reduction. We first evaluated the acceleration performance using retrospective undersampling of a fully sampled dataset. Second, a 20 min accelerated MRSI acquisition was performed on three healthy volunteers, resulting in metabolite maps with 5 mm isotropic resolution. The metabolite maps exhibited the detailed neurochemical composition of all brain regions and revealed parts of the underlying brain anatomy. The latter assessment used previous reported knowledge and a atlas-based analysis to show consistency of the concentration contrasts and ratio across all brain regions. These results acquired on a clinical 3 T MRI scanner successfully combined 3D ¹ H-FID-MRSI with a constrained reconstruction to produce detailed mapping of metabolite concentrations at high resolution over the whole brain, with an acquisition time suitable for clinical or research settings.

Noise reduction in diffusion weighted MRI of the pancreas using an L1-regularized iterative SENSE reconstruction

OBJECTIVES

To prospectively evaluate an L1 regularized iterative SENSE reconstruction (L1-R SENSE) to eliminate band-like artifacts frequently seen with parallel imaging (SENSE) at high acceleration factors in high resolution diffusion weighted magnetic resonance imaging of the pancreas.

METHODS

Fourteen patients with pancreatic ductal adenocarcinoma (PDAC) underwent respiratory triggered DWI ss-EPI at a resolution of 2.5 × 2.5 × 3 mm3 with uniform undersampling in the phase encoding direction (AP axis) with an acceleration factor of 4. Data were reconstructed using the standard SENSE reconstruction routine of the vendor and an iterative SENSE reconstruction employing L1 regularization after a wavelet sparsifying transformation (L1-R SENSE). Retrospective reconstruction of the data with a lower number of averages was performed using both reconstruction methods. Two radiologists independently assessed noise artifacts, anatomical details and image quality (IQ) subjectively with a 4-point scale. Apparent diffusion coefficient (ADC) and covariance (CV) of ADC estimated from images reconstructed at a different number of averages for PDAC and the normal pancreas were assessed.

RESULTS

L1-R SENSE resulted in higher IQ and less noise artifacts than SENSE. Anatomical details were significantly higher for SENSE in one reader. Mean ADC of PDAC and normal pancreas were significantly higher for L1-R SENSE than SENSE. L1-R SENSE revealed lower CV of ADC for normal pancreas compared to SENSE, whereas no difference was noted for PDAC.

CONCLUSION

Compared with traditional SENSE reconstruction, L1-R SENSE effectively reduces band-like noise and improves the robustness of the ADC estimation from acquisitions using single-shot DW-EPI of the pancreas.

Aliasing-free reduced field-of-view parallel imaging

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.

Achieving high-resolution 1H-MRSI of the human brain with compressed-sensing and low-rank reconstruction at 7 Tesla

Low sensitivity MR techniques such as magnetic resonance spectroscopic imaging (MRSI) greatly benefit from the gain in signal-to-noise provided by ultra-high field MR. High-resolution and whole-slab brain MRSI remains however very challenging due to lengthy acquisition, low signal, lipid contamination and field inhomogeneity. In this study, we propose an acquisition-reconstruction scheme that combines 1H free-induction-decay (FID)-MRSI sequence, short TR acquisition, compressed sensing acceleration and low-rank modeling with total-generalized-variation constraint to achieve metabolite imaging in two and three dimensions at 7 Tesla. The resulting images and volumes reveal highly detailed distributions that are specific to each metabolite and follow the underlying brain anatomy. The MRSI method was validated in a high-resolution phantom containing fine metabolite structures, and in five healthy volunteers. This new application of compressed sensing acceleration paves the way for high-resolution MRSI in clinical setting with acquisition times of 5 min for 2D MRSI at 2.5 mm and of 20 min for 3D MRSI at 3.3 mm isotropic.

GPU based parallel framework for receiver coil sensitivity estimation in SENSE reconstruction

Magnetic Resonance Imaging (MRI) uses non-ionizing radiations and is safer as compared to CT and X-ray imaging. MRI is broadly used around the globe for medical diagnostics. One main limitation of MRI is its long data acquisition time. Parallel MRI (pMRI) was introduced in late 1990's to reduce the MRI data acquisition time. In pMRI, data is acquired by under-sampling the Phase Encoding (PE) steps which introduces aliasing artefacts in the MR images. SENSitivity Encoding (SENSE) is a pMRI based method that reconstructs fully sampled MR image from the acquired under-sampled data using the sensitivity information of receiver coils. In SENSE, precise estimation of the receiver coil sensitivity maps is vital to obtain good quality images. Eigen-value method (a recently proposed method in literature for the estimation of receiver coil sensitivity information) does not require a pre-scan image unlike other conventional methods of sensitivity estimation. However, Eigen-value method is computationally intensive and takes a significant amount of time to estimate the receiver coil sensitivity maps. This work proposes a parallel framework for Eigen-value method of receiver coil sensitivity estimation that exploits its inherent parallelism using Graphics Processing Units (GPUs). We evaluated the performance of the proposed algorithm on in-vivo and simulated MRI datasets (i.e. human head and simulated phantom datasets) with Peak Signal-to-Noise Ratio (PSNR) and Artefact Power (AP) as evaluation metrics. The results show that the proposed GPU implementation reduces the execution time of Eigen-value method of receiver coil sensitivity estimation (providing up to 30 times speed up in our experiments) without degrading the quality of the reconstructed image.

Transfer learning in deep neural network-based receiver coil sensitivity map estimation

INTRODUCTION

The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming.

MATERIALS AND METHODS

A transfer learning approach, i.e., end-to-end fine-tuning is proposed to address the data scarcity and generalization problems of deep learning-based receiver coil sensitivity map estimation. First, generalization capabilities of a pre-trained U-Net (initially trained on 1.5T receiver coil sensitivity maps) are thoroughly assessed for 3T receiver coil sensitivity map estimation. Later, end-to-end fine-tuning is performed on the pre-trained U-Net to estimate the 3T receiver coil sensitivity maps.

RESULT AND CONCLUSION

Peak Signal-to-Noise Ratio, Root Mean Square Error and central line profiles (of the SENSE reconstructed images) show a successful SENSE reconstruction by utilizing the receiver coil sensitivity maps estimated by the proposed method.

Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging

Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence.

A dictionary-based graph-cut algorithm for MRI reconstruction

PURPOSE

Compressive sensing (CS)-based image reconstruction methods have proposed random undersampling schemes that produce incoherent, noise-like aliasing artifacts, which are easier to remove. The denoising process is critically assisted by imposing sparsity-enforcing priors. Sparsity is known to be induced if the prior is in the form of the L_p (0 ≤ p ≤ 1) norm. CS methods generally use a convex relaxation of these priors such as the L₁ norm, which may not exploit the full power of CS. An efficient, discrete optimization formulation is proposed, which works not only on arbitrary L_p -norm priors as some non-convex CS methods do, but also on highly non-convex truncated penalty functions, resulting in a specific type of edge-preserving prior. These advanced features make the minimization problem highly non-convex, and thus call for more sophisticated minimization routines.

THEORY AND METHODS

The work combines edge-preserving priors with random undersampling, and solves the resulting optimization using a set of discrete optimization methods called graph cuts. The resulting optimization problem is solved by applying graph cuts iteratively within a dictionary, defined here as an appropriately constructed set of vectors relevant to brain MRI data used here.

RESULTS

Experimental results with in vivo data are presented.

CONCLUSION

The proposed algorithm produces better results than regularized SENSE or standard CS for reconstruction of in vivo data.

Automated characterization of noise distributions in diffusion MRI data

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g., coils sensitivity maps or reconstruction coefficients), which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired on a 3T Philips scanner along with noise-only measurements. Finally, experiments on freely available datasets from a single subject acquired on a 3T GE scanner are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset acquired on a 3T Siemens scanner. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results for the bias correction and denoising task show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.

FPGA-based hardware accelerator for SENSE (a parallel MR image reconstruction method)

SENSE (Sensitivity Encoding) is a parallel MRI (pMRI) technique that allows accelerated data acquisition using multiple receiver coils and reconstructs the artifact-free images from the acquired under-sampled data. However, an increasing number of receiver coils has raised the computational demands of pMRI techniques to an extent where the reconstruction time on general purpose computers becomes impractically long for real-time MRI. Field Programmable Gate Arrays (FPGAs) have recently emerged as a viable hardware platform for accelerating pMRI algorithms (e.g. SENSE). However, recent efforts to accelerate SENSE using FPGAs have been focused on a fixed number of receiver coils (L=8) and acceleration factor (A_f=2). This paper presents a novel 32-bit floating-point FPGA-based hardware accelerator for SENSE (HW-ACC-SENSE); having an ability to work in coordination with an on-chip ARM processor performing reconstructions for different values of L and A_f. Moreover, the proposed design provides flexibility to integrate multiple units of HW-ACC-SENSE with an on-chip ARM processor, for low-latency image reconstruction. The VIVADO High-Level-Synthesis (HLS) tool has been used to design and implement the HW-ACC-SENSE on the Xilinx FPGA development board (ZCU102). A series of experiments has been performed on in-vivo datasets acquired using 8, 12 and 30 receiver coil elements. The performance of the proposed architecture is compared with the single thread and multi-thread CPU-based implementations of SENSE. The results show that the proposed design withstands the reconstruction quality of the SENSE algorithm while demonstrating a maximum speed-gain up to 298× over the CPU counterparts in our experiments.

Accelerated aortic 4D flow cardiovascular magnetic resonance using compressed sensing: applicability, validation and clinical integration

BACKGROUND

Three-dimensional time-resolved phase-contrast cardiovascular magnetic resonance (4D flow CMR) enables the quantification and visualisation of blood flow, but its clinical applicability remains hampered by its long scan time. The aim of this study was to evaluate the use of compressed sensing (CS) with on-line reconstruction to accelerate the acquisition and reconstruction of 4D flow CMR of the thoracic aorta.

METHODS

4D flow CMR of the thoracic aorta was acquired in 20 healthy subjects using CS with acceleration factors ranging from 4 to 10. As a reference, conventional parallel imaging (SENSE) with acceleration factor 2 was used. Flow curves, net flows, peak flows and peak velocities were extracted from six contours along the aorta. To measure internal data consistency, a quantitative particle trace analysis was performed. Additionally, scan-rescan, inter- and intraobserver reproducibility were assessed. Subsequently, 4D flow CMR with CS factor 6 was acquired in 3 patients with differing aortopathies. The flow patterns resulting from particle trace visualisation were qualitatively analysed.

RESULTS

All collected data were successfully acquired and reconstructed on-line. The average acquisition time including respiratory navigator efficiency with CS factor 6 was 5:02 ± 2:23 min while reconstruction took approximately 9 min. For CS factors of 8 or less, mean differences in net flow, peak flow and peak velocity as compared to SENSE were below 2.2 ± 7.8 ml/cycle, 4.6 ± 25.2 ml/s and - 7.9 ± 13.0 cm/s, respectively. For a CS factor of 10 differences reached 5.4 ± 8.0 ml/cycle, 14.4 ± 28.3 ml/s and - 4.0 ± 12.2 cm/s. Scan-rescan analysis yielded mean differences in net flow of - 0.7 ± 4.9 ml/cycle for SENSE and - 0.2 ± 8.5 ml/cycle for CS factor of 6.

CONCLUSIONS

A six- to eightfold acceleration of 4D flow CMR using CS is feasible. Up to a CS acceleration rate of 6, no statistically significant differences in measured flow parameters could be observed with respect to the reference technique. Acquisitions in patients with aortopathies confirm the potential to integrate the proposed method in a clinical routine setting, whereby its main benefits are scan-time savings and direct on-line reconstruction.

HF-SENSE: an improved partially parallel imaging using a high-pass filter

Background

One of the major limitations of MRI is its slow acquisition speed. To accelerate data acquisition, partially parallel imaging (PPI) methods have been widely used in clinical applications such as sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE is a popular image-domain partially parallel imaging method, which suffers from residual aliasing artifacts when the reduction factor goes higher. Undersampling the k-space data and then reconstruct images with artificial sparsity is an efficient way to accelerate data acquisition. By exploiting artificial sparsity with a high-pass filter, an improved SENSE method is proposed in this work, termed high-pass filtered SENSE (HF-SENSE).

Methods

First, a high-pass filter was applied to the raw k-space data, the result of which was used as the inputs of sensitivity estimation and undersampling process. Second, the adaptive array coil combination method was adopted to calculate sensitivity maps on a block-by-block basis. Third, Tikhonov regularized SENSE was then used to reconstruct magnetic resonance images. Fourth, the reconstructed images were transformed into k-space data, which was filtered with the corresponding inverse filter.

Results

Both simulation and in vivo experiments demonstrate that HF-SENSE method significantly reduces noise level of the reconstructed images compared with SENSE. Furthermore, it is found that HF-SENSE can achieve lower normalized root-mean-square error value than SENSE.

Conclusions

The proposed method explores artificial sparsity with a high-pass filter. Experiments demonstrate that the proposed HF-SENSE method can improve the image quality of SENSE reconstruction. The high-pass filter parameters can be predefined. With this image reconstruction method, high acceleration factors can be achieved, which will improve the clinical applicability of SENSE.

This retrospective study (HF-SENSE: an improved partially parallel imaging using a high-pass filter) was approved by Institute Review Board of 2nd Affiliated Hospital of Zhejiang University (ethical approval number 2018–314). Participant for all images have informed consent that he knew the risks and agreed to participate in the research.

Fast high-resolution brain metabolite mapping on a clinical 3T MRI by accelerated 1 H-FID-MRSI and low-rank constrained reconstruction

PURPOSE

Epitomizing the advantages of ultra short echo time and no chemical shift displacement error, high-resolution-free induction decay magnetic resonance spectroscopic imaging (FID-MRSI) sequences have proven to be highly effective in providing unbiased characterizations of metabolite distributions. However, its merits are often overshadowed in high-resolution settings by reduced signal-to-noise ratios resulting from the smaller voxel volumes procured by extensive phase encoding and the related acquisition times.

METHODS

To address these limitations, we here propose an acquisition and reconstruction scheme that offers both implicit dataset denoising and acquisition acceleration. Specifically, a slice selective high-resolution FID-MRSI sequence was implemented. Spectroscopic datasets were processed to remove fat contamination, and then reconstructed using a total generalized variation (TGV) regularized low-rank model. We further measured reconstruction performance for random undersampled data to assess feasibility of a compressed-sensing SENSE acceleration scheme. Performance of the lipid suppression was assessed using an ad hoc phantom, while that of the low-rank TGV reconstruction model was benchmarked using simulated MRSI data. To assess real-world performance, 2D FID-MRSI acquisitions of the brain in healthy volunteers were reconstructed using the proposed framework.

RESULTS

Results from the phantom and simulated data demonstrate that skull lipid contamination is effectively removed and that data reconstruction quality is improved with the low-rank TGV model. Also, we demonstrated that the presented acquisition and reconstruction methods are compatible with a compressed-sensing SENSE acceleration scheme.

CONCLUSIONS

An original reconstruction pipeline for 2D 1 H-FID-MRSI datasets was presented that places high-resolution metabolite mapping on 3T MR scanners within clinically feasible limits.

Over-discretized SENSE reconstruction and B0 correction for accelerated non-lipid-suppressed 1 H FID MRSI of the human brain at 9.4 T

The aim of this work was to use post-processing methods to improve the data quality of metabolite maps acquired on the human brain at 9.4 T with accelerated acquisition schemes. This was accomplished by combining an improved sensitivity encoding (SENSE) reconstruction with a B₀ correction of spatially over-discretized magnetic resonance spectroscopic imaging (MRSI) data. Since MRSI scans suffer from long scan duration, investigating different acceleration techniques has recently been the focus of several studies. Due to strong B₀ inhomogeneity and strict specific absorption rate (SAR) limitations at ultra-high fields, the use of a low-SAR sequence combined with an acceleration technique that is compatible with dynamic B₀ shim updating is preferable. Hence, in this study, a non-lipid-suppressed ultra-short T_E and T_R¹ H free induction decay MRSI sequence is combined with an in-plane SENSE acceleration technique to obtain high-resolution metabolite maps in a clinically feasible scan time. One of the major issues in applying parallel imaging techniques to non-lipid-suppressed MRSI is the presence of strong lipid aliasing artifacts, which if not thoroughly resolved will hinder the accurate quantification of the metabolites of interest. To achieve a more robust reconstruction, an over-discretized SENSE reconstruction (with direct control over the shape of the spatial response function) was combined with an over-discretized B₀ correction. This method is compared with conventional SENSE reconstruction for seven acceleration schemes on four healthy volunteers. The over-discretized method consistently outperformed conventional SENSE, resulting in an average of 23 ± 1.2% higher signal-to-noise ratio and 8 ± 2.9% less error in the fitting of the N-acetylaspartate signal over a whole brain slice. The highest achievable acceleration factor with the proposed technique was determined to be 4. Finally, using the over-discretized method, high-resolution (97 μL nominal voxel size) metabolite maps can be acquired in 3.75 min at 9.4 T. This enables the acquisition of high-resolution metabolite maps with more spatial coverage at ultra-high fields.

Targeted partial reconstruction for real-time fMRI with arbitrary trajectories

PURPOSE

A partial image reconstruction formalism is introduced for the targeted extraction of real-time feedback from arbitrary trajectories when full image reconstruction in real time is computationally too demanding.

METHODS

Explicit calculation and storage of linear combinations of lines of the reconstruction matrix by an incomplete basis change in spatial coordinates lead to translation of the expensive full reconstruction from a frame-wise application to a region of interest (ROI)-wise application. This step is independent from signal data and can be executed before the experiment. Subsequently, the results of the sum over fully reconstructed voxels can be evaluated directly. Data from a high-speed fMRI acquisition was used to investigate the targeted partial reconstruction of a functional ROI atlas, incorporating an intravolume dephasing correction. The same data and ROIs were used for a comparison of the time series obtained with those obtained from already existing methods for compartment-wise reconstruction. To examine real-time feasibility, the reconstruction was implemented and tested for online reconstruction performance.

RESULTS

The reconstruction yields results that are virtually identical to the standard reconstruction (i.e., the magnitude sums over the ROIs), with negligible discrepancies even after termination of the conjugate gradient algorithm at a feasible number of iterations. Notably, more discrepancies arise with existing compartment-wise reconstructions. The online real-time implementation evaluated 1 ROI within 2.8 ms in the case of a highly parallel 3D whole brain acquisition.

CONCLUSION

The high reconstruction fidelity and speed are satisfying for the exemplary application of real-time functional feedback using a highly parallel 3D whole brain acquisition.

Combining SENSE and reduced field-of-view for high-resolution diffusion weighted magnetic resonance imaging

BACKGROUND

In diffusion-weighted magnetic resonance imaging (DWI) using single-shot echo planar imaging (ss-EPI), both reduced field-of-view (FOV) excitation and sensitivity encoding (SENSE) alone can increase in-plane resolution to some degree. However, when the two techniques are combined to further increase resolution without pronounced geometric distortion, the resulted images are often corrupted by high level of noise and artifact due to the numerical restriction in SENSE. Hence, this study is aimed to provide a reconstruction method to deal with this problem.

METHODS

The proposed reconstruction method was developed and implemented to deal with the high level of noise and artifact in the combination of reduced FOV imaging and traditional SENSE, in which all the imaging data were considered jointly by incorporating the motion induced phase variations among excitations. The in vivo human spine diffusion images from ten subjects were acquired at 1.5 T and reconstructed using the proposed method, and compared with SENSE magnitude average results for a range of reduction factors in reduced FOV. These images were evaluated by two radiologists using visual scores (considering distortion, noise and artifact levels) from 1 to 10.

RESULTS

The proposed method was able to reconstruct images with greatly reduced noise and artifact compared to SENSE magnitude average. The mean g-factors were maintained close to 1 along with enhanced signal-to-noise ratio efficiency. The image quality scores of the proposed method were significantly higher (P < 0.01) than SENSE magnitude average for all the evaluated reduction factors.

CONCLUSION

The proposed method can improve the combination of SENSE and reduced FOV for high-resolution ss-EPI DWI with reduced noise and artifact.

Rapid compressed sensing reconstruction of 3D non-Cartesian MRI

PURPOSE

Conventional non-Cartesian compressed sensing requires multiple nonuniform Fourier transforms every iteration, which is computationally expensive. Accordingly, time-consuming reconstructions have slowed the adoption of undersampled 3D non-Cartesian acquisitions into clinical protocols. In this work we investigate several approaches to minimize reconstruction times without sacrificing accuracy.

METHODS

The reconstruction problem can be reformatted to exploit the Toeplitz structure of matrices that are evaluated every iteration, but it requires larger oversampling than what is strictly required by nonuniform Fourier transforms. Accordingly, we investigate relative speeds of the two approaches for various nonuniform Fourier transform kernel sizes and oversampling for both GPU and CPU implementations. Second, we introduce a method to minimize matrix sizes by estimating the image support. Finally, density compensation weights have been used as a preconditioning matrix to improve convergence, but this increases noise. We propose a more general approach to preconditioning that allows a trade-off between accuracy and convergence speed.

RESULTS

When using a GPU, the Toeplitz approach was faster for all practical parameters. Second, it was found that properly accounting for image support can prevent aliasing errors with minimal impact on reconstruction time. Third, the proposed preconditioning scheme improved convergence rates by an order of magnitude with negligible impact on noise.

CONCLUSION

With the proposed methods, 3D non-Cartesian compressed sensing with clinically relevant reconstruction times (<2 min) is feasible using practical computer resources. Magn Reson Med 79:2685-2692, 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.

Robust EPI Nyquist ghost removal by incorporating phase error correction with sensitivity encoding (PEC-SENSE)

PURPOSE

The existing approach of Nyquist ghost correction by parallel imaging in echo planar imaging (EPI) can suffer from image noise amplification. We propose a method that estimates a phase error map from multi-channel data itself and incorporates it into the sensitivity encoding (SENSE) reconstruction for Nyquist ghost correction without compromising the image SNR.

METHODS

This method first reconstructs two ghost-free images from positive and negative echoes using SENSE, respectively, from which the phase error map is computed. This map is then incorporated into the coil sensitivity maps for the negative echo image during the joint SENSE reconstruction of all k-space data to obtain the final ghost-free image. Phantom and in vivo EPI experiments at 7 T and 3 T were performed to evaluate the proposed method.

RESULTS

Nyquist ghost was effectively removed in all images even under oblique imaging and poor eddy current conditions. Resulting image signal-to-noise ratio (SNR) was comparable to that by the traditional linear phase error correction method and higher than that by a previous SENSE-based parallel imaging correction approach.

CONCLUSION

The proposed correction method can robustly eliminate Nyquist ghost while preserving the image SNR. This approach requires no additional calibration data beyond standard coil sensitivity maps and can be readily applied to all EPI applications. Magn Reson Med 79:943-951, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Interactions between head motion and coil sensitivity in accelerated fMRI

BACKGROUND

Parallel imaging is widely adopted to accelerate functional MRI (fMRI) data acquisition, through various strategies that involve multi-channel receiver coils. However, the non-uniform spatial sensitivity of multi-channel receiver coils may introduce unwanted artifacts when head motion occurs during the few-minute long fMRI scans. Although prospective correction provides a promising solution for alleviating the head motion artifacts in fMRI, the relative position of the fixed multi-channel receiver coils moves in the moving reference frame, potentially resulting in artifactual signal.

NEW METHOD

We used numerical simulations to investigate this effect on fMRI using two parallel imaging schemes: sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA) with acceleration factors 2 and 4, towards characterizing the regime over which parallel-imaging fMRI with prospective motion correction will benefit from updating coil sensitivities to reflect relative positional change between the head and the receiver coil. Moreover, six subjects were scanned with acceleration factors 2 and 4 while performing a simple finger-tapping task with and without overt head motion.

RESULTS

Updating coil sensitivities showed significant positive impact on standard deviation and activation maps in presence of overt head motion compared to that obtained with no overt head motion.

COMPARISON WITH EXISTING METHODS

The parallel imaging fMRI with updated coil sensitivity maps were compared to that with the coil sensitivity maps acquired at the reference position.

CONCLUSIONS

Head motion in relation to a fixed multi-channel coil can adversely affect the quality of parallel imaging fMRI data; and updating coil sensitivity map can mitigate this effect.

Two-dimensional-NGC-SENSE-GRAPPA for fast, ghosting-robust reconstruction of in-plane and slice-accelerated blipped-CAIPI echo planar imaging

Purpose

Ghosting‐robust reconstruction of blipped‐CAIPI echo planar imaging simultaneous multislice data with low computational load.

Methods

To date, Slice‐GRAPPA, with “odd–even” kernels that improve ghosting performance, has been the framework of choice for such reconstructions due to its predecessor SENSE‐GRAPPA being deemed unsuitable for blipped‐CAIPI data. Modifications to SENSE‐GRAPPA are used to restore CAIPI compatibility and to make it robust against ghosting. Two implementations are tested, one where slices and in‐plane unaliasing are dealt in the same serial manner as in Slice‐GRAPPA [referred to as one‐dimensional (1D)‐NGC‐SENSE‐GRAPPA, where NGC stands for Nyquist Ghost Corrected] and one where both are unaliased in a single step (2D‐NGC‐SENSE‐GRAPPA), which is analytically and experimentally shown to be computationally cheaper.

Results

The 1D‐NGC‐SENSE‐GRAPPA and odd‐even Slice‐GRAPPA perform identically, whereas 2D‐NGC‐SENSE‐GRAPPA shows reduced error propagation, less residual ghosting when reliable reference data were available. When the latter was not the case, error propagation was increased.

Conclusion

Unlike Slice‐GRAPPA, SENSE‐GRAPPA operates fully within the GRAPPA framework, for which improved reconstructions (e.g., iterative, nonlinear) have been developed over the past decade. It could, therefore, bring benefit to the reconstruction of SMS data as an attractive alternative to Slice‐GRAPPA. Magn Reson Med 77:998–1009, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Accelerating chemical exchange saturation transfer (CEST) MRI by combining compressed sensing and sensitivity encoding techniques

PURPOSE

To evaluate the feasibility of accelerated chemical-exchange-saturation-transfer (CEST) imaging using a combination of compressed sensing (CS) and sensitivity encoding (SENSE) at 3 Tesla.

THEORY AND METHODS

Two healthy volunteers and six high-grade glioma patients were recruited. Raw CEST image k-space data were acquired (with varied radiofrequency saturation power levels for the healthy volunteer study), and a sequential CS and SENSE reconstruction (CS-SENSE) was assessed. The MTR_asym (3.5 ppm) signals were compared with varied CS-SENSE acceleration factors.

RESULTS

In the healthy volunteer study, a CS-SENSE acceleration factor of R = 2 × 2 (CS × SENSE) was achieved without compromising the reconstructed MTR_asym (3.5 ppm) image quality. The MTR_asym (3.5 ppm) signals obtained from the CS-SENSE reconstruction with R = 2 × 2 were well preserved compared with the reference image (R = 2 for only SENSE). In the glioma patient study, the MTR_asym (3.5 ppm) signals were significantly higher in the tumor region (Gd-enhancing tumor core) than in the normal-appearing white matter (P < 0.001). There was no significant MTR_asym (3.5 ppm) difference between the reference image and CS-SENSE-reconstructed image in the acceleration factor of R = 2 × 2.

CONCLUSION

Combining the SENSE technique with CS (R = 2 × 2) enables considerable acceleration of CEST image acquisition and potentially has a wide range of clinical applications. Magn Reson Med 77:779-786, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Coil compression in simultaneous multislice functional MRI with concentric ring slice-GRAPPA and SENSE

PURPOSE

Simultaneous multislice (SMS) imaging is a useful way to accelerate functional magnetic resonance imaging (fMRI). As acceleration becomes more aggressive, an increasingly larger number of receive coils are required to separate the slices, which significantly increases the computational burden. We propose a coil compression method that works with concentric ring non-Cartesian SMS imaging and should work with Cartesian SMS as well. We evaluate the method on fMRI scans of several subjects and compare it to standard coil compression methods.

METHODS

The proposed method uses a slice-separation k-space kernel to simultaneously compress coil data into a set of virtual coils. Five subjects were scanned using both non-SMS fMRI and SMS fMRI with three simultaneous slices. The SMS fMRI scans were processed using the proposed method, along with other conventional methods. Code is available at https://github.com/alcu/sms.

RESULTS

The proposed method maintained functional activation with a fewer number of virtual coils than standard SMS coil compression methods. Compression of non-SMS fMRI maintained activation with a slightly lower number of virtual coils than the proposed method, but does not have the acceleration advantages of SMS fMRI.

CONCLUSION

The proposed method is a practical way to compress and reconstruct concentric ring SMS data and improves the preservation of functional activation over standard coil compression methods in fMRI. Magn Reson Med 76:1196-1209, 2016. © 2015 Wiley Periodicals, Inc.

Partial fourier and parallel MR image reconstruction with integrated gradient nonlinearity correction

PURPOSE

To describe how integrated gradient nonlinearity (GNL) correction can be used within noniterative partial Fourier (homodyne) and parallel (SENSE and GRAPPA) MR image reconstruction strategies, and demonstrate that performing GNL correction during, rather than after, these routines mitigates the image blurring and resolution loss caused by postreconstruction image domain based GNL correction.

METHODS

Starting from partial Fourier and parallel magnetic resonance imaging signal models that explicitly account for GNL, noniterative image reconstruction strategies for each accelerated acquisition technique are derived under the same core mathematical assumptions as their standard counterparts. A series of phantom and in vivo experiments on retrospectively undersampled data were performed to investigate the spatial resolution benefit of integrated GNL correction over conventional postreconstruction correction.

RESULTS

Phantom and in vivo results demonstrate that the integrated GNL correction reduces the image blurring introduced by the conventional GNL correction, while still correcting GNL-induced coarse-scale geometrical distortion. Images generated from undersampled data using the proposed integrated GNL strategies offer superior depiction of fine image detail, for example, phantom resolution inserts and anatomical tissue boundaries.

CONCLUSION

Noniterative partial Fourier and parallel imaging reconstruction methods with integrated GNL correction reduce the resolution loss that occurs during conventional postreconstruction GNL correction while preserving the computational efficiency of standard reconstruction techniques. Magn Reson Med 75:2534-2544, 2016. © 2015 Wiley Periodicals, Inc.

Rotary scanning acquisition in ultra-low-field MRI

PURPOSE

To develop a method of achieving large field of view (FOV) imaging with a smaller amount of data in ultra-low-field (ULF) MRI.

THEORY

In rotary scanning acquisition (RSA), data from the imaging object is acquired at multiple angles by rotating the object or the scanner. RSA is similar to radial-trajectory acquisition but simplifies the measurement and image reconstruction when concomitant fields are nonnegligible.

METHODS

RSA was implemented to achieve large FOV with only three localized superconductive quantum interference device (SQUID) sensors at the ULF-MRI field of 50 μT.

RESULTS

Simulations suggest benefits of RSA, including reduced concomitant field artifacts, large FOV imaging, and SNR improvement. Experimental data demonstrate the feasibility of reconstructing large FOV images using only three SQUID sensors with 33% of the amount of data collected using a Cartesian trajectory.

CONCLUSION

RSA can be useful in low-field, low-weight, or portable MRI to generate large FOV images with only a few sensors. Magn Reson Med 75:2255-2264, 2016. © 2015 Wiley Periodicals, Inc.

The effects of SENSE on PROPELLER imaging

PURPOSE

To study how sensitivity encoding (SENSE) impacts periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) image quality, including signal-to-noise ratio (SNR), robustness to motion, precision of motion estimation, and image quality.

METHODS

Five volunteers were imaged by three sets of scans. A rapid method for generating the g-factor map was proposed and validated via Monte Carlo simulations. Sensitivity maps were extrapolated to increase the area over which SENSE can be performed and therefore enhance the robustness to head motion. The precision of motion estimation of PROPELLER blades that are unfolded with these sensitivity maps was investigated. An interleaved R-factor PROPELLER sequence was used to acquire data with similar amounts of motion with and without SENSE acceleration. Two neuroradiologists independently and blindly compared 214 image pairs.

RESULTS

The proposed method of g-factor calculation was similar to that provided by the Monte Carlo methods. Extrapolation and rotation of the sensitivity maps allowed for continued robustness of SENSE unfolding in the presence of motion. SENSE-widened blades improved the precision of rotation and translation estimation. PROPELLER images with a SENSE factor of 3 outperformed the traditional PROPELLER images when reconstructing the same number of blades.

CONCLUSION

SENSE not only accelerates PROPELLER but can also improve robustness and precision of head motion correction, which improves overall image quality even when SNR is lost due to acceleration. The reduction of SNR, as a penalty of acceleration, is characterized by the proposed g-factor method.

Spatially variant noise estimation in MRI: a homomorphic approach

The reliable estimation of noise characteristics in MRI is a task of great importance due to the influence of noise features in extensively used post-processing algorithms. Many methods have been proposed in the literature to retrieve noise features from the magnitude signal. However, most of them assume a stationary noise model, i.e., the features of noise do not vary with the position inside the image. This assumption does not hold when modern scanning techniques are considered, e.g., in the case of parallel reconstruction and intensity correction. Therefore, new noise estimators must be found to cope with non-stationary noise. Some methods have been recently proposed in the literature. However, they require multiple acquisitions or extra information which is usually not available (biophysical models, sensitivity of coils). In this work we overcome this drawback by proposing a new method that can accurately estimate the non-stationary parameters of noise from just a single magnitude image. In the derivation, we considered the noise to follow a non-stationary Rician distribution, since it is the most common model in real acquisitions (e.g., SENSE reconstruction), though it can be easily generalized to other models. The proposed approach makes use of a homomorphic separation of the spatially variant noise in two terms: a stationary noise term and one low frequency signal that correspond to the x-dependent variance of noise. The non-stationary variance of noise is then estimated by a low pass filtering with a Rician bias correction. Results in real and synthetic experiments evidence the better performance and the lowest error variance of the proposed methodology when compared to the state-of-the-art methods.

SENSE and simultaneous multislice imaging

PURPOSE

Simultaneous multislice (SMS) acquisitions play an important role in the challenge of increasing single-shot imaging speed. We show that sensitivity encoding in two spatial dimensions (two-dimensional sensitivity encoding [2D-SENSE]) can be used to reconstruct SMS acquisitions with periodic but otherwise arbitrary undersampling patterns.

THEORY AND METHODS

By adopting a 3D k-space representation of the SMS sampling process, the accelerated in-plane and slice-encoding directions form a 2D-reconstruction problem that is equivalent to volumetric controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA). 2D-SENSE does not otherwise distinguish between standard volumetric and SMS imaging with arbitrary CAIPIRINHA sampling.

RESULTS

Use of the SENSE algorithm is demonstrated for in vivo brain data obtained with blipped-CAIPRINHA sampling in 2D SMS-echo planar imaging (EPI) and rapid acquisition with relaxation enhancement (RARE) acquisitions as well as 3D-EPI with various in-plane and through-plane acceleration factors and CAIPIRINHA shifts. The proposed SENSE reconstruction works for any combination of SMS-factor and CAIPIRINHA shift by the addition of "dummy slices" that allow for noninteger undersampling in the slice direction. Images with commonly used slice-generalized autocalibrating partially parallel acquisitions reconstruction are shown for reference.

CONCLUSION

SENSE is conceptually simple and provides a one-step reconstruction along both undersampled dimensions. It also provides a contrast-independent parallel imaging reconstruction for SMS.

Lipid elimination with an echo-shifting N/2-ghost acquisition (LEENA) MRI

PURPOSE

The Dixon techniques provide uniform water-fat separation but require multiple image sets, which extend the overall acquisition time. Here, an alternative rapid single acquisition method, lipid elimination with an echo-shifting N/2-ghost acquisition (LEENA), was introduced.

METHODS

The LEENA method utilized a fast imaging with steady-state free precession sequence to obtain a single k-space dataset in which successive k-space lines are acquired to allow the fat magnetization to precess 180°. The LEENA data were then unghosted using either image-domain (LEENA-S) or k-space domain (LEENA-G) parallel imaging techniques to reconstruct water-only and fat-only images. An off-resonance correction technique was incorporated to improve the uniformity of the water-fat separation.

RESULTS

Uniform water-fat separation was achieved for both the LEENA-S and LEENA-G methods for phantom and human body and leg imaging applications at 1.5T and 3T. The resultant water and fat images were qualitatively similar to conventional 2-point Dixon and fat-suppressed images.

CONCLUSION

The LEENA-S and LEENA-G methods provide uniform water and fat images from a single MRI acquisition. These straightforward methods can be adapted to 1.5T and 3T clinical MRI scanners and provide comparable fat/water separation with conventional 2-point Dixon and fat-suppression techniques.

Fast reconstruction for multichannel compressed sensing using a hierarchically semiseparable solver

PURPOSE

The adoption of multichannel compressed sensing (CS) for clinical magnetic resonance imaging (MRI) hinges on the ability to accurately reconstruct images from an undersampled dataset in a reasonable time frame. When CS is combined with SENSE parallel imaging, reconstruction can be computationally intensive. As an alternative to iterative methods that repetitively evaluate a forward CS+SENSE model, we introduce a technique for the fast computation of a compact inverse model solution.

METHODS

A recently proposed hierarchically semiseparable (HSS) solver is used to compactly represent the inverse of the CS+SENSE encoding matrix to a high level of accuracy. To investigate the computational efficiency of the proposed HSS-Inverse method, we compare reconstruction time with the current state-of-the-art. In vivo 3T brain data at multiple image contrasts, resolutions, acceleration factors, and number of receive channels were used for this comparison.

RESULTS

The HSS-Inverse method allows for >6× speedup when compared to current state-of-the-art reconstruction methods with the same accuracy. Efficient computational scaling is demonstrated for CS+SENSE with respect to image size. The HSS-Inverse method is also shown to have minimal dependency on the number of parallel imaging channels/acceleration factor.

CONCLUSIONS

The proposed HSS-Inverse method is highly efficient and should enable real-time CS reconstruction on standard MRI vendors' computational hardware.

Noise estimation in parallel MRI: GRAPPA and SENSE

Parallel imaging methods allow to increase the acquisition rate via subsampled acquisitions of the k-space. SENSE and GRAPPA are the most popular reconstruction methods proposed in order to suppress the artifacts created by this subsampling. The reconstruction process carried out by both methods yields to a variance of noise value which is dependent on the position within the final image. Hence, the traditional noise estimation methods - based on a single noise level for the whole image - fail. In this paper we propose a novel methodology to estimate the spatial dependent pattern of the variance of noise in SENSE and GRAPPA reconstructed images. In both cases, some additional information must be known beforehand: the sensitivity maps of each receiver coil in the SENSE case and the reconstruction coefficients for GRAPPA.

Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE

PURPOSE

To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation.

THEORY AND METHODS

It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on the properties of the reconstructed magnitude image. Using a root-sum-of-squares approach results in a magnitude signal that follows an effective noncentral-χ distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest.

RESULTS

In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fiber orientations, both for model-based and model-free approaches, when modern 32-channel coils are used. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, root-sum-of-squares can cause excessive overfitting and reduced precision in orientation estimation compared with the SENSE-based approach.

CONCLUSION

These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition.

Super-resolved parallel MRI by spatiotemporal encoding

Recent studies described an "ultrafast" scanning method based on spatiotemporal (SPEN) principles. SPEN demonstrates numerous potential advantages over EPI-based alternatives, at no additional expense in experimental complexity. An important aspect that SPEN still needs to achieve for providing a competitive ultrafast MRI acquisition alternative, entails exploiting parallel imaging algorithms without compromising its proven capabilities. The present work introduces a combination of multi-band frequency-swept pulses simultaneously encoding multiple, partial fields-of-view, together with a new algorithm merging a Super-Resolved SPEN image reconstruction and SENSE multiple-receiving methods. This approach enables one to reduce both the excitation and acquisition times of sub-second SPEN acquisitions by the customary acceleration factor R, without compromises in either the method's spatial resolution, SAR deposition, or capability to operate in multi-slice mode. The performance of these new single-shot imaging sequences and their ancillary algorithms were explored and corroborated on phantoms and human volunteers at 3 T. The gains of the parallelized approach were particularly evident when dealing with heterogeneous systems subject to major T2/T2* effects, as is the case upon single-scan imaging near tissue/air interfaces.

Chemical exchange saturation transfer MR imaging of articular cartilage glycosaminoglycans at 3 T: Accuracy of B0 Field Inhomogeneity corrections with gradient echo method

Glycosaminoglycan Chemical Exchange Saturation Transfer (gagCEST) is an important molecular MRI methodology developed to assess changes in cartilage GAG concentrations. The correction for B0 field inhomogeneity is technically crucial in gagCEST imaging. This study evaluates the accuracy of the B0 estimation determined by the dual gradient echo method and the effect on gagCEST measurements. The results were compared with those from the commonly used z-spectrum method. Eleven knee patients and three healthy volunteers were scanned. Dual gradient echo B0 maps with different ∆TE values (1, 2, 4, 8, and 10 ms) were acquired. The asymmetry of the magnetization transfer ratio at 1 ppm offset referred to the bulk water frequency, MTRasym(1 ppm), was used to quantify cartilage GAG levels. The B0 shifts for all knee patients using the z-spectrum and dual gradient echo methods are strongly correlated for all ∆TE values used (r = 0.997 to 0.786, corresponding to ∆TE = 10 to 1 ms). The corrected MTRasym(1 ppm) values using the z-spectrum method (1.34% ± 0.74%) highly agree only with those using the dual gradient echo methods with ∆TE = 10 ms (1.72% ± 0.80%; r = 0.924) and 8 ms (1.50% ± 0.82%; r = 0.712). The dual gradient echo method with longer ∆TE values (more than 8 ms) has an excellent correlation with the z-spectrum method for gagCEST imaging at 3T.

The influence of spatial resolution and smoothing on the detectability of resting-state and task fMRI

Functional MRI blood oxygen level-dependent (BOLD) signal changes can be subtle, motivating the use of imaging parameters and processing strategies that maximize the temporal signal-to-noise ratio (tSNR) and thus the detection power of neuronal activity-induced fluctuations. Previous studies have shown that acquiring data at higher spatial resolutions results in greater percent BOLD signal changes, and furthermore that spatially smoothing higher resolution fMRI data improves tSNR beyond that of data originally acquired at a lower resolution. However, higher resolution images come at the cost of increased acquisition time, and the number of image volumes also influences detectability. The goal of our study is to determine how the detection power of neuronally induced BOLD fluctuations acquired at higher spatial resolutions and then spatially smoothed compares to data acquired at the lower resolutions with the same imaging duration. The number of time points acquired during a given amount of imaging time is a practical consideration given the limited ability of certain populations to lie still in the MRI scanner. We compare acquisitions at three different in-plane spatial resolutions (3.50×3.50mm(2), 2.33×2.33mm(2), 1.75×1.75mm(2)) in terms of their tSNR, contrast-to-noise ratio, and the power to detect both task-related activation and resting-state functional connectivity. The impact of SENSE acceleration, which speeds up acquisition time increasing the number of images collected, is also evaluated. Our results show that after spatially smoothing the data to the same intrinsic resolution, lower resolution acquisitions have a slightly higher detection power of task-activation in some, but not all, brain areas. There were no significant differences in functional connectivity as a function of resolution after smoothing. Similarly, the reduced tSNR of fMRI data acquired with a SENSE factor of 2 is offset by the greater number of images acquired, resulting in few significant differences in detection power of either functional activation or connectivity after spatial smoothing.

Vascular masking for improved unfolding in 2D SENSE-accelerated 3D contrast-enhanced MR angiography

PURPOSE

To describe and evaluate the method we refer to as "vascular masking" for improving signal-to-noise ratio (SNR) retention in sensitivity encoding (SENSE)-accelerated contrast-enhanced magnetic resonance angiography (CE-MRA).

MATERIALS AND METHODS

Vascular masking is a technique that restricts the SENSE unfolding of an accelerated subtraction angiogram to the voxels within the field of view known to have enhancing signal. This is a more restricted voxel set than that identified with conventional masking, which excludes only voxels in the air around the object. Thus, improved retention of SNR is expected. Evaluation was done in phantom and in vivo studies by comparing SNR and the g-factor in results reconstructed using vascular versus conventional masking. A radiological evaluation was also performed comparing conventional and vascular masking in R = 8 accelerated CE-MRA studies of the thighs (n = 21) and calves (n = 13).

RESULTS

Images reconstructed with vascular masking showed a significant reduction in g-factor and improved retention of SNR versus those reconstructed with conventional masking. In the radiological evaluation, vascular masking consistently provided reduced background noise, improved luminal signal smoothness, and better small vessel conspicuity.

CONCLUSION

Vascular masking provides improved SNR retention and improved depiction of the vasculature in accelerated, subtraction 3D CE-MRA of the thighs and calves.

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.

Quantitative SENSE-MRSI of the human brain

PURPOSE

To develop a method for estimating metabolite concentrations using phased-array coils and sensitivity-encoded (SENSE) magnetic resonance spectroscopic images (MRSI) of the human brain.

MATERIALS AND METHODS

The method is based on the phantom replacement technique and uses receive coil sensitivity maps and body-coil loading factors to account for receive B(1) inhomogeneity and variable coil loading, respectively. Corrections for cerebrospinal fluid content from the MRSI voxel were also applied, and the total protocol scan time was less than 15 min. The method was applied to 10 normal human volunteers using a multislice 2D-MRSI sequence at 3 T, and seven different brain regions were quantified.

RESULTS

N-Acetyl aspartate (NAA) concentrations varied from 9.7 to 14.7 mM, creatine (Cr) varied from 6.6 to 10.6 mM and choline (Cho) varied from 1.6 to 3.0 mM, in good general agreement with prior literature values.

CONCLUSIONS

Quantitative SENSE-MRSI of the human brain is routinely possible using an adapted phantom-replacement technique. The method may also be applied to other MRSI techniques, including conventional phase encoding, with phased-array receiver coils, provided that coil sensitivity profiles can be measured.