POCSENSE: POCS-based reconstruction for sensitivity encoded magnetic resonance imaging

Reduced physiology-induced temporal instability achieved with variable-flip-angle fast low-angle excitation echo-planar technique with multishot echo planar time-resolved imaging

PURPOSE

Echo planar time-resolved imaging (EPTI) is a new imaging approach that addresses the limitations of EPI by providing high-resolution, distortion- and T2/T 2 * $$ {\mathrm{T}}_2^{\ast } $$  blurring-free imaging for functional MRI (fMRI). However, as in all multishot sequences, intershot phase variations induced by physiological processes can introduce temporal instabilities to the reconstructed time-series data. This study aims to reduce these instabilities in multishot EPTI.

THEORY AND METHODS

In conventional multishot EPTI, the time intervals between the shots comprising each slice can introduce intershot phase variations. Here, the fast low-angle excitation echo-planar technique (FLEET), in which all shots of each slice are acquired consecutively with minimal time delays, was combined with a variable flip angle (VFA) technique to improve intershot consistency and maximize signal. A recursive Shinnar-Le Roux RF pulse design algorithm was used to generate pulses for different shots to produce consistent slice profiles and signal intensities across shots. Blipped controlled aliasing in parallel imaging simultaneous multislice was also combined with the proposed VFA-FLEET EPTI to improve temporal resolution and increase spatial coverage.

RESULTS

The temporal stability of VFA-FLEET EPTI was compared with conventional EPTI at 7 T. The results demonstrated that VFA-FLEET can provide spatial-specific increase of temporal stability. We performed high-resolution task-fMRI experiments at 7 T using VFA-FLEET EPTI, and reliable BOLD responses to a visual stimulus were detected.

CONCLUSION

The intershot phase variations induced by physiological processes in multishot EPTI can manifest as specific spatial patterns of physiological noise enhancement and lead to reduced temporal stability. The VFA-FLEET technique can substantially reduce these physiology-induced instabilities in multishot EPTI acquisitions. The proposed method provides sufficient stability and sensitivity for high-resolution fMRI studies.

Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities

PURPOSE

To evaluate deep learning-reconstructed (DLR)-fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter hyperintensity evaluation.

MATERIALS AND METHODS

We examined 30 patients with white matter hyperintensities, obtaining fully sampled FLAIR images (standard FLAIR, std-FLAIR). We created accelerated FLAIR (acc-FLAIR) images using one-third of the fully sampled data and applied deep learning to generate DLR-FLAIR images. Three neuroradiologists assessed the quality (amount of noise and gray/white matter contrast) in all three image types. The reproducibility of hyperintensities was evaluated by comparing a subset of 100 hyperintensities in acc-FLAIR and DLR-FLAIR images with those in the std-FLAIR images. Quantitatively, similarities and errors of the entire image and the focused regions on white matter hyperintensities in acc-FLAIR and DLR-FLAIR images were measured against std-FLAIR images using structural similarity index measure (SSIM), regional SSIM, normalized root mean square error (NRMSE), and regional NRMSE values.

RESULTS

All three neuroradiologists evaluated DLR-FLAIR as having significantly less noise and higher image quality scores compared with std-FLAIR and acc-FLAIR (p < 0.001). All three neuroradiologists assigned significantly higher frontal lobe gray/white matter visibility scores for DLR-FLAIR than for acc-FLAIR (p < 0.001); two neuroradiologists attributed significantly higher scores for DLR-FLAIR than for std-FLAIR (p < 0.05). Regarding white matter hyperintensities, all three neuroradiologists significantly preferred DLR-FLAIR (p < 0.0001). DLR-FLAIR exhibited higher similarity to std-FLAIR in terms of visibility of the hyperintensities, with 97% of the hyperintensities rated as nearly identical or equivalent. Quantitatively, DLR-FLAIR demonstrated significantly higher SSIM and regional SSIM values than acc-FLAIR, with significantly lower NRMSE and regional NRMSE values (p < 0.0001).

CONCLUSIONS

DLR-FLAIR can reduce scan time and generate images of similar quality to std-FLAIR in patients with white matter hyperintensities. Therefore, DLR-FLAIR may serve as an effective method in traditional magnetic resonance imaging protocols.

The Potential of Phase Constraints for Non-Fourier Radiofrequency-Encoded MRI

In modern magnetic resonance imaging, it is common to use phase constraints to reduce sampling requirements along Fourier-encoded spatial dimensions. In this work, we investigate whether phase constraints might also be beneficial to reduce sampling requirements along spatial dimensions that are measured using non-Fourier encoding techniques, with direct relevance to approaches that use tailored spatially-selective radiofrequency (RF) pulses to perform spatial encoding along the slice dimension in a 3D imaging experiment. In the first part of the paper, we use the Cramér-Rao lower bound to examine the potential estimation theoretic benefits of using phase constraints. The results suggest that phase constraints can be used to improve experimental efficiency and enable acceleration, but only if the RF encoding matrix is complex-valued and appropriately designed. In the second part of the paper, we use simulations of RF-encoded data to test the benefits of phase constraints combined with optimized RF-encodings, and find that the theoretical benefits are indeed borne out empirically. These results provide new insights into the potential benefits of phase constraints for RF-encoded data, and provide a solid theoretical foundation for future practical explorations.

Three-dimensional echo-shifted EPI with simultaneous blip-up and blip-down acquisitions for correcting geometric distortion

PURPOSE

EPI with blip-up/down acquisition (BUDA) can provide high-quality images with minimal distortions by using two readout trains with opposing phase-encoding gradients. Because of the need for two separate acquisitions, BUDA doubles the scan time and degrades the temporal resolution when compared to single-shot EPI, presenting a major challenge for many applications, particularly fMRI. This study aims at overcoming this challenge by developing an echo-shifted EPI BUDA (esEPI-BUDA) technique to acquire both blip-up and blip-down datasets in a single shot.

METHODS

A 3D esEPI-BUDA pulse sequence was designed by using an echo-shifting strategy to produce two EPI readout trains. These readout trains produced a pair of k-space datasets whose k-space trajectories were interleaved with opposite phase-encoding gradient directions. The two k-space datasets were separately reconstructed using a 3D SENSE algorithm, from which time-resolved B0 -field maps were derived using TOPUP in FSL and then input into a forward model of joint parallel imaging reconstruction to correct for geometric distortion. In addition, Hankel structured low-rank constraint was incorporated into the reconstruction framework to improve image quality by mitigating the phase errors between the two interleaved k-space datasets.

RESULTS

The 3D esEPI-BUDA technique was demonstrated in a phantom and an fMRI study on healthy human subjects. Geometric distortions were effectively corrected in both phantom and human brain images. In the fMRI study, the visual activation volumes and their BOLD responses were comparable to those from conventional 3D echo-planar images.

CONCLUSION

The improved imaging efficiency and dynamic distortion correction capability afforded by 3D esEPI-BUDA are expected to benefit many EPI applications.

Technical note: Revised projections onto convex sets reconstruction of multi-shot diffusion-weighted imaging

BACKGROUND

High-resolution diffusion-weighted imaging (DWI) is usually achieved through multi-shot acquisitions and parallel imaging-based reconstructions. Multiple POCS (projections onto convex sets) based algorithms have been proposed for DWI reconstructions. However, the slow convergence of POCS and the suboptimal quality of the reconstructed images limit their applications.

PURPOSE

In this study, a revised POCS algorithm for multi-shot DWI reconstruction is proposed based on FISTA (fast iterative shrinkage-thresholding algorithm) to achieve faster convergence and higher accuracy.

METHODS

In FISTA, the next iteration is computed based on two previous iterations, instead of only the previous one, to improve the convergence speed. This scheme is adopted into the relevant POCS-based algorithms, including POCSENSE (POCS-based sensitivity-encoding), POCSMUSE (POCS-based multiplexed sensitivity-encoding), iPOCSMUSE (iterative POCSMUSE), and POCS-ICE (POCS-enhanced inherent correction of motion-induced phase errors) to address the slow convergence problem. Simulations and in vivo experiments were performed to evaluate the performance of the proposed method.

RESULTS

Experimental results show that the proposed method enables faster convergence compared to the original POCS. For example, for a spiral DWI simulation using eight-shot interleaves and having SNR of 20 dB, the iteration number needed for the revised POCS-ICE decreases by about 70% to achieve approximately the same nRMSE level as POCS-ICE. Additionally, it improves image quality in terms of fewer artifacts compared with the original POCS.

CONCLUSIONS

The revised DWI reconstruction methods can achieve higher convergence rates than the original POCS-based algorithms and higher image quality with the same iteration numbers. As such, the proposed method can serve as a practical and efficient reconstruction method for multi-shot DWI.

Parallel MR image reconstruction based on triple cycle optimization

The self-calibration parallel imaging (SC-SENSE) method reconstructs the image by estimating the coil sensitivity matrix. In order to obtain the sensitivity matrix, it is necessary to take a small amount of automatic calibration signal lines (ACSL) in the center of k-space. This method uses the data of the central region to obtain the sensitivity matrix, and then the reconstructed image is obtained. This paper proposed the triple cycle optimization (TCO) method to continuously optimize reconstructed images. The proposed TCO method takes the sensitivity matrix obtained by ACSL and substituted the reconstructed image as the initial data generation into the loop, and estimates the k-space data repeatedly. A new sensitivity matrix is obtained by using k-space data and the reconstructed image, and a stable triple cycle is obtained. In the cycle, all data are optimized to a certain extent, including the reconstructed image. Experimental results show that under the same sampling density, images reconstructed by using the triple cycle optimization method have lower noise and artifacts than those of the traditional method. When combined with the variable density sampling method, the effect is remarkable with a much low sampling rate.

Blip up-down acquisition for spin- and gradient-echo imaging (BUDA-SAGE) with self-supervised denoising enables efficient T2 , T2 *, para- and dia-magnetic susceptibility mapping

PURPOSE

To rapidly obtain high resolution T₂ , T₂ *, and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity.

METHODS

We propose Blip Up-Down Acquisition for Spin And Gradient Echo imaging (BUDA-SAGE), an efficient EPI sequence for quantitative mapping. The acquisition includes multiple T₂ *-, T₂ '-, and T₂ -weighted contrasts. We alternate the phase-encoding polarities across the interleaved shots in this multi-shot navigator-free acquisition. A field map estimated from interim reconstructions was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to eliminate distortion. A self-supervised neural network (NN), MR-Self2Self (MR-S2S), was used to perform denoising to boost SNR. Using Slider encoding allowed us to reach 1 mm isotropic resolution by performing super-resolution reconstruction on volumes acquired with 2 mm slice thickness. Quantitative T₂ (=1/R₂ ) and T₂ * (=1/R₂ *) maps were obtained using Bloch dictionary matching on the reconstructed echoes. QSM was estimated using nonlinear dipole inversion on the gradient echoes. Starting from the estimated R₂ /R₂ * maps, R₂ ' information was derived and used in source separation QSM reconstruction, which provided additional para- and dia-magnetic susceptibility maps.

RESULTS

In vivo results demonstrate the ability of BUDA-SAGE to provide whole-brain, distortion-free, high-resolution, multi-contrast images and quantitative T₂ /T₂ * maps, as well as yielding para- and dia-magnetic susceptibility maps. Estimated quantitative maps showed comparable values to conventional mapping methods in phantom and in vivo measurements.

CONCLUSION

BUDA-SAGE acquisition with self-supervised denoising and Slider encoding enables rapid, distortion-free, whole-brain T₂ /T₂ * mapping at 1 mm isotropic resolution under 90 s.

BUDA-MESMERISE: Rapid acquisition and unsupervised parameter estimation for T1 , T2 , M0 , B0 , and B1 maps

PURPOSE

Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion-free spin- and stimulated-echo signals and combine the signals with a physics-driven unsupervised network to estimate T₁ , T₂ , and proton density (M₀ ) parameter maps, along with B₀ and B₁ information from the acquired signals.

THEORY AND METHODS

An imaging sequence with three 90° RF pulses is utilized to acquire spin- and stimulated-echo signals. We utilize blip-up/-down acquisition to eliminate geometric distortion incurred by the effects of B₀ inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo-shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin- and stimulated-echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed.

RESULTS

The proposed acquisition provided distortion-free T₁ , T₂ , relative proton density (M0), B₀ , and B₁ maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin- and stimulated-echo signals, analytic fitting and the network-based method were able to estimate T₁ , T₂ , M₀ , B₀ , and B₁ maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels.

CONCLUSION

The proposed acquisition/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T₁ , T₂ , M₀ , B₀ , and B₁ maps at 1 × 1 × 5 mm³ resolution in 50 s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition.

POCS-Augmented CycleGAN for MR Image Reconstruction

Recent years have seen increased research interest in replacing the computationally intensive Magnetic resonance (MR) image reconstruction process with deep neural networks. We claim in this paper that the traditional image reconstruction methods and deep learning (DL) are mutually complementary and can be combined to achieve better image reconstruction quality. To test this hypothesis, a hybrid DL image reconstruction method was proposed by combining a state-of-the-art deep learning network, namely a generative adversarial network with cycle loss (CycleGAN), with a traditional data reconstruction algorithm: Projection Onto Convex Set (POCS). The output of the first iteration's training results of the CycleGAN was updated by POCS and used as the extra training data for the second training iteration of the CycleGAN. The method was validated using sub-sampled Magnetic resonance imaging data. Compared with other state-of-the-art, DL-based methods (e.g., U-Net, GAN, and RefineGAN) and a traditional method (compressed sensing), our method showed the best reconstruction results.

Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques

PURPOSE

We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates.

METHODS

Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2.

RESULTS

In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (Rnet) <2.5. VDR sampling with model-based SENSE showed the lowest RMSEs (10.5%-14.2%) and SDs (+1.7-2.4 ms) of T2 when Rnet>2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when Rnet>3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all Rnet (0.71-0.50) than volume-by-volume SENSE (0.68-0.30).

CONCLUSIONS

Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on Rnet. The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for Rnet>3.

Diagnostic quality assessment of compressed SENSE accelerated magnetic resonance images in standard neuroimaging protocol: Choosing the right acceleration

PURPOSE

To investigate the impact of compressed sensing - sensitivity encoding (CS-SENSE) acceleration factor on the diagnostic quality of magnetic resonance images within standard brain protocol.

METHODS

Three routine clinical neuroimaging sequences were chosen for this study due to their long acquisition time: T2-weighted turbo spin echo (TSE), fluid - attenuated inversion recovery (FLAIR), and 3D time of flight (TOF). Fully sampled reference scans and multiple prospectively 2x to 5x undersampled CS scans were acquired. Retrospectively, undersampled scans were compared to fully sampled scans and visually assessed for image quality and diagnostic quality by three independent radiologists.

RESULTS

Images obtained with CS-SENSE accelerated acquisition were of diagnostically acceptable quality at up to 3x acceleration for T2 TSE (average qualitative score 3.53 on a 4-point scale, with the acquisition time reduction of 64%), up to 2x for FLAIR (average qualitative score 3.27, with the acquisition time reduction of 43%) and 4x acceleration for 3D TOF sequence (average qualitative score 3.13, with the acquisition time reduction of 73%). There were no substantial differences between the readers' diagnostic quality scores (p > 0.05).

CONCLUSIONS

CS-SENSE accelerated T2 TSE, FLAIR, and 3D TOF sequences of the brain show image quality similar to that of conventional acquisitions with reduced acquisition time. CS-SENSE can moderately reduce scan time, providing many benefits without losing the image quality.

Efficient T2 mapping with blip-up/down EPI and gSlider-SMS (T2 -BUDA-gSlider)

PURPOSE

To rapidly obtain high isotropic-resolution T₂ maps with whole-brain coverage and high geometric fidelity.

METHODS

A T₂ blip-up/down EPI acquisition with generalized slice-dithered enhanced resolution (T₂ -BUDA-gSlider) is proposed. A RF-encoded multi-slab spin-echo (SE) EPI acquisition with multiple TEs was developed to obtain high SNR efficiency with reduced TR. This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and used for T₂ quantification.

RESULTS

In vivo results demonstrated that the T₂ values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated.

CONCLUSION

BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T₂ mapping in 2 min at ~1 mm³ isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications.

Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study

Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.

Simultaneous multi-slice image reconstruction using regularized image domain split slice-GRAPPA for diffusion MRI

The main goal of this work is to improve the quality of simultaneous multi-slice (SMS) reconstruction for diffusion MRI. We accomplish this by developing an image domain method that reaps the benefits of both SENSE and GRAPPA-type approaches and enables image regularization in an optimization framework. We propose a new approach termed regularized image domain split slice-GRAPPA (RI-SSG), which establishes an optimization framework for SMS reconstruction. Within this framework, we use a robust forward model to take advantage of both the SENSE model with explicit sensitivity estimations and the SSG model with implicit kernel relationship among coil images. The proposed approach also allows combining of coil images to increase the SNR and enables image domain regularization on estimated coil-combined single slices. We compare the performance of RI-SSG with that of SENSE and SSG using in-vivo diffusion EPI datasets with simulated and actual SMS acquisitions collected on a 3T MR scanner. Reconstructed diffusion-weighted images (DWIs) and the resulting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) maps are analyzed to evaluate the quantitative and qualitative performance of the three methods. The DWIs reconstructed by RI-SSG are closer to the single-band ground truth images than SENSE and SSG. Specifically, the proposed RI-SSG reduces the normalized root-mean-square-error (nRMSE) against ground truth images by ∼5% and increases the structural similarity index (SSIM) by ∼4% compared to SSG. All three methods produce similar fractional anisotropy (FA) maps using DTI representation, but mean diffusivity (MD) and fiber orientation estimates using RI-SSG are closer to the reference than SENSE and SSG. RI-SSG results in NODDI maps with noticeably smaller errors than those of SENSE and SSG and improves the accuracy of the mean value of orientation dispersion index (ODI) by ∼5% and the mean value of intracellular volume fraction by ∼7% in regions of interest in brain white matter compared to SSG.

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.

Quantitative T2 mapping using accelerated 3D stack-of-spiral gradient echo readout

PURPOSE

To develop a rapid T2 mapping protocol using optimized spiral acquisition, accelerated reconstruction, and model fitting.

MATERIALS AND METHODS

A T2-prepared stack-of-spiral gradient echo (GRE) pulse sequence was applied. A model-based approach joined with compressed sensing was compared with the two methods applied separately for accelerated reconstruction and T2 mapping. A 2-parameter-weighted fitting method was compared with 2- or 3-parameter models for accurate T2 estimation under the influences of noise and B1 inhomogeneity. The performance was evaluated using both digital phantoms and healthy volunteers. Mitigating partial voluming with cerebrospinal fluid (CSF) was also tested.

RESULTS

Simulations demonstrates that the 2-parameter-weighted fitting approach was robust to a large range of B1 scales and SNR levels. With an in-plane acceleration factor of 5, the model-based compressed sensing-incorporated method yielded around 8% normalized errors compared to references. The T2 estimation with and without CSF nulling was consistent with literature values.

CONCLUSION

This work demonstrated the feasibility of a T2 quantification technique with 3D high-resolution and whole-brain coverage in 2-3 min. The proposed iterative reconstruction method, which utilized the model consistency, data consistency and spatial sparsity jointly, provided reasonable T2 estimation. The technique also allowed mitigation of CSF partial volume effect.

Distortion correction of single-shot EPI enabled by deep-learning

PURPOSE

A distortion correction method for single-shot EPI was proposed. Point-spread-function encoded EPI (PSF-EPI) images were used as the references to correct traditional EPI images based on deep neural network.

THEORY AND METHODS

The PSF-EPI method can obtain distortion-free echo planar images. In this study, a 2D U-net based network was trained to achieve the distortion correction of single-shot EPI (SS-EPI) images, using PSF-EPI images as targets in the training stage. Anatomical T2W-TSE images were also fed into the network to improve the quality of the results. The applications in diffusion-weighted images were used as examples in this work. The network was trained on data acquired on healthy volunteers and tested on data of both healthy volunteers and patients. The corrected EPI images from the proposed method were also compared with those from field-mapping and top-up based distortion correction methods.

RESULTS

Experimental results showed that the proposed method can correct for EPI distortions better than both the field-mapping and top-up based methods, and the results were close to the distortion-free images from PSF-EPI. Additionally, inclusion of T2W-TSE images helped improve distortion correction of the SS-EPI images without contaminating the output noticeably. The experiments with patients and different MRI platforms demonstrated the generalization feasibility of the proposed method preliminarily.

CONCLUSION

Through the correction of diffusion-weighted images, the proposed deep-learning based method was demonstrated to have the feasibility to correct for the distortion of EPI images.

Venous cerebral blood volume mapping in the whole brain using venous-spin-labeled 3D turbo spin echo

PURPOSE

Venous cerebral blood volume (CBVv ) is a major contributor to BOLD contrast, and therefore is an important parameter for understanding the underlying mechanism. Here, we propose a velocity-selective venous spin labeling (VS-VSL)-prepared 3D turbo spin echo pulse sequence for whole-brain baseline CBVv mapping.

METHODS

Unlike previous CBVv measurement techniques that exploit the interrelationship between BOLD signals and CBVv , in the proposed VS-VSL technique both arterial blood and cerebrospinal fluid (CSF) signals were suppressed before the VS pulse train for exclusive labeling of venous blood, while a single-slab 3D turbo spin echo readout was used because of its relative immunity to magnetic field variations. Furthermore, two approximations were made to the VS-VSL signal model for simplified derivation of CBVv . In vivo studies were performed at 3T field strength in 8 healthy subjects. The performance of the proposed VS-VSL method in baseline CBVv estimation was first evaluated in comparison to the existing, hyperoxia-based method. Then, data were also acquired using VS-VSL under hypercapnic and hyperoxic gas breathing challenges for further validation of the technique.

RESULTS

The proposed technique yielded physiologically plausible baseline CBVv values, and when compared with the hyperoxia-based method, showed no statistical difference. Furthermore, data acquired using VS-VSL yielded average CBVv of 2.89%/1.78%, 3.71%/2.29%, and 2.88%/1.76% for baseline, hypercapnia, and hyperoxia, respectively, in gray/white matter regions. As expected, hyperoxia had negligible effect (P > .8), whereas hypercapnia demonstrated vasodilation (P << .01).

CONCLUSION

Upon further validation of the quantification model, the method is expected to have merit for 3D CBVv measurements across the entire brain.

Accelerating CEST MRI in the mouse brain at 9.4 T by exploiting sparsity in the Z-spectrum domain

PURPOSE

Chemical exchange saturation transfer (CEST) is an MR contrast modality offering an enhanced sensitivity for the detection of dilute metabolites with exchangeable protons. Quantitative analysis requires the acquisition of a number of images (usually between 20 and 50 RF offsets) per Z-spectrum, leading to long acquisition times of the order of 5-40 min in practice. In this work, we explore the possibility of employing sparsity in the Z-spectrum domain (irradiation offset dimension) to provide an accelerated acquisition scheme without compromising the quality of reconstructed CEST spectra.

METHOD AND THEORY

Ex vivo and in vivo data were acquired on an experimental, small animal 9.4 T system. Three different reconstruction methods were tested: k-Z SPARSE, k-Z SLR and k-Z principal component analysis (PCA) using retrospective undersampling with net acceleration factors R = 2, 3, 5. The quality of the reconstructed data was compared with respect to CEST spectra and full magnetization transfer ratio (MTR) asymmetry maps.

RESULTS

In both phantom and in vivo data, CEST spectra and the resulting MTR asymmetry maps were reconstructed without significant deterioration in data quality. For a low acceleration factor (R = 2, 3) all applied methods resulted in similar data quality, while for high acceleration factor (R = 5) only k-Z PCA and k-Z SLR could be used. Loss in spatial resolution was observed in reconstruction with k-Z PCA for all acceleration factors. An example of prospective undersampling with acceleration factor R = 3 and k-Z PCA reconstruction demonstrates improved CEST maps when compared with fully sampled data acquisition with either three times longer scan duration or threefold prolonged acquisition window per frequency offset.

CONCLUSION

The acquisition time of CEST spectra can be significantly accelerated by exploiting the sparsity of the Z-domain. For prospective and retrospective analysis using k-Z PCA, an acceleration factor of up to R = 3 can be used without significant loss in data quality.

Self-Navigated Three-Dimensional Ultrashort Echo Time Technique for Motion-Corrected Skull MRI

Ultrashort echo time (UTE) MRI is capable of detecting signals from protons with very short T₂ relaxation times, and thus has potential for skull-selective imaging as a radiation-free alternative to computed tomography. However, relatively long scan times make the technique vulnerable to artifacts from involuntary subject motion. Here, we developed a self-navigated, three-dimensional (3D) UTE pulse sequence, which builds on dual-RF, dual-echo UTE imaging, and a retrospective motion correction scheme for motion-resistant skull MRI. Full echo signals in the second readout serve as a self-navigator that yields a time-course of center of mass, allowing for adaptive determination of motion states. Furthermore, golden-means based k-space trajectory was employed to achieve a quasi-uniform distribution of sampling views on a spherical k-space surface for any subset of the entire data collected, thereby allowing reconstruction of low-resolution images pertaining to each motion state for subsequent estimation of rigid-motion parameters. Finally, the extracted trajectory of the head was used to make the whole k-space datasets motion-consistent, leading to motion-corrected, high-resolution images. Additionally, we posit that hardware-related k-space trajectory errors, if uncorrected, result in obscured bone contrast. Thus, a calibration scan was performed once to measure k-space encoding locations, subsequently used during image reconstruction of actual imaging data. In vivo studies were performed to evaluate the effectiveness of the proposed correction schemes in combination with approaches to accelerated bone-selective imaging. Results illustrating effective removal of motion artifacts and clear depiction of skull bone voxels suggest that the proposed method is robust to intermittent head motions during scanning.

Improving Quantitative Magnetic Resonance Imaging Using Deep Learning

Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parametric maps, allowing efficient and accurate T2 and T1ρ relaxometry analysis for monitoring and predicting MSK diseases. Deep learning methods have shown promising results for disease detection on quantitative MRI with diagnostic performance superior to conventional machine-learning methods for identifying knee osteoarthritis.

Upper Airway Narrowing during Central Apnea in Obese Adolescents

RATIONALE

The use of real-time magnetic resonance imaging (MRI) for the evaluation during sleep-related respiratory events can lead to better understanding of airway dynamics.

OBJECTIVES

To investigate the dynamic anatomy of the upper airway during central apnea.

METHODS

The study included obese adolescents who snore and were otherwise healthy. Subjects underwent an overnight baseline polysomnogram. Subjects slept during a 24-minute real-time upper airway MRI scan wearing a full face mask attached to a pneumotach. Sleep versus wakefulness during the MRI was inferred from the heart rate and respiratory patterns. Central apneas were scored using tracings of facemask airflow and abdominal bellows. The cross-sectional area of the upper airway before, during, and after each central apnea event was recorded.

RESULTS

Eight subjects were studied and 57 central apnea events were observed during real-time MRI scanning during natural sleep. The median age of subjects was 14.0 years (interquartile range [IQR], 13.5 to 15.5). The median average reduction in cross-sectional area during central apnea events was -38% (IQR, -27 to -51) for primary snorers and -45% (IQR, -40 to -54) for subjects with obstructive sleep apnea. The percentage decrease in cross-sectional area of upper airway during a central apnea event was positively correlated to the length of the central apnea (ρ = 0.389; r² = 0.152; P = 0.003).

CONCLUSIONS

We observed that there is upper airway narrowing during central apneas during natural sleep in obese adolescent subjects, using real-time MRI.

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

PURPOSE

To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging.

METHODS

Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution.

RESULTS

Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network.

CONCLUSION

Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.

Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER)

PURPOSE

Image acceleration provides multiple benefits to diffusion MRI, with in-plane acceleration reducing distortion and slice-wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem becomes ill-conditioned, particularly when using both in-plane acceleration and simultaneous multislice imaging. In this work, we develop a novel reconstruction method for in vivo MRI acquisition that provides acceleration beyond what conventional techniques can achieve.

THEORY AND METHODS

We propose to constrain the reconstruction in the spatial (k) domain by incorporating information from the angular (q) domain. This approach exploits smoothness of the signal in q-space using Gaussian processes, as has previously been exploited in post-reconstruction analysis. We demonstrate in-plane undersampling exceeding the theoretical parallel imaging limits, and simultaneous multislice combined with in-plane undersampling at a total factor of 12. This reconstruction is cast within a Bayesian framework that incorporates estimation of smoothness hyper-parameters, with no need for manual tuning.

RESULTS

Simulations and in vivo results demonstrate superior performance of the proposed method compared with conventional parallel imaging methods. These improvements are achieved without loss of spatial or angular resolution and require only a minor modification to standard pulse sequences.

CONCLUSION

The proposed method provides improvements over existing methods for diffusion acceleration, particularly for high simultaneous multislice acceleration with in-plane undersampling.

MR Image Reconstruction Using Deep Density Priors

Algorithms for magnetic resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this letter, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically variational autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers ( N=8 ), and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions.

Rapid dual-RF, dual-echo, 3D ultrashort echo time craniofacial imaging: A feasibility study

PURPOSE

To develop a dual-radiofrequency (RF), dual-echo, 3D ultrashort echo-time (UTE) pulse sequence and bone-selective image reconstruction for rapid high-resolution craniofacial MRI.

METHODS

The proposed pulse sequence builds on recently introduced dual-RF UTE imaging. While yielding enhanced bone specificity by exploiting high sensitivity of short T₂ signals to variable RF pulse widths, the parent technique exacts a 2-fold scan time penalty relative to standard dual-echo UTE. In the proposed method, the parent sequence's dual-RF scheme was incorporated into dual-echo acquisitions while radial view angles are varied every pulse-to-pulse repetition period. The resulting 4 echoes (2 for each RF) were combined by view-sharing to construct 2 sets of k-space data sets, corresponding to short and long TEs, respectively, leading to a 2-fold increase in imaging efficiency. Furthermore, by exploiting the sparsity of bone signals in echo-difference images, acceleration was achieved by solving a bone-sparsity constrained image reconstruction problem. In vivo studies were performed to evaluate the effectiveness of the proposed acceleration approaches in comparison to the parent method.

RESULTS

The proposed technique achieves 1.1-mm isotropic skull imaging in 3 minutes without visual loss of image quality, compared to the parent technique (scan time = 12 minutes). Bone-specific images and corresponding 3D renderings of the skull were found to depict the expected craniofacial anatomy over the entire head.

CONCLUSION

The proposed method is able to achieve high-resolution volumetric craniofacial images in a clinically practical imaging time, and thus may prove useful as a potential alternative to computed tomography.

On the limitations of partial Fourier acquisition in phase-contrast MRI of turbulent kinetic energy

PURPOSE

To investigate limitations of partial Fourier acquisition in phase-contrast MRI of turbulent kinetic energy (TKE).

METHODS

To assess the validity of partial Fourier reconstruction of TKE and phase images, computational fluid dynamics data of mean and turbulent velocities in a stenotic U-bend phantom was used. Partial Fourier acquisition with 75% k-space coverage was simulated and TKE data were reconstructed using zero-filling, homodyne reconstruction, and the method of projections onto convex sets (POCS). Results were compared to data from fully sampled k-space and 75% symmetric sampling. In addition, compressed sensing (CS) reconstruction was compared for a standard variable density sampling pattern and a variable density sampling pattern combined with 75% partial Fourier. For illustration purposes, in vivo examples of velocity magnitude and TKE maps of aortic flow reconstructed with the different methods are provided.

RESULTS

In accordance with theory, partial Fourier reconstruction of TKE maps from phase-contrast data results in artifacts relative to fully sampled data. It is demonstrated that neither homodyne reconstruction nor POCS can improve reconstruction of TKE data with respect to zero-filling reconstruction when compared to ground-truth (RMS error: 4.70%, 4.34%, and 2.45% for homodyne, POCS, and zero-filling reconstruction of in vivo data, respectively). CS reconstruction from data acquired with partial Fourier did not recover the resolution loss incurred by partial Fourier sampling.

CONCLUSION

Partial Fourier reconstruction of TKE maps from phase-contrast data does not yield a benefit over zero-filling reconstruction. In consequence, symmetric sampling is preferred over partial Fourier acquisition for a given number of phase-encodes in phase-contrast MRI.

Free-breathing abdominal MRI improved by repeated k-t-subsampling and artifact-minimization (ReKAM)

PURPOSE

We report an approach, termed Repeated k-t-subsampling and artifact-minimization (ReKAM), for removing motion artifacts in free-breathing abdominal MRI. The method is particularly valuable for challenging patients who may not hold their breath for a long time or have irregular respiratory rate.

METHODS

The ReKAM framework comprises one acquisition module and two reconstruction modules. A fast MRI sequence is used to repeatedly acquire multiple sets of k-t space data. Motion artifacts are then minimized by two reconstruction modules: (a) a bootstrapping module in k-t-space is used to identify a low-artifact image; (b) a constrained reconstruction module that integrates projection onto convex set (POCS) and multiplexed sensitivity encoding (MUSE), termed POCSMUSE, is applied to further remove residual artifact. The ReKAM framework is compatible with different pulse sequences, and generally applicable to irregular data sampling patterns in k-space. Free-breathing fast spin-echo MRI data, acquired from healthy volunteers and patients, were used to evaluate the developed ReKAM method.

RESULTS

Experimental results show that the ReKAM technique can produce high-quality free-breathing images with the artifact levels comparable to that of breath-holding MRI.

CONCLUSION

The ReKAM framework improves the quality of free-breathing abdominal MRI data, and is compatible with various MRI pulse sequences.

Rapid whole-brain gray matter imaging using single-slab three-dimensional dual-echo fast spin echo: A feasibility study

PURPOSE

To achieve rapid, high resolution whole-brain gray matter (GM) imaging by developing a novel, single-slab three-dimensional dual-echo fast-spin-echo pulse sequence and GM-selective reconstruction.

METHODS

Unlike conventional GM imaging that uses time-consuming double-inversion-recovery preparation, the proposed pulse sequence was designed to have two split portions along the echo train, in which the first half was dedicated to yield short inversion recovery (IR)-induced white matter suppression and variable-flip-angle-induced two-step GM signal evolution while the second half cerebrospinal fluid-only signals. Multi-step variable-flip-angle schedules and sampling reordering were optimized to yield high GM signals while balancing cerebrospinal fluid signals between ECHOes. GM-selective images were then reconstructed directly from the weighted subtraction between ECHOes by solving a sparse signal recovery problem. In vivo studies were performed to validate the effectiveness of the proposed method over conventional double-inversion-recovery.

RESULTS

The proposed method, while achieving one millimeter isotropic, whole-brain GM imaging within 5.5 min, showed superior performance than conventional double-inversion-recovery in producing GM-only images without apparent artifacts and noise.

CONCLUSION

We successfully demonstrated the feasibility of the proposed method in achieving whole-brain GM imaging in a clinically acceptable imaging time. The proposed method is expected to be a promising alternative to conventional double-inversion-recovery in clinical applications. Magn Reson Med 78:1691-1699, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Partial Discreteness: A Novel Prior for Magnetic Resonance Image Reconstruction

An important factor influencing the quality of magnetic resonance (MR) images is the reconstruction method that is employed, and specifically, the type of prior knowledge that is exploited during reconstruction. In this work, we introduce a new type of prior knowledge, partial discreteness (PD), where a small number of regions in the image are assumed to be homogeneous and can be well represented by a constant magnitude. In particular, we mathematically formalize the partial discreteness property based on a Gaussian Mixture Model (GMM) and derive a partial discreteness image representation that characterizes the salient features of partially discrete images: a constant intensity in homogeneous areas and texture in heterogeneous areas. The partial discreteness representation is then used to construct a novel prior dedicated to the reconstruction of partially discrete MR images. The strength of the proposed prior is demonstrated on various simulated and real k-space data-based experiments with partially discrete images. Results demonstrate that the PD algorithm performs competitively with state-of-the-art reconstruction methods, being flexible and easy to implement.

A New Joint-Blade SENSE Reconstruction for Accelerated PROPELLER MRI

PROPELLER technique is widely used in MRI examinations for being motion insensitive, but it prolongs scan time and is restricted mainly to T2 contrast. Parallel imaging can accelerate PROPELLER and enable more flexible contrasts. Here, we propose a multi-step joint-blade (MJB) SENSE reconstruction to reduce the noise amplification in parallel imaging accelerated PROPELLER. MJB SENSE utilizes the fact that PROPELLER blades contain sharable information and blade-combined images can serve as regularization references. It consists of three steps. First, conventional blade-combined images are obtained using the conventional simple single-blade (SSB) SENSE, which reconstructs each blade separately. Second, the blade-combined images are employed as regularization for blade-wise noise reduction. Last, with virtual high-frequency data resampled from the previous step, all blades are jointly reconstructed to form the final images. Simulations were performed to evaluate the proposed MJB SENSE for noise reduction and motion correction. MJB SENSE was also applied to both T2-weighted and T1-weighted in vivo brain data. Compared to SSB SENSE, MJB SENSE greatly reduced the noise amplification at various acceleration factors, leading to increased image SNR in all simulation and in vivo experiments, including T1-weighted imaging with short echo trains. Furthermore, it preserved motion correction capability and was computationally efficient.

Analysis of spatiotemporal fidelity in quantitative 3D first-pass perfusion cardiovascular magnetic resonance

BACKGROUND

Whole-heart first-pass perfusion cardiovascular magnetic resonance (CMR) relies on highly accelerated image acquisition. The influence of undersampling on myocardial blood flow (MBF) quantification has not been systematically investigated yet. In the present work, the effect of spatiotemporal scan acceleration on image reconstruction accuracy and MBF error was studied using a numerical phantom and validated in-vivo.

METHODS

Up to 10-fold scan acceleration using k-t PCA and k-t SPARSE-SENSE was simulated using the MRXCAT CMR numerical phantom framework. Image reconstruction results were compared to ground truth data in the k-f domain by means of modulation transfer function (MTF) analysis. In the x-t domain, errors pertaining to specific features of signal intensity-time curves and MBF values derived using Fermi model deconvolution were analysed. In-vivo first-pass CMR data were acquired in ten healthy volunteers using a dual-sequence approach assessing the arterial input function (AIF) and myocardial enhancement. 10x accelerated 3D k-t PCA and k-t SPARSE-SENSE were compared and related to non-accelerated 2D reference images.

RESULTS

MTF analysis revealed good recovery of data upon k-t PCA reconstruction at 10x undersampling with some attenuation of higher temporal frequencies. For 10x k-t SPARSE-SENSE the MTF was found to decrease to zero at high spatial frequencies for all temporal frequencies indicating a loss in spatial resolution. Signal intensity-time curve errors were most prominent in AIFs from 10x k-t PCA, thereby emphasizing the need for separate AIF acquisition using a dual-sequence approach. These findings were confirmed by MBF estimation based on AIFs from fully sampled and undersampled simulations. Average in-vivo MBF estimates were in good agreement between both accelerated and the fully sampled methods. Intra-volunteer MBF variation for fully sampled 2D scans was lower compared to 10x k-t PCA and k-t SPARSE-SENSE data.

CONCLUSION

Quantification of highly undersampled 3D first-pass perfusion CMR yields accurate MBF estimates provided the AIF is obtained using fully sampled or moderately undersampled scans as part of a dual-sequence approach. However, relative to fully sampled 2D perfusion imaging, intra-volunteer variation is increased using 3D approaches prompting for further developments.

Phase-updated regularized SENSE for navigator-free multishot diffusion imaging

PURPOSE

Either SENSE+CG or POCS-ICE methods can be used to correct for motion-induced phase errors in navigator-free multishot diffusion imaging. SENSE+CG has the advantage of a fast convergence, however, occasionally the convergence can be unstable, thus degrading the image quality. POCS-ICE has a stable convergence and can be used with a high number of shots, but its convergence is slow, which limits its practical usage. The study here proposes an improved method based on both SENSE+CG and POCS-ICE, called Phase-updated Regularized SENSE (PR-SENSE), for navigator-free multishot diffusion imaging.

THEORY AND METHODS

In PR-SENSE, a total variation regularization method is used to solve the SENSE inverse problem instead of the conjugate gradient method used in SENSE+CG. This method is implemented by using a lagged diffusivity fixed point iteration algorithm. Additionally, the phase is updated during the iteration process to improve the image accuracy.

RESULTS

Simulations and in vivo experiments demonstrated that PR-SENSE can successfully correct for the motion-induced phase errors in multi-shot DWI. It integrates the advantages of SENSE+CG and POCS-ICE, resulting in a fast and stable convergence with improved image quality.

CONCLUSION

Given its advantages, PR-SENSE is a significant improvement over other methods for navigator-free high-resolution DWI. Magn Reson Med 78:172-181, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Radial simultaneous multi-slice CAIPI for ungated myocardial perfusion

OBJECTIVE

Simultaneous multi-slice (SMS) imaging is a slice acceleration technique that acquires multiple slices in the same time as a single slice. Radial controlled aliasing in parallel imaging results in higher acceleration (radial CAIPIRINHA or CAIPI) is a promising SMS method with less severe slice aliasing artifacts as compared to its Cartesian counterpart. Here we use radial CAIPI with data undersampling and constrained reconstruction to improve the utility of ungated cardiac perfusion acquisitions. We test the proposed framework with a traditional saturation recovery fast low-angle shot (turboFLASH) sequence and also without saturation recovery as a steady-state spoiled gradient echo (SPGR) sequence on animal and human studies.

METHODS

Simulations and phantom studies were performed for both the turboFLASH and the SPGR radial CAIPI methods. Ungated undersampled golden ratio radial CAIPI data with saturation recovery were acquired in 8 dogs and 2 human subjects. The CAIPI data without saturation pulses were acquired in 4 human subjects. For both methods, slice acceleration factors of two and three were used. A new spatio-temporal reconstruction using total variation and patch-based low rank constraints was used to jointly reconstruct the multi-slice multi-coil images.

RESULTS

Phantom scans and computer simulations showed that ungated SPGR generally provides better contrast to noise ratio (CNR) than the saturation recovery sequence if the saturation recovery time is less than 100ms. Both of the ungated radial CAIPI methods demonstrated promising image quality in terms of preserving dynamics of the contrast agent and maintaining anatomical structures, even with three slices acquired simultaneously.

CONCLUSION

Ungated simultaneous multi-slice acquisitions with either a saturation recovery turboFLASH sequence or a steady-state gradient echo SPGR sequence are feasible and provide increased slice coverage without loss of temporal resolution. Compared with a sensitivity encoding (SENSE) SMS reconstruction, the constrained reconstruction method provides better image quality for undersampled radial CAIPI data.

Hybrid-Space SENSE Reconstruction for Simultaneous Multi-Slice MRI

Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.

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

PURPOSE

Parallel imaging and partial Fourier acquisition are two classical approaches for accelerated MRI. Methods that combine these approaches often rely on prior knowledge of the image phase, but the need to obtain this prior information can place practical restrictions on the data acquisition strategy. In this work, we propose and evaluate SENSE-LORAKS, which enables combined parallel imaging and partial Fourier reconstruction without requiring prior phase information.

THEORY AND METHODS

The proposed formulation is based on combining the classical SENSE model for parallel imaging data with the more recent LORAKS framework for MR image reconstruction using low-rank matrix modeling. Previous LORAKS-based methods have successfully enabled calibrationless partial Fourier parallel MRI reconstruction, but have been most successful with nonuniform sampling strategies that may be hard to implement for certain applications. By combining LORAKS with SENSE, we enable highly accelerated partial Fourier MRI reconstruction for a broader range of sampling trajectories, including widely used calibrationless uniformly undersampled trajectories.

RESULTS

Our empirical results with retrospectively undersampled datasets indicate that when SENSE-LORAKS reconstruction is combined with an appropriate k-space sampling trajectory, it can provide substantially better image quality at high-acceleration rates relative to existing state-of-the-art reconstruction approaches.

CONCLUSION

The SENSE-LORAKS framework provides promising new opportunities for highly accelerated MRI. Magn Reson Med 77:1021-1035, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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.

Diagnostic quality assessment of compressed sensing accelerated magnetic resonance neuroimaging

PURPOSE

To determine the efficacy of compressed sensing (CS) reconstructions for specific clinical magnetic resonance neuroimaging applications beyond more conventional acceleration techniques such as parallel imaging (PI) and low-resolution acquisitions.

MATERIALS AND METHODS

Raw k-space data were acquired from five healthy volunteers on a 3T scanner using a 32-channel head coil using T2 -FLAIR, FIESTA-C, time of flight (TOF), and spoiled gradient echo (SPGR) sequences. In a series of blinded studies, three radiologists independently evaluated CS, PI (GRAPPA), and low-resolution images at up to 5× accelerations. Synthetic T2 -FLAIR images with artificial lesions were used to assess diagnostic accuracy for CS reconstructions.

RESULTS

CS reconstructions were of diagnostically acceptable quality at up to 4× acceleration for T2 -FLAIR and FIESTA-C (average qualitative scores 3.7 and 4.3, respectively, on a 5-point scale at 4× acceleration), and at up to 3× acceleration for TOF and SPGR (average scores 4.0 and 3.7, respectively, at 3× acceleration). The qualitative scores for CS reconstructions were significantly better than low-resolution images for T2 -FLAIR, FIESTA-C, and TOF and significantly better than GRAPPA for TOF and SPGR (Wilcoxon signed rank test, P < 0.05) with no significant difference found otherwise. Diagnostic accuracy was acceptable for both CS and low-resolution images at up to 3× acceleration (area under the ROC curve 0.97 and 0.96, respectively.)

CONCLUSION

Mild to moderate accelerations are possible for those sequences by a combined CS and PI reconstruction. Nevertheless, for certain sequences/applications one might mildly reduce the acquisition time by appropriately reducing the imaging resolution rather than the more complicated CS reconstruction. J. Magn. Reson. Imaging 2016;44:433-444.

Spectrally resolved fully phase-encoded three-dimensional fast spin-echo imaging

PURPOSE

To develop and test the feasibility of a spectrally resolved fully phase-encoded (SR-FPE) three-dimensional fast spin-echo technique and to demonstrate its application for distortion-free imaging near metal and chemical species separation.

METHODS

In separate scans at 1.5 T, a hip prosthesis phantom and a sphere filled with gadolinium solution were imaged with SR-FPE and compared to conventional three-dimensional-fast spin-echo. Spectral modeling was performed on the SR-FPE data to generate the following parametric maps: species-specific signal (ρspecies), B0 field inhomogeneity, and R*2. The prosthesis phantom was also scanned using a 16-channel coil at 1.5 T. The fully sampled k-space data were retrospectively undersampled to demonstrate the feasibility of parallel imaging acceleration in all three phase-encoding directions, in combination with corner-cutting and half-Fourier sampling. Finally, SR-FPE was performed with an acetone/water/oil phantom to test chemical species separation.

RESULTS

High quality distortion-free images and parametric maps were generated from SR-FPE. A 4 h SR-FPE scan was retrospectively accelerated to 12 min while preserving spectral information and 7.5 min without preserving spectral data. Chemical species separation was demonstrated in the acetone/water/oil phantom.

CONCLUSION

This work demonstrates the feasibility of SR-FPE to perform chemical species separation and spectrally resolved imaging near metal without distortion, in scan times appropriate for the clinical setting.

ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA

PURPOSE

Parallel imaging allows the reconstruction of images from undersampled multicoil data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and GRAPPA, which makes use of learned correlations in k-space. The purpose of this work is to clarify their relationship and to develop and evaluate an improved algorithm.

THEORY AND METHODS

A theoretical analysis shows: (1) The correlations in k-space are encoded in the null space of a calibration matrix. (2) Both approaches restrict the solution to a subspace spanned by the sensitivities. (3) The sensitivities appear as the main eigenvector of a reconstruction operator computed from the null space. The basic assumptions and the quality of the sensitivity maps are evaluated in experimental examples. The appearance of additional eigenvectors motivates an extended SENSE reconstruction with multiple maps, which is compared to existing methods.

RESULTS

The existence of a null space and the high quality of the extracted sensitivities are confirmed. The extended reconstruction combines all advantages of SENSE with robustness to certain errors similar to GRAPPA.

CONCLUSION

In this article the gap between both approaches is finally bridged. A new autocalibration technique combines the benefits of both.