POCS-enhanced inherent correction of motion-induced phase errors (POCS-ICE) for high-resolution multishot diffusion MRI

3D distortion-free, reduced FOV diffusion-prepared gradient echo at 3 T

PURPOSE

To develop a 3D distortion-free reduced-FOV diffusion-prepared gradient-echo sequence and demonstrate its application in vivo for diffusion imaging of the spinal cord in healthy volunteers.

METHODS

A 3D multi-shot reduced-FOV diffusion-prepared gradient-echo acquisition is achieved using a slice-selective tip-down pulse in the phase-encoding direction in the diffusion preparation, combined with magnitude stabilizers, centric k-space encoding, and 2D phase navigators to correct for intershot phase errors. The accuracy of the ADC values obtained using the proposed approach was evaluated in a diffusion phantom and compared to the tabulated reference ADC values and to the ADC values obtained using a standard spin echo diffusion-weighted single-shot EPI sequence (DW-SS-EPI). Five healthy volunteers were scanned at 3 T using the proposed sequence, DW-SS-EPI, and a clinical diffusion-weighted multi-shot readout-segmented EPI sequence (RESOLVE) for cervical spinal cord imaging. Image quality, perceived SNR, and image distortion were assessed by two expert radiologists. ADC maps were calculated, and ADC values obtained with the proposed sequence were compared to those obtained using DW-SS-EPI and RESOLVE.

RESULTS

Consistent ADC estimates were measured in the diffusion phantom with the proposed sequence and the conventional DW-SS-EPI sequence, and the ADC values were in close agreement with the reference values provided by the manufacturer of the phantom. In vivo, the proposed sequence demonstrated improved image quality, improved perceived SNR, and reduced perceived distortion compared to DW-SS-EPI, whereas all measures were comparable against RESOLVE. There were no significant differences in ADC values estimated in vivo for each of the sequences.

CONCLUSION

3D distortion-free diffusion-prepared imaging can be achieved using the proposed sequence.

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.

Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging

PURPOSE

To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions.

METHODS

A controlled breathing DWI acquisition was first employed at 3 T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC.

RESULTS

Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF.

CONCLUSION

Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800 s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.

Motion-compensated diffusion encoding in multi-shot human brain acquisitions: Insights using high-performance gradients

PURPOSE

To evaluate the utility of up to second-order motion-compensated diffusion encoding in multi-shot human brain acquisitions.

METHODS

Experiments were performed with high-performance gradients using three forms of diffusion encoding motion-compensated through different orders: conventional zeroth-order-compensated pulsed gradients (PG), first-order-compensated gradients (MC1), and second-order-compensated gradients (MC2). Single-shot acquisitions were conducted to correlate the order of motion compensation with resultant phase variability. Then, multi-shot acquisitions were performed at varying interleaving factors. Multi-shot images were reconstructed using three levels of shot-to-shot phase correction: no correction, channel-wise phase correction based on FID navigation, and correction based on explicit phase mapping (MUSE).

RESULTS

In single-shot acquisitions, MC2 diffusion encoding most effectively suppressed phase variability and sensitivity to brain pulsation, yielding residual variations of about 10° and of low spatial order. Consequently, multi-shot MC2 images were largely satisfactory without phase correction and consistently improved with the navigator correction, which yielded repeatable high-quality images; contrarily, PG and MC1 images were inadequately corrected using the navigator approach. With respect to MUSE reconstructions, the MC2 navigator-corrected images were in close agreement for a standard interleaving factor and considerably more reliable for higher interleaving factors, for which MUSE images were corrupted. Finally, owing to the advanced gradient hardware, the relative SNR penalty of motion-compensated diffusion sensitization was substantially more tolerable than that faced previously.

CONCLUSION

Second-order motion-compensated diffusion encoding mitigates and simplifies shot-to-shot phase variability in the human brain, rendering the multi-shot acquisition strategy an effective means to circumvent limitations of retrospective phase correction methods.

Motion compensated structured low-rank reconstruction for 3D multi-shot EPI

PURPOSE

The 3D multi-shot EPI imaging offers several benefits including higher SNR and high isotropic resolution compared to 2D single shot EPI. However, it suffers from shot-to-shot inconsistencies arising from physiologically induced phase variations and bulk motion. This work proposed a motion compensated structured low-rank (mcSLR) reconstruction method to address both issues for 3D multi-shot EPI.

METHODS

Structured low-rank reconstruction has been successfully used in previous work to deal with inter-shot phase variations for 3D multi-shot EPI imaging. It circumvents the estimation of phase variations by reconstructing an individual image for each phase state which are then sum-of-squares combined, exploiting their linear interdependency encoded in structured low-rank constraints. However, structured low-rank constraints become less effective in the presence of inter-shot motion, which corrupts image magnitude consistency and invalidates the linear relationship between shots. Thus, this work jointly models inter-shot phase variations and motion corruptions by incorporating rigid motion compensation for structured low-rank reconstruction, where motion estimates are obtained in a fully data-driven way without relying on external hardware or imaging navigators.

RESULTS

Simulation and in vivo experiments at 7T have demonstrated that the mcSLR method can effectively reduce image artifacts and improve the robustness of 3D multi-shot EPI, outperforming existing methods which only address inter-shot phase variations or motion, but not both.

CONCLUSION

The proposed mcSLR reconstruction compensates for rigid motion, and thus improves the validity of structured low-rank constraints, resulting in improved robustness of 3D multi-shot EPI to both inter-shot motion and phase variations.

Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging

Purpose

To overcome the major challenges in dMRI acquisition, including low SNR, distortion/blurring, and motion vulnerability.

Methods

A novel Romer-EPTI technique is developed to provide distortion-free dMRI with significant SNR gain, high motion-robustness, sharp spatial resolution, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free EPTI encoding. Romer enhances SNR by a simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness through a motion-aware super-resolution reconstruction, which also incorporates slice-profile and real-value diffusion, to resolve high-isotropic-resolution volumes. The in-plane encoding is performed using distortion/blurring-free EPTI, which further improves effective spatial resolution and motion robustness by preventing not only T2/T2*-blurring but also additional blurring resulting from combining encoded volumes with inconsistent geometries caused by dynamic distortions. Self-navigation was incorporated to enable efficient phase correction. Additional developments include strategies to address slab-boundary artifacts, achieve minimal TE for SNR gain at 7T, and achieve high robustness to strong phase variations at high b-values.

Results

Using Romer-EPTI, we demonstrate distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm-iso) and 7T (485-μm-iso) for the first time, with high SNR efficiency (e.g., 25 × ), and high image quality free from distortion and slab-boundary artifacts with minimal blurring. Motion experiments demonstrate Romer-EPTI's high motion-robustness and ability to recover sharp images in the presence of motion. Romer-EPTI also demonstrates significant SNR gain and robustness in high b-value (b=5000s/mm2) and time-dependent dMRI.

Conclusion

Romer-EPTI significantly improves SNR, motion-robustness, and image quality, providing a highly efficient acquisition for high-resolution dMRI and microstructure imaging.

Structured low-rank reconstruction for navigator-free water/fat separated multi-shot diffusion-weighted EPI

PURPOSE

Multi-shot diffusion-weighted EPI allows an increase in image resolution and reduced geometric distortions and can be combined with chemical-shift encoding (Dixon) to separate water/fat signals. However, such approaches suffer from physiological motion-induced shot-to-shot phase variations. In this work, a structured low-rank-based navigator-free algorithm is proposed to address the challenge of simultaneously separating water/fat signals and correcting for physiological motion-induced shot-to-shot phase variations in multi-shot EPI-based diffusion-weighted MRI.

THEORY AND METHODS

We propose an iterative, model-based reconstruction pipeline that applies structured low-rank regularization to estimate and eliminate the shot-to-shot phase variations in a data-driven way, while separating water/fat images. The algorithm is tested in different anatomies, including head-neck, knee, brain, and prostate. The performance is validated in simulations and in-vivo experiments in comparison to existing approaches.

RESULTS

In-vivo experiments and simulations demonstrated the effectiveness of the proposed algorithm compared to extra-navigated and an alternative self-navigation approach. The proposed algorithm demonstrates the capability to reconstruct in the multi-shot/Dixon hybrid space domain under-sampled datasets, using the same number of acquired EPI shots compared to conventional fat-suppression techniques but eliminating fat signals through chemical-shift encoding. In addition, partial Fourier reconstruction can also be achieved by using the concept of virtual conjugate coils in conjunction with the proposed algorithm.

CONCLUSION

The proposed algorithm effectively eliminates the shot-to-shot phase variations and separates water/fat images, making it a promising solution for future DWI on different anatomies.

Comparison of uniform-density, variable-density, and dual-density spiral samplings for multi-shot DWI

PURPOSE

To compare the performances of uniform-density spiral (UDS), variable-density spiral (VDS), and dual-density spiral (DDS) samplings in multi-shot diffusion imaging, and determine a sampling strategy that balances reliability of shot navigator and overall DWI image quality.

THEORY AND METHODS

UDS, VDS, and DDS trajectories were implemented to achieve four-shot diffusion-weighted spiral imaging. First, the static B0 off-resonance effects in UDS, VDS, and DDS acquisitions were analyzed based on a signal model. Then, in vivo experiments were performed to verify the theoretical analyses, and fractional anisotropy (FA) fitting residuals were used to quantitatively assess the quality of spiral diffusion data for tensor estimation. Finally, the SNR performances and g-factor behavior of the three spiral samplings were evaluated using a Monte Carlo-based pseudo multiple replica method.

RESULTS

Among the three spiral trajectories with the same readout duration, UDS sampling exhibited the least off-resonance artifacts. This was most evident when the static B0 off-resonance effect was severe. The UDS diffusion images had higher anatomical fidelity and lower FA fitting residuals than the other two counterparts. Furthermore, the four-shot UDS acquisition achieved the best SNR performance in diffusion imaging with 12.11% and 40.85% improvements over the VDS and DDS acquisitions with the same readout duration, respectively.

CONCLUSION

UDS sampling is an efficient spiral acquisition scheme for high-resolution diffusion imaging with reliable navigator information. It provides superior off-resonance performance and SNR efficiency over the VDS and DDS samplings for the tested scenarios.

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.

Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping

PURPOSE

To develop a joint reconstruction method for multi-band multi-shot diffusion MRI.

THEORY AND METHODS

Multi-band multi-shot EPI acquisition is an effective approach for high-resolution diffusion MRI, but requires specific algorithms to correct the inter-shot phase variations. The phase correction can be done by first estimating the explicit phase map and then feeding it into the k-space signal formulation model. Alternatively, the phase information can be used indirectly as structured low-rank constraints in k-space. The 2 methods differ in reconstruction accuracy and efficiency. We aim to combine the 2 different approaches for improved image quality and reconstruction efficiency simultaneously, termed "joint usage of structured low-rank constraints and explicit phase mapping" (JULEP). The proposed JULEP reconstruction is tested on both single-band and multi-band, multi-shot diffusion data, with different resolutions and b values. The results of JULEP are compared with conventional methods with explicit phase mapping (i.e., multiplexed sensitivity-encoding [MUSE]) and structured low-rank constraints (i.e., MUSSELS), and another joint reconstruction method (i.e., network estimated artifacts for tempered reconstruction [NEATR]).

RESULTS

JULEP improves the quality of the navigator and subsequently facilitates the reconstruction of final diffusion images. Compared with all 3 other methods (MUSE, MUSSELS, and NEATR), JULEP mitigates residual structural bias and improves temporal SNRs in the final diffusion image, particularly at high multi-band factors. Compared with MUSSELS, JULEP also improves computational efficiency.

CONCLUSION

The proposed JULEP method significantly improves the image quality and reconstruction efficiency of multi-band multi-shot diffusion MRI, which can promote a broader application of high-resolution diffusion MRI.

Water/fat separation for self-navigated diffusion-weighted multishot echo-planar imaging

The purpose of this study was to develop a self-navigation strategy to improve scan efficiency and image quality of water/fat-separated, diffusion-weighted multishot echo-planar imaging (ms-EPI). This is accomplished by acquiring chemical shift-encoded diffusion-weighted data and using an appropriate water-fat and diffusion-encoded signal model to enable reconstruction directly from k-space data. Multishot EPI provides reduced geometric distortion and improved signal-to-noise ratio in diffusion-weighted imaging compared with single-shot approaches. Multishot acquisitions require corrections for physiological motion-induced shot-to-shot phase errors using either extra navigators or self-navigation principles. In addition, proper fat suppression is important, especially in regions with large B₀ inhomogeneity. This makes the use of chemical shift encoding attractive. However, when combined with ms-EPI, shot-to-shot phase navigation can be challenging because of the spatial displacement of fat signals along the phase-encoding direction. In this work, a new model-based, self-navigated water/fat separation reconstruction algorithm is proposed. Experiments in legs and in the head-neck region of 10 subjects were performed to validate the algorithm. The results are compared with an image-based, two-dimensional (2D) navigated water/fat separation approach for ms-EPI and with a conventional fat saturation approach. Compared with the 2D navigated method, the use of self-navigation reduced the shot duration time by 30%-35%. The proposed algorithm provided improved diffusion-weighted water images in both leg and head-neck regions compared with the 2D navigator-based approach. The proposed algorithm also produced better fat suppression compared with the conventional fat saturation technique in the B₀ inhomogeneous regions. In conclusion, the proposed self-navigated reconstruction algorithm can produce superior water-only diffusion-weighted EPI images with less artefacts compared with the existing methods.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

White matter regeneration induced by aligned fibrin nanofiber hydrogel contributes to motor functional recovery in canine T12 spinal cord injury

A hierarchically aligned fibrin hydrogel (AFG) that possesses soft stiffness and aligned nanofiber structure has been successfully proven to facilitate neuroregeneration in vitro and in vivo. However, its potential in promoting nerve regeneration in large animal models that is critical for clinical translation has not been sufficiently specified. Here, the effects of AFG on directing neuroregeneration in canine hemisected T12 spinal cord injuries were explored. Histologically obvious white matter regeneration consisting of a large area of consecutive, compact and aligned nerve fibers is induced by AFG, leading to a significant motor functional restoration. The canines with AFG implantation start to stand well with their defective legs from 3 to 4 weeks postoperatively and even effortlessly climb the steps from 7 to 8 weeks. Moreover, high-resolution multi-shot diffusion tensor imaging illustrates the spatiotemporal dynamics of nerve regeneration rapidly crossing the lesion within 4 weeks in the AFG group. Our findings indicate that AFG could be a potential therapeutic vehicle for spinal cord injury by inducing rapid white matter regeneration and restoring locomotion, pointing out its promising prospect in clinic practice.

Segmented simultaneous multi-slice diffusion-weighted imaging with navigated 3D rigid motion correction

PURPOSE

To improve the robustness of diffusion-weighted imaging (DWI) data acquired with segmented simultaneous multi-slice (SMS) echo-planar imaging (EPI) against in-plane and through-plane rigid motion.

THEORY AND METHODS

The proposed algorithm incorporates a 3D rigid motion correction and wavelet denoising into the image reconstruction of segmented SMS-EPI diffusion data. Low-resolution navigators are used to estimate shot-specific diffusion phase corruptions and 3D rigid motion parameters through SMS-to-volume registration. The shot-wise rigid motion and phase parameters are integrated into a SENSE-based full-volume reconstruction for each diffusion direction. The algorithm is compared to a navigated SMS reconstruction without gross motion correction in simulations and in vivo studies with four-fold interleaved 3-SMS diffusion tensor acquisitions.

RESULTS

Simulations demonstrate high fidelity was achieved in the SMS-to-volume registration, with submillimeter registration errors and improved image reconstruction quality. In vivo experiments validate successful artifact reduction in 3D motion-compromised in vivo scans with a temporal motion resolution of approximately 0.3 s.

CONCLUSION

This work demonstrates the feasibility of retrospective 3D rigid motion correction from shot navigators for segmented SMS DWI.

RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors

PURPOSE

To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning.

METHODS

An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation.

RESULTS

The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts.

CONCLUSION

A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.

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 half-century of innovation in technology-preparing MRI for the 21st century

MRI developed during the last half-century from a very basic concept to an indispensable non-ionising medical imaging technique that has found broad application in diagnostics, therapy control and far beyond. Due to its excellent soft-tissue contrast and the huge variety of accessible tissue- and physiological-parameters, MRI is often preferred to other existing modalities. In the course of its development, MRI underwent many substantial transformations. From the beginning, starting as a proof of concept, much effort was expended to develop the appropriate basic scanning technology and methodology, and to establish the many clinical contrasts (e.g., T₁, T₂, flow, diffusion, water/fat, etc.) that MRI is famous for today. Beyond that, additional prominent innovations to the field have been parallel imaging and compressed sensing, leading to significant scanning time reductions, and the move towards higher static magnetic field strengths, which led to increased sensitivity and improved image quality. Improvements in workflow and the use of artificial intelligence are among many current trends seen in this field, paving the way for a broad use of MRI. The 125th anniversary of the BJR is a good point to reflect on all these changes and developments and to offer some slightly speculative ideas as to what the future may bring.

Phase-constrained reconstruction of high-resolution multi-shot diffusion weighted image

Diffusion weighted imaging (DWI) is a unique examining method in tumor diagnosis, acute stroke evaluation. Single-shot echo planar imaging is currently conventional method for DWI. However, single-shot DWI suffers from image distortion, blurring and low spatial resolution. Although multi-shot DWI improves image resolution, it brings phase variations among different shots at the same time. In this paper, we introduce a smooth phase constraint of each shot image into multi-shot navigator-free DWI reconstruction by imposing the low-rankness of Hankel matrix constructed from the k-space data. Furthermore, we exploit the partial sum minimization of singular values to constrain the low-rankness of Hankel matrix. Results on brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of artifacts removal and our method potentially has the ability to reconstruct high number of shot of DWI.

Application of multishot diffusion tensor imaging in spinal cord tumors

Objective:

To explore the usefulness of multishot diffusion tensor imaging (DTI) for evaluating the neurological function of patients with spinal cord tumors

Methods:

Routine magnetic resonance imaging and multishot DTI were performed in five patients with spinal cord tumors. The values of fractional anisotropy (FA) and radial diffusivity (RD) were analyzed.

Results:

Multishot DTI of spinal cord tumors allowed for defining the margins of tumors and determining the relationship of tumors with the adjacent white matter structures of the spinal cord. Multishot DTI demonstrated significantly increased RD and decreased FA of spinal cord tumors compared with those of the normal spinal cord.

Conclusions:

Multishot DTI is a potentially useful modality for differentiating resectable tumors from nonresectable ones based on preoperative imaging alone as well as for differentiating intramedullary tumors from extramedullary ones. Further prospective studies are warranted to confirm these results.

Improved MUSSELS reconstruction for high-resolution multi-shot diffusion weighted imaging

PURPOSE

MUSSELS is a one-step iterative reconstruction method for multishot diffusion weighted (msDW) imaging. The current work presents an efficient implementation, termed IRLS MUSSELS, that enables faster reconstruction to enhance its utility for high-resolution diffusion MRI studies.

METHODS

The recently proposed MUSSELS reconstruction belongs to a new class of parallel imaging-based methods that recover artifact-free DWIs from msDW data without needing phase compensation. The reconstruction is achieved via structured low-rank matrix completion algorithms, which are computationally demanding due to the large size of the Hankel matrices and their associated computations involving singular value decompositions. Because of this, computational demands of the MUSSELS reconstruction scales as the matrix size and the number of shots increases, which hinders its practical utility for high-resolution applications. In this work, we derive a computationally efficient MUSSELS formulation by modifying the iterative reweighted least squares (IRLS) method that were proposed earlier to solve such problems. Using whole-brain in vivo data, we show the utility of the IRLS MUSSELS for routine high-resolution studies with reduced computational burden.

RESULTS

IRLS MUSSELS provides about five times faster reconstruction for matrix sizes 192 × 192 and 256 × 256 compared to the earlier MUSSELS implementation. The widely employed conjugate symmetry priors can also be incorporated into IRLS MUSSELS to reduce blurring of the partial Fourier acquisitions, without incurring much computational burden.

CONCLUSIONS

The proposed method is observed to be computationally efficient to enable routine high-resolution studies. The computational complexity matches the traditional msDWI reconstruction methods and provides improved reconstruction results with the additional constraints.

Multi-shot diffusion-weighted MRI reconstruction with magnitude-based spatial-angular locally low-rank regularization (SPA-LLR)

PURPOSE

To resolve the motion-induced phase variations in multi-shot multi-direction diffusion-weighted imaging (DWI) by applying regularization to magnitude images.

THEORY AND METHODS

A nonlinear model was developed to estimate phase and magnitude images separately. A locally low-rank regularization (LLR) term was applied to the magnitude images from all diffusion-encoding directions to exploit the spatial and angular correlation. In vivo experiments with different resolutions and b-values were performed to validate the proposed method.

RESULTS

The proposed method significantly reduces the noise level compared to the conventional reconstruction method and achieves submillimeter (0.8mm and 0.9mm isotropic resolutions) DWI with a b-value of 1,000  s / mm 2 and 1-mm isotropic DWI with a b-value of 2,000  s / mm 2 without modification of the sequence.

CONCLUSIONS

A joint reconstruction method with spatial-angular LLR regularization on magnitude images substantially improves multi-direction DWI reconstruction, simultaneously removes motion-induced phase artifacts, and denoises images.

Common Information Enhanced Reconstruction for Accelerated High-resolution Multi-shot Diffusion Imaging

PURPOSE

Multi-shot technique can effectively achieve high-resolution diffusion weighted images, but the acquisition time of multi-shot technique is prolonged, especially for multiple direction diffusion encoding. Thus, increasing acquisition efficiency is highly desirable for high-resolution diffusion tensor imaging (DTI). In this study, based on the assumption that different diffusion directions share the common information, image ratio constrained reconstruction (IRCR) combined with iterative self-consistent parallel imaging reconstruction (SPIRiT) is proposed to improve data sampling efficiency and image reconstruction fidelity for high-resolution DTI.

THEORY AND METHODS

The proposed reconstruction framework is named Common Information Enhanced Reconstruction (CIER). Inter-image correlation among different direction diffusion-weighted images is used through common information, which is an isotropic component and structure, for improving the performance of reconstruction. The framework consists of three steps. (i) Pre-processing: three intermediate multi-shot images, low-resolution composite image, high-resolution composite image and low-resolution diffusion weighted image, are generated based on the SPIRiT method. (ii) IRCR: the initial high-resolution diffusion weighted image is calculated from the images in step (i) based on that the ratio map between high-resolution images is approximated by the ratio map between the corresponding low-resolution images. (iii) Final SPIRiT reconstruction: the final image is generated with the image from IRCR as initialization by considering data consistency only in the SPIRiT calculation. A specific implementation based on multishot variable density spiral (VDS) DTI is used to demonstrate the method.

RESULTS

The proposed CIER method was compared with the traditional reconstruction methods, conjugate gradient SENSE (CG-SENSE), L1-regularized SPIRiT (L1-SPIRiT), and anisotropic-sparsity SPIRiT (AS-SPIRiT) in brain DTI at acceleration factors of 3 to 7. CIER provided better diffusion image quality than other methods shown by both qualitative and quantitative results, especially at higher undersampling acceleration factors.

CONCLUSION

CIER offers better diffusion image quality at higher undersampling acceleration factors for high-resolution DTI. Both qualitative and quantitative results prove that common information can be used to improve sampling efficiency and maintain the image quality of diffusion-weighted images.

The quest for high spatial resolution diffusion-weighted imaging of the human brain in vivo

Diffusion-weighted imaging, a contrast unique to MRI, is used for assessment of tissue microstructure in vivo. However, this exquisite sensitivity to finer scales far above imaging resolution comes at the cost of vulnerability to errors caused by sources of motion other than diffusion motion. Addressing the issue of motion has traditionally limited diffusion-weighted imaging to a few acquisition techniques and, as a consequence, to poorer spatial resolution than other MRI applications. Advances in MRI imaging methodology have allowed diffusion-weighted MRI to push to ever higher spatial resolution. In this review we focus on the pulse sequences and associated techniques under development that have pushed the limits of image quality and spatial resolution in diffusion-weighted MRI.

Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization

PURPOSE

The goal of this work is to propose a motion robust reconstruction method for diffusion-weighted MRI that resolves shot-to-shot phase mismatches without using phase estimation.

METHODS

Assuming that shot-to-shot phase variations are slowly varying, spatial-shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low-rank constraint on the spatial-shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method.

RESULTS

The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase-estimation-based methods to achieve even higher image quality.

CONCLUSION

We introduced the shot-locally low-rank method, a reconstruction technique for multishot diffusion-weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.

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

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

Image formation in diffusion MRI: A review of recent technical developments

Diffusion magnetic resonance imaging (MRI) is a standard imaging tool in clinical neurology, and is becoming increasingly important for neuroscience studies due to its ability to depict complex neuroanatomy (eg, white matter connectivity). Single-shot echo-planar imaging is currently the predominant formation method for diffusion MRI, but suffers from blurring, distortion, and low spatial resolution. A number of methods have been proposed to address these limitations and improve diffusion MRI acquisition. Here, the recent technical developments for image formation in diffusion MRI are reviewed. We discuss three areas of advance in diffusion MRI: improving image fidelity, accelerating acquisition, and increasing the signal-to-noise ratio.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:646-662.

Diffusion-prepared stimulated-echo turbo spin echo (DPsti-TSE): An eddy current-insensitive sequence for three-dimensional high-resolution and undistorted diffusion-weighted imaging

In this study, we present a new three-dimensional (3D), diffusion-prepared turbo spin echo sequence based on a stimulated-echo read-out (DPsti-TSE) enabling high-resolution and undistorted diffusion-weighted imaging (DWI). A dephasing gradient in the diffusion preparation module and rephasing gradients in the turbo spin echo module create stimulated echoes, which prevent signal loss caused by eddy currents. Near to perfect agreement of apparent diffusion coefficient (ADC) values between DPsti-TSE and diffusion-weighted echo planar imaging (DW-EPI) was demonstrated in both phantom transient signal experiments and phantom imaging experiments. High-resolution and undistorted DPsti-TSE was demonstrated in vivo in prostate and carotid vessel wall. 3D whole-prostate DWI was achieved with four b values in only 6 min. Undistorted ADC maps of the prostate peripheral zone were obtained at low and high imaging resolutions with no change in mean ADC values [(1.60 ± 0.10) × 10^-3 versus (1.60 ± 0.02) × 10^-3  mm² /s]. High-resolution 3D DWI of the carotid vessel wall was achieved in 12 min, with consistent ADC values [(1.40 ± 0.23) × 10^-3  mm² /s] across different subjects, as well as slice locations through the imaging volume. This study shows that DPsti-TSE can serve as a robust 3D diffusion-weighted sequence and is an attractive alternative to the traditional two-dimensional DW-EPI approaches.

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.

Joint correction of Nyquist artifact and minuscule motion-induced aliasing artifact in interleaved diffusion weighted EPI data using a composite two-dimensional phase correction procedure

Diffusion-weighted imaging (DWI) obtained with interleaved echo-planar imaging (EPI) pulse sequence has great potential of characterizing brain tissue properties at high spatial-resolution. However, interleaved EPI based DWI data may be corrupted by various types of aliasing artifacts. First, inconsistencies in k-space data obtained with opposite readout gradient polarities result in Nyquist artifact, which is usually reduced with 1D phase correction in post-processing. When there exist eddy current cross terms (e.g., in oblique-plane EPI), 2D phase correction is needed to effectively reduce Nyquist artifact. Second, minuscule motion induced phase inconsistencies in interleaved DWI scans result in image-domain aliasing artifact, which can be removed with reconstruction procedures that take shot-to-shot phase variations into consideration. In existing interleaved DWI reconstruction procedures, Nyquist artifact and minuscule motion-induced aliasing artifact are typically removed subsequently in two stages. Although the two-stage phase correction generally performs well for non-oblique plane EPI data obtained from well-calibrated system, the residual artifacts may still be pronounced in oblique-plane EPI data or when there exist eddy current cross terms. To address this challenge, here we report a new composite 2D phase correction procedure, which effective removes Nyquist artifact and minuscule motion induced aliasing artifact jointly in a single step. Our experimental results demonstrate that the new 2D phase correction method can much more effectively reduce artifacts in interleaved EPI based DWI data as compared with the existing two-stage artifact correction procedures. The new method robustly enables high-resolution DWI, and should prove highly valuable for clinical uses and research studies of DWI.