Motion immune diffusion imaging using augmented MUSE for high-resolution multi-shot EPI

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.

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.

Rapid T 2 ∗ -weighted MRI using multishot EPI with retrospective motion and phase correction in the emergency department

PURPOSE

Brain MRI is increasingly used in the emergency department (ED), whereT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted MRI is an essential tool for detecting hemorrhage and stroke. The goal of this study was to develop a rapidT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted MRI technique capable of correcting motion-induced artifacts, thereby simultaneously improving scan time and motion robustness for ED applications.

METHODS

A 2D gradient-echo (GRE)-based multishot EPI (msEPI) technique was implemented using a navigator echo for estimating motion-induced errors. Bulk rigid head motion and phase errors were retrospectively corrected using an iterative conjugate gradient approach in the reconstruction pipeline. Three volunteers and select patients were imaged at 3 T and/or 1.5 T with an approximately 1-min full-brain protocol using the proposed msEPI technique and compared to an approximately 3-min standard-of-care GRE protocol to examine its performance.

RESULTS

Data from volunteers demonstrated that in-plane motion artifacts could be effectively corrected with the proposed msEPI technique, and through-plane motion artifacts could be mitigated. Patient images were qualitatively reviewed by one radiologist without a formal statistical analysis. These results suggested the proposed technique could correct motion-induced artifacts in the clinical setting. In addition, the conspicuity of susceptibility-related lesions using the proposed msEPI technique was comparable, or improved, compared to GRE.

CONCLUSION

A 1-min full-brainT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted MRI technique was developed using msEPI with a navigator echo to correct motion-induced errors. Preliminary clinical results suggest faster scans and improved motion robustness and lesion conspicuity make msEPI a competitive alternative to traditionalT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted MRI techniques for brain studies in the ED.

Evaluation of Motion-Corrected Multishot Echo-Planar Imaging as an Alternative to Gradient Recalled-Echo for Blood-Sensitive Imaging

We evaluated motion-corrected multishot EPI compared with gradient recalled-echo imaging to determine whether it can be used as a faster technique for blood-sensitive imaging in the emergency department setting. Multishot EPI was found to be superior to gradient recalled-echo (P < .05) in motion artifacts, overall image quality, and lesion detection. These results and reduced scan time make motion-corrected multishot EPI a viable alternative for blood-sensitive imaging in the emergency department setting.

High-resolution diffusion-weighted imaging at 7 Tesla: single-shot readout trajectories and their impact on signal-to-noise ratio, spatial resolution and accuracy

Diffusion MRI (dMRI) is a valuable imaging technique to study the connectivity and microstructure of the brain in vivo. However, the resolution of dMRI is limited by the low signal-to-noise ratio (SNR) of this technique. Various multi-shot acquisition strategies have been developed to achieve sub-millimeter resolution, but they require long scan times which can be restricting for patient scans. Alternatively, the SNR of single-shot acquisitions can be increased by using a spiral readout trajectory to minimize the sequence echo time. Imaging at ultra-high fields (UHF) could further increase the SNR of single-shot dMRI; however, the shorter T2* of brain tissue and the greater field non-uniformities at UHFs will degrade image quality, causing image blurring, distortions, and signal loss. In this study, we investigated the trade-off between the SNR and resolution of different k-space trajectories, including echo planar imaging (EPI), partial Fourier EPI, and spiral trajectories, over a range of dMRI resolutions at 7T. The effective resolution, spatial specificity and sharpening effect were measured from the point spread function (PSF) of the simulated diffusion sequences for a nominal resolution range of 0.6-1.8 mm. In-vivo partial brain scans at a nominal resolution of 1.5 mm isotropic were acquired using the three readout trajectories to validate the simulation results. Field probes were used to measure dynamic magnetic fields offline up to the 3^rd order of spherical harmonics. Image reconstruction was performed using static ΔB₀ field maps and the measured trajectories to correct image distortions and artifacts, leaving T2* effects as the primary source of blurring. The effective resolution was examined in fractional anisotropy (FA) maps calculated from a multi-shell dataset with b-values of 300, 1000, and 2000 s/mm² in 5, 16, and 48 directions, respectively. In-vivo scans at nominal resolutions of 1, 1.2, and 1.5 mm were acquired and the SNR of the different trajectories calculated using the multiple replica method to investigate the SNR. Finally, in-vivo whole brain scans with an effective resolution of 1.5 mm isotropic were acquired to explore the SNR and efficiency of different trajectories at a matching effective resolution. FA and intra-cellular volume fraction (ICVF) maps calculated using neurite orientation dispersion and density imaging (NODDI) were used for the comparison. The simulations and in vivo imaging results showed that for matching nominal resolutions, EPI trajectories had the highest specificity and effective resolution with maximum image sharpening effect. However, spirals have a significantly higher SNR, in particular at higher resolutions and even when the effective image resolutions are matched. Overall, this work shows that the higher SNR of single-shot spiral trajectories at 7T allows us to achieve higher effective resolutions compared to EPI and PF-EPI to map the microstructure and connectivity of small brain structures.

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.

Motion-corrected 3D-EPTI with efficient 4D navigator acquisition for fast and robust whole-brain quantitative imaging

PURPOSE

To develop a motion estimation and correction method for motion-robust three-dimensional (3D) quantitative imaging with 3D-echo-planar time-resolved imaging.

THEORY AND METHODS

The 3D-echo-planar time-resolved imaging technique was designed with additional four-dimensional navigator acquisition (x-y-z-echoes) to achieve fast and motion-robust quantitative imaging of the human brain. The four-dimensional-navigator is inserted into the relaxation-recovery deadtime of the sequence in every pulse TR (∼2 s) to avoid extra scan time, and to provide continuous tracking of the 3D head motion and B0 -inhomogeneity changes. By using an optimized spatiotemporal encoding combined with a partial-Fourier scheme, the navigator acquires a large central k-t data block for accurate motion estimation using only four small-flip-angle excitations and readouts, resulting in negligible signal-recovery reduction to the 3D-echo-planar time-resolved imaging acquisition. By incorporating the estimated motion and B0 -inhomogeneity changes into the reconstruction, multi-contrast images can be recovered with reduced motion artifacts.

RESULTS

Simulation shows the cost to the SNR efficiency from the added navigator acquisitions is <1%. Both simulation and in vivo retrospective experiments were conducted, that demonstrate the four-dimensional navigator provided accurate estimation of the 3D motion and B0 -inhomogeneity changes, allowing effective reduction of image artifacts in quantitative maps. Finally, in vivo prospective undersampling acquisition was performed with and without head motion, in which the motion corrupted data after correction show close image quality and consistent quantifications to the motion-free scan, providing reliable quantitative measurements even with head motion.

CONCLUSION

The proposed four-dimensional navigator acquisition provides reliable tracking of the head motion and B0 change with negligible SNR cost, equips the 3D-echo-planar time-resolved imaging technique for motion-robust and efficient quantitative imaging.

Sheared two-dimensional radiofrequency excitation for off-resonance robustness and fat suppression in reduced field-of-view imaging

PURPOSE

Two-dimensional (2D) echo-planar radiofrequency (RF) pulses are widely used for reduced field-of-view (FOV) imaging in applications such as diffusion-weighted imaging. However, long pulse durations render the 2D RF pulses sensitive to off-resonance effects, causing local signal losses in reduced-FOV images. This work aims to achieve off-resonance robustness for 2D RF pulses via a sheared trajectory design.

THEORY AND METHODS

A sheared 2D RF pulse design is proposed to reduce pulse durations while covering identical excitation k-space extent as a standard 2D RF pulse. For a given shear angle, the number of sheared trajectory lines is minimized to obtain the shortest pulse duration, such that the excitation replicas are repositioned outside the slice stack to guarantee unlimited slice coverage. A target fat/water signal ratio of 5% is chosen to achieve robust fat suppression.

RESULTS

Simulations, imaging experiments on a custom head and neck phantom, and in vivo imaging experiments in the spinal cord at 3 T demonstrate that the sheared 2D RF design provides significant improvement in image quality while preserving profile sharpnesses. In regions with high off-resonance effects, the sheared 2D RF pulse improves the signal by more than 50% when compared to the standard 2D RF pulse.

CONCLUSION

The proposed sheared 2D RF design successfully reduces pulse durations, exhibiting significantly improved through-plane off-resonance robustness, while providing unlimited slice coverage and high fidelity fat suppression. This method will be especially beneficial in regions suffering from a variety of off-resonance effects, such as spinal cord and breast.

Pediatric skeletal diffusion-weighted magnetic resonance imaging: part 1 - technical considerations and optimization strategies

Diffusion-weighted MRI, or DWI, is a fast, quantitative technique that is easily integrated into a morphological MR acquisition. The ability of DWI to aid in detecting multifocal skeletal pathology and in characterizing tissue cellularity to a level beyond that possible with other techniques makes it a niche component of multiparametric MR imaging of the skeleton. Besides its role in disease detection and establishing cellularity and character of osseous lesions, DWI continues to be examined as a surrogate biomarker for therapeutic response of several childhood bone tumors. There is increasing interest in harnessing DWI as a potential substitute to alternative modes of imaging evaluation that involve radiation or administration of intravenous contrast agent or radiopharmaceuticals, for example in early detection and diagnosis of capital femoral epiphyseal ischemia in cases of Legg-Calvé-Perthes disease, or diagnosis and staging of lymphoma. The expected evolution of skeletal diffusivity characteristics with maturation and the unique disease processes that affect the pediatric skeleton necessitate a pediatric-specific discussion. In this article, the author examines the developmentally appropriate normal appearances, technique, artifacts and pitfalls of pediatric skeletal DWI.

qModeL: A plug-and-play model-based reconstruction for highly accelerated multi-shot diffusion MRI using learned priors

PURPOSE

To introduce a joint reconstruction method for highly undersampled multi-shot diffusion weighted (msDW) scans.

METHODS

Multi-shot EPI methods enable higher spatial resolution for diffusion MRI, but at the expense of long scan-time. Highly accelerated msDW scans are needed to enable their utilization in advanced microstructure studies, which require high q-space coverage. Previously, joint k-q undersampling methods coupled with compressed sensing were shown to enable very high acceleration factors. However, the reconstruction of this data using sparsity priors is challenging and is not suited for multi-shell data. We propose a new reconstruction that recovers images from the combined k-q data jointly. The proposed qModeL reconstruction brings together the advantages of model-based iterative reconstruction and machine learning, extending the idea of plug-and-play algorithms. Specifically, qModeL works by prelearning the signal manifold corresponding to the diffusion measurement space using deep learning. The prelearned manifold prior is incorporated into a model-based reconstruction to provide a voxel-wise regularization along the q-dimension during the joint recovery. Notably, the learning does not require in vivo training data and is derived exclusively from biophysical modeling. Additionally, a plug-and-play total variation denoising provides regularization along the spatial dimension. The proposed framework is tested on k-q undersampled single-shell and multi-shell msDW acquisition at various acceleration factors.

RESULTS

The qModeL joint reconstruction is shown to recover DWIs from 8-fold accelerated msDW acquisitions with error less than 5% for both single-shell and multi-shell data. Advanced microstructural analysis performed using the undersampled reconstruction also report reasonable accuracy.

CONCLUSION

qModeL enables the joint recovery of highly accelerated multi-shot dMRI utilizing learning-based priors. The bio-physically driven approach enables the use of accelerated multi-shot imaging for multi-shell sampling and advanced microstructure studies.

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.

Diffusion-prepared 3D gradient spin-echo sequence for improved oscillating gradient diffusion MRI

PURPOSE

Oscillating gradient (OG) enables the access of short diffusion times for time-dependent diffusion MRI (dMRI); however, it poses several technical challenges for clinical use. This study proposes a 3D oscillating gradient-prepared gradient spin-echo (OGprep-GRASE) sequence to improve SNR and shorten acquisition time for OG dMRI on clinical scanners.

METHODS

The 3D OGprep-GRASE sequence consisted of global saturation, diffusion encoding, fat saturation, and GRASE readout modules. Multiplexed sensitivity-encoding reconstruction was used to correct the phase errors between multiple shots. We compared the scan time and SNR of the proposed sequence and the conventional 2D-EPI sequence for OG dMRI at 30-90-mm slice coverage. We also examined the time-dependent diffusivity changes with OG dMRI acquired at frequencies of 50 Hz and 25 Hz and pulsed-gradient dMRI at diffusion times of 30 ms and 60 ms.

RESULTS

The OGprep-GRASE sequence reduced the scan time by a factor of 1.38, and increased the SNR by 1.74-2.27 times compared with 2D EPI for relatively thick slice coverage (60-90 mm). The SNR gain led to improved diffusion-tensor reconstruction in the multishot protocols. Image distortion in 2D-EPI images was also reduced in GRASE images. Diffusivity measurements from the pulsed-gradient dMRI and OG dMRI showed clear diffusion-time dependency in the white matter and gray matter of the human brain, using both the GRASE and EPI sequences.

CONCLUSION

The 3D OGprep-GRASE sequence improved scan time and SNR and reduced image distortion compared with the 2D multislice acquisition for OG dMRI on a 3T clinical system, which may facilitate the clinical translation of time-dependent dMRI.

Minimizing the echo time in diffusion imaging using spiral readouts and a head gradient system

PURPOSE

Diffusion weighted imaging (DWI) is commonly limited by low signal-to-noise ratio (SNR) as well as motion artifacts. To address this limitation, a method that allows to maximize the achievable signal yield and increase the resolution in motion robust single-shot DWI is presented.

METHODS

DWI was performed on a 3T scanner equipped with a recently developed gradient insert (gradient strength: 200 mT/m, slew rate: 600 T/m/s). To further shorten the echo time, Stejskal-Tanner diffusion encoding with a single-shot spiral readout was implemented. To allow non-Cartesian image reconstruction using such strong and fast gradients, the characterization of eddy current and concomitant field effects was performed based on field-camera measurements.

RESULTS

An echo time of only 19 ms was achieved for a b-factor of 1000 s/mm² . An in-plane resolution of 0.68 mm was encoded by a single-shot spiral readout of 40.5 ms using 3-fold undersampling. The resulting images did not suffer from off-resonance artifacts and T 2 ∗ blurring that are common to single-shot images acquired with regular gradient systems.

CONCLUSION

Spiral diffusion imaging using a head gradient system, together with an accurate characterization of the encoding process allows for a substantial reduction of the echo time, and improves the achievable resolution in motion-insensitive single-shot DWI.

High-Spatial-Resolution Multishot Multiplexed Sensitivity-encoding Diffusion-weighted Imaging for Improved Quality of Breast Images and Differentiation of Breast Lesions: A Feasibility Study

Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images.Purpose: To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast.Materials and Methods: In this prospective institutional review board-approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27-70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was performed by calculating apparent diffusion coefficient (ADC) values and image quality parameters (signal-to-noise ratio [SNR] for lesions and fibroglandular tissue; contrast-to-noise ratio) by manually drawing regions of interest within the phantoms and breast tumor tissue. Interreader variability was determined using the Cohen κ coefficient, and quantitative differences between MUSE DWI and single-shot DWI were assessed using the Mann-Whitney U test; significance was defined at P < .05.Results: MUSE DWI yielded significantly improved image quality compared with single-shot DWI in phantoms (SNR, P = .001) and participants (lesion SNR, P = .009; fibroglandular tissue SNR, P = .05; contrast-to-noise ratio, P = .008). MUSE DWI ADC values showed a significant difference between malignant and benign lesions (P < .001). No significant differences were found between MUSE DWI and single-shot DWI in the mean, maximum, and minimum ADC values (P = .96, P = .28, and P = .49, respectively). Visual qualitative analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70).Conclusion: MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening.Keywords: Breast, MR-Diffusion Weighted Imaging, OncologySupplemental material is available for this article.© RSNA, 2020.

High-resolution diffusion MRI studies of development in pregnant mice visualized by novel spatiotemporal encoding schemes

This study introduces an MRI approach to map diffusion of water in vivo with high resolution under challenging conditions; the approach's potential is then used in diffusivity characterizations of embryos and fetoplacental units in pregnant mice, as well as of newborn mice in their initial postnatal period. The method relies on performing self-referenced spatiotemporal encoded MRI acquisitions, which can achieve the motional and susceptibility immunities needed to target challenging regions such as a mouse's abdominal cavity in a single shot. When suitably combined with zooming-in and novel interleaving procedures, these scans can overcome the inhomogeneity and sensitivity challenges arising upon targeting ≈100 μm in-plane resolutions, and thereby enable longitudinal development studies of abdominal organs that have hitherto eluded in vivo diffusion-weighted imaging. This is employed here to follow processes related to embryonic implantation and placentation, including the final stages of mouse gastrulation, the development of white matter in fetal brains, the maturation of fetal spines, and the evolution of the different layers making up mouse hemochorial placentas. The protocol's ability to extract diffusivity information in challenging regions as a function of embryonic mouse development is thus demonstrated, and its usefulness as a tool for visualizing pregnancy-related developmental changes in rodents is discussed.

SMS MUSSELS: A navigator-free reconstruction for simultaneous multi-slice-accelerated multi-shot diffusion weighted imaging

PURPOSE

To introduce a novel reconstruction method for simultaneous multi-slice (SMS)-accelerated multi-shot diffusion weighted imaging (ms-DWI).

METHODS

SMS acceleration using blipped-CAIPI schemes have been proposed to speed up the acquisition of ms-DWIs. The reconstruction of the data requires (a) phase compensation to combine data from different shots and (b) slice unfolding to separate the data of different slices. The traditional approaches first estimate the phase maps corresponding to each shot and slice which are then employed to iteratively recover the slice unfolded DWIs without phase artifacts. In contrast, the proposed reconstruction directly recovers the slice-unfolded k-space data of the multiple shots for each slice in a single-step recovery scheme. The proposed method is enabled by the low-rank property inherent in the k-space samples of ms-DW acquisition. This enabled to formulate a joint recovery scheme that simultaneously (a) unfolds the k-space data of each slice using a SENSE-based scheme and (b) recover the missing k-space samples in each slice of the multi-shot acquisition employing a structured low-rank matrix completion. Additional smoothness regularization is also utilized for higher acceleration factors. The proposed joint recovery is tested on simulated and in vivo data and compared to similar un-navigated methods.

RESULTS

Our experiments show effective slice unfolding and successful recovery of DWIs with minimal phase artifacts using the proposed method. The performance is comparable to existing methods at low acceleration factors and better than existing methods for higher acceleration factors.

CONCLUSIONS

For the slice accelerations considered in this study, the proposed method can successfully recover DWIs from SMS-accelerated ms-DWI acquisitions.

Echo planar time-resolved imaging (EPTI)

PURPOSE

To develop an efficient distortion- and blurring-free multi-shot EPI technique for time-resolved multiple-contrast and/or quantitative imaging.

METHODS

EPI is a commonly used sequence but suffers from geometric distortions and blurring. Here, we introduce a new multi-shot EPI technique termed echo planar time-resolved imaging (EPTI), which has the ability to rapidly acquire distortion- and blurring-free multi-contrast data set. The EPTI approach performs encoding in ky -t space and uses a new highly accelerated spatio-temporal CAIPI sampling trajectory to take advantage of signal correlation along these dimensions. Through this acquisition and a B0 -informed parallel imaging reconstruction, hundreds of "time-resolved" distortion- and blurring-free images at different TEs across the EPI readout window can be created at sub-millisecond temporal increments using a small number of EPTI shots. Moreover, a method for self-estimation and correction of shot-to-shot B0 variations was developed. Simultaneous multi-slice acquisition was also incorporated to further improve the acquisition efficiency.

RESULTS

We evaluated EPTI under varying simulated acceleration factors, B0 -inhomogeneity, and shot-to-shot B0 variations to demonstrate its ability to provide distortion- and blurring-free images at multiple TEs. Two variants of EPTI were demonstrated in vivo at 3T: (1) a combined gradient- and spin-echo EPTI for quantitative mapping of T2 , T2* , proton density, and susceptibility at 1.1 × 1.1 × 3 mm3 whole-brain in 28 s (0.8 s/slice), and (2) a gradient-echo EPTI, for multi-echo and quantitative T2* fMRI at 2 × 2 × 3 mm3 whole-brain at a 3.3 s temporal resolution.

CONCLUSION

EPTI is a new approach for multi-contrast and/or quantitative imaging that can provide fast acquisition of distortion- and blurring-free images at multiple TEs.

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.

Technical Note: Clustering-based motion compensation scheme for multishot diffusion tensor imaging

PURPOSE

To extend image reconstruction using image-space sampling function (IRIS) to address large-scale motion in multishot diffusion-weighted imaging (DWI).

METHODS

A clustered IRIS (CIRIS) algorithm that would extend IRIS was proposed to correct for large-scale motion. For DWI, CIRIS initially groups the shots into clusters without intracluster large-scale motion and reconstructs each cluster by using IRIS. Then, CIRIS registers these cluster images and combines the registered images by using a weighted average to correct for voxel mismatch caused by intercluster large-scale motion. For diffusion tensor imaging (DTI), CIRIS further reduces the effect of motion on diffusion directions by treating motion-induced direction changes as additional diffusion directions. CIRIS also introduces the detection and rejection of motion-corrupted data to avoid corresponding image degradation. The proposed method was evaluated by simulation and in vivo diffusion datasets.

RESULTS

Experiments demonstrated that CIRIS can reduce motion-induced blurring and artifacts in DWI and provide more accurate DTI estimations in the presence of large-scale motion, compared with IRIS.

CONCLUSION

The proposed method presents a novel approach to correct for large-scale in-plane motion for multishot DWI and is expected to benefit the practical application of high-resolution diffusion imaging.

Structure tensor informed fibre tractography at 3T

Structure tensor informed fibre tractography (STIFT) based on informing tractography for diffusion-weighted images at 3T and by utilising the structure tensor obtained from gradient-recalled echo (GRE) images at 7T is able to delineate fibres when seed voxels are placed close to the fibre boundaries. However, incorporating data from two different field strengths limits the applicability of STIFT. In this study, STIFT was implemented with both diffusion-weighted images and GRE images acquired at 3T. Instead of using the magnitude GRE data directly for STIFT as in the previous work, the utility of T₂ * maps and quantitative susceptibility maps derived from complex-valued GRE data to improve fibre delineation was explored. Single-seed tractography was performed and the results show that the optic radiation reconstructed with STIFT is more distinguishable from the inferior longitudinal fasciculus/inferior fronto-occipital fasciculus complex when compared to standard diffusion-weighted imaging tractography. We further investigated the quantitative effects of STIFT in a group of five healthy volunteers and evaluated its impact on measures of structural connectivity. The framework was extended to evaluate implementations of STIFT based on T₂ *-weighted and quantitative susceptibility-weighted images in a whole-brain connectivity study. In terms of connectivity, no systematic differences were found between STIFT and diffusion-weighted imaging tractography, suggesting that local improvements in tractography are not translated to the atlas-based structural connectivity analysis. Nevertheless, the reduction in the number of statistically significant connections in the STIFT connectivity matrix suggests that STIFT can potentially reduce the false-positive connections in fibre tractography.

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.

Motion-robust sub-millimeter isotropic diffusion imaging through motion corrected generalized slice dithered enhanced resolution (MC-gSlider) acquisition

PURPOSE

To develop an efficient MR technique for ultra-high resolution diffusion MRI (dMRI) in the presence of motion.

METHODS

gSlider is an SNR-efficient high-resolution dMRI acquisition technique. However, subject motion is inevitable during a prolonged scan for high spatial resolution, leading to potential image artifacts and blurring. In this study, an integrated technique termed Motion Corrected gSlider (MC-gSlider) is proposed to obtain high-quality, high-resolution dMRI in the presence of large in-plane and through-plane motion. A motion-aware reconstruction with spatially adaptive regularization is developed to optimize the conditioning of the image reconstruction under difficult through-plane motion cases. In addition, an approach for intra-volume motion estimation and correction is proposed to achieve motion correction at high temporal resolution.

RESULTS

Theoretical SNR and resolution analysis validated the efficiency of MC-gSlider with regularization, and aided in selection of reconstruction parameters. Simulations and in vivo experiments further demonstrated the ability of MC-gSlider to mitigate motion artifacts and recover detailed brain structures for dMRI at 860 μm isotropic resolution in the presence of motion with various ranges.

CONCLUSION

MC-gSlider provides motion-robust, high-resolution dMRI with a temporal motion correction sensitivity of 2 s, allowing for the recovery of fine detailed brain structures in the presence of large subject movements.

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.

Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS)

PURPOSE

To introduce a novel method for the recovery of multi-shot diffusion weighted (MS-DW) images from echo-planar imaging (EPI) acquisitions.

METHODS

Current EPI-based MS-DW reconstruction methods rely on the explicit estimation of the motion-induced phase maps to recover artifact-free images. In the new formulation, the k-space data of the artifact-free DWI is recovered using a structured low-rank matrix completion scheme, which does not require explicit estimation of the phase maps. The structured matrix is obtained as the lifting of the multi-shot data. The smooth phase-modulations between shots manifest as null-space vectors of this matrix, which implies that the structured matrix is low-rank. The missing entries of the structured matrix are filled in using a nuclear-norm minimization algorithm subject to the data-consistency. The formulation enables the natural introduction of smoothness regularization, thus enabling implicit motion-compensated recovery of the MS-DW data.

RESULTS

Our experiments on in-vivo data show effective removal of artifacts arising from inter-shot motion using the proposed method. The method is shown to achieve better reconstruction than the conventional phase-based methods.

CONCLUSION

We demonstrate the utility of the proposed method to effectively recover artifact-free images from Cartesian fully/under-sampled and partial Fourier acquired data without the use of explicit phase estimates. Magn Reson Med 78:494-507, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging

The parameter selection for diffusion MRI experiments is dominated by the "k-q tradeoff" whereby the Signal to Noise Ratio (SNR) of the images is traded for either high spatial resolution (determined by the maximum k-value collected) or high diffusion sensitivity (effected by b-value or the q vector) but usually not both. Furthermore, different brain regions (such as gray matter and white matter) likely require different tradeoffs between these parameters due to the size of the structures to be visualized or the length-scale of the microstructure being probed. In this case, it might be advantageous to combine information from two scans - a scan with high q but low k (high angular resolution in diffusion but low spatial resolution in the image domain) to provide maximal information about white matter fiber crossing, and one low q but high k (low angular resolution but high spatial resolution) for probing the cortex. In this study, we propose a method, termed HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging, for acquiring and combining the information from these two complementary types of scan with the goal of studying diffusion in the cortex without compromising white matter fiber information. The white-gray boundary and pial surface obtained from anatomical scans are incorporated as prior information to guide the fusion. We study the complementary advantages of the fused datasets, and assess the quality of the HIBRID data compared to either alone.

Reduced field-of-view DWI with robust fat suppression and unrestricted slice coverage using tilted 2D RF excitation

PURPOSE

Reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) using 2D echo-planar radiofrequency (2DRF) excitation has been widely and successfully applied in clinical settings. The purpose of this work is to further improve its clinical utility by overcoming slice coverage limitations without any scan time penalty while providing robust fat suppression.

THEORY AND METHODS

During multislice imaging with 2DRF pulses, periodic sidelobes in the slice direction cause partial saturation, limiting the slice coverage. In this work, a tilting of the excitation plane is proposed to push the sidelobes out of the imaging section while preserving robust fat suppression. The 2DRF pulse is designed using Shinnar-Le Roux algorithm on a rotated excitation k-space. The performance of the method is validated via simulations, phantom experiments, and high in-plane resolution in vivo DWI of the spinal cord.

RESULTS

Results show that rFOV DWI using the tilted 2DRF pulse provides increased signal-to-noise ratio, extended coverage, and robust fat suppression, without any scan time penalty.

CONCLUSION

Using a tilted 2DRF excitation, a high-resolution rFOV DWI method with robust fat suppression and unrestricted slice coverage is presented. This method will be beneficial in clinical applications needing large slice coverage, for example, axial imaging of the spine, prostate, or breast. Magn Reson Med 76:1668-1676, 2016. © 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.

Human brain diffusion tensor imaging at submillimeter isotropic resolution on a 3Tesla clinical MRI scanner

The advantages of high-resolution diffusion tensor imaging (DTI) have been demonstrated in a recent post-mortem human brain study (Miller et al., NeuroImage 2011;57(1):167-181), showing that white matter fiber tracts can be much more accurately detected in data at a submillimeter isotropic resolution. To our knowledge, in vivo human brain DTI at a submillimeter isotropic resolution has not been routinely achieved yet because of the difficulty in simultaneously achieving high resolution and high signal-to-noise ratio (SNR) in DTI scans. Here we report a 3D multi-slab interleaved EPI acquisition integrated with multiplexed sensitivity encoded (MUSE) reconstruction, to achieve high-quality, high-SNR and submillimeter isotropic resolution (0.85×0.85×0.85mm(3)) in vivo human brain DTI on a 3Tesla clinical MRI scanner. In agreement with the previously reported post-mortem human brain DTI study, our in vivo data show that the structural connectivity networks of human brains can be mapped more accurately and completely with high-resolution DTI as compared with conventional DTI (e.g., 2×2×2mm(3)).