Accelerating quantitative susceptibility imaging acquisition using compressed sensing

Magnetic resonance metrics for identification of cuprizone-induced demyelination in the mouse model of neurodegeneration: a review

Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler's Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T1 Weighted (T1W) and T2 Weighted (T2W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology.

Extracellular vesicles-based point-of-care testing for the diagnosis and monitoring of Alzheimer's disease

Alzheimer's disease (AD) is a debilitating condition that affects millions of people worldwide. One promising strategy for detecting and monitoring AD early on is using extracellular vesicles (EVs)-based point-of-care testing; however, diagnosing AD using EVs poses a challenge due to the low abundance of EV-biomarkers. Here, we present a fully integrated organic electrochemical transistor (OECT) that enables high accuracy, speed, and convenience in the detection of EVs from AD patients. We incorporated self-aligned acoustoelectric enhancement of EVs on a chip that rapidly propels, enriches, and specifically binds EVs to the OECT detection area. With our enhancement of pre-concentration, we increased the sensitivity to a limit of detection of 500 EV particles/μL and reduced the required detection time to just two minutes. We also tested the sensor on an AD mouse model to monitor AD progression, examined mouse Aβ EVs at different time courses, and compared them with intraneuronal Aβ cumulation using MRI. This innovative technology has the potential to diagnose Alzheimer's and other neurodegenerative diseases accurately and quickly, enabling monitoring of disease progression and treatment response.

Accelerating TOF-MRA: The impact of the combined use of compressed sensitivity encoding and spiral imaging

PURPOSE

To evaluate the image quality of the combined technique of compressed sensitivity encoding (CS) and spiral imaging in time-of-flight magnetic resonance angiography (TOF-MRA), which is approximately 2.5 times faster than conventional methods.

METHODS

Twenty volunteers underwent four TOF-MRA sequences: sensitivity encoding (SENSE) with acceleration factor of 4 (acquisition time: 4:55 min), CS with acceleration factor of 10.9, and spiral and CS-spiral (both 1:55 min). A quantitative image analysis (signal-to-noise ratio [SNR], contrast, and full width at half maximum [FWHM] edge criterion measurements) was performed on four TOF sequences. For qualitative image analysis, two board-certified radiologists evaluated the overall depiction of the proximal, intermediate, and distal branches in CS, spiral, and CS-spiral images using SENSE as a reference.

RESULTS

The SNR of BA in spiral and CS-spiral imaging was significantly lower than that in SENSE (p = 0.009). The contrasts of ACA and BA in CS-spiral were significantly higher and those in spiral were significantly lower than those in SENSE (p < 0.001). The FWHM in the CS image was significantly higher than that of SENSE; however, no significant differences were observed between the spiral or CS-spiral and SENSE. In qualitative analysis, the depiction of proximal vascular branches was significantly impaired in spiral than in others and that of distal vascular branches was significantly impaired in CS than in others (p < 0.001).

CONCLUSIONS

In TOF-MRA, which is approximately 2.5 times faster than conventional methods, the combined use of CS and spiral imaging demonstrated an improvement in image quality compared to either CS or spiral imaging alone.

SUMMARY STATEMENT

The image quality of Compressed SENSE and spiral imaging is particularly poor in the proximal and distal vascular branches, respectively at an extremely high acceleration factor; however, CS-spiral provided stable image quality in all regions as compared with the SENSE technique.

Hippocampal demyelination is associated with increased magnetic susceptibility in a mouse model of concussion

Structural and functional deficits in the hippocampus are a prominent feature of moderate-severe traumatic brain injury (TBI). In this work, we investigated the potential of Quantitative Susceptibility Imaging (QSM) to reveal the temporal changes in myelin integrity in a mouse model of concussion (mild TBI). We employed a cross-sectional design wherein we assigned 43 mice to cohorts undergoing either a concussive impact or a sham procedure, with QSM imaging at day 2, 7, or 14 post-injury, followed by Luxol Fast Blue (LFB) myelin staining to assess the structural integrity of hippocampal white matter (WM). We assessed spatial learning in the mice using the Active Place Avoidance Test (APA), recording their ability to use visual cues to locate and avoid zone-dependent mild electrical shocks. QSM and LFB staining indicated changes in the stratum lacunosum-molecular layer of the hippocampus in the concussion groups, suggesting impairment of this key relay between the entorhinal cortex and the CA1 regions. These imaging and histology findings were consistent with demyelination, namely increased magnetic susceptibility to MR imaging and decreased LFB staining. In the APA test, sham animals showed fewer entries into the shock zone compared to the concussed cohort. Thus, we present radiological, histological, and behavioral findings that concussion can induce significant and alterations in hippocampal integrity and function that evolve over time after the injury.

Phase-regularized and displacement-regularized compressed sensing for fast magnetic resonance elastography

Liver magnetic resonance elastography (MRE) is a noninvasive stiffness measurement technique that captures the tissue displacement in the phase of the signal. To limit the scanning time to a single breath-hold, liver MRE usually involves advanced readout techniques such as simultaneous multislice (SMS) or multishot methods. Furthermore, all these readout techniques require additional in-plane acceleration using either parallel imaging capabilities, such as sensitivity encoding (SENSE), or k -space undersampling, such as compressed sensing (CS). However, these methods apply a single regularization function on the complex image. This study aims to design and evaluate methods that use separate regularization on the magnitude and phase of MRE to exploit their distinct spatiotemporal characteristics. Specifically, we introduce two compressed sensing methods. The first method, termed phase-regularized compressed sensing (PRCS), applies a two-dimensional total variation (TV) prior to the magnitude and two-dimensional wavelet regularization to the phase. The second method, termed displacement-regularized compressed sensing (DRCS), exploits the spatiotemporal redundancy using 3D total variation on the magnitude. Additionally, DRCS includes a displacement fitting function to apply wavelet regularization to the displacement phasor. Both DRCS and PRCS were evaluated with different levels of compression factors in three datasets: an in silico abdomen dataset, an in vitro tissue-mimicking phantom, and an in vivo liver dataset. The reconstructed images were compared with the full sampled reconstruction, zero-filling reconstruction, wavelet-regularized compressed sensing, and a low rank plus sparse reconstruction. The metrics used for quantitative evaluation were the structural similarity index (SSIM) of magnitude (M-SSIM), displacement (D-SSIM), and shear modulus (S-SSIM), and mean shear modulus. Results from highly undersampled in silico and in vitro datasets demonstrate that the DRCS method provides higher reconstruction quality than the conventional compressed sensing method for a wide range of stiffness values. Notably, DRCS provides 24% and 22% increase in D-SSIM compared with CS for the in silico and in vitro datasets, respectively. Comparison with liver stiffness measured from full sampled data and highly undersampled data (CR=4) demonstrates that the DRCS method provided the strongest correlation ( R 2 =0.95), second-lowest mean bias (-0.18 kPa, lowest for CS with -0.16 kPa), and lowest coefficient of variation (CV=3.6%). Our results demonstrate the potential of using DRCS to improve the reconstruction quality of accelerated MRE.

Restoring morphology of light sheet microscopy data based on magnetic resonance histology

The combination of cellular-resolution whole brain light sheet microscopy (LSM) images with an annotated atlas enables quantitation of cellular features in specific brain regions. However, most existing methods register LSM data with existing canonical atlases, e.g., The Allen Brain Atlas (ABA), which have been generated from tissue that has been distorted by removal from the skull, fixation and physical handling. This limits the accuracy of the regional morphologic measurement. Here, we present a method to combine LSM data with magnetic resonance histology (MRH) of the same specimen to restore the morphology of the LSM images to the in-skull geometry. Our registration pipeline which maps 3D LSM big data (terabyte per dataset) to MRH of the same mouse brain provides registration with low displacement error in ∼10 h with limited manual input. The registration pipeline is optimized using multiple stages of transformation at multiple resolution scales. A three-step procedure including pointset initialization, automated ANTs registration with multiple optimized transformation stages, and finalized application of the transforms on high-resolution LSM data has been integrated into a simple, structured, and robust workflow. Excellent agreement has been seen between registered LSM data and reference MRH data both locally and globally. This workflow has been applied to a collection of datasets with varied combinations of MRH contrasts from diffusion tensor images and LSM with varied immunohistochemistry, providing a routine method for streamlined registration of LSM images to MRH. Lastly, the method maps a reduced set of the common coordinate framework (CCFv3) labels from the Allen Brain Atlas onto the geometrically corrected full resolution LSM data. The pipeline maintains the individual brain morphology and allows more accurate regional annotations and measurements of volumes and cell density.

Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging

Purpose

To evaluate the age-dependent microstructure changes in 5xFAD mice using high-resolution diffusion tensor imaging (DTI).

Methods

The 5xFAD mice at 4, 7.5, and 12 months and the wild-type controls at 4 months were scanned at 9.4T using a 3D echo-planar imaging (EPI) pulse sequence with the isotropic spatial resolution of 100 μm. The b-value was 3000 s/mm² for all the diffusion MRI scans. The samples were also acquired with a gradient echo pulse sequence at 50 μm isotropic resolution. The microstructure changes were quantified with DTI metrics, including fractional anisotropy (FA) and mean diffusivity (MD). The conventional histology was performed to validate with MRI findings.

Results

The FA values (p = 0.028) showed significant differences in the cortex between wild-type (WT) and 5xFAD mice at 4 months, while hippocampus, anterior commissure, corpus callosum, and fornix showed no significant differences for either FA and MD. FA values of 5xFAD mice gradually decreased in cortex (0.140 ± 0.007 at 4 months, 0.132 ± 0.008 at 7.5 months, 0.126 ± 0.013 at 12 months) and fornix (0.140 ± 0.007 at 4 months, 0.132 ± 0.008 at 7.5 months, 0.126 ± 0.013 at 12 months) with aging. Both FA (p = 0.029) and MD (p = 0.037) demonstrated significant differences in corpus callosum between 4 and 12 months age old. FA and MD were not significantly different in the hippocampus or anterior commissure. The age-dependent microstructure alterations were better captured by FA when compared to MD.

Conclusion

FA showed higher sensitivity to monitor amyloid deposition in 5xFAD mice. DTI may be utilized as a sensitive biomarker to monitor beta-amyloid progression for preclinical studies.

Resolution and b value dependent Structural Connectome in ex vivo Mouse Brain

Diffusion magnetic resonance imaging has been widely used in both clinical and preclinical studies to characterize tissue microstructure and structural connectivity. The diffusion MRI protocol for the Human Connectome Project (HCP) has been developed and optimized to obtain high-quality, high-resolution diffusion MRI (dMRI) datasets. However, such efforts have not been fully explored in preclinical studies, especially for rodents. In this study, high quality dMRI datasets of mouse brains were acquired at 9.4T system from two vendors. In particular, we acquired a high-spatial resolution dMRI dataset (25 μm isotropic with 126 diffusion encoding directions), which we believe to be the highest spatial resolution yet obtained; and a high-angular resolution dMRI dataset (50 μm isotropic with 384 diffusion encoding directions), which we believe to be the highest angular resolution compared to the dMRI datasets at the microscopic resolution. We systematically investigated the effects of three important parameters that affect the final outcome of the connectome: b value (1000s/mm² to 8000 s/mm²), angular resolution (10 to 126), and spatial resolution (25 μm to 200 μm). The stability of tractography and connectome increase with the angular resolution, where more than 50 angles is necessary to achieve consistent results. The connectome and quantitative parameters derived from graph theory exhibit a linear relationship to the b value (R² > 0.99); a single-shell acquisition with b value of 3000 s/mm² shows comparable results to the multi-shell high angular resolution dataset. The dice coefficient decreases and both false positive rate and false negative rate gradually increase with coarser spatial resolution. Our study provides guidelines and foundations for exploration of tradeoffs among acquisition parameters for the structural connectome in ex vivo mouse brain.

Acceleration of Brain Susceptibility-Weighted Imaging with Compressed Sensitivity Encoding: A Prospective Multicenter Study

BACKGROUND AND PURPOSE

While three-dimensional susceptibility-weighted imaging has been widely suggested for intracranial vessel imaging, hemorrhage detection, and other neuro-diseases, its relatively long scan time has necessitated the clinical verification of recent progresses of fast imaging techniques. Our aim was to evaluate the effectiveness of brain SWI accelerated by compressed sensitivity encoding to identify the optimal acceleration factors for clinical practice.

MATERIALS AND METHODS

Ninety-nine subjects, prospectively enrolled from 5 centers, underwent 8 brain SWI sequences: 5 different folds of compressed sensitivity encoding acceleration (CS2, CS4, CS6, CS8, and CS10), 2 different folds of sensitivity encoding acceleration (SF2 and SF4), and 1 without acceleration. Images were assessed quantitatively on both the SNR of the red nucleus and its contrast ratio to the CSF and, subjectively, with scoring on overall image quality; visibility of the substantia nigra-red nucleus, basilar artery, and internal cerebral vein; and diagnostic confidence of the cerebral microbleeds and other intracranial diseases.

RESULTS

Compressed sensitivity encoding showed a promising ability to reduce the acquisition time (from 202 to 41 seconds) of SWI while increasing the acceleration factor from 2 to 10, though at the cost of decreasing the SNR, contrast ratio, and the scores of visual assessments. The visibility of the substantia nigra-red nucleus and internal cerebral vein became unacceptable in CS6 to CS10. The basilar artery was well-distinguished, and diseases including cerebral microbleeds, cavernous angiomas, intracranial gliomas, venous malformations, and subacute hemorrhage were well-diagnosed in all compressed sensitivity encoding sequences.

CONCLUSIONS

Compressed sensitivity encoding factor 4 is recommended in routine practice. Compressed sensitivity encoding factor 10 is potentially a fast surrogate for distinguishing the basilar artery and detecting susceptibility-related abnormalities (eg, cerebral microbleeds, cavernous angiomas, gliomas, and venous malformation) at the sacrifice of visualization of the substantia nigra-red nucleus and internal cerebral vein.

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with parallel imaging. Incoherent undersampling and compressed sensing reconstruction techniques have been used to accelerate traditional magnitude-based MRI acquisitions; however, most do not recover the full phase signal, as required by QSM, due to its non-convex nature. In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM and R2* acquisition. Magnitude, phase, R2*, and QSM results from DCRNet were compared with two iterative and one deep learning methods on retrospectively undersampled acquisitions from six healthy volunteers, one intracranial hemorrhage and one multiple sclerosis patients, as well as one prospectively undersampled healthy subject using a 7T scanner. Peak signal to noise ratio (PSNR), structural similarity (SSIM), root-mean-squared error (RMSE), and region-of-interest susceptibility and R2* measurements are reported for numerical comparisons. The proposed DCRNet method substantially reduced artifacts and blurring compared to the other methods and resulted in the highest PSNR, SSIM, and RMSE on the magnitude, R2*, local field, and susceptibility maps. Compared to two iterative and one deep learning methods, the DCRNet method demonstrated a 3.2% to 9.1% accuracy improvement in deep grey matter susceptibility when accelerated by a factor of four. The DCRNet also dramatically shortened the reconstruction time of single 2D brain images from 36-140 seconds using conventional approaches to only 15-70 milliseconds.

Mapping the peripheral nervous system in the whole mouse via compressed sensing tractography

Objective.The peripheral nervous system (PNS) connects the central nervous system with the rest of the body to regulate many physiological functions and is therapeutically targeted to treat diseases such as epilepsy, depression, intestinal dysmotility, chronic pain, and more. However, we still lack understanding of PNS innervation in most organs because the large span, diffuse nature, and small terminal nerve bundle fibers have precluded whole-organism, high resolution mapping of the PNS. We sought to produce a comprehensive peripheral nerve atlas for use in future interrogation of neural circuitry and selection of targets for neuromodulation.Approach.We used diffusion tensor magnetic resonance imaging (DT-MRI) with high-speed compressed sensing to generate a tractogram of the whole mouse PNS. The tractography generated from the DT-MRI data is validated using lightsheet microscopy on optically cleared, antibody stained tissue.Main results.Herein we demonstrate the first comprehensive PNS tractography in a whole mouse. Using this technique, we scanned the whole mouse in 28 h and mapped PNS innervation and fiber network in multiple organs including heart, lung, liver, kidneys, stomach, intestines, and bladder at 70µm resolution. This whole-body PNS tractography map has provided unparalleled information; for example, it delineates the innervation along the gastrointestinal tract by multiple sacral levels and by the vagal nerves. The map enabled a quantitative tractogram that revealed relative innervation of the major organs by each vertebral foramen as well as the vagus nerve.Significance.This novel high-resolution nerve atlas provides a potential roadmap for future neuromodulation therapies and other investigations into the neural circuits which drive homeostasis and disease throughout the body.

Automatic determination of the regularization weighting for wavelet-based compressed sensing MRI reconstructions

PURPOSE

To present a method that automatically, rapidly, and in a noniterative manner determines the regularization weighting for wavelet-based compressed sensing reconstructions. This method determines level-specific regularization weighting factors from the wavelet transform of the image obtained from zero-filling in k-space.

METHODS

We compare reconstruction results obtained by our method, λ auto , to the ones obtained by the L-curve, λ Lcurve , and the minimum NMSE, λ NMSE . The comparisons are done using in vivo data; then, simulations are used to analyze the impact of undersampling and noise. We use NMSE, Pearson's correlation coefficient, high-frequency error norm, and structural similarity as reconstruction quality indices.

RESULTS

Our method, λ auto , provides improved reconstructed image quality to that obtained by λ Lcurve regardless of undersampling or SNR and comparable quality to λ NMSE at high SNR. The method determines the regularization weighting prospectively with negligible computational time.

CONCLUSION

Our main finding is an automatic, fast, noniterative, and robust procedure to determine the regularization weighting. The impact of this method is to enable prospective and tuning-free wavelet-based compressed sensing reconstructions.

Susceptibility Weighted Imaging for evaluation of musculoskeletal lesions

The presence of blood or calcium in the musculoskeletal (MSK) system may be linked to specific pathological conditions. The ability of MRI for calcium detection is usually limited compared with other techniques such as CT. In a similar manner, the accuracy of MRI for detection and evaluation of hemorrhage in soft tissues is closely linked to the degree of degradation of blood products. Blood and calcium are substances that cause local inhomogeneity of the magnetic field resulting in susceptibility artifacts. To try to evaluate these substances, specific MRI sequences which are highly sensitive to these local magnetic field inhomogeneities such as Susceptibility Weighted Imaging (SWI) have been developed and successfully applied in the Central Nervous System, but scarcely used in MSK. SWI may increase the overall sensitivity of MRI to detect blood and calcium in several clinical scenarios such as degenerative joint disease or bone and soft tissue lesion assessment and discriminate between both compounds, something which is not always possible with conventional MRI approaches. In this paper, physical basis and technical adjustment for SWI acquisition at MSK are detailed reviewing the potential application of SWI in different MSK clinical scenarios.

Multi-spin echo T2 relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging

PURPOSE

Myelin water imaging (MWI) provides a valuable biomarker for myelin, but clinical application has been restricted by long acquisition times. Accelerating the standard multi-echo T₂ acquisition with gradient echoes (GRASE) or by 2D multi-slice data collection results in image blurring, contrast changes, and other issues. Compressed sensing (CS) can vastly accelerate conventional MRI. In this work, we assessed the use of CS for in vivo human MWI, using a 3D multi spin-echo sequence.

METHODS

We implemented multi-echo T₂ relaxation imaging with compressed sensing (METRICS) and METRICS with partial Fourier acceleration (METRICS-PF). Scan-rescan data were acquired from 12 healthy controls for assessment of repeatability. MWI data were acquired for METRICS in 9 m:58 s and for METRICS-PF in 7 m:25 s, both with 1.5 × 2 × 3 mm³ voxels, 56 echoes, 7 ms ΔTE, and 240 × 240 × 170 mm³ FOV. METRICS was compared with a novel multi-echo spin-echo gold-standard (MSE-GS) MWI acquisition, acquired for a single additional subject in 2 h:2 m:40 s.

RESULTS

METRICS/METRICS-PF myelin water fraction had mean: repeatability coefficient 1.5/1.1, coefficient of variation 6.2/4.5%, and intra-class correlation coefficient 0.79/0.84. Repeatability metrics comparing METRICS with METRICS-PF were similar, and both sequences agreed with reference values from literature. METRICS images and quantitative maps showed excellent qualitative agreement with those of MSE-GS.

CONCLUSION

METRICS and METRICS-PF provided highly repeatable MWI data without the inherent disadvantages of GRASE or 2D multi-slice acquisition. CS acceleration allows MWI data to be acquired rapidly with larger FOV, higher estimated SNR, more isotropic voxels and more echoes than with previous techniques. The approach introduced here generalizes to any multi-component T₂ mapping application.

Probing demyelination and remyelination of the cuprizone mouse model using multimodality MRI

BACKGROUND

Various studies by MRI exhibit that the corpus callosum (CC) is the most vulnerable to cuprizone administration, detecting the demyelination and remyelination process using different MRI parameters are, however, lacking.

PURPOSE

To investigate the sensitivity of multiparametric MRI both in vivo and ex vivo for demyelination and remyelination.

STUDY TYPE

Prospective.

ANIMAL MODEL

A cuprizone mice model with an age-matched control group (n = 5), 4-week cuprizone exposure group followed by 9-week on a normal diet (n = 6), and a 13-week cuprizone exposure group (n = 6).

FIELD STRENGTH/SEQUENCE

3D gradient recalled echo, T2 -weighted, and diffusion tensor imaging (DTI) at 7.0T and 9.4T.

ASSESSMENT

Quantification of DTI metrics, quantitative susceptibility mapping (QSM), and T2 -weighted imaging intensity in major white matter bundles.

STATISTICAL TESTS

Nonparametric permutation tests were used with a cluster-forming threshold as 3.09 (equivalent to P = 0.001), and the significant level as P = 0.05 with family-wise correction.

RESULTS

In vivo susceptibility values increased from -11.7 to -0.7 ppb (P < 0.001) in CC and from -13.7 to -5.1 ppb (P < 0.001) in the anterior commissure (AC) after the 13-week cuprizone exposure. Ex vivo susceptibility values increased from -25.4 to 7.4 ppb (P < 0.001) in CC and from -41.6 to -15.8 ppb (P < 0.001) in AC. Susceptibility values showed high variations to demyelination for in vivo studies (94.0% in CC, 62.8% in AC). Susceptibility values exhibited higher variations than radial diffusivity for ex vivo studies (129.1% vs. 28.3% in CC, 62.0% vs. 25.0% in AC). In addition to the differential susceptibility variations in different white matter tracts, intraregional demyelination variation was also present not only in CC but also in the AC area by voxel-based analysis.

DATA CONCLUSION

QSM is sensitive to the demyelination process of cuprizone exposure, which can be a complementary technique to conventional T2 -weighted images and DTI metrics.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1852-1865.