Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI

A synoptic literature review of animal models for investigating the biomechanics of knee osteoarthritis

Osteoarthritis (OA) is a common chronic disease largely driven by mechanical factors, causing significant health and economic burdens worldwide. Early detection is challenging, making animal models a key tool for studying its onset and mechanically-relevant pathogenesis. This review evaluate current use of preclinical in vivo models and progressive measurement techniques for analysing biomechanical factors in the specific context of the clinical OA phenotypes. It categorizes preclinical in vivo models into naturally occurring, genetically modified, chemically-induced, surgically-induced, and non-invasive types, linking each to clinical phenotypes like chronic pain, inflammation, and mechanical overload. Specifically, we discriminate between mechanical and biological factors, give a new explanation of the mechanical overload OA phenotype and propose that it should be further subcategorized into two subtypes, post-traumatic and chronic overloading OA. This review then summarises the representative models and tools in biomechanical studies of OA. We highlight and identify how to develop a mechanical model without inflammatory sequelae and how to induce OA without significant experimental trauma and so enable the detection of changes indicative of early-stage OA in the absence of such sequelae. We propose that the most popular post-traumatic OA biomechanical models are not representative of all types of mechanical overloading OA and, in particular, identify a deficiency of current rodent models to represent the chronic overloading OA phenotype without requiring intraarticular surgery. We therefore pinpoint well standardized and reproducible chronic overloading models that are being developed to enable the study of early OA changes in non-trauma related, slowly-progressive OA. In particular, non-invasive models (repetitive small compression loading model and exercise model) and an extra-articular surgical model (osteotomy) are attractive ways to present the chronic natural course of primary OA. Use of these models and quantitative mechanical behaviour tools such as gait analysis and non-invasive imaging techniques show great promise in understanding the mechanical aspects of the onset and progression of OA in the context of chronic knee joint overloading. Further development of these models and the advanced characterisation tools will enable better replication of the human chronic overloading OA phenotype and thus facilitate mechanically-driven clinical questions to be answered.

Magic angle effect on diffusion tensor imaging in ligament and brain

PURPOSE

To evaluate the magic angle effect on diffusion tensor imaging (DTI) measurements in rat ligaments and mouse brains.

METHODS

Three rat knee joints and three mouse brains were scanned at 9.4 T using a modified 3D diffusion-weighted spin echo pulse sequence with the isotropic spatial resolution of 45 μm. The b value was 1000 s/mm2 for rat knee and 4000 s/mm2 for mouse brain. DTI model was used to investigate the quantitative metrics at different orientations with respect to the main magnetic field. The collagen fiber structure of the ligament was validated with polarized light microscopy (PLM) imaging.

RESULTS

The signal intensity, signal-to-noise ratio (SNR), and DTI metrics in the ligament were strongly dependent on the collagen fiber orientation with respect to the main magnetic field from both simulation and actual MRI scans. The variation of fractional anisotropy (FA) was about ~32%, and the variation of mean diffusivity (MD) was ~11%. These findings were further validated with the numerical simulation at different SNRs (~10.0 to 86.0). Compared to the ligament, the DTI metrics showed little orientation dependence in mouse brains.

CONCLUSION

Magic angle effect plays an important role in DTI measurements in the highly ordered collagen-rich tissues, while MD showed less orientation dependence than FA.

Effects of Angular Resolution and b Value on Diffusion Tensor Imaging in Knee Joint

OBJECTIVE

To investigate the influences of the diffusion gradient directions (angular resolution) and the strength of the diffusion gradient (b value) on diffusion tensor imaging (DTI) metrics and tractography of various connective tissues in knee joint.

DESIGN

Two rat knee joints were scanned on a preclinical 9.4-T system using a 3-dimensional diffusion-weighted spin echo pulse sequence. One protocol with b value of 500, 1500, and 2500 s/mm² were acquired separately using 43 diffusion gradient directions. The other protocol with b value of 1000 s/mm² was performed using 147 diffusion gradient directions. The in-plane resolution was 45 µm isotropic. Fractional anisotropy (FA) and mean diffusivity (MD) were compared at different angular resolution. Tractography was quantitatively evaluated at different b values and angular resolutions in cartilage, ligament, meniscus, and growth plate.

RESULTS

The ligament showed higher FA value compared with growth plate and cartilage. The FA values were largely overestimated at the angular resolution of 6. Compared with FA, MD showed less sensitivity to the angular resolution. The fiber tracking was failed at low angular resolution (6 diffusion gradient directions) or high b value (2500 s/mm²). The quantitative measurements of tract length and track volume were strongly dependent on angular resolution and b value.

CONCLUSIONS

To obtain consistent DTI outputs and tractography in knee joint, the scan may require a proper b value (ranging from 500 to 1500 s/mm²) and sufficient angular resolution (>14) with signal-to-noise ratio >10.

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.

Resolution-dependent influences of compressed sensing in quantitative T2 mapping of articular cartilage

This study evaluates the resolution-dependent influences of compressed sensing (CS) in MRI quantification of T2 mapping in articular cartilage with osteoarthritis (OA). T2-weighed 2D experiments of healthy and OA cartilage were fully sampled in k-space with five echo times at both 17.6 μm and 195.3 μm in-plane resolutions; termed as microscopic MRI (μMRI) and macroscopic MRI (mMRI) respectively. These fully sampled k-space data were under-sampled at various 2D CS accelerating factors (AF = 4-32). The under-sampled data were reconstructed individually into 2D images using nonlinear reconstruction, which were used to calculate the T2 maps. The bulk and zonal variations of T2 values in cartilage were evaluated at different AFs. The study finds that the T2 images at AFs up to 8 preserved major visual information and produced negligible artifacts for μMRI. The T2 values remained accurate for different sub-tissue zones at various AFs. The absolute difference between the CS (AF up to 32) and the Ground Truth (i.e., using 100% of the k-space data) of the mean T2 values through the whole tissue depth was higher in mMRI versus μMRI. For mMRI (where the resolution mimics the clinical MRI of human cartilage), the quantitative T2 mapping at AFs up to 4 showed negligible variations. This study demonstrates that both clinical MRI and μMRI can benefit from the use of CS in image acquisition, and μMRI benefits more from the use of CS by acquiring much less data, without losing significant accuracy in the quantification of T2 maps in osteoarthritic cartilage.

Cytoarchitecture of the mouse brain by high resolution diffusion magnetic resonance imaging

MRI has been widely used to probe the neuroanatomy of the mouse brain, directly correlating MRI findings to histology is still challenging due to the limited spatial resolution and various image contrasts derived from water relaxation or diffusion properties. Magnetic resonance histology has the potential to become an indispensable research tool to mitigate such challenges. In the present study, we acquired high spatial resolution MRI datasets, including diffusion MRI (dMRI) at 25 ​μm isotropic resolution and quantitative susceptibility mapping (QSM) at 21.5 ​μm isotropic resolution to validate with conventional mouse brain histology. Diffusion weighted images (DWIs) show better delineation of cortical layers and glomeruli in the olfactory bulb than fractional anisotropy (FA) maps. However, among all the image contrasts, including quantitative susceptibility mapping (QSM), T1/T2∗ images and DTI metrics, FA maps highlight unique laminar architecture in sub-regions of the hippocampus, including the strata of the dentate gyrus and CA fields of the hippocampus. The mean diffusivity (MD) and axial diffusivity (AD) yield higher correlation with DAPI (0.62 and 0.71) and NeuN (0.78 and 0.74) than with NF-160 (-0.34 and -0.49). The correlations between FA and DAPI, NeuN, and NF-160 are 0.31, -0.01, and -0.49, respectively. Our findings demonstrate that MRI at microscopic resolution deliver a three-dimensional, non-invasive and non-destructive platform for characterization of fine structural detail in both gray matter and white matter of the mouse brain.

Characterization complex collagen fiber architecture in knee joint using high-resolution diffusion imaging

PURPOSE

To evaluate the complex fiber orientations and 3D collagen fiber network of knee joint connective tissues, including ligaments, muscle, articular cartilage, and meniscus using high spatial and angular resolution diffusion imaging.

METHODS

Two rat knee joints were scanned using a modified 3D diffusion-weighted spin echo pulse sequence with the isotropic spatial resolution of 45 μm at 9.4T. The b values varied from 250 to 1250 s/mm² with 31 diffusion encoding directions for 1 rat knee. The b value was fixed to 1000 s/mm² with 147 diffusion encoding directions for the second knee. Both the diffusion tensor imaging (DTI) model and generalized Q-sampling imaging (GQI) method were used to investigate the fiber orientation distributions and tractography with the validation of polarized light microscopy.

RESULTS

To better resolve the crossing fibers, the b value should be great than or equal to 1000 s/mm² . The tractography results were comparable between the DTI model and GQI method in ligament and muscle. However, the tractography exhibited apparent difference between DTI and GQI in connective tissues with more complex collagen fibers network, such as cartilage and meniscus. In articular cartilage, there were numerous crossing fibers found in superficial zone and transitional zone. Tractography generated with GQI also resulted in more intact tracts in articular cartilage than DTI.

CONCLUSION

High-resolution diffusion imaging with GQI method can trace the complex collagen fiber orientations and architectures of the knee joint at microscopic resolution.

Compressed sensing MRI of different organs: ready for clinical daily practice?

OBJECTIVES

The aim was to evaluate the image quality and sensitivity to artifacts of compressed sensing (CS) acceleration technique, applied to 3D or breath-hold sequences in different clinical applications from brain to knee.

METHODS

CS with an acceleration from 30 to 60% and conventional MRI sequences were performed in 10 different applications in 107 patients, leading to 120 comparisons. Readers were blinded to the technique for quantitative (contrast-to-noise ratio or functional measurements for cardiac cine) and qualitative (image quality, artifacts, diagnostic findings, and preference) image analyses.

RESULTS

No statistically significant difference in image quality or artifacts was found for each sequence except for the cardiac cine CS for one of both readers and for the wrist 3D proton density (PD)-weighted CS sequence which showed less motion artifacts due to the reduced acquisition time. The contrast-to-noise ratio was lower for the elbow CS sequence but not statistically different in all other applications. Diagnostic findings were similar between conventional and CS sequence for all the comparisons except for four cases where motion artifacts corrupted either the conventional or the CS sequence.

CONCLUSIONS

The evaluated CS sequences are ready to be used in clinical daily practice except for the elbow application which requires a lower acceleration. The CS factor should be tuned for each organ and sequence to obtain good image quality. It leads to 30% to 60% acceleration in the applications evaluated in this study which has a significant impact on clinical workflow.

KEY POINTS

• Clinical implementation of compressed sensing (CS) reduced scan times of at least 30% with only minor penalty in image quality and no change in diagnostic findings. • The CS acceleration factor has to be tuned separately for each organ and sequence to guarantee similar image quality than conventional acquisition. • At least 30% and up to 60% acceleration is feasible in specific sequences in clinical routine.

Neurite orientation dispersion and density imaging of mouse brain microstructure

Advanced biophysical models like neurite orientation dispersion and density imaging (NODDI) have been developed to estimate the microstructural complexity of voxels enriched in dendrites and axons for both in vivo and ex vivo studies. NODDI metrics derived from high spatial and angular resolution diffusion MRI using the fixed mouse brain as a reference template have not yet been reported due in part to the extremely long scan time required. In this study, we modified the three-dimensional diffusion-weighted spin-echo pulse sequence for multi-shell and undersampling acquisition to reduce the scan time. This allowed us to acquire several exhaustive datasets that would otherwise not be attainable. NODDI metrics were derived from a complex 8-shell diffusion (1000-8000 s/mm²) dataset with 384 diffusion gradient-encoding directions at 50 µm isotropic resolution. These provided a foundation for exploration of tradeoffs among acquisition parameters. A three-shell acquisition strategy covering low, medium, and high b values with at least angular resolution of 64 is essential for ex vivo NODDI experiments. The good agreement between neurite density index (NDI) and the orientation dispersion index (ODI) with the subsequent histochemical analysis of myelin and neuronal density highlights that NODDI could provide new insight into the microstructure of the brain. Furthermore, we found that NDI is sensitive to microstructural variations in the corpus callosum using a well-established demyelination cuprizone model. The study lays the ground work for developing protocols for routine use of high-resolution NODDI method in characterizing brain microstructure in mouse models.

Diffusion tractography of the rat knee at microscopic resolution

PURPOSE

To evaluate whole knee joint tractography, including articular cartilage, ligaments, meniscus, and growth plate using diffusion tensor imaging (DTI) at microscopic resolution.

METHODS

Three rat knee joints were scanned using a modified 3D diffusion-weighted spin echo pulse sequence with 90- and 45-μm isotropic spatial resolution at 9.4T. The b values varied from 250 to 1250 s/mm² with 4 times undersampling in phase directions. Fractional anisotropy (FA) and mean diffusivity (MD) were compared at different spatial resolution and b values. Tractography was evaluated at multiple b values and angular resolutions in different connective tissues, and compared with conventional histology. The mean tract length and tract volume in various types of tissues were also quantified.

RESULTS

DTI metrics (FA and MD) showed consistent quantitative results at 90- and 45-μm isotropic spatial resolutions. Tractography of various connective tissues was found to be sensitive to the spatial resolution, angular resolution, and diffusion weightings. Higher spatial resolution (45 μm) supported tracking the cartilage collagen fiber tracts from the superficial zone to the deep zone, in a continuous and smooth progression in the transitional zone. Fiber length and fiber volume in the growth plate were strongly dependent on angular resolution and b values, whereas tractography in ligaments was found to be less dependent on spatial resolution.

CONCLUSION

High spatial and angular resolution DTI and diffusion tractography can be valuable for knee joint research because of its visualization capacity for collagen fiber orientations and quantitative evaluation of tissue's microscopic properties.

Rapid compositional mapping of knee cartilage with compressed sensing MRI

More than a decade after the introduction of compressed sensing (CS) in MRI, researchers are still working on ways to translate it into different research and clinical applications. The greatest advantage of CS in MRI is the reduced amount of k-space data needed to reconstruct images, which can be exploited to reduce scan time or to improve spatial resolution and volumetric coverage. Efficient data acquisition using CS is extremely important for compositional mapping of the musculoskeletal system in general and knee cartilage mapping techniques in particular. High-resolution quantitative information about tissue biochemical composition could be obtained in just a few minutes using CS MRI. However, in order to make this goal a reality, some issues still need to be addressed. In this article we review the current state of the art of CS methods for rapid compositional mapping of knee cartilage. Specifically, data acquisition strategies, image reconstruction algorithms, and data fitting models are discussed. Different CS studies for T2 and T1ρ mapping of knee cartilage are reviewed, with illustrative results. Future directions, opportunities, and challenges of rapid compositional mapping techniques are also discussed. Level of Evidence: 4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;47:1185-1198.

Whole mouse brain structural connectomics using magnetic resonance histology

Diffusion tensor histology holds great promise for quantitative characterization of structural connectivity in mouse models of neurological and psychiatric conditions. There has been extensive study in both the clinical and preclinical domains on the complex tradeoffs between the spatial resolution, the number of samples in diffusion q-space, scan time, and the reliability of the resultant data. We describe here a method for accelerating the acquisition of diffusion MRI data to support quantitative connectivity measurements in the whole mouse brain using compressed sensing (CS). The use of CS allows substantial increase in spatial resolution and/or reduction in scan time. Compared to the fully sampled results at the same scan time, the subtle anatomical details of the brain, such as cortical layers, dentate gyrus, and cerebellum, were better visualized using CS due to the higher spatial resolution. Compared to the fully sampled results at the same spatial resolution, the scalar diffusion metrics, including fractional anisotropy (FA) and mean diffusivity (MD), showed consistently low error across the whole brain (< 6.0%) even with 8.0 times acceleration. The node properties of connectivity (strength, cluster coefficient, eigenvector centrality, and local efficiency) demonstrated correlation of better than 95.0% between accelerated and fully sampled connectomes. The acceleration will enable routine application of this technology to a wide range of mouse models of neurologic diseases.