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•An unsupervised ML approach, SLED, was developed for T2* MWI data analysis.•SLED outperformed NLLS for MWF estimation using simulated phantom and real data.•SLED MWF sensitively measured graded hypomyelination in a novel KO mouse model.•SLED was efficiently trained on each dataset without the need for a signal library. Reconstruction of high quality myelin water imaging (MWI) maps is challenging, particularly for data acquired using multi-echo gradient echo (mGRE) sequences. A non-linear least squares fitting (NLLS) approach has often been applied for MWI. However, this approach may produce maps with limited detail and, in some cases, sub-optimal signal to noise ratio (SNR), due to the nature of the voxel-wise fitting. In this study, we developed a novel, unsupervised learning method called self-labelled encoder-decoder (SLED) to improve gradient echo-based MWI data fitting. Ultra-high resolution, MWI data was collected from five mouse brains with variable levels of myelination, using a mGRE sequence. Imaging data was acquired using a 7T preclinical MRI system. A self-labelled, encoder-decoder network was implemented in TensorFlow for calculation of myelin water fraction (MWF) based on the mGRE signal decay. A simulated MWI phantom was also created to evaluate the performance of MWF estimation. Compared to NLLS, SLED demonstrated improved MWF estimation, in terms of both stability and accuracy in phantom tests. In addition, SLED produced less noisy MWF maps from high resolution MR microscopy images of mouse brain tissue. It specifically resulted in lower noise amplification for all mouse genotypes that were imaged and yielded mean MWF values in white matter ROIs that were highly correlated with those derived from standard NLLS fitting. Lastly, SLED also exhibited higher tolerance to low SNR data. Due to its unsupervised and self-labeling nature, SLED offers a unique alternative to analyze gradient echo-based MWI data, providing accurate and stable MWF estimations.

•The role of MBP gene expression on white matter myelin elaboration was identified.•We validated a model-free reconstruction method for robust brain MWF mapping.•The findings were augmented with detailed, tract-wise measures of AWF.•Our hypomyelinated mouse models act as a tool for calibrated myelin-sensitive MRI. Non-invasive myelin water fraction (MWF) and g-ratio mapping using microstructural MRI have the potential to offer critical insights into brain microstructure and our understanding of neuroplasticity and neuroinflammation. By leveraging a unique panel of variably hypomyelinating mouse strains, we validated a high-resolution, model-free image reconstruction method for whole-brain MWF mapping. Further, by employing a bipolar gradient echo MRI sequence, we achieved high spatial resolution and robust mapping of MWF and g-ratio across the whole mouse brain. Our regional white matter-tract specific analyses demonstrated a graded decrease in MWF in white matter tracts which correlated strongly with myelin basic protein gene (Mbp) mRNA levels. Using these measures, we derived the first sensitive calibrations between MWF and Mbp mRNA in the mouse. Minimal changes in axonal density supported our hypothesis that observed MWF alterations stem from hypomyelination. Overall, our work strongly emphasizes the potential of non-invasive, MRI-derived MWF and g-ratio modeling for both preclinical model validation and ultimately translation to humans.

Purpose Hemorrhagic transformation (HT) following mechanical thrombectomy (MT) is a critical concern in the management of ischemic stroke patients. While both CT and MRI are used to detect HT, the clinical implications of discrepancies between these modalities remain unclear. This study aims to investigate the clinical implications of discordant findings between gradient echo MRI and CT in detecting HT after MT. Methods In this retrospective study, patients who underwent MT for ischemic stroke between 2016 and 2023 were analyzed. Patients were categorized into two groups based on imaging results: CT negative and MRI positive versus CT positive and MRI positive. Results A total of 286 patients were included in the study, with 104 in the CT negative and MRI positive group and 182 in the CT positive and MRI positive group. More patients in the CT negative and MRI positive group were started on antithrombotic medications in less than 2 days compared to the patients in the CT positive and MRI positive group (47.3% vs. 34.5%; OR: 1.94, 95% CI: 0.88–4.27, p  = 0.09) and still exhibited significantly higher rates of good functional outcomes (mRS 0–2) at discharge (OR: 3.27, 95% CI: 1.03–10.36, p  = 0.04). Additionally, the CT negative and MRI positive group had a lower, though not statistically significant, likelihood of readmission for diagnosis of intracranial hemorrhage (ICH) within 30 days (0% vs. 2.7%, p  = 0.16). Conclusion Gradient echo MRI can detect subtle hemorrhages not seen on CT, and both modalities offer complementary insights. In our cohort of MRI-positive patients, those with CT-negative findings had better functional outcomes and earlier antithrombotic resumption without increased risk of ICH readmission. These results suggest that in select scenarios, a negative CT may help inform safe antithrombotic management, though this does not diminish the diagnostic value of MRI. Further prospective studies are warranted to establish thresholds for MRI findings and refine clinical protocols post-MT.

Fiber orientation mapping through diffusion tensor imaging (DTI) is a powerful MRI-based technique for visualising white matter (WM) microstructure in the brain. Although DTI provides a robust way to measure fiber orientation, it has some limitations linked to the use of EPI read-outs and long diffusion encoding periods, including relatively low spatial resolution. Development of alternative MRI-based methods for fiber orientation mapping is therefore valuable, in part to allow validation of DTI results. In this study, we used the orientation dependence of R2* (1/T2*) and frequency difference measurements to generate three dimensional maps of the fiber orientation in WM from multi-echo gradient-echo (GE) images acquired from post mortem brain tissue samples oriented at multiple angles to B0. Through analytical derivation and numerical simulation, the relationships connecting variations in R2* and frequency difference values to the angle between the underlying WM fiber orientation and the direction of B0 were characterised. High resolution 3D fiber orientation maps (FOM) were then formed by comparing R2* and frequency difference data, acquired with the sample at multiple orientations to the field, to generalised models based on the derived expressions for the angular dependence of each parameter. By comparing the resulting GE-based FOM with DTI-based FOM from the same tissue sample, we demonstrate that fiber orientation mapping based on gradient echo MRI has the potential to become an important tool for investigating microstructure in brain tissue. •R2* and frequency difference mapping (FDM) are sensitive to fiber orientation.•We show that R2* and FDM data can be used to produce fiber orientation maps (FOM).•We tested this approach on small tissue samples.•Good agreement was observed between the gradient echo- and DTI-based FOM.

This review presents an overview of the spectrum of the current and cutting-edge MRI techniques for pelvic bone imaging in clinical practice. The current MRI sequences and their advantages, disadvantages and usefullness in the imaging of this complex anatomical region are addressed. Finally, cutting-edge techniques are discussed, including susceptibility weighted MRI, ultrashort echo time MRI, zero echo time MRI and a deep learning-based multiparametric MRI technique named 'synthetic CT,' creating CT-like images without ionizing radiaton. GRE, SWI, UTE, ZTE MRI and synthetic CT sequences depict the cortical outline of the bones better in comparison to conventional MR images.MRI-based synthetic CT can create HU maps and allows for automated segmentation of pelvic bones.The current and cutting-edge MR techniques for bone imaging are complementary in the characterization of a variety of musculoskeletal disorders.

Previous attempts at characterizing the spatial specificity of the blood oxygenation level dependent functional MRI (BOLD fMRI) response by estimating its point-spread function (PSF) have conventionally relied on retinotopic spatial representations of visual stimuli in area V1. Consequently, their estimates were confounded by the width and scatter of receptive fields of V1 neurons. Here, we circumvent these limits by instead using the inherent cortical spatial organization of ocular dominance columns (ODCs) to determine the PSF for both Gradient Echo (GE) and Spin Echo (SE) BOLD imaging at 7 Tesla. By applying Markov chain Monte Carlo sampling on a probabilistic generative model of imaging ODCs, we quantified the PSFs that best predict the spatial structure and magnitude of differential ODCs' responses. Prior distributions for the ODC model parameters were determined by analyzing published data of cytochrome oxidase patterns from post-mortem histology of human V1 and of neurophysiological ocular dominance indices. The average PSF full-widths at half-maximum obtained from differential ODCs’ responses following the removal of voxels influenced by contributions from macroscopic blood vessels were 0.86 mm (SE) and 0.99 mm (GE). Our results provide a quantitative basis for the spatial specificity of BOLD fMRI at ultra-high fields, which can be used for planning and interpretation of high-resolution differential fMRI of fine-scale cortical organizations. [Display omitted] •The spread of BOLD response was previously estimated with retinotopic stimuli.•Previous estimates were confounded by width and scatter of neuronal receptive fields.•We apply Markov Chain Monte Carlo sampling to fit a model of imaging columns to data.•Average FWHM of BOLD spread, following blood vessel removal: 0.86 mm (SE) and 0.99 mm (GE).•Our findings support planning and interpretation of high-resolution differential fMRI.

The capacity of functional MRI (fMRI) to resolve cortical columns depends on several factors. These include the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the signal-to-noise ratio (SNR) considering thermal and physiological noise. However, it remains unknown how these factors combine, and what is the voxel size that optimizes fMRI of cortical columns. Here we combine current knowledge into a quantitative model of fMRI of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We compare different approaches for identifying patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and high-resolution imaging and reconstruction of the pattern of cortical columns. We present the dependence of the performance of each approach on the parameters of the imaged pattern as well as those of the data acquisition. In addition, we predict voxel sizes that optimize fMRI of cortical columns under various scenarios. We found that all measures associated with multivariate detection and decoding could be approximately calculated from a measure we termed “multivariate contrast-to-noise ratio” (mv-CNR), which is a function of the contrast-to-noise ratio (CNR) and number of voxels. Furthermore, mv-CNR implied that the optimal voxel width for detection and decoding is independent of changes in response amplitude, SNR and imaged volume that are not caused by changes in voxel size. For regular patterns, optimal voxel widths for detection, decoding and imaging/reconstructing the pattern of cortical columns were approximately half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T, and Spin-Echo fMRI at 7T on the detection, decoding, and reconstruction measures considered and found that in all cases the width of the fMRI point-spread had the most significant effect. In contrast, different response amplitudes and noise characteristics played a relatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for high-resolution imaging of cortical columns under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of high-resolution fMRI of cortical columns and the decoding of information conveyed by these columns. [Display omitted] •We model differential fMRI of realistic patterns of cortical columns.•We evaluate detection probability, MVPA, and high-resolution imaging of the columns.•Optimal voxel sizes for detection and MVPA differ than those for imaging the columns.•We recommend specific voxel widths for imaging under different scenarios at 3T and 7T.•We propose how to approach fMRI of unknown columnar patterns.

Non-heme iron is an important element supporting the structure and functioning of biological tissues. Imbalance in non-heme iron can lead to different neurological disorders. Several MRI approaches have been developed for iron quantification relying either on the relaxation properties of MRI signal or measuring tissue magnetic susceptibility. Specific quantification of the non-heme iron can, however, be constrained by the presence of the heme iron in the deoxygenated blood and contribution of cellular composition. The goal of this paper is to introduce theoretical background and experimental MRI method allowing disentangling contributions of heme and non-heme irons simultaneously with evaluation of tissue neuronal density in the iron-rich basal ganglia. Our approach is based on the quantitative Gradient Recalled Echo (qGRE) MRI technique that allows separation of the total R2* metric characterizing decay of GRE signal into tissue-specific (R2t*) and the baseline blood oxygen level-dependent (BOLD) contributions. A combination with the QSM data (also available from the qGRE signal phase) allowed further separation of the tissue-specific R2t* metric in a cell-specific and non-heme-iron-specific contributions. It is shown that the non-heme iron contribution to R2t* relaxation can be described with the previously developed Gaussian Phase Approximation (GPA) approach. qGRE data were obtained from 22 healthy control participants (ages 26–63 years). Results suggest that the ferritin complexes are aggregated in clusters with an average radius about 100nm comprising approximately 2600 individual ferritin units. It is also demonstrated that the concentrations of heme and non-heme iron tend to increase with age. The strongest age effect was seen in the pallidum region, where the highest age-related non-heme iron accumulation was observed.

Purpose Cerebellar herniation in Chiari patients can be dynamic, following the cerebrospinal fluid pulsatility during the cardiac cycle. We present a voxel intensity distribution method (VIDM) to automatically extract the pulsatility-dependent herniation in time-resolved MRI (CINE MRI) and compare it to the simple linear measurements. The degree of herniation is furthermore compared on CINE and static sequences, and the cerebellar movement is correlated to the presence of hydrocephalus and syringomyelia. Methods The cerebellar movement in 27 Chiari patients is analyzed with VIDM and the results were compared to linear measurements on an image viewer (visual inspection, VI) using a paired t test. Second, an ANOVA test is applied to compare the degree of herniation on static 3D MRI and CINE. Finally, the Pearson’s correlation coefficient is calculated for the correlation between cerebellar movement and the presence of hydrocephalus and syringomyelia. Results VIDM showed significant movement in 85% of our patients. Assuming that movement < 1 mm cannot be detected reliably on an image viewer, VI identified movement in 29.6% of the patients ( p  = 0.002). The herniation was greater on static sequences than on CINE in most cases, but this was not statistically significant. The cerebellar movement was not correlated with hydrocephalus or syringomyelia (Pearson’s coefficient < 0.3). Conclusions VIDM is a sensitive method to detect tissue movement on CINE MRI and could be used for Chiari patients, but also for the evaluation of cyst membranes, ventriculostomies, etc. The cerebellar movement appears not to correlate with hydrocephalus and syringomyelia in Chiari patients.

Background Brain aging is a major risk factor in the progression of cognitive diseases including Alzheimer’s disease (AD) and vascular dementia. We investigated a mouse model of brain aging up to 24 months old (mo). Methods A high field (11.7T) MRI protocol was developed to characterize specific features of brain aging including the presence of cerebral microbleeds (CMBs), morphology of grey and white matter, and tissue diffusion properties. Mice were selected from age categories of either young (3 mo), middle-aged (18 mo), or old (24 mo) and fed normal chow over the duration of the study. Mice were imaged in vivo with multimodal MRI, including conventional T2-weighted (T2W) and T2*-weighted (T2*W) imaging, followed by ex vivo diffusion-weighted imaging (DWI) and T2*W MR-microscopy to enhance the detection of microstructural features. Results Structural changes observed in the mouse brain with aging included reduced cortical grey matter volume and enlargement of the brain ventricles. A remarkable age-related change in the brains was the development of CMBs found starting at 18 mo and increasing in total volume at 24 mo, primarily in the thalamus. CMBs presence was confirmed with high resolution ex vivo MRI and histology. DWI detected further brain tissue changes in the aged mice including reduced fractional anisotropy, increased radial diffusion, increased mean diffusion, and changes in the white matter fibers visualized by color-coded tractography, including around a large cortical CMB. Conclusions The mouse is a valuable model of age-related vascular contributions to cognitive impairment and dementia (VCID). In composite, these methods and results reveal brain aging in older mice as a multifactorial process including CMBs and tissue diffusion alterations that can be well characterized by high field MRI.