The astrocyte-produced growth factor HB-EGF limits autoimmune CNS pathology.

Central nervous system (CNS)-resident cells such as microglia, oligodendrocytes and astrocytes are gaining increasing attention in respect to their contribution to CNS pathologies including multiple sclerosis (MS). Several studies have demonstrated the involvement of pro-inflammatory glial subsets in the pathogenesis and propagation of inflammatory events in MS and its animal models. However, it has only recently become clear that the underlying heterogeneity of astrocytes and microglia can not only drive inflammation, but also lead to its resolution through direct and indirect mechanisms. Failure of these tissue-protective mechanisms may potentiate disease and increase the risk of conversion to progressive stages of MS, for which currently available therapies are limited. Using proteomic analyses of cerebrospinal fluid specimens from patients with MS in combination with experimental studies, we here identify Heparin-binding EGF-like growth factor (HB-EGF) as a central mediator of tissue-protective and anti-inflammatory effects important for the recovery from acute inflammatory lesions in CNS autoimmunity. Hypoxic conditions drive the rapid upregulation of HB-EGF by astrocytes during early CNS inflammation, while pro-inflammatory conditions suppress trophic HB-EGF signaling through epigenetic modifications. Finally, we demonstrate both anti-inflammatory and tissue-protective effects of HB-EGF in a broad variety of cell types in vitro and use intranasal administration of HB-EGF in acute and post-acute stages of autoimmune neuroinflammation to attenuate disease in a preclinical mouse model of MS. Altogether, we identify astrocyte-derived HB-EGF and its epigenetic regulation as a modulator of autoimmune CNS inflammation and potential therapeutic target in MS.

Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data.

PURPOSE: To enable a fast and automatic deep learning-based QSM reconstruction of tissues with diverse chemical shifts, relevant to most regions outside the brain. METHODS: A UNET was trained to reconstruct susceptibility maps using synthetically generated, unwrapped, multi-echo phase data as input. The RMS error with respect to synthetic validation data was computed. The method was tested on two in vivo knee and two pelvis data sets. Comparisons were made to a conventional fat-water separation pipeline by applying a commonly used graph-cut algorithm, both without and with an extended mask for background field removal (FWS-CONV-QSM and FWS-MASK-CONV-QSM, respectively). Several regions of interest were segmented and compared. Furthermore, the approach was tested on a prostate cancer patient receiving low-dose-rate brachytherapy, to detect and localize the seeds by MRI. RESULTS: The RMS error was 0.292 ppm with FWS-CONV-QSM and 0.123 ppm for the UNET approach. Susceptibility maps were reconstructed much faster (< 10 s) and completely automatically (no background masking needed) by the UNET compared with the other applied techniques (5 min 51 s and 22 min 44 s for CONV-QSM and FWS-MASK-CONV-QSM, respectively. Background artifacts, fat-water swaps, and hypointense artifacts between I-125 seeds of a patient receiving low-dose brachytherapy in the prostate were largely reduced in the UNET approach. CONCLUSIONS: Deep learning-based QSM reconstruction, trained solely with synthetic data, is well-suited to rapidly reconstructing high-quality susceptibility maps in the presence of fat without needing masking for background field removal.

Brain tissues have single-voxel signatures in multi-spectral MRI.

Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.

The traveling heads 2.0: Multicenter reproducibility of quantitative imaging methods at 7 Tesla.

OBJECT: This study evaluates inter-site and intra-site reproducibility at ten different 7 T sites for quantitative brain imaging. MATERIAL AND METHODS: Two subjects - termed the "traveling heads" - were imaged at ten different 7 T sites with a harmonized quantitative brain MR imaging protocol. In conjunction with the system calibration, MP2RAGE, QSM, CEST and multi-parametric mapping/relaxometry were examined. RESULTS: Quantitative measurements with MP2RAGE showed very high reproducibility across sites and subjects, and errors were in concordance with previous results and other field strengths. QSM had high inter-site reproducibility for relevant subcortical volumes. CEST imaging revealed systematic differences between the sites, but reproducibility was comparable to results in the literature. Relaxometry had also very high agreement between sites, but due to the high sensitivity, differences caused by different applications of the B1 calibration of the two RF coil types used were observed. CONCLUSION: Our results show that quantitative brain imaging can be performed with high reproducibility at 7 T and with similar reliability as found at 3 T for multicenter studies of the supratentorial brain.

Sample size estimation: Current practice and considerations for original investigations in MRI technical development studies.

PURPOSE: To investigate and to provide guidance for sample size selection based on the current practice in MR technical development studies in which healthy volunteers are examined. METHODS: All original articles published in Magnetic Resonance in Medicine between 2017 and 2019 were investigated and categorized according to technique, anatomical region, and magnetic field strength. The number of examined healthy volunteers (ie, the sample size) was collected and evaluated, whereas the number of patients was not considered. Papers solely measuring patients, animals, phantoms, specimens, or studies using existing data, for example, from an open databank, or consisting only of theoretical work or simulations were excluded. RESULTS: The median sample size of the 882 included studies was 6. There were some peaks in the sample size distribution (eg, 1, 5, and 10). In 49.9%, 82.1%, and 95.6% of the studies, the sample size was smaller or equal to 5, 10, and 20, respectively. CONCLUSION: We observed a large variance in sample sizes reflecting the variety of studies published in Magnetic Resonance in Medicine. Therefore, it can be concluded that it is current practice to balance the need for statistical power with the demand to minimize experiments involving healthy humans, often by choosing small sample sizes between 1 and 10. Naturally, this observation does not release an investigator from ensuring that sufficient data are acquired to reach statistical conclusions.

MRI Brain Signal Intensity and Relaxation Times in Individuals with Prior Exposure to Gadobutrol.

Purpose To perform follow-up brain MRI in volunteer participants who had previously received multiple doses of gadobutrol and to assess for changes in signal intensities and relaxation times. Materials and Methods This prospective study included 160 participants who received gadobutrol only between 2007 and 2017. The participants were separated into two groups, including participants with at least five contrast agent-enhanced examinations and normal kidney function (group 1) or at least one examination and impaired renal function (group 2). Control groups with normal and impaired renal function (groups 3 and 4) without history of contrast agent exposure were included for comparison. Unenhanced brain MRI was performed in 220 participants (76, 84, 25, and 35 participants in groups 1-4, respectively) with T1-weighted spin-echo and T1 and T2 mapping to determine visual signal intensity changes, signal intensity ratios (globus pallidus-to-thalamus and dentate nucleus-to-pons ratios), and T1 and T2 relaxation times. Results In groups 1 and 2, neither visual signal alterations nor differences in signal intensity ratio or T2 mapping were found. T1 mapping showed no changes for dentate nucleus, pons, and thalamus. However, shorter T1 relaxation times in the globus pallidus were found in group 1 compared with group 3 (difference of -26.2 msec; P = .002), which correlated with the number of previous gadobutrol doses in this group (P = .001). Conclusion In study participants who had previously received gadobutrol, brain MRI showed no differences relative to healthy control participants without gadobutrol exposure. However, quantitative T1 measurements might indicate gadolinium retention in the globus pallidus. © RSNA, 2018 Online supplemental material is available for this article.

Mapping of thalamic magnetic susceptibility in multiple sclerosis indicates decreasing iron with disease duration: A proposed mechanistic relationship between inflammation and oligodendrocyte vitality.

Recent advances in susceptibility MRI have dramatically improved the visualization of deep gray matter brain regions and the quantification of their magnetic properties in vivo, providing a novel tool to study the poorly understood iron homeostasis in the human brain. In this study, we used an advanced combination of the recent quantitative susceptibility mapping technique with dedicated analysis methods to study intra-thalamic tissue alterations in patients with clinically isolated syndrome (CIS) and multiple sclerosis (MS). Thalamic pathology is one of the earliest hallmarks of MS and has been shown to correlate with cognitive dysfunction and fatigue, but the mechanisms underlying the thalamic pathology are poorly understood. We enrolled a total of 120 patients, 40 with CIS, 40 with Relapsing Remitting MS (RRMS), and 40 with Secondary Progressive MS (SPMS). For each of the three patient groups, we recruited 40 controls, group matched for age- and sex (120 total). We acquired quantitative susceptibility maps using a single-echo gradient echo MRI pulse sequence at 3 T. Group differences were studied by voxel-based analysis as well as with a custom thalamus atlas. We used threshold-free cluster enhancement (TFCE) and multiple regression analyses, respectively. We found significantly reduced magnetic susceptibility compared to controls in focal thalamic subregions of patients with RRMS (whole thalamus excluding the pulvinar nucleus) and SPMS (primarily pulvinar nucleus), but not in patients with CIS. Susceptibility reduction was significantly associated with disease duration in the pulvinar, the left lateral nuclear region, and the global thalamus. Susceptibility reduction indicates a decrease in tissue iron concentration suggesting an involvement of chronic microglia activation in the depletion of iron from oligodendrocytes in this central and integrative brain region. Not necessarily specific to MS, inflammation-mediated iron release may lead to a vicious circle that reduces the protection of axons and neuronal repair.

Methods for the computation of templates from quantitative magnetic susceptibility maps (QSM): Toward improved atlas- and voxel-based analyses (VBA).

PURPOSE: To develop and assess a method for the creation of templates for voxel-based analysis (VBA) and atlas-based approaches using quantitative magnetic susceptibility mapping (QSM). MATERIALS AND METHODS: We studied four strategies for the creation of magnetic susceptibility brain templates, derived as successive extensions of the conventional template generation (CONV) based on only T1 -weighted (T1 w) images. One method that used only T1 w images involved a minor improvement of CONV (U-CONV). One method used only magnetic susceptibility maps as input for template generation (DIRECT), and the other two used a linear combination of susceptibility and T1 w images (HYBRID) and an algorithm that directly used both image modalities (MULTI), respectively. The strategies were evaluated in a group of N = 10 healthy human subjects and semiquantitatively assessed by three experienced raters. Template quality was compared statistically via worth estimates (WEs) obtained with a log-linear Bradley-Terry model. RESULTS: The overall quality of the templates was better for strategies including both susceptibility and T1 w contrast (MULTI: WE = 0.62; HYBRID: WE = 0.21), but the best method depended on the anatomical region of interest. While methods using only one modality resulted in lower WEs, lowest overall WEs were obtained when only T1 w images were used (DIRECT: WE = 0.12; U-CONV: WE = 0.05). CONCLUSION: Template generation strategies that employ only magnetic susceptibility contrast or both magnetic susceptibility and T1 w contrast produce templates with the highest quality. The optimal approach depends on the anatomical structures of interest. The established approach of using only T1 w images (CONV) results in reduced image quality compared to all other approaches studied. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1474-1484.

Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that is associated with additional radiation exposure and poor soft tissue contrast. To establish a magnetic resonance imaging (MRI) only workflow combining improved tissue contrast and high seed detectability, a deep learning-approach for automatic seed segmentation on MRI-scans was developed. MATERIAL AND METHODS: Patients treated with I-125 seed brachytherapy received a postplan-CT and a 1.5 T MRI-scan on nominal day 30 after implantation. For MRI-based seed visualization, DIXON-sequences were acquired and deep learning-based quantitative susceptibility maps (QSM) were generated from 3D-gradient-echo-sequences from 20 patients. Seed segmentations created on CT served as ground truth. For automatic seed segmentation on MRI, a 3D nnU-net model was trained using QSM and DIXON, both solely and combined. RESULTS: Of the implanted seeds 94.8 ± 2.4% were detected with deep learning automatic segmentation entrained on both QSM and DIXON data. Models trained on the individual sequence data-sets performed worse with detection rates of 87.5 ± 2.6% or 88.6 ± 7.5% for QSM and DIXON respectively. The seed centers identified on CT versus QSM and DIXON were on average 1.8 ± 1.3 mm apart. Postimplant dosimetry for evaluation of positioning inaccuracies revealed only small variations of up to 0.4 ± 4.26 Gy in D90 (dose 90% of the prostate receives) between the standard CT-approach and our MRI-only workflow. CONCLUSION: The proposed deep learning-based MRI-only workflow provided a promisingly accurate and robust seed localization and thus has the potential to compete with current state-of-the-art CT-based postimplant dosimetry in the future.

Parkinson's disease or multiple system atrophy: potential separation by quantitative susceptibility mapping.

Background: Due to the absence of robust biomarkers, and the low sensitivity and specificity of routine imaging techniques, the differential diagnosis between Parkinson's disease (PD) and multiple system atrophy (MSA) is challenging. High-field magnetic resonance imaging (MRI) opened up new possibilities regarding the analysis of pathological alterations associated with neurodegenerative processes. Recently, we have shown that quantitative susceptibility mapping (QSM) enables visualization and quantification of two major histopathologic hallmarks observed in MSA: reduced myelin density and iron accumulation in the basal ganglia of a transgenic murine model of MSA. It is therefore emerging as a promising imaging modality on the differential diagnosis of Parkinsonian syndromes. Objectives: To assess QSM on high-field MRI for the differential diagnosis of PD and MSA. Methods: We assessed 23 patients (nine PDs and 14 MSAs) and nine controls using QSM on 3T and 7T MRI scanners at two academic centers. Results: We observed increased susceptibility in MSA at 3T in prototypical subcortical and brainstem regions. Susceptibility measures of putamen, pallidum, and substantia nigra reached excellent diagnostic accuracy to separate both synucleinopathies. Increase toward 100% sensitivity and specificity was achieved using 7T MRI in a subset of patients. Magnetic susceptibility correlated with age in all groups, but not with disease duration in MSA. Sensitivity and specificity were particularly high for possible MSA, and reached 100% in the putamen. Conclusion: Putaminal susceptibility measures, in particular on ultra-high-field MRI, may distinguish MSA patients from both, PD and controls, allowing an early and sensitive diagnosis of MSA.

Quantitative susceptibility mapping depicts severe myelin deficit and iron deposition in a transgenic model of multiple system atrophy.

Despite internationally established diagnostic criteria, multiple system atrophy (MSA) is frequently misdiagnosed, particularly at disease onset. While neuropathological changes such as demyelination and iron deposition are typically detected in MSA, these structural hallmarks were so far only demonstrated post-mortem. Here, we examine whether myelin deficit observed in a transgenic murine model of MSA can be visualized and quantified in vivo using specific magnetic resonance imaging (MRI) approaches. Reduced myelin content was measured histologically in prototypical white matter as well as mixed grey-white matter regions i.e. corpus callosum, anterior commissure, and striatum of transgenic mice overexpressing human α-synuclein under the control of the myelin basic protein promotor (MBP29-hα-syn mice). Correspondingly, in vivo quantitative susceptibility mapping (QSM) showed a strongly reduced susceptibility contrast in white matter regions and T2-weighted MR imaging revealed a significantly reduced grey-white matter contrast in MBP29-hα-syn mice. In addition, morphological analysis suggested a pronounced, white matter-specific deposition of iron in MBP29-hα-syn mice. Importantly, in vivo MRI results were matched by comprehensive structural characterization of myelin, iron, and axonal directionality. Taken together, our results provide strong evidence that QSM is a very sensitive tool measuring changes in myelin density in conjunction with iron deposition in MBP29-hα-syn mice. This multimodal neuroimaging approach may pave the way towards a novel non-invasive technique to detect crucial neuropathological changes specifically associated with MSA.