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In MRI the transverse relaxation rate, R

Homodyne filtering is a standard preprocessing step in the estimation of SWI. Unfortunately, SWI is not quantitative, and QSM cannot be accurately estimated from filtered phase images. Compared with gradient-echo sequences suitable for computing QSM, SWI is more readily available and is often the only susceptibility-sensitive sequence acquired in the clinical setting. In this project, we aimed to quantify susceptibility from the homodyne-filtered phase (HFP), acquired for computing susceptibility-weighted images, using convolutional neural networks to solve the compounded problem of (1) computing the solution to the inverse dipole problem, and (2) compensating for the effects of the homodyne filtering.Two convolutional neural networks, the U-Net and a modified QSMGAN architecture (HFP-QSMGAN), were trained to predict QSM maps at different TEs from HFP images. The QSM maps were quantified from a gradient-echo sequence acquired in the same individuals using total generalized variation (TGV)-QSM. The QSM maps estimated directly from the HFP were also included for comparison. Voxel-wise predictions and, importantly, regional predictions of susceptibility with adjustment to a reference region, were compared.Our results indicate that the U-Net model provides more accurate voxel-wise predictions of susceptibility compared with HFP-QSMGAN and HFP-QSM. However, regional estimates of susceptibility predicted by HFP-QSMGAN are more strongly correlated with the values from TGV-QSM compared with those of U-Net and HFP-QSM.Accurate prediction of susceptibility can be achieved from filtered SWI phase using convolutional neural networks.

The fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) sequence aims at suppressing the signal of the cerebrospinal fluid (CSF) by acquiring images at the time point at which the longitudinal magnetization and therefore the signal of CSF is zero. This time point is also called the null point inversion time (TI

Purpose To develop a deep neural network to recover filtered phase from clinical MR phase images to enable the computation of QSMs. Methods Eighteen deep learning networks were trained to recover combinations of 13 SWI phase-filtering pipelines. SWI-filtered data were computed offline from five multiorientation, multiecho MRI scans yielding 132 3D volumes (118/7/7 training/validation/testing). Two experiments were conducted to show the efficacy of the networks. First, using QSM processing, local fields were computed from the raw phase and subsequently filtered using the SWI-filtering pipelines. The networks were then trained to invert the filtering operation. Second, the trained networks were fine-tuned to recover unfiltered local fields from filtered local fields computed by applying QSM processing to the SWI-filtered phase. Susceptibility maps were computed from the recovered fields and compared with gold standard multiple orientation sampling reconstructions. Results Susceptibility maps computed from the raw phase using standard QSM processing have a normalized root mean square error (NRMSE) of 0.732 ± 0.095. Susceptibility maps computed from the recovered phase obtained NRMSEs of 0.725 ± 0.095. The network trained using all 13 processing methods generalized well, obtaining NRMSEs of 0.725 ± 0.89 on filters it has not seen, while matching the reconstruction accuracy of networks trained to recover a single filter. Conclusion It is feasible to recover SWI-filtered phase using deep learning. QSM can be computed from the recovered phase from SWI acquisition with comparable accuracy to standard QSM processing.

Post mortem in situ and in vivo MRI scans were conducted at 3T.Although the mean diffusivity is strongly reduced post mortem, we could successfully estimate the fiber angle using DTI. Due to the strong correlation of the higher order orientation dependent

The multiexponential T2 decay of the magnetic resonance imaging (MRI) signal from cerebral white matter can be separated into short components sensitive to myelin water and long components related to intra- and extracellular water. In this study we investigated to what degree the myelin water fraction (MWF) depends on the angle between white matter fibers and the main magnetic filed. Maps of MWF were acquired using multi echo CPMG and GRASE sequences. The CPMG sequence was acquired with a TR of 1073 ms, 1500 ms and 2000 ms. The fiber orientation was mapped with diffusion tensor imaging. By angle-wise pooling the voxels across the brain’s white matter, an orientation dependent MWF curve was generated. We found that MWF varied between 25% and 35% across different fiber orientations. The orientation dependency of the MWF is characterized by a dipole-dipole interaction model. Furthermore, the selection of the TR influences the orientation dependent and global white matter MWF. White matter fiber orientation induces a strong systematic bias on the estimation of MWF. This finding has important implications for future research and the interpretation of MWI results in previously published studies.

Studies on brain iron content in restless legs syndrome (RLS) using magnetic resonance imaging (MRI) are heterogeneous. In this study, we sought to leverage the availability of a large dataset including a range of iron-sensitive MRI techniques to reassess the association between brain iron content and RLS with added statistical power and to compare these results to previous studies.The relaxation rates RIn our cohort, RLS patients had increased RNormal and increased iron content of subcortical brain areas detected in this study is not in line with the hypothesis of reduced brain iron storage, but favors CSF investigations and post mortem studies indicating alteration of brain iron mobilization and homeostasis in RLS.

Investigations of the ultrastructural features of neurons and their synapses are only possible with electron microscopy. Especially for comparative studies of the changes in densities and distributions of such features, an unbiased sampling protocol is vital for reliable results. Here, we present a workflow for the image acquisition of brain samples. The workflow allows systematic uniform random sampling within a defined brain region, and the images can be analyzed using a disector. This technique is much faster than extensive examination of serial sections but still presents a feasible approach to estimate the densities and distributions of ultrastructure features. Before embedding, stained vibratome sections were used as a reference to identify the brain region under investigation, which helped speed up the overall specimen preparation process. This approach was used for comparative studies investigating the effect of an enriched-housing environment on several ultrastructural parameters in the mouse brain. Based on the successful use of the workflow, we adapted it for the purpose of elemental analysis of brain samples. We optimized the protocol in terms of the time of user-interaction. Automating all the time-consuming steps by compiling a script for the open source software SerialEM helps the user to focus on the main work of acquiring the elemental maps. As in the original workflow, we paid attention to the unbiased sampling approach to guarantee reliable results.

This study explored whether autoregulatory shifts in cerebral blood volume induce susceptibility changes large enough to be depicted by quantitative susceptibility mapping. Eight healthy subjects underwent fast quantitative susceptibility mapping at 3T while lying down to slowly decrease mean arterial pressure. A linear relationship between mean arterial pressure and susceptibility was observed in cortical and subcortical structures, likely representing vessels involved in autoregulation. The slope of this relationship is assumed to indicate the extent of cerebral vascular compliance.

A variety of Magnetic Resonance Imaging (MRI) techniques are known to be sensitive to brain iron content. In principle, iron sensitive MRI techniques are based on local magnetic field variations caused by iron particles in tissue. The purpose of this study was to investigate the sensitivity of MR relaxation and magnetization transfer parameters to changes in iron oxidation state compared to changes in iron concentration. Therefore, quantitative MRI parameters including R1, R2, R2∗, quantitative susceptibility maps (QSM) and magnetization transfer ratio (MTR) of post mortem human brain tissue were acquired prior and after chemical iron reduction to change the iron oxidation state and chemical iron extraction to decrease the total iron concentration. All assessed parameters were shown to be sensitive to changes in iron concentration whereas only R2, R2∗ and QSM were also sensitive to changes in iron oxidation state. Mass spectrometry confirmed that iron accumulated in the extraction solution but not in the reduction solution. R2∗ and QSM are often used as markers for iron content. Changes in these parameters do not necessarily reflect variations in iron content but may also be a result of changes in the iron’s oxygenation state from ferric towards more ferrous iron or vice versa.

Current MRI techniques cannot reliably assess iron content in white matter due to the confounding diamagnetic effect of myelin. The purpose of this study was to validate with histology a novel iron mapping technique that uses the temperature dependency of the paramagnetic susceptibility in multiple sclerosis (MS) brains, where white matter has been reported to show significant variations in iron content.We investigated post mortem brain tissue from three MS patients and one control subject. Temperature-dependent R2* relaxometry was performed between 4°C and 37°C. The resulting temperature coefficient ( TcR2*) maps were compared with immunohistochemical stains for ferritin light chain.Good agreement between TcR2* maps and ferritin staining was found by way of visual comparison and quantitative analysis. The highest iron concentrations were detected at the edge of MS lesions and in the basal ganglia. For all regions, except the subcortical U-fibers, there was a significant negative correlation between the TcR2* values and the ferritin count.This study provides further evidence that TcR2* may be a reliable measure of white matter iron content due to the elimination of myelin-induced susceptibility changes and is well suited for further research into neurological diseases with distortions of the iron homeostasis. Magn Reson Med 79:1609-1615, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Formalin fixation prevents tissue autolysis by crosslinking proteins and changes tissue microstructure and MRI signal characteristics. Previous studies showed high variations in MR relaxation time constants of formalin fixed brain tissue, which has been attributed to the use of different formalin concentrations. Our investigations confirmed the influence of formalin concentration on relaxation times and unexpectedly revealed an influence of vendor specific formalin composition, which has not been investigated so far.We systematically analyzed relaxation times of human brain tissue fixed with 4% and 10% formalin compared with unfixed condition at 3 Tesla MRI. Furthermore, we assessed relaxation times of nine formalin solutions from different vendors and performed comparisons of their magnetic susceptibility by SQUID (superconducting quantum interference device) magnetometry.Tissue relaxation times decreased approximately twice as fast using 10% than in 4% formalin fixation. The vendor specific composition of the formalin solutions and concentration dependent paramagnetic effects showed a substantial contribution to differences in relaxation times of formalin.Our study demonstrates that differences of the formalin composition have substantial effects on MRI signal characteristics after fixation, which can explain the divergence of reported relaxation times beyond the effect of differences in formalin concentration. Magn Reson Med 79:1111-1115, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Post-mortem MRI of the brain is increasingly applied in neuroscience for a better understanding of the contrast mechanisms of disease induced tissue changes. However, the influence of chemical processes caused by formalin fixation and differences in temperature may hamper the comparability with results from in vivo MRI. In this study we investigated how formalin fixation and temperature affect T1, T2 and T2* relaxation times of brain tissue. Fixation effects were examined with respect to changes in water content and crosslinking. Relaxometry was performed in brain slices from five deceased subjects at different temperatures. All measurements were repeated after 190 days of formaldehyde immersion. The water content of unfixed and fixed tissue was determined using the wet-to-dry ratio following drying. Protein weight was determined with sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Fixation caused a strong decrease of all relaxation times, the strongest effect being seen on T1, with a reduction of up to 76%. The temperature coefficient of T1 was lower in the fixed than unfixed tissue, which was in contrast to T2, where an increase of the temperature coefficient was observed following fixation. The reduction of the water content after fixation was in the range of 1-6% and thus not sufficient to explain the changes in relaxation time. Results from SDS-PAGE indicated a strong increase of the protein size above 260 kDa in all brain structures examined. Our results suggest that crosslinking induced changes of the macromolecular matrix are responsible for T1 shortening and a decreased temperature dependency. The relaxation times provided in this work should allow optimization of post-mortem MRI protocols for the brain.

Magnetic resonance relaxation times of most tissues are expected to depend on temperature, which can impact findings in postmortem magnetic resonance imaging or when using magnetic resonance imaging for relaxation-based thermometry. The purpose of this study was to investigate the exact temperature dependency of the relaxation times T(1), T(2), T(2) *, and the magnetization transfer ratio in different structures of the human brain.To prevent fixation and autolysis effects, this study was performed with fresh postmortem brain tissues. Following autopsy, coronal brain slices from five deceased subjects were subjected to relaxometry at 3T in a temperature range between 4°C and 37°C. Heating of the tissue was achieved by flushing the vacuum packed brain slices with water at a predefined temperature.T1 showed a linear dependency on temperature with the highest temperature coefficient in the cortex (17.4 ms/°C) and the lowest in the white matter (3.4 ms/°C). T(2) did not depend on temperature. T(2) * and magnetization transfer ratio scaled with temperature only in deep gray matter.The temperature coefficient for T(1) is higher than expected from previous reports and varies across brain structures. The coefficients obtained in this study can serve as reference for thermometry or for correcting quantitative postmortem magnetic resonance imaging.