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Purpose: Diffuse cortical damage in relapsing–remitting multiple sclerosis (RRMS) is clinically relevant but cannot be directly assessed with conventional MRI. In this study, it was aimed to use diffusion tensor imaging (DTI) techniques with optimized intrinsic eddy current compensation to quantify and characterize cortical mean diffusivity (MD) and fractional anisotropy (FA) changes in RRMS and to analyze the distribution of these changes across the cortex.
Materials and Methods: Three-Tesla MRI acquisition, mapping of the MD providing information about the integrity of microstructural barriers and of the FA reflecting axonal density and surface-based analysis with Freesurfer were performed for 24 RRMS patients and 25 control subjects.
Results: Across the whole cortex, MD was increased in patients (p < 0.001), while surface-based analysis revealed focal cortical FA decreases. MD and FA changes were distributed inhomogeneously across the cortex, the MD increase being more widespread than the FA decrease. Cortical MD correlated with the Expanded Disability Status Scale (EDSS, r = 0.38, p = 0.03).
Conclusion: Damage of microstructural barriers occurs inhomogeneously across the cortex in RRMS and might be spatially more widespread than axonal degeneration. The results and, in particular, the correlation with the clinical status indicate that DTI might be a promising technique for the monitoring of cortical damage under treatment in larger clinical studies.
Magnetic resonance imaging (MRI) is the gold standard imaging technique for diagnosis and monitoring of many neurological diseases. However, the application of conventional MRI in clinical routine is mainly limited to the visual detection of macroscopic tissue pathology since mixed tissue contrasts depending on hardware and protocol parameters hamper its application for the assessment of subtle or diffuse impairment of the structural tissue integrity. Multiparametric quantitative (q)MRI determines tissue parameters quantitatively, enabling the detection of microstructural processes related to tissue remodeling in aging and neurological diseases. In contrast to measuring tissue atrophy via structural imaging, multiparametric qMRI allows for investigating biologically distinct microstructural processes, which precede changes of the tissue volume. This facilitates a more comprehensive characterization of tissue alterations by revealing early impairment of the microstructural integrity and specific disease-related patterns. So far, qMRI techniques have been employed in a wide range of neurological diseases, including in particular conditions with inflammatory, cerebrovascular and neurodegenerative pathology. Numerous studies suggest that qMRI might add valuable information, including the detection of microstructural tissue damage in areas appearing normal on conventional MRI and unveiling the microstructural correlates of clinical manifestations. This review will give an overview of current qMRI techniques, the most relevant tissue parameters and potential applications in neurological diseases, such as early (differential) diagnosis, monitoring of disease progression, and evaluating effects of therapeutic interventions.
Purpose: To investigate cortical thickness and cortical quantitative T2 values as imaging markers of microstructural tissue damage in patients with unilateral high-grade internal carotid artery occlusive disease (ICAOD).
Methods: A total of 22 patients with ≥70% stenosis (mean age 64.8 years) and 20 older healthy control subjects (mean age 70.8 years) underwent structural magnetic resonance imaging (MRI) and high-resolution quantitative (q)T2 mapping. Generalized linear mixed models (GLMM) controlling for age and white matter lesion volume were employed to investigate the effect of ICAOD on imaging parameters of cortical microstructural integrity in multivariate analyses.
Results: There was a significant main effect (p < 0.05) of the group (patients/controls) on both cortical thickness and cortical qT2 values with cortical thinning and increased cortical qT2 in patients compared to controls, irrespective of the hemisphere. The presence of upstream carotid stenosis had a significant main effect on cortical qT2 values (p = 0.01) leading to increased qT2 in the poststenotic hemisphere, which was not found for cortical thickness. The GLMM showed that in general cortical thickness was decreased and cortical qT2 values were increased with increasing age (p < 0.05).
Conclusion: Unilateral high-grade carotid occlusive disease is associated with widespread cortical thinning and prolongation of cortical qT2, presumably reflecting hypoperfusion-related microstructural cortical damage similar to accelerated aging of the cerebral cortex. Cortical thinning and increase of cortical qT2 seem to reflect different aspects and different pathophysiological states of cortical degeneration. Quantitative T2 mapping might be a sensitive imaging biomarker for early cortical microstructural damage.
Highlights
• Increased values in SVD, suggesting reduced oxygen extraction fraction (OEF).
• Vascular dysfunction and microstructural impairment limit OEF capacity.
• Association between prolonged and more alkaline intracellular pH.
• Adaptation of intracellular energy metabolism compensates for reduced OEF.
Abstract
Background: We aimed to investigate whether combined phosphorous (31P) magnetic resonance spectroscopic imaging (MRSI) and quantitative T′2 mapping are able to detect alterations of the cerebral oxygen extraction fraction (OEF) and intracellular pH (pHi) as markers the of cellular energy metabolism in cerebral small vessel disease (SVD).
Materials and methods: 32 patients with SVD and 17 age-matched healthy control subjects were examined with 3-dimensional 31P MRSI and oxygenation-sensitive quantitative T′2 mapping (1/T′2 = 1/T2* - 1/T2) at 3 Tesla (T). PHi was measured within the white matter hyperintensities (WMH) in SVD patients. Quantitative T′2 values were averaged across the entire white matter (WM). Furthermore, T′2 values were extracted from normal-appearing WM (NAWM) and the WMH and compared between patients and controls.
Results: Quantitative T′2 values were significantly increased across the entire WM and in the NAWM in patients compared to control subjects (149.51 ± 16.94 vs. 138.19 ± 12.66 ms and 147.45 ± 18.14 vs. 137.99 ± 12.19 ms, p < 0.05). WM T′2 values correlated significantly with the WMH load (ρ=0.441, p = 0.006). Increased T′2 was significantly associated with more alkaline pHi (ρ=0.299, p < 0.05). Both T′2 and pHi were significantly positively correlated with vascular pulsatility in the distal carotid arteries (ρ=0.596, p = 0.001 and ρ=0.452, p = 0.016).
Conclusions: This exploratory study found evidence of impaired cerebral OEF in SVD, which is associated with intracellular alkalosis as an adaptive mechanism. The employed techniques provide new insights into the pathophysiology of SVD with regard to disease-related consequences on the cellular metabolic state.
Longitudinal changes of cortical microstructure in Parkinson's disease assessed with T1 relaxometry
(2016)
Background: Histological evidence suggests that pathology in Parkinson's disease (PD) goes beyond nigrostriatal degeneration and also affects the cerebral cortex. Quantitative MRI (qMRI) techniques allow the assessment of changes in brain tissue composition. However, the development and pattern of disease-related cortical changes have not yet been demonstrated in PD with qMRI methods. The aim of this study was to investigate longitudinal cortical microstructural changes in PD with quantitative T1 relaxometry.
Methods: 13 patients with mild to moderate PD and 20 matched healthy subjects underwent high resolution T1 mapping at two time points with an interval of 6.4 years (healthy subjects: 6.5 years). Data from two healthy subjects had to be excluded due to MRI artifacts. Surface-based analysis of cortical T1 values was performed with the FreeSurfer toolbox.
Results: In PD patients, a widespread decrease of cortical T1 was detected during follow-up which affected large parts of the temporo-parietal and occipital cortices and also frontal areas. In contrast, age-related T1 decrease in the healthy control group was much less pronounced and only found in lateral frontal, parietal and temporal areas. Average cortical T1 values did not differ between the groups at baseline (p = 0.17), but were reduced in patients at follow-up (p = 0.0004). Annualized relative changes of cortical T1 were higher in patients vs. healthy subjects (patients: − 0.72 ± 0.64%/year; healthy subjects: − 0.17 ± 0.41%/year, p = 0.007).
Conclusions: In patients with PD, the development of widespread changes in cortical microstructure was observed as reflected by a reduction of cortical T1. The pattern of T1 decrease in PD patients exceeded the normal T1 decrease as found in physiological aging and showed considerable overlap with the pattern of cortical thinning demonstrated in previous PD studies. Therefore, cortical T1 might be a promising additional imaging marker for future longitudinal PD studies. The biological mechanisms underlying cortical T1 reductions remain to be further elucidated.
The detection of cortical malformations in conventional MR images can be challenging. Prominent examples are focal cortical dysplasias (FCD), the most common cause of drug‐resistant focal epilepsy. The two main MRI hallmarks of cortical malformations are increased cortical thickness and blurring of the gray (GM) and white matter (WM) junction. The purpose of this study was to derive synthetic anatomies from quantitative T1 maps for the improved display of the above imaging characteristics in individual patients.
On the basis of a T1 map, a mask comprising pixels with T1 values characteristic for GM is created from which the local cortical extent (CE) is determined. The local smoothness (SM) of the GM‐WM junctions is derived from the T1 gradient. For display of cortical malformations, the resulting CE and SM maps serve to enhance local intensities in synthetic double inversion recovery (DIR) images calculated from the T1 map.
The resulting CE‐ and/or SM‐enhanced DIR images appear hyperintense at the site of cortical malformations, thus facilitating FCD detection in epilepsy patients. However, false positives may arise in areas with naturally elevated CE and/or SM, such as large GM structures and perivascular spaces.
In summary, the proposed method facilitates the detection of cortical abnormalities such as cortical thickening and blurring of the GM‐WM junction which are typical FCD markers. Still, subject motion artifacts, perivascular spaces, and large normal GM structures may also yield signal hyperintensity in the enhanced synthetic DIR images, requiring careful comparison with clinical MR images by an experienced neuroradiologist to exclude false positives.
Purpose: In the clinical routine, detection of focal cortical dysplasia (FCD) by visual inspection is challenging. Still, information about the presence and location of FCD is highly relevant for prognostication and treatment decisions. Therefore, this study aimed to develop, describe and test a method for the calculation of synthetic anatomies using multiparametric quantitative MRI (qMRI) data and surface-based analysis, which allows for an improved visualization of FCD.
Materials and Methods: Quantitative T1-, T2- and PD-maps and conventional clinical datasets of patients with FCD and epilepsy were acquired. Tissue segmentation and delineation of the border between white matter and cortex was performed. In order to detect blurring at this border, a surface-based calculation of the standard deviation of each quantitative parameter (T1, T2, and PD) was performed across the cortex and the neighboring white matter for each cortical vertex. The resulting standard deviations combined with measures of the cortical thickness were used to enhance the signal of conventional FLAIR-datasets. The resulting synthetically enhanced FLAIR-anatomies were compared with conventional MRI-data utilizing regions of interest based analysis techniques.
Results: The synthetically enhanced FLAIR-anatomies showed higher signal levels than conventional FLAIR-data at the FCD sites (p = 0.005). In addition, the enhanced FLAIR-anatomies exhibited higher signal levels at the FCD sites than in the corresponding contralateral regions (p = 0.005). However, false positive findings occurred, so careful comparison with conventional datasets is mandatory.
Conclusion: Synthetically enhanced FLAIR-anatomies resulting from surface-based multiparametric qMRI-analyses have the potential to improve the visualization of FCD and, accordingly, the treatment of the respective patients.
Background and purpose: In patients with epilepsies of structural origin, brain atrophy and pathological alterations of the tissue microstructure extending beyond the putative epileptogenic lesion have been reported. However, in patients without any evidence of epileptogenic lesions on diagnostic magnetic resonance imaging (MRI), impairment of the brain microstructure has been scarcely elucidated. Using multiparametric quantitative (q) magnetic resonance imaging MRI, we aimed to investigate diffuse impairment of the microstructural tissue integrity in MRI-negative focal epilepsy patients.
Methods: 27 MRI-negative patients with focal epilepsy (mean age 33.1 ± 14.2 years) and 27 matched healthy control subjects underwent multiparametric qMRI including T1, T2, and PD mapping at 3 T. After tissue segmentation based on synthetic anatomies, mean qMRI parameter values were extracted from the cerebral cortex, the white matter (WM) and the deep gray matter (GM) and compared between patients and control subjects. Apart from calculating mean values for the qMRI parameters across the respective compartments, voxel-wise analyses were performed for each tissue class.
Results: There were no significant differences for mean values of quantitative T1, T2, and PD obtained from the cortex, the WM and the deep GM between the groups. Furthermore, the voxel-wise analyses did not reveal any clusters indicating significant differences between patients and control subjects for the qMRI parameters in the respective compartments.
Conclusions: Based on the employed methodology, no indication for an impairment of the cerebral microstructural tissue integrity in MRI-negative patients with focal epilepsy was found in this study. Further research will be necessary to identify relevant factors and mechanisms contributing to microstructural brain tissue damage in various subgroups of patients with epilepsy.
Multimodal quantitative mri reveals no evidence for tissue pathology in idiopathic cervical dystonia
(2019)
Background: While in symptomatic forms of dystonia cerebral pathology is by definition present, it is unclear so far whether disease is associated with microstructural cerebral changes in idiopathic dystonia. Previous quantitative MRI (qMRI) studies assessing cerebral tissue composition in idiopathic dystonia revealed conflicting results.
Objective: Using multimodal qMRI, the presented study aimed to investigate alterations in different cerebral microstructural compartments associated with idiopathic cervical dystonia in vivo.
Methods: Mapping of T1, T2, T∗2, and proton density (PD) was performed in 17 patients with idiopathic cervical dystonia and 29 matched healthy control subjects. Statistical comparisons of the parametric maps between groups were conducted for various regions of interest (ROI), including major basal ganglia nuclei, the thalamus, white matter, and the cerebellum, and voxel-wise for the whole brain.
Results: Neither whole brain voxel-wise statistics nor ROI-based analyses revealed significant group differences for any qMRI parameter under investigation.
Conclusions: The negative findings of this qMRI study argue against the presence of overt microstructural tissue change in patients with idiopathic cervical dystonia. The results seem to support a common view that idiopathic cervical dystonia might primarily resemble a functional network disease.
Highlights
• The goal was to assess the intra- and inter-scanner reproducibility of qMRI data.
• Mean scan-rescan variations were not exceeding 2.14%.
• Mean inter-scanner model deviations were not exceeding 5.21%.
• Provided that identical acquisition sequences are used, discrepancies between qMRI data acquired with different scanner models are low.
Abstract
Background: Quantitative MRI (qMRI) techniques allow assessing cerebral tissue properties. However, previous studies on the accuracy of quantitative T1 and T2 mapping reported a scanner model bias of up to 10% for T1 and up to 23% for T2. Such differences would render multi-centre qMRI studies difficult and raise fundamental questions about the general precision of qMRI. A problem in previous studies was that different methods were used for qMRI parameter mapping or for measuring the transmitted radio frequency field B1 which is critical for qMRI techniques requiring corrections for B1 non-uniformities.
Aims: The goal was to assess the intra- and inter-scanner reproducibility of qMRI data at 3 T, using two different scanner models from the same vendor with exactly the same multiparametric acquisition protocol.
Methods: Proton density (PD), T1, T2* and T2 mapping was performed on healthy subjects and on a phantom, performing each measurement twice for each of two scanner models. Although the scanners had different hardware and software versions, identical imaging sequences were used for PD, T1 and T2* mapping, adapting the codes of an existing protocol on the older system line by line to match the software version of the newer scanner. For T2-mapping, the respective manufacturer’s sequence was used which depended on the software version. However, system-dependent corrections were carried out in this case. Reproducibility was assessed by average values in regions of interest.
Results: Mean scan-rescan variations were not exceeding 2.14%, with average values of 1.23% and 1.56% for the new and old system, respectively. Inter-scanner model deviations were not exceeding 5.21% with average values of about 2.2–3.8% for PD, 2.5–3.0% for T2*, 1.6–3.1% for T1 and 3.3–5.2% for T2.
Conclusions: Provided that identical acquisition sequences are used, discrepancies between qMRI data acquired with different scanner models are low. The level of systematic differences reported in this work may help to interpret multi-centre data.