A method is presented for rapid simultaneous quantification of the longitudinal T 1 relaxation, the transverse T 2 relaxation, the proton density (PD), and the amplitude of the local radio frequency B 1 field. All four parameters are measured in one single scan by means of a multislice, multiecho, and multidelay acquisition. It is based on a previously reported method, which was substantially improved for routine clinical usage. The improvements comprise of the use of a multislice spin-echo technique, a background phase correction, and a spin system simulation to compensate for the slice-selective RF pulse profile effects. The aim of the optimization was to achieve the optimal result for the quantification of magnetic resonance parameters within a clinically acceptable time. One benchmark was high-resolution coverage of the brain within 5 min. In this scan time the measured intersubject standard deviation (SD) in a group of volunteers was 2% to 8%, depending on the tissue (voxel size ‫؍‬ 0.8 ؋ 0.8 ؋ 5 mm). As an example, the method was applied to a patient with multiple sclerosis in whom the diseased tissue could clearly be distinguished from healthy reference values. Additionally it was shown that, using the approach of synthetic MRI, both accurate conventional contrast images as well as quantification maps can be generated based on the same scan. Tissues in the human body can be distinguished with magnetic resonance imaging (MRI) depending on their MR parameters, such as the longitudinal T 1 relaxation, the transverse T 2 relaxation, and the proton density (PD). In clinical routine, the MR scanner settings, such as echo time (T E ), repetition time (T R ), and flip angle (␣), are most often chosen to highlight, or saturate, the image intensity of tissues, resulting in T 1 -weighting or T 2 -weighting in a contrast image. These procedures are well-established and relatively quick. A major disadvantage of using such contrast images is that the absolute intensity has no direct meaning and diagnosis relies on comparison with surrounding tissues in the image. In many cases it is therefore necessary to perform several different contrast scans. A more direct approach is the absolute quantification of the tissue parameters T 1 , T 2 , and PD. In this case, pathology can be examined on a pixel basis to establish the absolute deviation compared to the normal values. Automatic segmentation of such tissue images would be straightforward and the progress of the disease could then be expressed in absolute numbers. An excellent overview of the use of absolute quantification on neurodegenerative diseases is provided in Ref. 1.Although the advantages of absolute quantification are obvious, its clinical use is still limited. At least two major hurdles need to be addressed to stimulate widespread clinical usage. For many methods, the excessive scan time associated with the measurement of the three parameters has so far prohibited its clinical application. However, in recent years there has been substantial progress (see, e.g., R...

An imaging method called "quantification of relaxation times and proton density by twin-echo saturation-recovery turbofield echo" (QRAPTEST) is presented as a means of quickly determining the longitudinal T 1 and transverse T* 2 relaxation time and proton density (PD) within a single sequence. The method also includes an estimation of the B 1 field inhomogeneity. High-resolution images covering large volumes can be achieved within clinically acceptable times of 5-10 min. Constant progress in the time efficiency and accuracy of magnetic resonance imaging (MRI) has increased interest in quantifying, rather than qualifying, tissue parameters in large volumes of interest. Quick measurement of the longitudinal T 1 relaxation time (1-7), as well as the transverse T 2 and T* 2 relaxation time (6 -13), has been the subject of active investigation over the recent years. New fast imaging methods for measuring the proton density (PD) have also been published, although less frequently (6,8). The effective scan times have diminished, allowing the methods to enter the clinical arena. The quantification of MR relaxation times and water concentration may improve the detection and staging of various diseases. Examples include the use of T 1 relaxation time for diseases such as Parkinson's (14), Alzheimer's, and multiple sclerosis (15,16), and T* 2 relaxation time to assess iron deposition in thalassemia (17,18). One can expect that in many circumstances the combined measurement of several MR parameters would lead to a better diagnostic accuracy. For example, characterization of atherosclerotic plaques in the main vessels is improved when quantification of T 1 , T 2 , and PD replaces qualitative assessment of the vessel walls (19). In another example, the rapid quantification of metabolites in spectroscopic imaging is critically dependent on the accurate determination of water concentration (20,21).Beyond these immediate clinical benefits, rapid quantification could profoundly alter the way MRI is performed. Once the relaxation times and PD are measured for a region of interest (ROI), in principle any contrast image with a certain combination of echo time (TE) and repetition time (TR) or prepulses can be reconstructed in postprocessing. This would make the application of MR more similar to the CT approach (i.e., acquisition of a single quantification scan with subsequent generation of images with the desired contrast and orientation). This may significantly reduce patient scanning and planning time for MRI because instead of a performing a survey and reference scan, and acquiring all of the contrast images at different orientations, one only has to perform a single quantification scan. Unfortunately, most existing methods for measuring relaxation times and PD are limited by scan times that are clinically unacceptable. Several scans are generally required to measure both T 1 and T 2 , or T* 2 and the parameters might even depend on each other. Multiple images increase the chance of misregistration. Moreover, fast methods often hav...

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Background & aims Only a fraction of IBS patients show increased perceptual sensitivity to rectal distension, suggesting possible differences in processing and/or modulation of visceral afferent signals within this group. The aim was to identify brain mechanisms which may underlie these perceptual differences. Methods 44 women with IBS and 20 female healthy control subjects (HCs) were included. Symptom severity in IBS was determined by Severity Scoring System (IBS-SSS). Anxiety and depression symptoms were assessed using the Hospital anxiety & depression score (HAD). Blood oxygen level dependent (BOLD) signals were measured by functional Magnetic Resonance Imaging (fMRI) during expectation and delivery of high (45mmHg) and low (15mmHg) intensity rectal distensions. Perception thresholds to rectal distension were determined in the scanner. Brain imaging data from 18 normosensitive, 15 hypersensitive IBS patients and 18 HCs were compared. Results were reported significant if peak p-value ≤ 0.05 with family wise error correction in regions of interest. Results The two IBS subgroups were similar in age, symptom duration, psychological symptoms and IBS symptom severity. While brain responses to distension were similar in normosensitive patients and HCs, hypersensitive IBS demonstrated greater activation of insula and reduced deactivation in pregenual anterior cingulate cortex during noxious rectal distensions, compared to both HCs and normosensitive IBS. During expectation of rectal distension, normosensitive IBS had more activation in right hippocampus than HCs. Conclusions Despite similarities in symptoms, hyper- and normosensitive IBS patients differed substantially in cerebral response to standardized rectal distensions and their expectation, consistent with differences in ascending visceral afferent input.

BackgroundThere is a need for clinically useful biomarkers of disease activity in clinically isolated syndrome (CIS) and relapsing remitting MS (RRMS). The aim of this study was to assess the correlation between neurofilament light chain (NFL) in cerebrospinal fluid (CSF) and serum and the relationship between NFL and other biomarkers, subsequent disease activity, and brain volume loss in CIS and RRMS.MethodsA panel of neurodegenerative and neuroinflammatory markers were analyzed in repeated CSF samples from 41 patients with CIS or RRMS in a prospective longitudinal cohort study and from 22 healthy controls. NFL in serum was analyzed using a single-molecule array (Simoa) method. “No evidence of disease activity-3” (NEDA-3) status and brain volume (brain parenchymal fraction calculated using SyMRI®) were recorded during 4 years of follow-up.ResultsNFL levels in CSF and serum correlated significantly (all samples, n = 63, r 0.74, p < 0.001), but CSF-NFL showed an overall stronger association profile with NEDA-3 status, new T2 lesions, and brain volume loss. CSF-NFL was associated with both new T2 lesions and brain volume loss during follow-up, whereas CSF-CHI3L1 was associated mainly with brain volume loss and CXCL1, CXCL10, CXCL13, CCL22, and MMP-9 were associated mainly with new T2 lesions.ConclusionsSerum and CSF levels of NFL correlate, but CSF-NFL predicts and reflects disease activity better than S-NFL. CSF-NFL levels are associated with both new T2 lesions and brain volume loss. Our findings further add to the accumulating evidence that CSF-NFL is a clinically useful biomarker in CIS and RRMS and should be considered in the expanding NEDA concept. CSF-CXCL10 and CSF-CSF-CHI3L1 are potential markers of disease activity and brain volume loss, respectively.Electronic supplementary materialThe online version of this article (10.1186/s12974-018-1249-7) contains supplementary material, which is available to authorized users.

A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t‐tests, F‐tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated. Magn Reson Med 45:323–330, 2001. © 2001 Wiley‐Liss, Inc.

Background: Conventional magnetic resonance imaging (MRI) has relatively long scan

The aim of this study was to present a model that uses multi-parametric quantitative MRI to estimate the presence of myelin and edema in the brain. The model relates simultaneous measurement of R1 and R2 relaxation rates and proton density to four partial volume compartments, consisting of myelin partial volume, cellular partial volume, free water partial volume, and excess parenchymal water partial volume. The model parameters were obtained using spatially normalized brain images of a group of 20 healthy controls. The pathological brain was modeled in terms of the reduction of myelin content and presence of excess parenchymal water, which indicates the degree of edema. The method was tested on spatially normalized brain images of a group of 20 age-matched multiple sclerosis (MS) patients. Clear differences were observed with respect to the healthy controls: the MS group had a 79 mL smaller brain volume (1069 vs. 1148 mL), a 38 mL smaller myelin volume (119 vs. 157 mL), and a 21 mL larger excess parenchymal water volume (78 vs. 57 mL). Template regions of interest of various brain structures indicated that the myelin partial volume in the MS group was 1.6 ± 1.5% lower for gray matter (GM) structures and 2.8 ± 1.0% lower for white matter (WM) structures. The excess parenchymal water partial volume was 9 ± 10% larger for GM and 5 ± 2% larger for WM. Manually placed ROIs indicated that the results using the template ROIs may have suffered from loss of anatomical detail due to the spatial normalization process. Examples of the application of the method on high-resolution images are provided for three individual subjects: a 45-year-old healthy subject, a 72-year-old healthy subject, and a 45-year-old MS patient. The observed results agreed with the expected behavior considering both age and disease. In conclusion, the proposed model may provide clinically important parameters, such as the total brain volume, degree of myelination, and degree of edema, based on a single qMRI acquisition with a clinically acceptable scan time.