Background: Brain volume loss measured from magnetic resonance imaging (MRI) is a marker of neurodegeneration and predictor of disability progression in MS, and is commonly used to assess drug efficacy at the group level in clinical trials. Whether measures of brain volume loss could be useful to help guide management of individual patients depends on the relative magnitude of the changes over a given interval to physiological and technical sources of variability. Goal: To understand the relative contributions of neurodegeneration vs. physiological and technical sources of variability to measurements of brain volume loss in individuals. Material and methods: Multiple T1-weighted 3D MPRAGE images were acquired from a healthy volunteer and MS patient over varying time intervals: 7 times on the first day (before breakfast at 7:30AM and then every 2 h for 12 h), each day for the next 6 working days, and 6 times over the remainder of the year, on 2 Siemens MRI scanners: 1.5T Sonata (S1) and 3.0T TIM Trio (S2). Scan-reposition-rescan data were acquired on S2 for daily, monthly and 1-year visits. Percent brain volume change (PBVC) was measured from baseline to each follow-up scan using FSL/SIENA. We estimated the effect of physiologic fluctuations on brain volume using linear regression of the PBVC values over hourly and daily intervals. The magnitude of the physiological effect was estimated by comparing the root-mean-square error (RMSE) of the regression of all the data points relative to the regression line, for the hourly scans vs the daily scans. Variance due to technical sources was assessed as the RMSE of the regression over time using the intracranial volume as a reference. Results: The RMSE of PBVC over 12 h, for both scanners combined, ("Hours", 0.15%), was similar to the day-to-day variation over 1 week ("Days", 0.14%), and both were smaller than the RMS error over the year (0.21%). All of these variations, however, were smaller than the scan-reposition-rescan RMSE (0.32%). The variability of PBVC for the individual scanners followed the same trend. The standard error of the mean (SEM) for PBVC was 0.26 for 51, and 0.22 for S2. From these values, we computed the minimum detectable change (MDC) to be 0.7% on 51 and 0.6% on S2. The location of the brain along the z-axis of the magnet inversely correlated with brain volume change for hourly and daily brain volume fluctuations (p < 0.01). Conclusion: Consistent diurnal brain volume fluctuations attributable to physiological shifts were not detectable in this small study. Technical sources of variation dominate measured changes in brain volume in individuals until the volume loss exceeds around 0.6-0.7%. Reliable interpretation of measured brain volume changes as pathological (greater than normal aging) in individuals over 1 year requires changes in excess of about 1.1% (depending on the scanner). Reliable brain atrophy detection in an individual may be feasible if the rate of brain volume loss is large, or if the measurement interval is sufficiently long.

The in vivo detection of subpial cortical gray matter lesions in multiple sclerosis is challenging. We quantified the spatial extent of subpial decreases in the magnetization transfer ratio (MTR) of cortical gray matter in subjects with multiple sclerosis, as such reductions may indicate regions of cortical demyelination. We exploited the unique geometry of cortical lesions by using two-dimensional parametric surface models of the cortex instead of traditional three-dimensional voxel-wise analyses. MTR images were mapped onto intermediate surfaces between the pial and white matter surfaces and were used to compute differences between secondary-progressive MS (n = 12), relapsing-remitting MS (n = 12), and normal control (n = 12) groups as well as between each individual patient and the normal controls. We identified large regions of significantly reduced cortical MTR in secondary-progressive patients when compared with normal controls. We also identified large regions of reduced cortical MTR in 11 individual patients (8 secondary-progressive, 3 relapsing-remitting). The secondary-progressive patients showed larger areas of abnormally low MTR compared with relapsing-remitting patients both at the group level and on an individual basis. The spatial distributions of abnormal MTR preferentially involved cingulate cortex, insula, and the depths of sulci, in agreement with pathological descriptions of subpial gray matter lesion distribution. These findings suggest that our method is a plausible in vivo imaging technique for quantifying subpial cortical demyelinating lesions in patients with multiple sclerosis and, furthermore, can be applied at the typical clinical field strength of 1.5 T.

BackgroundAcute paralytic poliomyelitis is associated with encephalitis. Early brain inflammation may produce permanent neuronal injury with brain atrophy, which may result in symptoms such as fatigue. Brain volume has not been assessed in postpoliomyelitis syndrome (PPS).ObjectiveTo determine whether brain volume is decreased compared with that in normal controls, and whether brain volume is associated with fatigue in patients with PPS.DesignA cross-sectional study.SettingTertiary university-affiliated hospital postpolio and multiple sclerosis (MS) clinics.ParticipantsForty-nine ambulatory patients with PPS, 28 normal controls, and 53 ambulatory patients with MS.MethodsWe studied the brains of all study subjects with magnetic resonance imaging by using a 1.5 T Siemens Sonata machine. The subjects completed the Fatigue Severity Scale. Multivariable linear regression models were computed to evaluate the contribution of PPS and MS compared with controls to explain brain volume.Main Outcome MeasurementsNormalized brain volume (NBV) was assessed with the automated program Structured Image Evaluation, using Normalization, of Atrophy method from the acquired magnetic resonance images. This method may miss brainstem atrophy.ResultsTechnically adequate NBV measurements were available for 42 patients with PPS, 27 controls, and 49 patients with MS. The mean (standard deviation) age was 60.9 ± 7.6 years for patients with PPS, 47.0 ± 14.6 years for controls, and 46.2 ± 9.4 years for patients with MS. In a multivariable model adjusted for age and gender, NBV was not significantly different in patients with PPS compared with that in controls (P = .28). As expected, when using a similar model for patients with MS, NBV was significantly decreased compared with that in controls (P = .006). There was no significant association between NBV and fatigue in subjects with PPS (Spearman ρ = 0.23; P = .19).ConclusionsNo significant whole-brain atrophy was found, and no association of brain volume with fatigue in PPS. Brain atrophy was confirmed in MS. It is possible that brainstem atrophy was not recognized by this study.

Multiple sclerosis (MS), the most frequent demyelinating disease, is characterized by a variable disease course. The majority of patients starts with relapsing remitting (RR) disease; approximately 50–60% of these patients progress to secondary progressive (SP) disease. Only about 15% of the patients develop a progressive disease course from onset, termed primary progressive multiple sclerosis (PPMS); the underlying pathogenic mechanisms responsible for onset of the disease with either PPMS or relapsing remitting multiple sclerosis (RRMS) are unknown. Patients with PPMS do not show a female predominance and usually have a later onset of disease compared to patients with RRMS. Monozygous twins can be concordant or discordant for disease courses indicating that the disease course is not only genetically determined. Primary progressive multiple sclerosis and secondary progressive multiple sclerosis (SPMS) share many similarities in imaging and pathological findings. Differences observed among the different disease courses are more of a quantitative than qualitative nature suggesting that the different phenotypes are part of a disease spectrum modulated by individual genetic predisposition and environmental influences. In this review, we summarize the knowledge regarding the clinical, epidemiological, imaging, and pathological characteristics of PPMS and compare those characteristics with RRMS and SPMS.

Objective: To better characterize the relationship between cerebral white matter lesion load (CWM-LL) and clinical disability by (1) covering the entire range of the Kurtzke Expanded Disability Status Scale (EDSS), (2) minimizing nonbiological sources of variability, and (3) increasing pathologic specificity by studying CWM lesions that are hypointense on T1-weighted magnetic resonance imaging.Design: Cross-sectional, retrospective study.Setting: Hospital-based multiple sclerosis (MS) clinic.Patients: A total of 110 patients with untreated MS were recruited and studied from June 1, 1997, through June 30, 2003.Main Outcome Measures: Cube-rooted CWM-LL and EDSS-measured clinical disability scores.Results: We found a large, nonplateauing relationship between cube-rooted CWM-LL and concurrent EDSS scores, more so for T1-hypointense than T2-hyperintense lesions (r=0.619 vs 0.548). Correlations between the EDSS scores and CWM-LL diminished when, as typically done in clinical trials, only those patients with EDSS scores of 0 to 6.0 were studied(n=92; r=0.523 for T1-hypointense lesions and r=0.457 for T2-hyperintense lesions); more important, a series of boot-strapped correlations suggested that this decrease was not simply due to smaller sample size, and these relationships remained even after correcting for disease duration.Conclusion: A large, nonplateauing relationship exists between CWM-LL and EDSS-measured clinical disability when patients with MS are studied to examine the entire range of disability, minimize nonbiological sources of variability, and increase pathologic specificity.

PM&RVolume 4, Issue 10S p. S281-S281 2012 AAPM&R Annual Assembly Abstract Poster 271 Post-Poliomyelitis Syndrome Is Not Associated With Brain Atrophy Daria A. Trojan MD, Daria A. Trojan MD Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorDouglas L. Arnold MD, Douglas L. Arnold MD Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorZografos Caramanos MSc, Zografos Caramanos MSc Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorSimon Francis MSc, Simon Francis MSc Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorSridar Narayanan PhD, Sridar Narayanan PhD Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorAnn Robinson RN, Ann Robinson RN Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this author Daria A. Trojan MD, Daria A. Trojan MD Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorDouglas L. Arnold MD, Douglas L. Arnold MD Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorZografos Caramanos MSc, Zografos Caramanos MSc Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorSimon Francis MSc, Simon Francis MSc Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorSridar Narayanan PhD, Sridar Narayanan PhD Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this authorAnn Robinson RN, Ann Robinson RN Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, CanadaSearch for more papers by this author First published: 23 October 2012 https://doi.org/10.1016/j.pmrj.2012.09.884Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume4, Issue10SOctober 2012Pages S281-S281 RelatedInformation

Reducing measurement variability in MRI-based morphometric analysis of human brain structures will increase statistical power to detect changes between groups and longitudinally over time in individual subjects. One source of measurement error in anatomical MR is magnetic field gradient-induced geometric distortion. This work proposes a method to characterize and compensate for these distortions using a novel image processing technique relying on the image acquisition of a phantom with known geometrical dimensions, without the need to acquire the magnetic field mapping. The method is not specific to any particular shape of the phantom, as long as it provides enough coverage of the volume of interest and enough structure to densely sample the distortion field. The distortions are expressed in terms of spherical harmonic functions, which are then used to define the distortion correction field for the volume of interest. Accuracy of the distortion measurement was evaluated using numerical simulation and reproducibility was estimated using multiple scans of the phantom in the same scanner. Finally, scan-rescan experiments with nine healthy subjects demonstrated that 90% of the distortion (in terms of local volume change) can be corrected with this technique.

“Simulated between-scan Z-shifts of 5 mm resulted in only very small absolute errors throughout the range of simulations (Fig. 10A); interestingly, these increased slightly as the center of the simulated Z-shifts was located further into the magnet (and farther away frommagnet isocenter), and they decreased slightly as the center of the simulated Z-shifts was located further out of the magnet (and closer to magnet isocenter).”

Several methods exist and are frequently used to quantify grey matter (GM) atrophy in multiple sclerosis (MS). Fundamental to all available techniques is the accurate segmentation of GM in the brain, a difficult task confounded even further by the pathology present in the brains of MS patients. In this paper, we examine the segmentations of six different automated techniques and compare them to a manually defined reference standard. Results demonstrate that, although the algorithms perform similarly to manual segmentations of cortical GM, severe shortcomings are present in the segmentation of deep GM structures. This deficiency is particularly relevant given the current interest in the role of GM in MS and the numerous reports of atrophy in deep GM structures.

Background: Assessing the impact of glioma location on prognosis remains elusive. We approached the problem using multivoxel proton magnetic resonance spectroscopic imaging (1H-MRSI) to define a tumor “metabolic epicenter”, and examined the relationship of metabolic epicenter location to survival and histopathological grade. Methods: We studied 54 consecutive patients with a supratentorial glioma (astrocytoma or oligodendroglioma, WHO grades II-IV). The metabolic epicenter in each tumor was defined as the 1H-MRSI voxel containing maximum intra-tumoral choline on preoperative imaging. Tumor location was considered the X-Y-Z coordinate position, in a standardized stereotactic space, of the metabolic epicenter. Correlation between epicenter location and survival or grade was assessed. Results: Metabolic epicenter location correlated significantly with patient survival for all tumors (r2 = 0.30, p = 0.0002) and astrocytomas alone (r2 = 0.32, p = 0.005). A predictive model based on both metabolic epicenter location and histopathological grade accounted for 70% of the variability in survival, substantially improving on histology alone to predict survival. Location also correlated significantly with grade (r2 = 0.25, p = 0.001): higher grade tumors had a metabolic epicenter closer to the midpoint of the brain. Conclusions: The concept of the metabolic epicenter eliminates several problems related to existing methods of classifying glioma location. The location of the metabolic epicenter is strongly correlated with overall survival and histopathological grade, suggesting that it reflects biological factors underlying glioma growth and malignant dedifferentiation. These findings may be clinically relevant to predicting patterns of local glioma recurrence, and in planning resective surgery or radiotherapy.

Accurate, reliable quantification of brain volume change (BVC) in AD patients is important. This can be affected, however, by local-volume changes related to MRI-scanner-gradient nonlinearities and inconsistent positioning of subjects within the scanner (particularly along the Z-axis). Furthermore, standard canthomeatal (CM) alignment results in the cerebrum being centered several cms away from isocenter (Fig-A). Use actual MRI data to quantify and correct the effect of Z-shift-associated gradient-distortion (GD) on SIENA-generated measures of %-BVC (PBVC). High-resolution, volumetric T1-weighted MRI data were acquired in 9 normal adults on a Siemens Sonata 1.5 T scanner after: (i) CM-alignment (CM, Fig-A), (ii) moving the scanner bed 50-mm out of the magnet (Z50), and (iii) accurate-as-possible CM repositioning (Repos). A GD field was generated using spherical harmonic expansion to map coordinates from an “ideal” coordinate system (a Lego-DUPLO® phantom) to the imaging coordinate system of the scanner (Fig-B). SIENA v2.5 was used to quantify the amount of PBVC observed between each subject's: (i) CM and Z50 scans, and (ii) CM and Repos scans; both before and after correcting for the observed GD. Relative to CM images, the mean (range) MRI-measured Z-shift was 4.3-mm (-9.0 to 21.1) for Repos images, and -49.2-mm (-50.9 to -48.4) for Z50 images. As shown in Fig-C, Repos-vs.-CM SIENAs had: (i) a median (mean) absolute-error (AE) of 0.15% (0.17%), precision similar to the original SIENA validation studies; and (ii) a max-AE of only 0.35%. Z50-vs.-CM SIENAs had: (i) a higher max-AE of 0.81%, and (ii) a significantly-higher mean-AE of 0.40% (p = 0.003). Correcting for GD: (i) reduced the Z50-vs.-CM max-AE to 0.23%; and (ii) significantly reduced the Z50-vs.-CM mean-AE to 0.15% (p = 0.001), which did not differ from the Repos-vs.-CM mean-AE (p = 0.969). As shown in Fig-D, a strong relationship was found between Z50-vs.-CM PBVC and CM-image Z-location (r = 0.81). Z-shift-associated GDs can have significant effects on observed SIENA-PBVC values, with Z-shifts of similar magnitude having different effects depending on where they center. Accordingly, inadvertent Z-shifts should be avoided or corrected. Consistently aligning the centers of the cerebrum and magnet may decrease the effects of GD on observed BVC.

Introduction: Evidence from pathological studies continues to underscore the significant involvement of cortical grey matter (GM) in multiple sclerosis (MS) [Stadelmann, C., et al., Curr Opin Neurol, 2008. 21(3):229-34]. Although some of this cortical pathology is now detectable as a result of advances in sequence development (e.g., double inversion recovery), contiguous areas of subpial demyelination, which are the most common subtype of cortical lesions sampled at autopsy, remain largely undetectable with conventional methods [Geurts JJ, et al., AJNR., 2005 26(3):572-7]. Magnetization transfer imaging has already been shown to be sensitive to changes in myelin content in white matter [Pike, G B, et al., Radiology, 2000. 215(3):824-30; Schmierer, K., et al., Ann Neurol, 2004. 56(3): 407-15]. We used a novel surface-based method to quantify the extent of subpial decreases in magnetization transfer ratio (MTR) in order to identify regions of cortical demyelination. Our method was applied to group data from MS patients and normal controls, as well as to data from individuals to identify areas that differed from the control group.

Precise and accurate quantification of whole-brain atrophy based on magnetic resonance imaging (MRI) data is an important goal in understanding the natural progression of neurodegenerative disorders such as Alzheimer's disease and multiple sclerosis. We found that inconsistent MRI positioning of subjects is common in typically acquired clinical trial data - particularly along the magnet's long (i.e., Z) axis. We also found that, if not corrected for, the gradient distortion effects associated with such Z-shifts can significantly decrease the accuracy and precision of MRI-derived measures of whole-brain atrophy - negative effects that increase in magnitude with (i) increases in the Z-distance between the brains to be compared and (ii) increases in the Z-distance from magnet isocenter of the center of the pair of brains to be compared. These gradient distortion effects can be reduced by accurate subject positioning, and they can also be corrected post hoc with the use of appropriately-generated gradient-distortion correction fields. We used a novel DUPLO-based phantom to develop a spherical-harmonics-based gradient distortion field that was used to (i) correct for observed Z-shift-associated gradient distortion effects on SIENA-generated measures of brain atrophy and (ii) simulate the gradient distortion effects that might be expected with a greater range of Z-shifts than those that we were able to acquire. Our results suggest that consistent alignment to magnet isocenter and/or correcting for the observed effects of gradient distortion should lead to more accurate and precise estimates of brain-related changes and, as a result, to increased statistical power in studies aimed at understanding the natural progression and the effective treatment of neurodegenerative disorders.