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.

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

We present a fully automated technique to segment lesions from multimodal brain MRIs of patients with Multiple Sclerosis. We describe an adapted Markov Random Field that uses intensity at every voxel, its neighbourhood intensity difference information and neighbouring voxel class information to infer voxel labels at every voxel. We test our technique on 25 real, clinical MS volumes evaluated by five experts. Our method outperforms two state of the art methods: one an outlier based MRF technique and the other a hybrid Bayesian-MRF technique both qualitatively and according to the Dice similarity coefficients and the number of present negative lesions.

Accurate and precise identification of multiple sclerosis (MS) lesions in longitudinal MRI is important for monitoring disease progression and for assessing treatment effects. We present a probabilistic framework to automatically detect new, enlarging and resolving lesions in longitudinal scans of MS patients based on multimodal subtraction magnetic resonance (MR) images. Our Bayesian framework overcomes registration artifact by explicitly modeling the variability in the difference images, the tissue transitions, and the neighbourhood classes in the form of likelihoods, and by embedding a classification of a reference scan as a prior. Our method was evaluated on (a) a scan-rescan data set consisting of 3 MS patients and (b) a multicenter clinical data set consisting of 212 scans from 89 RRMS (relapsing-remitting MS) patients. The proposed method is shown to identify MS lesions in longitudinal MRI with a high degree of precision while remaining sensitive to lesion activity.

“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.

Tissue intensity standardization is an important preprocessing step in the study and analysis of Magnetic Resonance Images (MRI) of human brain. Sources of variations in the intensity ranges across different MRI volumes, even after intensity inhomogeneity correction, can result from heterogeneity of data due to difference in scanners, presence of Multiple Sclerosis (MS) lesions and the stage of disease progression in the brain. As a result, intensity normalization plays a significant role in standardizing the tissue intensity ranges across MRI volumes on which most automatic image analysis methods base their distributional assumptions. The method of Nyul et. al. [3] has become a widely used standard for intensity normalization. However, an extensive validation of this approach on multi-site multi-scanner data in the presence of MS has yet not been performed. We aim to undertake this validation in this work and show the effectiveness of this procedure on standardizing tissue intensities in the MRI volumes.

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.

Objective To validate the use of the magnetization transfer ratio (MTR) as a practical imaging marker of demyelination and remyelination in acute multiple sclerosis lesions. Design Case study. Setting University hospital multiple sclerosis clinic. Patients Six patients with relapsing-remitting multiple sclerosis and acute gadolinium-enhancing lesions were studied serially using a quantitative magnetization transfer examination. Main Outcome Measures Changes in the water content and macromolecular content, a marker of myelin content that, unlike MTR, is not affected by changes in water content (edema) associated with acute inflammation, and changes in MTR of lesions. Results Both the macromolecular content and MTR were lower than normal in acute lesions and recovered over several months. The decrease in macromolecular content relative to contralateral normal-appearing white matter was greater than the decrease in MTR (0.46 vs 0.75 at the time of gadolinium enhancement), likely because edema in the acute lesion increased the T1 relaxation time of water and attenuated the decrease in MTR. Nevertheless, there was still a strong correlation between changes in the relative MTR and macromolecular content (R2 = 0.70;P < .001). Conclusion Our data support the use of MTR as a practical marker of demyelination and remyelination, even in acute lesions where decreases in MTR are attenuated because of the effects of edema.

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.

We evaluated the incidence, volume, and spatial distribution of T2-weighted magnetic resonance imaging lesions in 58 children with clinically isolated syndromes at risk for multiple sclerosis compared with 58 adults with relapsing-remitting multiple sclerosis. Pediatric patients with clinically isolated syndromes who had brain lesions had supratentorial lesion volumes similar to adult multiple sclerosis patients, but greater infratentorial lesion volumes (p < 0.009), particularly in the pons of male patients. The predilection for infratentorial lesions the pediatric patients with clinically isolated syndromes may reflect immunological differences or differences in myelin, possibly related to the caudorostral temporal gradient in myelin maturation.

We present a method to detect intensity changes in longitudinal volumetric MRI data from patients with multiple sclerosis (MS). Preprocessing includes spatial and intensity normalization. The intra-subject intensity normalization is achieved using a polynomial least trimmed squares method to match the histograms of all images in the series. Viewing the detection of disease activity in MRI as a change-point problem, we present two statistical tests and apply them to a patient's series of grey-level images on a voxel-by-voxel basis. Results are compared with manual lesion segmentation for one MS patient scanned approximately every 5 months for 5 years. Results are also shown for 12 MS patients with 30 monthly scans.

The clinical course of multiple sclerosis (MS) is highly variable ranging from benign to aggressive, and is difficult to predict. Since magnetization transfer (MT) imaging can detect focal abnormalities in normal-appearing white matter (NAWM) before the appearance of lesions on conventional MRI, we hypothesized that changes in MT might be able to predict the clinical evolution of MS. We assessed MR data from MS patients who were subsequently followed clinically for 5 years. We computed the mean MT ratio (MTr) in gray matter, in lesions identified on T2-weighted MRI, and in NAWM, as well as in a thick central brain slice for each patient. Patients were divided into stable and worsening groups according to their change in Expanded Disability Status Scale (EDSS) scores over 5 years. We calculated the sensitivity, specificity, predictive value, and odds ratio of the baseline MTr measures in order to assess their prognostic utility. We found significant differences in baseline MTr values in NAWM (p = 0.005) and brain slice (p = 0.03) between clinically stable and worsening MS patients. When these MTr values were compared with changes in EDSS over 5 years, a strong correlation was found between the EDSS changes and MTr values in both NAWM (SRCC = −0.76, p < 0.001) and in the brain slice (SRCC = 0.59, p = 0.01). Baseline NAWM MTr correctly predicted clinical evolution in 15/18 patients (1 false positive and 2 false negatives), yielding a positive predictive value of 77.78 %, a negative predictive value of 88.89 %, and an odds ratio of 28. The relationship between 5-year changes in EDSS and MTr values in T2 weighted MRI lesions was weaker (SRCC = −0.43, p = 0.07). Our data support the notion that the quantification of MTr in the NAWM can predict the clinical evolution of MS. Lower MTr values predict poorer long-term clinical outcome. Abnormalities of MTr values in the NAWM are more relevant to the development of future patient disability than those in the T2-weighted MRI lesions.