Quantification of Global Cerebral Atrophy in Multiple Sclerosis from 3T MRI Using SPM: The Role of Misclassification Errors

Abstract

PurposeWe tested the validity of a freely available segmentation pipeline to measure compartmental brain volumes from 3T MRI in patients with multiple sclerosis (MS). Our primary focus was methodological to explore the effect of segmentation corrections on the clinical relevance of the output metrics.MethodsThree-dimensional T1-weighted images were acquired to compare 61 MS patients to 30 age- and gender-matched normal controls (NC). We also tested the within patient MRI relationship to disability (eg, expanded disability status scale [EDSS] score) and cognition. Statistical parametric mapping v. 8 (SPM8)-derived gray matter (GMF), white matter (WMF), and total brain parenchyma fractions (BPF) were derived before and after correcting errors from T1 hypointense MS lesions and/or ineffective deep GM contouring.ResultsMS patients had lower GMF and BPF as compared to NC (P<.05). Cognitively impaired patients had lower BPF than cognitively preserved patients (P<.05). BPF was related to EDSS; BPF and GMF were related to disease duration (all P<.05). Errors caused bias in GMFs and WMFs but had no discernable influence on BPFs or any MRI-clinical associations.ConclusionsWe report the validity of a segmentation pipeline for the detection of MS-related brain atrophy with 3T MRI. Longitudinal studies are warranted to extend these results.

Whole‐brain atrophy assessed by proportional‐ versus registration‐based pipelines from 3TMRIin multiple sclerosis

“…We prospectively enrolled 61 patients with MS and 30 NC; part of the data from these subjects and the recruitment/collection procedures have been published previously (Dell'Oglio et al., 2015). In brief, inclusion criteria were: age 18–55, no significant medical comorbidities, no changes in disease‐modifying therapy in the 6 months prior to examination.…”

“…Complete neuropsychological evaluation methods are described previously (Dell'Oglio et al., 2015). In brief, this was based on the previously validated Minimal Assessment of Cognitive Function in MS (MACFIMS) battery (Benedict et al., 2006), which was administered by a clinical psychologist and her supervised research fellow.…”

“…All images were inspected for quality, and processed through two separate pipelines (Figure 1); BPF: as previously described (Dell'Oglio et al., 2015), raw MDEFT images were manually de‐skulled, aligned to the MNI152 template, intensity normalized using N3 nonparametric nonuniform parameters, and automatically segmented using the SPM8 (Statistical Parametric Mapping, http://www.fil.ion.ucl.ac.uk/spm/software/) unified segmentation model into GM, white matter (WM), and CSF volumes. Intracranial volume (ICV) was calculated as the sum of GM + WM + CSF.…”

“…Images then underwent automated segmentation and template normalization using SIENAX, (Smith et al., 2002) part of FSL (v5.0) (Smith et al., 2004) using a previously optimized brain extraction tool (BET) threshold of 0.2 (Chu et al., 2016). T2‐hyperintense lesion volumes were obtained by expert semiautomated segmentation with an edge‐finding tool based on local image intensity thresholds using JIM (v5) as previously published (Dell'Oglio et al., 2015); manual corrections were applied as needed (Ceccarelli et al., 2012). To determine if manual versus default (automated) deskulling would affect the results in SIENAX, we analyzed scans from three subjects using manually skull‐stripped images with a BET threshold of 0.01 (for maximal brain extraction) normalized with the original scaling factor from nonskull‐stripped data; however, this approach provided similar BPVs (within 20 ml versus the nonskull‐stripped extraction, mean ±  SD  = −5.66 ± 22.2, range: −19 to 20 ml).…”

“…The goal of this study was to compare the validity of two freely available widely used automated postprocessing algorithms for the assessment of normalized whole‐brain volume from 3T MRI. We examined patients with MS and normal controls (NC) using two methods: both proportional‐based [SPM8 to measure BPF (Dell'Oglio et al., 2015)] and registration‐based (SIENAX to measure BPV).…”