Fast semi-automated lesion demarcation in stroke.

Lesion-behaviour mapping analyses require the demarcation of the brain lesion on each (usually transverse) slice of the individual stroke patient's brain image. To date, this is generally thought to be most precise when done manually, which is, however, both time-consuming and potentially observer-dependent. Fully automated lesion demarcation methods have been developed to address these issues, but these are often not practicable in acute stroke research where for each patient only a single image modality is available and the available image modality differs over patients. In the current study, we evaluated a semi-automated lesion demarcation approach, the so-called Clusterize algorithm, in acute stroke patients scanned in a range of common image modalities. Our results suggest that, compared to the standard of manual lesion demarcation, the semi-automated Clusterize algorithm is capable of significantly speeding up lesion demarcation in the most commonly used image modalities, without loss of either lesion demarcation precision or lesion demarcation reproducibility. For the three investigated acute datasets (CT, DWI, T2FLAIR), containing a total of 44 patient images obtained in a regular clinical setting at patient admission, the reduction in processing time was on average 17.8 min per patient and this advantage increased with increasing lesion volume (up to 60 min per patient for the largest lesion volumes in our datasets). Additionally, our results suggest that performance of the Clusterize algorithm in a chronic dataset with 11 T1 images was comparable to its performance in the acute datasets. We thus advocate the use of the Clusterize algorithm, integrated into a simple, freely available SPM toolbox, for the precise, reliable and fast preparation of imaging data for lesion-behaviour mapping analyses.

Cerebral gray and white matter changes and clinical course in metachromatic leukodystrophy.

OBJECTIVE: Metachromatic leukodystrophy (MLD) is a rare metabolic disorder leading to demyelination and rapid neurologic deterioration. As therapeutic options evolve, it seems essential to understand and quantify progression of the natural disease. The aim of this study was to assess cerebral volumetric changes in children with MLD in comparison to normal controls and in relation to disease course. METHOD: Eighteen patients with late-infantile MLD and 42 typically developing children in the same age range (20-59 months) were analyzed in a cross-sectional study. Patients underwent detailed genetic, biochemical, electrophysiologic, and clinical characterization. Cerebral gray matter (GM) and white matter (WM) volumes were assessed by multispectral segmentation of T1- and T2-weighted MRI. In addition, the demyelinated WM (demyelination load) was automatically quantified in T2-weighted images of the patients, and analyzed in relation to the clinical course. RESULTS: WM volumes of patients did not differ from controls, although their growth curves were slightly different. GM volumes of patients, however, were on average 10.7% (confidence interval 6.0%-14.9%, p < 0.001) below those of normally developing children. The demyelination load (corrected for total WM volume) increased with disease duration (p < 0.003) and motor deterioration (p < 0.001). CONCLUSION: GM volume in patients with MLD is reduced when compared with healthy controls, already at young age. This supports the notion that, beside demyelination, neuronal dysfunction caused by neuronal storage plays an additional role in the disease process. The demyelination load may be a useful noninvasive imaging marker for disease progression and may serve as reference for therapeutic intervention.

A semi-automatic algorithm for determining the demyelination load in metachromatic leukodystrophy.

RATIONALE AND OBJECTIVES: Metachromatic leukodystrophy is a lysosomal storage disorder leading to progressive demyelination of brain white matter. This is sensitively detected using magnetic resonance imaging. The volume of demyelination, the "demyelination load," could serve as a useful parameter for assessing both the natural course of the disease and treatment effects. The aim of this study was to develop and validate a semiautomated approach for determining the demyelination load to achieve reliable and time-efficient segmentation results. MATERIALS AND METHODS: The demyelination load was determined in 77 magnetic resonance imaging data sets from 35 patients both manually and semiautomatically. For manual segmentation, regarded as the gold standard, the software ITK-Snap was used. For semiautomatic segmentation, a new algorithm called Clusterize was developed and implemented in MATLAB, consisting of automatic iterative region growing followed by the interactive selection of clusters. Results were compared in terms of the obtained volumes, spatial overlap, and time taken to conduct the segmentation. RESULTS: Performance of the semiautomatic algorithm was excellent, with the volumes generated by the new algorithm showing good agreement with the ones generated by the gold standard (93.4 ± 45.5 vs 96.1 ± 49.0 mL, P = NS) with high spatial overlap (Dice's similarity coefficient = 0.7861 ± 0.0697). The semiautomatic algorithm was significantly faster than the gold standard (8.2 vs 27.0 min, P < .001). Intrarater and interrater reliability determined high reproducibility of the method. CONCLUSION: The demyelination load in metachromatic leukodystrophy can be determined in a time-efficient manner using a semiautomatic algorithm, showing high agreement with the current gold standard.