Related Experiment Video
Updated: May 29, 2026
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
Published on: April 14, 2014
Evaluating and reducing the impact of white matter lesions on brain volume measurements
Marco Battaglini1, Mark Jenkinson, Nicola De Stefano
1Department of Neurological and Behavioral Sciences, University of Siena, Italy.
Abstract:
MR-based measurements of brain volumes may be affected by the presence of white matter (WM) lesions. Here, we assessed how and to what extent this may happen for WM lesions of various sizes and intensities. After inserting WM lesions of different sizes and intensities into T1-W brain images of healthy subjects, we assessed the effect on two widely used automatic methods for brain volume measurement such as SIENAX (segmentation-based) and SIENA (registration-based). To explore the relevance of partial volume (PV) estimation, we performed the experiments with two different PV models, implemented by the same segmentation algorithm (FAST) of SIENAX and SIENA. Finally, we tested potential solutions to this issue. The presence of WM lesions did not bias measurements for registration-based method such as SIENA. By contrast, the presence of WM lesions affected segmentation-based brain volume measurements such as SIENAx. The misclassification of both gray matter (GM) and WM volumes varied considerably with lesion size and intensity, especially when the lesion intensity was similar to that of the GM/WM interface. The extent to which the presence of WM lesions could affect tissue-class measures was clearly driven by the PV modeling used, with the mixel-type PV model giving a lower error in the presence of WM lesions. The tissue misclassification due to WM lesions was still present when they were masked out. By contrast, refilling the lesions with intensities matching the surrounding normal-appearing WM ensured accurate tissue-class measurements and thus represents a promising approach for accurate tissue classification and brain volume measurements.
Insights
White matter lesions can impact brain volume measurements. Refilling lesions with normal-appearing white matter intensity ensures accurate segmentation-based brain volume analysis, improving diagnostic reliability.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Brain Anatomy
Background:
- Magnetic resonance (MR)-based brain volume measurements are crucial for diagnosing neurological conditions.
- White matter (WM) lesions can introduce inaccuracies in these measurements.
- Understanding the impact of lesion characteristics on automated analysis is essential.
Purpose of the Study:
- To evaluate how WM lesions of varying sizes and intensities affect automated brain volume measurement methods.
- To compare the performance of segmentation-based (SIENAX) and registration-based (SIENA) approaches.
- To assess the role of partial volume (PV) modeling in mitigating lesion-induced errors.
Main Methods:
- Simulated WM lesions of diverse sizes and intensities were introduced into T1-weighted brain MR images.
- The effects on SIENAX and SIENA brain volume quantification were assessed.
- Experiments were conducted using two different PV models within the FAST segmentation algorithm.
Main Results:
- SIENA (registration-based) measurements remained unbiased by WM lesions.
- SIENAX (segmentation-based) measurements were significantly affected, with misclassification varying by lesion size and intensity.
- Mixel-type PV modeling reduced errors, but refilling lesions with normal-appearing WM intensity yielded the most accurate results.
Conclusions:
- Segmentation-based brain volume analysis is susceptible to WM lesion artifacts.
- Partial volume modeling and lesion refilling strategies can improve accuracy.
- Lesion refilling offers a promising solution for reliable tissue classification and brain volume measurement in the presence of WM lesions.
