MRI T2 Hypointensities in basal ganglia of premanifest Huntington's disease.

Increased iron levels have been demonstrated in the basal ganglia of manifest Huntington's disease (HD). An excess in iron accumulation correlates with MRI T2-weighted hypointensity. Determination of the amount of hypointensities in the basal ganglia in the premanifest phase of HD may give more insight in the role of iron in the pathogenesis of HD. Therefore, the present study assessed whether the degree of hypointensities on T2-w MRI in the basal ganglia of premanifest gene carriers differs from non-carriers. Seventeen HD gene carriers without clinical motor signs and 15 non-carriers underwent clinical evaluation and MRI scanning. The amount of T2-w hypointensities was determined using a computer-assisted quantitative method that classified each pixel in the basal ganglia as hypointense or not, resulting in a total of hypointense pixels for each individual. Carriers showed an increased amount of hypointensities in the basal ganglia compared to non-carriers. More hypointensities were furthermore associated with a higher UHDRS total motor score, a longer CAG repeat length and a greater probability of developing symptoms within 5 years. We concluded that the increased amount of hypointensities in the basal ganglia of premanifest carriers of the HD gene may reflect excessive iron deposition and a role for iron in the neuropathology of HD. Furthermore, this phenomenon is associated with clinical and biological disease characteristics. An increased amount of hypointensities on T2-w MRI in the basal ganglia may be considered a biomarker for HD.

Local structure-based region-of-interest retrieval in brain MR images.

The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been developing computerized methods to help the medical expert, these efforts have primarily emphasized to improve the effectiveness in single patient data, such as computing a brain lesion size. However, patient-to-patient comparison that should help improve diagnosis and therapy has not received much attention. To this effect, this paper introduces a fast and robust region-of-interest retrieval method for brain MR images. We make the following various contributions to the domains of brain MR image analysis, and search and retrieval system: 1) we show the potential and robustness of local structure information in the search and retrieval of brain MR images; 2) we provide analysis of two complementary features, local binary patterns (LBPs) and Kanade-Lucas-Tomasi feature points, and their comparison with a baseline method; 3) we show that incorporating spatial context in the features substantially improves accuracy; and 4) we automatically extract dominant LBPs and demonstrate their effectiveness relative to the conventional LBP approach. Comprehensive experiments on real and simulated datasets revealed that dominant LBPs with spatial context is robust to geometric deformations and intensity variations, and have high accuracy and speed even in pathological cases. The proposed method can not only aid the medical expert in disease diagnosis, or be used in scout (localizer) scans for optimization of acquisition parameters, but also supports low-power handheld devices.

Robustness of local binary patterns in brain MR image analysis.

The aging population in developed countries has shifted considerable research attention to diseases related to age. Because age is one of the highest risk factors for neurodegenerative diseases, the need for automated brain image analysis has significantly increased. Magnetic Resonance Imaging (MRI) is a commonly used modality to image brain. MRI provides high tissue contrast; hence, the existing brain image analysis methods have often preferred the intensity information to others, such as texture. Recently, an easy-to-compute texture descriptor, Local Binary Pattern (LBP), has shown promise in various applications outside the medical field. In this paper, after extensive experiments, we show that rotation-invariant LBP is invariant to some common MRI artifacts that makes it possible to use it in various high-level brain MR image analysis applications.