Methodological improvements in voxel-based analysis of diffusion tensor images: applications to study the impact of apolipoprotein E on white matter integrity.

PURPOSE: To identify regional differences in apparent diffusion coefficient (ADC) and fractional anisotropy (FA) using customized preprocessing before voxel-based analysis (VBA) in 14 normal subjects with the specific genes that decrease (apolipoprotein [APO] E ε2) and that increase (APOE ε4) the risk of Alzheimer's disease. MATERIALS AND METHODS: Diffusion tensor images (DTI) acquired at 1.5 Tesla were denoised with a total variation tensor regularization algorithm before affine and nonlinear registration to generate a common reference frame for the image volumes of all subjects. Anisotropic and isotropic smoothing with varying kernel sizes was applied to the aligned data before VBA to determine regional differences between cohorts segregated by allele status. RESULTS: VBA on the denoised tensor data identified regions of reduced FA in APOE ε4 compared with the APOE ε2 healthy older carriers. The most consistent results were obtained using the denoised tensor and anisotropic smoothing before statistical testing. In contrast, isotropic smoothing identified regional differences for small filter sizes alone, emphasizing that this method introduces bias in FA values for higher kernel sizes. CONCLUSION: Voxel-based DTI analysis can be performed on low signal to noise ratio images to detect subtle regional differences in cohorts using the proposed preprocessing techniques.

Initial results on development and application of statistical atlas of femoral cartilage in osteoarthritis to determine sex differences in structure: data from the Osteoarthritis Initiative.

PURPOSE: To create an average atlas of knee femoral cartilage morphology, to apply the atlas for quantitative assessment of osteoarthritis (OA), and to study localized sex differences. MATERIALS AND METHODS: High-resolution 3D magnetic resonance imaging (MRI) data of the knee cartilage collected at 3 T as part of the Osteoarthritis Initiative (OAI) were used. An atlas was created based on images from 30 male Caucasian high-risk subjects with no symptomatic OA at baseline. A female cohort of age- and disease-matched Caucasian subjects was also selected from the OAI database. The Jacobian determinant was calculated from the deformation vector fields that nonlinearly registered each subject to the atlas. Statistical analysis based on the general linear model was used to test for regions of significant differences in the Jacobian values between the two cohorts. RESULTS: The average Jacobian was larger in women (1.2 ± 0.078) than in men (1.08 ± 0.097), showing that after global scaling to the male template, the female cartilage was thicker in most regions. Regions showing significant structural differences include the medial weight bearing region, the trochlear (femoral) side of the patellofemoral compartment, and the lateral posterior condyle. CONCLUSION: Sex-based differences in cartilage structure were localized using tensor based morphometry in a cohort of high-risk subjects.

Morphological atlases of knee cartilage: shape indices to analyze cartilage degradation in osteoarthritic and non-osteoarthritic population.

3D magnetic resonance imaging of the articular cartilage allows accurate morphological assessment of the cartilage with relevance for identifying osteoarthritis (OA) status and to monitor progression and response to treatment. We propose the creation of morphological atlases of the cartilage using normal subjects segregated by age, sex, and gender. These atlases capture the variation of shape in normal subjects and are then used to classify new imaging studies as belonging to ;normal (asymptomatic of OA)' or ;abnormal (symptomatic of OA)'. The classification is performed by (i) analysis of the 3D deformation field required to move voxels to their corresponding locations in the atlas. Deformations beyond +/-2SD of normal variations constitute regions with large morphological changes; (ii) generating active shape models from the normal subject data and using the shape coefficients to classify cartilage morphology. The methodology is evaluated with an atlas of 20 normal subjects in one sub-type and testing the classification potential with 3 subjects symptomatic and 3 subjects asymptomatic of OA.

Texture measure from low resolution MR images to determine trabecular bone integrity in osteoporosis.

Bone architectural information can be derived from structural indices calculated from high resolution magnetic resonance (MR) images. However, high resolution scans are time consuming and prone to motion artifacts and hence are not routinely feasible. The purpose of this study is to determine if a correlation exists between 3D structural indices calculated from high-resolution MR images and 3D co-occurrence texture features calculated from lower resolution MR images. Regression analysis indicates a strong correlation between the structural indices and the texture features. This study highlights the potential of using surrogate texture markers extracted from readily acquired clinical MR images to quantify bone architecture, circumventing the need for high resolution MR imaging.

MR atlas for articular cartilage morphology: potential to detect shape differences.

An atlas of the cartilage was created using free form transformation of MR images of the cartilage from 20 subjects. The deformation required to move each voxel to its corresponding location in the atlas is used to determine the differences in shape between cartilages of subjects in a population. Based on these active shape models, it is possible to localize regions of high morphological variance in population cohorts. The atlas, reported here, is based on 20 male subjects; ten symptomatic of arthritis and ten asymptomatic. The active shape models based on this atlas show regions of high morphological variance corresponding to cartilage thinning in the arthritic group. This method has the potential to differentiate between normal and arthritic population groups by detecting subtle morphological changes in articular cartilage.

Automated image processing and analysis of cartilage MRI: enabling technology for data mining applied to osteoarthritis.

Osteoarthritis (OA) is a heterogeneous and multi-factorial disease characterized by the progressive loss of articular cartilage. Magnetic Resonance Imaging has been established as an accurate technique to assess cartilage damage through both cartilage morphology (volume and thickness) and cartilage water mobility (Spin-lattice relaxation, T2). The Osteoarthritis Initiative, OAI, is a large scale serial assessment of subjects at different stages of OA including those with pre-clinical symptoms. The electronic availability of the comprehensive data collected as part of the initiative provides an unprecedented opportunity to discover new relationships in complex diseases such as OA. However, imaging data, which provides the most accurate non-invasive assessment of OA, is not directly amenable for data mining. Changes in morphometry and relaxivity with OA disease are both complex and subtle, making manual methods extremely difficult. This chapter focuses on the image analysis techniques to automatically localize the differences in morphometry and relaxivity changes in different population sub-groups (normal and OA subjects segregated by age, gender, and race). The image analysis infrastructure will enable automatic extraction of cartilage features at the voxel level; the ultimate goal is to integrate this infrastructure to discover relationships between the image findings and other clinical features.