MRI Texture Analysis and Diffusion Tensor Imaging in Chronic Right Hemisphere Ischemic Stroke.

BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) is shown to reveal changes caused by cerebral infarction. The aim of this study is to reveal those changes also in the conventional magnetic resonance (MR) images using a quantitative image analysis method, texture analysis (TA). METHODS: Thirty patients who had suffered their first ever infarction located on the right hemisphere underwent DTI and conventional MRI studies in the chronic phase. DTI parameters fractional anisotropy and mean diffusivity, as well as four second-order texture parameters were calculated. Interhemispheric differences and correlations between DTI and TA parameters were evaluated. RESULTS: Our DTI findings supported earlier studies as fractional anisotropy values were lowered and mean diffusivity values elevated in the lesion site, and ipsilateral cerebral peduncle, thalamus, and centrum semiovale compared to the unaffected side. Textural homogeneity parameters showed lower and complexity parameters higher values in the lesion site and ipsilateral centrum semiovale compared to the contralateral hemisphere. Correlation between the two methods was found in ipsilateral mesencephalon. CONCLUSIONS: In addition to DTI method, TA could assist in revealing the changes caused by infarction, also outside the lesion site. Damaged areas were found more heterogeneous and random in texture compared to unaffected sites.

MR image texture in Parkinson's disease: a longitudinal study.

BACKGROUND: Few of the structural changes caused by Parkinson's disease (PD) are visible in magnetic resonance imaging (MRI) with visual inspection but there is a need for a method capable of observing the changes beyond the human eye. Texture analysis offers a technique that enables the quantification of the image gray-level patterns. PURPOSE: To investigate the value of quantitative image texture analysis method in diagnosis and follow-up of PD patients. MATERIAL AND METHODS: Twenty-six PD patients underwent MRI at baseline and after 2 years of follow-up. Four co-occurrence matrix-based texture parameters, describing the image homogeneity and complexity, were calculated within clinically interesting areas of the brain. In addition, correlations with clinical characteristics (Unified Parkinson's Disease Ranking Scales I-III and Mini-Mental State Examination score) along with a comparison to healthy controls were evaluated. RESULTS: Patients at baseline and healthy volunteers differed in their brain MR image textures mostly in the areas of substantia nigra pars compacta, dentate nucleus, and basilar pons. During the 2-year follow-up of the patients, textural differences appeared mainly in thalamus and corona radiata. Texture parameters in all the above mentioned areas were also found to be significantly related to clinical scores describing the severity of PD. CONCLUSION: Texture analysis offers a quantitative method for detecting structural changes in brain MR images. However, the protocol and repeatability of the method must be enhanced before possible clinical use.

Influence of exercise loading on magnetic resonance image texture of thigh soft tissues.

Adaptation to exercise training can affect bone marrow adiposity; muscle-fat distribution; and muscle volume, strength and architecture. The objective of this study was to identify exercise-load-associated differences in magnetic resonance image textures of thigh soft tissues between various athlete groups and non-athletes. Ninety female athletes representing five differently loading sport types (high impact, odd impact, high magnitude, repetitive low impact and repetitive non-impact), and 20 non-athletic clinically healthy female controls underwent magnetic resonance imaging. Five thigh muscles, subcutaneous fat and femoral bone marrow were analysed with co-occurrence matrix-based quantitative texture analysis at two anatomical levels of the dominant leg. Compared with the controls thigh muscle textures differed especially in high-impact and odd-impact exercise-loading groups. However, all sports appeared to modulate muscle textures to some extent. Fat tissue was found different among the low-impact group, and bone marrow was different in the high-impact group when compared to the controls. Exercise loading was associated with textural variation in magnetic resonance images of thigh soft tissues. Texture analysis proved a potential method for detecting apparent structural differences in the muscle, fat and bone marrow.

MRI texture analysis of femoral neck: Detection of exercise load-associated differences in trabecular bone.

PURPOSE: To assess the ability of co-occurrence matrix-based texture parameters to detect exercise load-associated differences in MRI texture at the femoral neck cross-section. MATERIALS AND METHODS: A total of 91 top-level female athletes representing five differently loading sports and 20 referents participated in this cross-sectional study. Axial T1-weighted FLASH and T2*-weighted MEDIC sequence images of the proximal femur were obtained with a 1.5T MRI. The femoral neck trabecular bone at the level of the insertion of articular capsule was divided manually into regions of interest representing four anatomical sectors (anterior, posterior, superior, and inferior). Selected co-occurrence matrix-based texture parameters were used to evaluate differences in apparent trabecular structure between the exercise loading groups and anatomical sectors of the femoral neck. RESULTS: Significant differences in the trabecular bone texture, particularly at the superior femoral neck, were observed between athletes representing odd-impact (soccer and squash) and high-magnitude exercise loading (power-lifting) groups and the nonathletic reference group. CONCLUSION: MRI texture analysis provides a quantitative method for detecting and classifying apparent structural differences in trabecular bone that are associated with specific exercise loading.

Parkinson's disease: interhemispheric textural differences in MR images.

RATIONALE AND OBJECTIVES: Early-stage diagnosis of Parkinson's disease (PD) is essential in making decisions related to treatment and prognosis. However, there is no specific diagnostic test for the diagnosis of PD. The aim of this study was to evaluate the role of texture analysis (TA) of magnetic resonance images in detecting subtle changes between the hemispheres in various brain structures in patients with early symptoms of parkinsonism. In addition, functional TA parameters for detecting textural changes are presented. MATERIALS AND METHODS: Fifty-one patients with symptoms of PD and 20 healthy controls were imaged using a 3-T magnetic resonance device. Co-occurrence matrix-based TA was applied to detect changes in textures between the hemispheres in the following clinically interesting areas: dentate nucleus, basilar pons, substantia nigra, globus pallidus, thalamus, putamen, caudate nucleus, corona radiata, and centrum semiovale. The TA results were statistically evaluated using the Mann-Whitney U test. RESULTS: The results showed interhemispheric textural differences among the patients, especially in the area of basilar pons and midbrain. Concentrating on this clinically interesting area, the four most discriminant parameters were defined: co-occurrence matrix correlation, contrast, difference variance, and sum variance. With these parameters, differences were also detected in the dentate nucleus, globus pallidus, and corona radiata. CONCLUSIONS: On the basis of this study, interhemispheric differences in the magnetic resonance images of patients with PD can be identified by the means of co-occurrence matrix-based TA. The detected areas correlate with the current pathophysiologic and neuroanatomic knowledge of PD.

Effect of slice thickness on brain magnetic resonance image texture analysis.

BACKGROUND: The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients. METHODS: We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy. RESULTS: Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets. CONCLUSIONS: Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue.

MRI texture analysis in multiple sclerosis: toward a clinical analysis protocol.

RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI)-based texture analysis has been shown to be effective in classifying multiple sclerosis lesions. Regarding the clinical use of texture analysis in multiple sclerosis, our intention was to show which parts of the analysis are sensitive to slight changes in textural data acquisition and which steps tolerate interference. MATERIALS AND METHODS: The MRI datasets of 38 multiple sclerosis patients were used in this study. Three imaging sequences were compared in quantitative analyses, including a comparison of anatomical levels of interest, variance between sequential slices and two methods of region of interest drawing. We focused on the classification of white matter and multiple sclerosis lesions in determining the discriminatory power of textural parameters. Analyses were run with MaZda software for texture analysis, and statistical tests were performed for raw parameters. RESULTS: MRI texture analysis based on statistical, autoregressive-model and wavelet-derived texture parameters provided an excellent distinction between the image regions corresponding to multiple sclerosis plaques and white matter or normal-appearing white matter with high accuracy (nonlinear discriminant analysis 96%-100%). There were no significant differences in the classification results between imaging sequences or between anatomical levels. Standardized regions of interest were tolerant of changes within an anatomical level when intra-tissue variance was tested. CONCLUSION: The MRI texture analysis protocol with fixed imaging sequence and anatomical levels of interest shows promise as a robust quantitative clinical means for evaluating multiple sclerosis lesions.

Non-Hodgkin lymphoma response evaluation with MRI texture classification.

BACKGROUND: To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis. METHODS: A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests. RESULTS: NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images.Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue. CONCLUSION: Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.