Lara Harrison

Targeted exercises against hip fragility

SummaryCompared to high-impact exercises, moderate-magnitude impacts from odd-loading directions have similar ability to thicken vulnerable cortical regions of the femoral neck. Since odd-impact exercises are mechanically less demanding to the body, this type of exercise can provide a reasonable basis for devising feasible, targeted bone training against hip fragility.IntroductionRegional cortical thinning at the femoral neck is associated with hip fragility. Here, we investigated whether exercises involving high-magnitude impacts, moderate-magnitude impacts from odd directions, high-magnitude muscle forces, low-magnitude impacts at high repetition rate, or non-impact muscle forces at high repetition rate were associated with thicker femoral neck cortex.MethodsUsing three-dimensional magnetic resonance imaging, we scanned the proximal femur of 91 female athletes, representing the above-mentioned five exercise-loadings, and 20 referents. Cortical thickness at the inferior, anterior, superior, and posterior regions of the femoral neck was evaluated. Between-group differences were analyzed with ANCOVA.ResultsFor the inferior cortical thickness, only the high-impact group differed significantly (~60%, p = 0.012) from the reference group, while for the anterior cortex, both the high-impact and odd-impact groups differed (~20%, p = 0.042 and p = 0.044, respectively). Also, the posterior cortex was ~20% thicker (p = 0.014 and p = 0.006, respectively) in these two groups.ConclusionsOdd-impact exercise-loading was associated, similar to high-impact exercise-loading, with ~20% thicker cortex around the femoral neck. Since odd-impact exercises are mechanically less demanding to the body than high-impact exercises, it is argued that this type of bone training would offer a feasible basis for targeted exercise-based prevention of hip fragility.

Characterization of breast cancer types by texture analysis of magnetic resonance images.

RATIONALE AND OBJECTIVES This novel study aims to investigate texture parameters in distinguishing healthy breast tissue and breast cancer in breast magnetic resonance imaging (MRI). A specific aim was to identify possible differences in the texture characteristics of histological types (lobular and ductal) of invasive breast cancer and to determine the value of these differences for computer-assisted lesion classification. MATERIALS AND METHODS Twenty patients (mean age 50.6 + or - SD 10.6; range 37-70 years), with histopathologically proven invasive breast cancer (10 lobular and 10 ductal) were included in this preliminary study. The median MRI lesion size was 25 mm (range, 7-60 mm). The selected T1-weighted precontrast, post-contrast, and subtracted images were analyzed and classified with texture analysis (TA) software MaZda and additional statistical tests were used for testing the parameters separability. RESULTS All classification methods employed were able to differentiate between cancer and healthy breast tissue and also invasive lobular and ductal carcinoma with classification accuracy varying between 80% and 100%, depending on the used imaging series and the type of region of interest. We found several parameters to be significantly different between the regions of interest studied. The co-occurrence matrix based parameters proved to be superior to other texture parameters used. CONCLUSIONS The results of this study indicate that MRI TA differentiates breast cancer from normal tissue and may be able to distinguish between two histological types of breast cancer providing more accurate characterization of breast lesions thereby offering a new tool for radiological analysis of breast MRI.

Mild Traumatic Brain Injury: Tissue Texture Analysis Correlated to Neuropsychological and DTI Findings

RATIONALE AND OBJECTIVES The aim of this study was to evaluate whether texture analysis (TA) can detect subtle changes in cerebral tissue caused by mild traumatic brain injury (MTBI) and to determine whether these changes correlate with neuropsychological and diffusion tensor imaging (DTI) findings. MATERIALS AND METHODS Forty-two patients with MTBIs were imaged using 1.5T magnetic resonance imaging within 3 weeks after head injury. TA was performed for the regions corresponding to the mesencephalon, centrum semiovale, and corpus callosum. Using DTI, the fractional anisotropic and apparent diffusion coefficient values for the same regions were evaluated. The same analyses were performed on a group of 10 healthy volunteers. Patients also underwent a battery of neurocognitive tests within 6 weeks after injury. RESULTS TA revealed textural differences between the right and left hemispheres in patients with MTBIs, whereas differences were minimal in healthy controls. A significant correlation was found between scores on memory tests and texture parameters (sum of squares, sum entropy, inverse difference moment, and sum average) in patients in the area of the mesencephalon and the genu of the corpus callosum. Significant correlations were also found between texture parameters for the left mesencephalon and both fractional anisotropic and apparent diffusion coefficient values. CONCLUSIONS The data suggest that heterogeneous texture and abnormal DTI patterns in the area of the mesencephalon may be linked with verbal memory deficits among patients with MTBIs. Therefore, TA combined with DTI in patients with MTBIs may increase the ability to detect early and subtle neuropathologic changes.

Non-Hodgkin lymphoma response evaluation with MRI texture classification

BackgroundTo show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.MethodsA 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.ResultsNHL 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.ConclusionTexture 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.

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.

Texture analysis on MRI images of non-Hodgkin lymphoma

The aim here is to show that texture parameters of magnetic resonance imaging (MRI) data changes in lymphoma tissue during chemotherapy. Ten patients having non-Hodgkin lymphoma masses in the abdomen were imaged for chemotherapy response evaluation three consecutive times. The analysis was performed with MaZda texture analysis (TA) application. The best discrimination in lymphoma MRI texture was obtained within T2-weighted images between the pre-treatment and the second response evaluation stage. TA proved to be a promising quantitative means of representing lymphoma tissue changes during medication follow-up.

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

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.

Effect of slice thickness on brain magnetic resonance image texture analysis

BackgroundThe 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.MethodsWe 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. Wilcoxons 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.ResultsOnly 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.ConclusionsThree-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.

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.