Lara C V Harrison

Tampere University Hospital (TAUH), Tammerfors, Province of Western Finland, Finland

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Publications (17)23.55 Total impact

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    ABSTRACT: 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.
    Clinical Physiology and Functional Imaging 11/2013; · 1.33 Impact Factor
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    ABSTRACT: 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. 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. 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. MRI texture analysis provides a quantitative method for detecting and classifying apparent structural differences in trabecular bone that are associated with specific exercise loading.
    Journal of Magnetic Resonance Imaging 09/2011; 34(6):1359-66. · 2.57 Impact Factor
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    ABSTRACT: 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. 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. 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. 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.
    Academic radiology 07/2011; 18(10):1217-24. · 2.09 Impact Factor
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    ABSTRACT: In this study, we have studied the effect of background noise on the texture analysis of muscle, bone marrow and fat tissues in 1.5 T magnetic resonance (MR) images using different statistical methods. Variable levels of noise were first added on 3-mm thick T2 weighted image slices of voluntary subjects to simulate several signal-to-noise ratio (SNR) levels. For each original and simulated image, the values for 264 texture parameters were calculated using MaZda, a texture analysis toolkit. We also determined Fisher coefficients based on the texture parameter values in order to enable high discrimination between different tissues. Linear discriminant analysis (LDA) and two different nearest neighbour (NN) methods were then applied for the texture parameters with the highest Fisher coefficient values. Several training and test sets were used to approximate the variation in the classification results. All the above-mentioned methods had the same classification accuracy, which in turn depended on the image SNR. We conclude that these tissues can be detected by texture analysis methods with a sufficient accuracy (90%) especially if SNR is at least 30-40 dB, even though the separation of different muscles remains a very challenging task.
    Proc SPIE 03/2011;
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    ABSTRACT: 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. 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. 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. 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.
    BioMedical Engineering OnLine 10/2010; 9:60. · 1.61 Impact Factor
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    ABSTRACT: 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. 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. 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. 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.
    Academic radiology 09/2010; 17(9):1096-102. · 2.09 Impact Factor
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    ABSTRACT: 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. 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. 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. 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.
    Academic radiology 06/2010; 17(6):696-707. · 2.09 Impact Factor
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    ABSTRACT: Magnetic resonance imaging based texture analysis has been shown effective on classifying multiple sclerosis lesions. Quantitative analysis of images contains several manual and automatic steps. Results of subtle changes between tissues may suffer from errors in analysis protocol. For the development of clinical analysis protocol we evaluate the potential of non-specialized medics to carry out image slice selection and manual segmentation of brain tissue for texture analysis purposes as an assistant for a specialist. Our results indicate manual region of interest definition performed by non-specialists requires sufficient education with practical training to achieve successful textural data. KeywordsMagnetic resonance imaging (MRI)-texture analysis (TA)-multiple sclerosis (MS)-region of interest (ROI)-segmentation
    03/2010: pages 300-303;
  • Acad Radiol. 01/2010;
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    ABSTRACT: Our objective was to study the effect of trauma on texture features in cerebral tissue in mild traumatic brain injury (MTBI). Our hypothesis was that a mild trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection but could be detected with texture analysis (TA). We imaged 42 MTBI patients by using 1.5 T MRI within three weeks of onset of trauma. TA was performed on the area of mesencephalon, cerebral white matter at the levels of mesencephalon, corona radiata and centrum semiovale and in different segments of corpus callosum (CC) which have been found to be sensitive to damage. The same procedure was carried out on a control group of ten healthy volunteers. Patients' TA data was compared with the TA results of the control group comparing the amount of statistically significantly differing TA parameters between the left and right sides of the cerebral tissue and comparing the most discriminative parameters. There were statistically significant differences especially in several co-occurrence and run-length matrix based parameters between left and right side in the area of mesencephalon, in cerebral white matter at the level of corona radiata and in the segments of CC in patients. Considerably less difference was observed in the healthy controls. TA revealed significant changes in texture parameters of cerebral tissue between hemispheres and CC segments in TBI patients. TA may serve as a novel additional tool for detecting the conventionally invisible changes in cerebral tissue in MTBI and help the clinicians to make an early diagnosis.
    BMC Medical Imaging 01/2010; 10:8. · 1.09 Impact Factor
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    ABSTRACT: Texture analysis (TA) is a potential tool for analysis of medical images. It can be used for classification of pathological tissue. Co-occurrence matrix is one of the most promising TA methods. In this study we have analysed this method by using software phantoms instead of physical phantoms. This choice was made because the construction process of a real phantom is slow and only one set of parameter results is available. The software phantoms were implemented with Matlab program by constructing 16x16 matrices containing four different grey level values. Value from 0 to 1, 0 corresponding to black and 1 being white, was given for each matrix element to perform different texture patterns. Grey scale images were drawn from the matrices and the texture analysis was performed with MaZda. Software phantoms were proved to be an effective method to study the parameter value distribution because of the easy construction and modification of the matrices. However, more complex patterns should be used for further studies.
    01/2010;
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    ABSTRACT: Texture analysis (TA) is a quantitative approach for characterizing subtle changes in magnetic resonance (MR) images of different tissues. The aim of this study was to detect changes in tissue of corpus callosum (CC) in mild traumatic brain injury (MTBI) patients by the means of TA. TA was performed in the sagittal T1-weighted MR images of 42 MTBI patients, focusing on different segments of CC by using the tissue characterization software MaZda. Results were compared with the control group of ten healthy volunteers. The most discriminant texture features were identified with a combination of feature selection algorithms mutual information (MI), classification error probability combined with average correlation coefficients (POE+ACC) and Fisher coefficient. Linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) were performed. Nearestneighbor (1-NN) classification for LDA and artificial neural network (ANN) for NDA was used for tissue classification. The results revealed differences in the textures between the selected segments of CC in MTBI patients. There were also differences in the CC between healthy volunteers and MTBI patients. The best classification results between healthy volunteers and patients were achieved in the area of splenium of CC, with accuracy of 96% for the 1-NN classifier, and accuracy of 98 % for the ANN classifier. TA results revealed changes in the texture parameters of the segments of CC between healthy volunteers and MTBI patients and therefore may provide a novel additional tool for detecting subtle changes in CC tissue on MTBI, but evidently larger data is necessary to confirm the clinical value of TA in diagnosing MTBI.
    12/2009: pages 37-40;
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    ABSTRACT: 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. 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. 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. 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.
    Academic radiology 11/2009; 17(2):135-41. · 2.09 Impact Factor
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    ABSTRACT: To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis. 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. 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. 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.
    Journal of Experimental & Clinical Cancer Research 07/2009; 28:87. · 3.07 Impact Factor
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    ABSTRACT: Breast cancer is the most common cancer in women. Breast MRI (BMRI) has emerged as a promising technique for detecting, diagnosing, and staging the condition. Automated image analysis aims to extract relevant information from MR images of the breast and improve the accuracy and consistency of image interpretation. Texture analysis (TA) is one possible means of detecting tissue features in biomedical images. The aim of this study was to evaluate the parameters which identify the most important breast cancer characteristics and to assess the ability of MRI-based TA to characterize breast cancer tissue. Seven patients with histopathologically proven breast cancer were included in this preliminary study. The texture analysis was performed with MaZda texture application. The most discriminant texture features identified by Fisher coefficients and POE+ACC (probability of classification error and average correlation coefficients) between breast cancer tissue and reference tissue from the healthy breast and tissue adjoining the cancer area were evaluated. This evaluation was made between patients, different imaging series and two histological types of (ductal vs. lobular) carcinomas. Raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) were run for each subset of images and chosen texture features. The results revealed differences in the textures in every imaging series when non-cancer and cancer tissue were compared and the best discrimination results were obtained within two dynamic contrast-enhanced MRI subtraction series. Furthermore, the texture parameters obtained differed between the two histological groups. The preliminary results show potential in discriminating between normal and abnormal breast tissue elements, encouraging us to continue with larger data sets.
    12/2008: pages 517-521;
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    ABSTRACT: Compared 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. Regional 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. Using 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. For the inferior cortical thickness, only the high-impact group differed significantly (approximately 60%, p = 0.012) from the reference group, while for the anterior cortex, both the high-impact and odd-impact groups differed (approximately 20%, p = 0.042 and p = 0.044, respectively). Also, the posterior cortex was approximately 20% thicker (p = 0.014 and p = 0.006, respectively) in these two groups. Odd-impact exercise-loading was associated, similar to high-impact exercise-loading, with approximately 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.
    Osteoporosis International 12/2008; 20(8):1321-8. · 4.04 Impact Factor
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    ABSTRACT: 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.
    Computers in Biology and Medicine 05/2008; 38(4):519-24. · 1.48 Impact Factor

Publication Stats

137 Citations
23.55 Total Impact Points

Institutions

  • 2009–2011
    • Tampere University Hospital (TAUH)
      Tammerfors, Province of Western Finland, Finland
  • 2008–2011
    • Tampere University of Technology
      • Department of Electronics and Communications Engineering
      Tammerfors, Province of Western Finland, Finland
    • University of Tampere
      Tammerfors, Province of Western Finland, Finland
    • Pirkanmaa Hospital District
      Tammerfors, Province of Western Finland, Finland