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Texture mri

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https://www.readbyqxmd.com/read/29664902/a-general-prediction-model-for-the-detection-of-adhd-and-autism-using-structural-and-functional-mri
#1
Bhaskar Sen, Neil C Borle, Russell Greiner, Matthew R G Brown
This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder...
2018: PloS One
https://www.readbyqxmd.com/read/29659431/preliminary-study-on-molecular-subtypes-of-breast-cancer-based-on-magnetic-resonance-imaging-texture-analysis
#2
Xinru Sun, Bing He, Xin Luo, Yuhua Li, Jinfeng Cao, Jinlan Wang, Jun Dong, Xiaoyu Sun, Guangxia Zhang
OBJECTIVE: The aim of the study was to investigate the molecular subtypes of breast cancer based on the texture features derived from magnetic resonance images (MRIs). METHODS: One hundred seven patients with preoperative confirmed breast cancer were recruited. One hundred eight breast lesions were divided into 4 subtypes according to the status of estrogen receptor, progesterone receptor, human epidermal growth factor receptor type 2, and Ki67. Fisher discriminant analysis was performed on the texture features that extracted from the enhanced high-resolution T1-weighted images and diffusion weighted images to establish the classification model of molecular subtypes...
April 13, 2018: Journal of Computer Assisted Tomography
https://www.readbyqxmd.com/read/29656584/improving-lymph-node-characterization-in-staging-malignant-lymphoma-using-first-order-adc-texture-analysis-from-whole-body-diffusion-weighted-mri
#3
Katja N De Paepe, Frederik De Keyzer, Pascal Wolter, Oliver Bechter, Daan Dierickx, Ann Janssens, Gregor Verhoef, Raymond Oyen, Vincent Vandecaveye
BACKGROUND: Correct staging and treatment initiation in malignant lymphoma depends on accurate lymph node characterization. However, nodal assessment based on conventional and diffusion-weighted (DWI) MRI remains challenging, particularly in smaller nodes. PURPOSE: To evaluate first-order apparent diffusion coefficient (ADC) texture parameters compared to mean ADC for lymph node characterization in non-Hodgkin lymphoma (NHL) using whole-body DWI (WB-DWI). STUDY TYPE: Retrospective...
April 14, 2018: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/29626238/primary-central-nervous-system-lymphoma-and-atypical-glioblastoma-differentiation-using-radiomics-approach
#4
Hie Bum Suh, Yoon Seong Choi, Sohi Bae, Sung Soo Ahn, Jong Hee Chang, Seok-Gu Kang, Eui Hyun Kim, Se Hoon Kim, Seung-Koo Lee
OBJECTIVES: To evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating primary central nervous system lymphoma (PCNSL) from non-necrotic atypical glioblastoma (GBM). METHODS: Seventy-seven patients (54 individuals with PCNSL and 23 with non-necrotic atypical GBM), diagnosed from January 2009 to April 2017, were enrolled in this retrospective study. A total of 6,366 radiomics features, including shape, volume, first-order, texture, and wavelet-transformed features, were extracted from multi-parametric (post-contrast T1- and T2-weighted, and fluid attenuation inversion recovery images) and multiregional (enhanced and non-enhanced) tumour volumes...
April 6, 2018: European Radiology
https://www.readbyqxmd.com/read/29609039/computer-aided-diagnosis-of-cavernous-malformations-in-brain-mr-images
#5
Huiquan Wang, S Nizam Ahmed, Mrinal Mandal
Cavernous malformation or cavernoma is one of the most common epileptogenic lesions. It is a type of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage, and various neurological disorders. Manual detection of cavernomas by physicians in a large set of brain MRI slices is a time-consuming and labor-intensive task and often delays diagnosis. In this paper, we propose a computer-aided diagnosis (CAD) system for cavernomas based on T2-weighted axial plane MRI image analysis...
March 21, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29581455/structural-and-functional-connectivity-changes-beyond-visual-cortex-in-a-later-phase-of-visual-perceptual-learning
#6
Dong-Wha Kang, Dongho Kim, Li-Hung Chang, Yong-Hwan Kim, Emi Takahashi, Matthew S Cain, Takeo Watanabe, Yuka Sasaki
The neural mechanisms of visual perceptual learning (VPL) remain unclear. Previously we found that activation in the primary visual cortex (V1) increased in the early encoding phase of training, but returned to baseline levels in the later retention phase. To examine neural changes during the retention phase, we measured structural and functional connectivity changes using MRI. After weeks of training on a texture discrimination task, the fractional anisotropy of the inferior longitudinal fasciculus, a major tract connecting visual and anterior areas, was increased, as well as the functional connectivity between V1 and anterior regions mediated by the ILF...
March 26, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29573085/radiomics-strategy-for-glioma-grading-using-texture-features-from-multiparametric-mri
#7
Qiang Tian, Lin-Feng Yan, Xi Zhang, Xin Zhang, Yu-Chuan Hu, Yu Han, Zhi-Cheng Liu, Hai-Yan Nan, Qian Sun, Ying-Zhi Sun, Yang Yang, Ying Yu, Jin Zhang, Bo Hu, Gang Xiao, Ping Chen, Shuai Tian, Jie Xu, Wen Wang, Guang-Bin Cui
BACKGROUND: Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays. PURPOSE/HYPOTHESIS: To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps. STUDY TYPE: Retrospective; radiomics. POPULATION: A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively...
March 23, 2018: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/29562574/wavelet-based-joint-ct-mri-reconstruction
#8
Xuelin Cui, Lamine Mili, Ge Wang, Hengyong Yu
Since their inceptions, the multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. In this work, a novel joint reconstruction framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI) data is developed and evaluated. CT and MRI images are synchronously acquired and registered from a hybrid CT-MRI platform. Because image data are highly undersampled, analytic methods are unable to generate decent image quality. To overcome this drawback, we resort to the compressed sensing (CS) techniques, which employ sparse priors that result from an application of a wavelet transform...
March 15, 2018: Journal of X-ray Science and Technology
https://www.readbyqxmd.com/read/29556054/appearance-constrained-semi-automatic-segmentation-from-dce-mri-is-reproducible-and-feasible-for-breast-cancer-radiomics-a-feasibility-study
#9
Harini Veeraraghavan, Brittany Z Dashevsky, Natsuko Onishi, Meredith Sadinski, Elizabeth Morris, Joseph O Deasy, Elizabeth J Sutton
We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR)...
March 19, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29552288/mri-texture-analysis-in-predicting-treatment-response-to-neoadjuvant-chemoradiotherapy-in-rectal-cancer
#10
Yankai Meng, Chongda Zhang, Shuangmei Zou, Xinming Zhao, Kai Xu, Hongmei Zhang, Chunwu Zhou
To evaluate the importance of MRI texture analysis in prediction and early assessment of treatment response before and early neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study comprised of 59 patients. The tumoral texture parameters were compared between pre- and early nCRT. Area Under receiver operating characteristic (ROC) Curves [AUCs] were used to compare the diagnostic performance of statistically significant difference parameters and logistic regression analysis predicted probabilities for discriminating responders and nonresponders...
February 23, 2018: Oncotarget
https://www.readbyqxmd.com/read/29551853/deep-learning-and-texture-based-semantic-label-fusion-for-brain-tumor-segmentation
#11
L Vidyaratne, M Alam, Z Shboul, K M Iftekharuddin
Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset...
2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29547053/explorative-investigation-of-whole-lesion-histogram-mri-metrics-for-differentiating-uterine-leiomyomas-and-leiomyosarcomas
#12
Luke Gerges, Dorota Popiolek, Andrew B Rosenkrantz
OBJECTIVE: The purpose of this study is to assess the utility of texture analysis of multiple MRI sequences for the differentiation of uterine leiomyomas and leiomyosarcomas. MATERIALS AND METHODS: Seventeen leiomyosarcomas and 51 leiomyomas undergoing MRI before resection were included. Whole-lesion volumes of interest were placed on T2-weighted images, contrast-enhanced T1-weighted images, and apparent diffusion coefficient (ADC) maps. The diagnostic performance of histogram metrics was assessed...
March 16, 2018: AJR. American Journal of Roentgenology
https://www.readbyqxmd.com/read/29532781/value-of-internal-carotid-artery-stenosis-in-the-differential-diagnosis-between-invasive-pituitary-adenoma-and-invasive-meningioma
#13
Zhu Zhang, Jun-Wei Gong, Ming Wen, Li-Qiang Zhang
Objective To assess the value of internal carotid artery stenosis in differentiating invasive pituitary adenoma (IPA) from invasive meningiomas (IM). Methods The clinical and imaging data of 28 IPA patients and 15 IM patients who were treated in our center from January 2012 to December 2016 were retrospectively analyzed. The magnetic resonance imaging (MRI) features were analyzed. The narrowest diameter (Dstenosis ) and area (Astenosis ) of internal carotid artery around the tumor were measured by computed tomography angiography (CTA),followed by the calculation of the stenosis score (%stenosis )...
February 28, 2018: Zhongguo Yi Xue Ke Xue Yuan Xue Bao. Acta Academiae Medicinae Sinicae
https://www.readbyqxmd.com/read/29527478/mri-features-predict-p53-status-in-lower-grade-gliomas-via-a-machine-learning-approach
#14
Yiming Li, Zenghui Qian, Kaibin Xu, Kai Wang, Xing Fan, Shaowu Li, Tao Jiang, Xing Liu, Yinyan Wang
Background: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. Methods: Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training ( n  = 180) or validation ( n  = 92) set...
2018: NeuroImage: Clinical
https://www.readbyqxmd.com/read/29497775/texture-analysis-of-paraspinal-musculature-in-mri-of-the-lumbar-spine-analysis-of-the-lumbar-stenosis-outcome-study-lsos-data
#15
Manoj Mannil, Jakob M Burgstaller, Arjun Thanabalasingam, Sebastian Winklhofer, Michael Betz, Ulrike Held, Roman Guggenberger
OBJECTIVE: To evaluate association of fatty infiltration in paraspinal musculature with clinical outcomes in patients suffering from lumbar spinal stenosis (LSS) using qualitative and quantitative grading in magnetic resonance imaging (MRI). MATERIALS AND METHODS: In this retrospective study, texture analysis (TA) was performed on postprocessed axial T2 weighted (w) MR images at level L3/4 using dedicated software (MaZda) in 62 patients with LSS. Associations in fatty infiltration between qualitative Goutallier and quantitative TA findings with two clinical outcome measures, Spinal stenosis measure (SSM) score and walking distance, at baseline and regarding change over time were assessed using machine learning algorithms and multiple logistic regression models...
March 1, 2018: Skeletal Radiology
https://www.readbyqxmd.com/read/29488715/-quantitative-analysis-of-enhanced-mri-features-for-predicting-epidermal-growth-factor-receptor-gene-amplification-in-glioblastoma-multiforme-with-radiomic-method
#16
Fei Dong, Qian Li, Biao Jiang, Qiang Zeng, Jianming Hua, Minming Zhang
OBJECTIVE: To assess the value of contrast enhanced MRI features for predicting epidermal growth factor receptor ( EGFR ) gene amplification in glioblastoma multiforme (GBM) with radiomic method. METHODS: Eighty patients with EGFR status examined GBM were retrospectively reviewed. The data were randomly divided into a training dataset (60%) and test dataset (40%). Texture features of each case were extracted from the enhanced region and the edema region in contrast enhanced MR images...
May 25, 2017: Zhejiang da Xue Xue Bao. Yi Xue Ban, Journal of Zhejiang University. Medical Sciences
https://www.readbyqxmd.com/read/29488179/an-efficient-implementation-of-deep-convolutional-neural-networks-for-mri-segmentation
#17
Farnaz Hoseini, Asadollah Shahbahrami, Peyman Bayat
Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms...
February 27, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29477436/supervised-learning-based-multimodal-mri-brain-tumour-segmentation-using-texture-features-from-supervoxels
#18
Mohammadreza Soltaninejad, Guang Yang, Tryphon Lambrou, Nigel Allinson, Timothy L Jones, Thomas R Barrick, Franklyn A Howe, Xujiong Ye
BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI)...
April 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29464462/gestational-and-lactational-exposure-to-dichlorinated-bisphenol-a-induces-early-alterations-of-hepatic-lipid-composition-in-mice
#19
Dounia El Hamrani, Amandine Chepied, William Même, Marc Mesnil, Norah Defamie, Sandra Même
OBJECTIVE: Using non-invasive magnetic resonance (MR) techniques and a histological approach, we assessed the outcomes of perinatal exposure at a low dose of 3,3'-DCBPA (2-chloro-4-[1-(3-chloro-4-hydroxyphenyl)-1-methylethyl]phenol) and/or 3,5-DCBPA (2,6-dichloro-4-[1-(4-hydroxyphenyl)-1-methylethyl]phenol) on mice livers. MATERIALS AND METHODS: Fertilized female Swiss mice were injected intraperitoneally during gestation and lactation with either vehicle control, 20 μg/kg/day of BPA, 3,5-DCBPA, 3,3'-DCBPA or a mixture (mix-DCBPA)...
February 20, 2018: Magma
https://www.readbyqxmd.com/read/29427064/diagnostic-and-prognostic-value-of-amyloid-pet-textural-and-shape-features-comparison-with-classical-semi-quantitative-rating-in-760-patients-from-the-adni-2-database
#20
Fayçal Ben Bouallègue, Fabien Vauchot, Denis Mariano-Goulart, Pierre Payoux
We evaluated the performance of amyloid PET textural and shape features in discriminating normal and Alzheimer's disease (AD) subjects, and in predicting conversion to AD in subjects with mild cognitive impairment (MCI) or significant memory concern (SMC). Subjects from the Alzheimer's Disease Neuroimaging Initiative with available baseline 18F-florbetapir and T1-MRI scans were included. The cross-sectional cohort consisted of 181 controls and 148 AD subjects. The longitudinal cohort consisted of 431 SMC/MCI subjects, 85 of whom converted to AD during follow-up...
February 9, 2018: Brain Imaging and Behavior
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