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Texture mri

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https://www.readbyqxmd.com/read/28712700/radiomic-analysis-of-dce-mri-for-prediction-of-response-to-neoadjuvant-chemotherapy-in-breast-cancer-patients
#1
Ming Fan, Guolin Wu, Hu Cheng, Juan Zhang, Guoliang Shao, Lihua Li
OBJECTIVES: To enhance the accurate prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer patients by using a quantitative analysis of dynamic enhancement magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: A dataset of 57 cancer patients with breast DCE-MR images acquired before NAC was used. Among them, 47 patients were Responders, and 10 patients were non-Responders based on the RECIST criteria. The breast regions were segmented on the MR images, and a total of 158 radiomic features were computed to represent the morphologic, dynamic, and the texture of the tumors as well as the background parenchymal features...
June 28, 2017: European Journal of Radiology
https://www.readbyqxmd.com/read/28708850/tumor-spatial-heterogeneity-in-myxoid-containing-soft-tissue-using-texture-analysis-of-diffusion-weighted-mri
#2
Hyun Su Kim, Jae-Hun Kim, Young Cheol Yoon, Bong Keun Choe
The objective of this study was to examine the tumor spatial heterogeneity in myxoid-containing soft-tissue tumors (STTs) using texture analysis of diffusion-weighted imaging (DWI). A total of 40 patients with myxoid-containing STTs (23 benign and 17 malignant) were included in this study. The region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map. For texture analysis, the global (mean, standard deviation, skewness, and kurtosis), regional (intensity variability and size-zone variability), and local features (energy, entropy, correlation, contrast, homogeneity, variance, and maximum probability) were extracted from the ADC map...
2017: PloS One
https://www.readbyqxmd.com/read/28707546/a-computer-aided-diagnosis-cad-scheme-for-pretreatment-prediction-of-pathological-response-to-neoadjuvant-therapy-using-dynamic-contrast-enhanced-mri-texture-features
#3
Valentina Giannini, Simone Mazzetti, Agnese Marmo, Filippo Montemurro, Daniele Regge, Laura Martincich
OBJECTIVES: To assess whether a computer aided diagnosis (CAD) system can predict pathological response (pCR) to neoadjuvant chemotherapy (NAC) prior to treatment using texture features. MATERIALS AND METHODS: Response to treatment of 44 patients was defined according to the histopatology of resected tumour and extracted axillary nodes in two ways: a) pCR+ (Smith's grade=5) vs pCR- (Smith's grade<5); b) pCRN+ (pCR+ and absence of residual lymph node metastases) vs pCRN-...
July 14, 2017: British Journal of Radiology
https://www.readbyqxmd.com/read/28705145/texture-based-classification-of-different-single-liver-lesion-based-on-spair-t2w-mri-images
#4
Zhenjiang Li, Yu Mao, Wei Huang, Hongsheng Li, Jian Zhu, Wanhu Li, Baosheng Li
BACKGROUND: To assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic metastases (HM) and hepatocellular carcinoma (HCC). METHODS: The SPAIR T2W-MRI data of 162 patients with HH (n=55), HM (n=67) and HCC (n=40) were retrospectively analyzed. We used two independent cohorts for training (n = 112 patients) and validation (n = 50 patients)...
July 13, 2017: BMC Medical Imaging
https://www.readbyqxmd.com/read/28691102/dce-mri-texture-features-for-early-prediction-of-breast-cancer-therapy-response
#5
Guillaume Thibault, Alina Tudorica, Aneela Afzal, Stephen Y-C Chui, Arpana Naik, Megan L Troxell, Kathleen A Kemmer, Karen Y Oh, Nicole Roy, Neda Jafarian, Megan L Holtorf, Wei Huang, Xubo Song
This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6-8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion...
March 2017: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28687979/-radio-oncomics-the-potential-of-radiomics-in-radiation-oncology
#6
Jan Caspar Peeken, Fridtjof Nüsslin, Stephanie E Combs
INTRODUCTION: Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. METHODS: After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features...
July 7, 2017: Strahlentherapie und Onkologie: Organ der Deutschen Röntgengesellschaft ... [et Al]
https://www.readbyqxmd.com/read/28680910/compressed-sensing-magnetic-resonance-imaging-based-on-shearlet-sparsity-and-nonlocal-total-variation
#7
Ali Pour Yazdanpanah, Emma E Regentova
Compressed sensing (CS) has been utilized for acceleration of data acquisition in magnetic resonance imaging (MRI). MR images can then be reconstructed with an undersampling rate significantly lower than that required by the Nyquist sampling criterion. However, the CS usually produces images with artifacts, especially at high reduction rates. We propose a CS MRI method called shearlet sparsity and nonlocal total variation (SS-NLTV) that exploits SS-NLTV regularization. The shearlet transform is an optimal sparsifying transform with excellent directional sensitivity compared with that by wavelet transform...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28670164/texture-analysis-of-torn-rotator-cuff-on-preoperative-magnetic-resonance-arthrography-as-a-predictor-of-postoperative-tendon-status
#8
Yeonah Kang, Guen Young Lee, Joon Woo Lee, Eugene Lee, Bohyoung Kim, Su Jin Kim, Joong Mo Ahn, Heung Sik Kang
OBJECTIVE: To evaluate texture data of the torn supraspinatus tendon (SST) on preoperative T2-weighted magnetic resonance arthrography (MRA) using the gray-level co-occurrence matrix (GLCM) for prediction of post-operative tendon state. MATERIALS AND METHODS: Fifty patients who underwent arthroscopic rotator cuff repair for full-thickness tears of the SST were included in this retrospective study. Based on 1-year follow-up, magnetic resonance imaging showed that 30 patients had intact SSTs, and 20 had rotator cuff retears...
July 2017: Korean Journal of Radiology: Official Journal of the Korean Radiological Society
https://www.readbyqxmd.com/read/28660250/shape-selectivity-of-middle-superior-temporal-sulcus-body-patch-neurons
#9
Ioannis Kalfas, Satwant Kumar, Rufin Vogels
Functional MRI studies in primates have demonstrated cortical regions that are strongly activated by visual images of bodies. The presence of such body patches in macaques allows characterization of the stimulus selectivity of their single neurons. Middle superior temporal sulcus body (MSB) patch neurons showed similar stimulus selectivity for natural, shaded, and textured images compared with their silhouettes, suggesting that shape is an important determinant of MSB responses. Here, we examined and modeled the shape selectivity of single MSB neurons...
May 2017: ENeuro
https://www.readbyqxmd.com/read/28654820/the-potential-value-of-preoperative-mri-texture-and-shape-analysis-in-grading-meningiomas-a-preliminary-investigation
#10
Peng-Fei Yan, Ling Yan, Ting-Ting Hu, Dong-Dong Xiao, Zhen Zhang, Hong-Yang Zhao, Jun Feng
OBJECT: Preoperative knowledge of meningioma grade is essential for planning treatment and surgery. The purpose of this study was to investigate the diagnostic value of MRI texture and shape analysis in grading meningiomas. METHODS: A surgical database was reviewed to identify meningioma patients who had undergone tumor resection between January 2015 and December 2016. Preoperative MR images were retrieved and analyzed. Texture and shape analysis was conducted to quantitatively evaluate tumor heterogeneity and morphology...
June 24, 2017: Translational Oncology
https://www.readbyqxmd.com/read/28653477/radiomic-analysis-of-soft-tissues-sarcomas-can-distinguish-intermediate-from-high-grade-lesions
#11
Valentina D A Corino, Eros Montin, Antonella Messina, Paolo G Casali, Alessandro Gronchi, Alfonso Marchianò, Luca T Mainardi
PURPOSE: To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics). MATERIALS AND METHODS: MRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features...
June 27, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28647484/neural-representation-of-geometry-and-surface-properties-in-object-and-scene-perception
#12
Matthew X Lowe, Jason Rajsic, Jason P Gallivan, Susanne Ferber, Jonathan S Cant
Multiple cortical regions are crucial for perceiving the visual world, yet the processes shaping representations in these regions are unclear. To address this issue, we must elucidate how perceptual features shape representations of the environment. Here, we explore how the weighting of different visual features affects neural representations of objects and scenes, focusing on the scene-selective parahippocampal place area (PPA), but additionally including the retrosplenial complex (RSC), occipital place area (OPA), lateral occipital (LO) area, fusiform face area (FFA) and occipital face area (OFA)...
June 21, 2017: NeuroImage
https://www.readbyqxmd.com/read/28642629/improved-brain-tumor-segmentation-by-utilizing-tumor-growth-model-in-longitudinal-brain-mri
#13
Linmin Pei, Syed M S Reza, Wei Li, Christos Davatzikos, Khan M Iftekharuddin
In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. In order to model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28642480/haralick-texture-features-from-apparent-diffusion-coefficient-adc-mri-images-depend-on-imaging-and-pre-processing-parameters
#14
Patrik Brynolfsson, David Nilsson, Turid Torheim, Thomas Asklund, Camilla Thellenberg Karlsson, Johan Trygg, Tufve Nyholm, Anders Garpebring
In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image...
June 22, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28630883/detection-of-prostate-cancer-in-multiparametric-mri-using-random-forest-with-instance-weighting
#15
Nathan Lay, Yohannes Tsehay, Matthew D Greer, Baris Turkbey, Jin Tae Kwak, Peter L Choyke, Peter Pinto, Bradford J Wood, Ronald M Summers
A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28624924/evaluation-of-hepatic-fibrosis-a-review-from-the-society-of-abdominal-radiology-disease-focus-panel
#16
REVIEW
Jeanne M Horowitz, Sudhakar K Venkatesh, Richard L Ehman, Kartik Jhaveri, Patrick Kamath, Michael A Ohliger, Anthony E Samir, Alvin C Silva, Bachir Taouli, Michael S Torbenson, Michael L Wells, Benjamin Yeh, Frank H Miller
Hepatic fibrosis is potentially reversible; however early diagnosis is necessary for treatment in order to halt progression to cirrhosis and development of complications including portal hypertension and hepatocellular carcinoma. Morphologic signs of cirrhosis on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) alone are unreliable and are seen with more advanced disease. Newer imaging techniques to diagnose liver fibrosis are reliable and accurate, and include magnetic resonance elastography and US elastography (one-dimensional transient elastography and point shear wave elastography or acoustic radiation force impulse imaging)...
June 17, 2017: Abdominal Radiology
https://www.readbyqxmd.com/read/28624024/differentiation-between-acute-and-chronic-myocardial-infarction-by-means-of-texture-analysis-of-late-gadolinium-enhancement-and-cine-cardiac-magnetic-resonance-imaging
#17
Andrés Larroza, Andrzej Materka, María P López-Lereu, José V Monmeneu, Vicente Bodí, David Moratal
The purpose of this study was to differentiate acute from chronic myocardial infarction using machine learning techniques and texture features extracted from cardiac magnetic resonance imaging (MRI). The study group comprised 22 cases with acute myocardial infarction (AMI) and 22 cases with chronic myocardial infarction (CMI). Cine and late gadolinium enhancement (LGE) MRI were analyzed independently to differentiate AMI from CMI. A total of 279 texture features were extracted from predefined regions of interest (ROIs): the infarcted area on LGE MRI, and the entire myocardium on cine MRI...
July 2017: European Journal of Radiology
https://www.readbyqxmd.com/read/28622412/textural-analysis-of-early-phase-spatiotemporal-changes-in-contrast-enhancement-of-breast-lesions-imaged-with-an-ultrafast-dce-mri-protocol
#18
Jana Milenković, Mehmet Ufuk Dalmış, Janez Žgajnar, Bram Platel
PURPOSE: New ultrafast view-sharing sequences have enabled breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to be performed at high spatial and temporal resolution. The aim of this study is to evaluate the diagnostic potential of textural features that quantify the spatiotemporal changes of the contrast-agent uptake in computer-aided diagnosis of malignant and benign breast lesions imaged with high spatial and temporal resolution DCE-MRI. METHOD: The proposed approach is based on the textural analysis quantifying the spatial variation of six dynamic features of the early-phase contrast-agent uptake of a lesion's largest cross-sectional area...
June 16, 2017: Medical Physics
https://www.readbyqxmd.com/read/28608163/prostate-specific-membrane-antigen-pet-mri-validation-of-mr-textural-analysis-for-detection-of-transition-zone-prostate-cancer
#19
Anthony Bates, Kenneth Miles
OBJECTIVES: To validate MR textural analysis (MRTA) for detection of transition zone (TZ) prostate cancer through comparison with co-registered prostate-specific membrane antigen (PSMA) PET-MR. METHODS: Retrospective analysis was performed for 30 men who underwent simultaneous PSMA PET-MR imaging for staging of prostate cancer. Thirty texture features were derived from each manually contoured T2-weighted, transaxial, prostatic TZ using texture analysis software that applies a spatial band-pass filter and quantifies texture through histogram analysis...
June 12, 2017: European Radiology
https://www.readbyqxmd.com/read/28599282/optimizing-a-machine-learning-based-glioma-grading-system-using-multi-parametric-mri-histogram-and-texture-features
#20
Xin Zhang, Lin-Feng Yan, Yu-Chuan Hu, Gang Li, Yang Yang, Yu Han, Ying-Zhi Sun, Zhi-Cheng Liu, Qiang Tian, Zi-Yang Han, Le-De Liu, Bin-Quan Hu, Zi-Yu Qiu, Wen Wang, Guang-Bin Cui
Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study...
May 18, 2017: Oncotarget
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