Short Run Emphasis Research Articles

SU-F-R-19: Novel Radiomics Quantifying Tumor Structural Evolution Using Deformation Vector Field: Application for Tumor Response Assessment

Purpose: to quantify tumor volume/shape evolution due to chemoradiotherapy using structural evolution maps computed from deformation field. To extract radiomics from these maps for tumor response assessment. Methods: In 20 patients with esophageal cancer, BSpline deformable registration was performed to register post-treatment CT image to pre-treatment CT image. The resulting deformation (vector) field represented tissue displacement at every voxel. We computed three structural evolution maps from the deformation field: (1) contraction map, to detect local tumor shrinking/growth, (2) Jacobian map, to characterize the ratio of local volumetric variation, and (3) Hessian map, to track dynamic evolving process. We then extracted intensity and textural radiomics from the evolution maps for both the GTV and the 1-cm ring surrounding the GTV, to characterize tumor structural evolution. ROC analysis and Wilcoxon rank sum test were employed to identify important predictors for tumor response. Results: We visually overlaid structural evolution maps on registered CT image. Local tumor shrinking/growth regions were identified with contraction map and Jacobian map. Hessian map dynamically tracked the evolving process of a tumor. Nine radiomics (four intensity, five texture) characterizing tumor structural evolution were found useful for tumor response assessment. A tumor was more likely to achieve complete pathologic response when skewness of the contraction map in the 1-cm ring was lower (AUC = 0.84, p = 0.03), or when Kurtosis of the Jacobian map was lower in the GTV (AUC = 0.77, p = 0.08), or when short run emphasis of the Hessian map was higher (AUC = 0.87, p = 0.01). Conclusion: Structural evolution maps computed from the deformation field can quantify structural changes in a tumor. The radiomics extracted from these maps were useful predictors of tumor response. This work was supported in part by the National Cancer Institute Grants R01CA172638.

Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer

Radiomics is being explored for potential applications in radiation therapy. How various imaging protocols affect quantitative image features is currently a highly active area of research. To assess the variability of image features derived from conventional [three-dimensional (3D)] and respiratory-gated (RG) positron emission tomography (PET)/computed tomography (CT) images of lung cancer patients, image features were computed from 23 lung cancer patients. Both protocols for each patient were acquired during the same imaging session. PET tumor volumes were segmented using an adaptive technique which accounted for background. CT tumor volumes were delineated with a commercial segmentation tool. Using RG PET images, the tumor center of mass motion, length, and rotation were calculated. Fifty-six image features were extracted from all images consisting of shape descriptors, first-order features, and second-order texture features. Overall, 26.6% and 26.2% of total features demonstrated less than 5% difference between 3D and RG protocols for CT and PET, respectively. Between 10 RG phases in PET, 53.4% of features demonstrated percent differences less than 5%. The features with least variability for PET were sphericity, spherical disproportion, entropy (first and second order), sum entropy, information measure of correlation 2, Short Run Emphasis (SRE), Long Run Emphasis (LRE), and Run Percentage (RPC); and those for CT were minimum intensity, mean intensity, Root Mean Square (RMS), Short Run Emphasis (SRE), and RPC. Quantitative analysis using a 3D acquisition versus RG acquisition (to reduce the effects of motion) provided notably different image feature values. This study suggests that the variability between 3D and RG features is mainly due to the impact of respiratory motion.

3D Texture Analysis of Corpus Caliosum Based on MR Images Inpatients with Alzheimer’s Disease and Mild Cognitive Impairment

This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimers disease (AD). Methods: T1-weighted MRI of 18 AD patients, 18 Mild Cognitive Impairment (MCI) patients and 18 normal controls (NC) were selected.3D Texture parameters of the corpus callosum,including contrast, inverse difference moment , entropy, short run emphasis, long run emphasis, grey level nonuniformity, run length nonuniformity and fraction were extracted from the gray level co-occurrence matrix and run length matrix. Finally statistic significance was tested among three groups, and the correlations between parameters and Mini-Mental State Examination (MMSE) scores were calculated. Results: The results showed that the 3D texture features had significant differences (p<0.05) among three groups except grey level nonuniformity and run length nonuniformity that the difference was not significant (p>0.05) between MCI and NC or AD and MCI , and they were correlated with MMSE scores.Conclusions: 3D texture analysis can reflect the pathological changes of corpus callosum in patients with AD and MCI, and it may be helpful to AD early diagnosis.

3D Texture Analysis Reveals Imperceptible MRI Textural Alterations in the Thalamus and Putamen in Progressive Myoclonic Epilepsy Type 1, EPM1

Progressive myoclonic epilepsy type 1 (EPM1) is an autosomal recessively inherited neurodegenerative disorder characterized by young onset age, myoclonus and tonic-clonic epileptic seizures. At the time of diagnosis, the visual assessment of the brain MRI is usually normal, with no major changes found later. Therefore, we utilized texture analysis (TA) to characterize and classify the underlying properties of the affected brain tissue by means of 3D texture features. Sixteen genetically verified patients with EPM1 and 16 healthy controls were included in the study. TA was performed upon 3D volumes of interest that were placed bilaterally in the thalamus, amygdala, hippocampus, caudate nucleus and putamen. Compared to the healthy controls, EPM1 patients had significant textural differences especially in the thalamus and right putamen. The most significantly differing texture features included parameters that measure the complexity and heterogeneity of the tissue, such as the co-occurrence matrix-based entropy and angular second moment, and also the run-length matrix-based parameters of gray-level non-uniformity, short run emphasis and long run emphasis. This study demonstrates the usability of 3D TA for extracting additional information from MR images. Textural alterations which suggest complex, coarse and heterogeneous appearance were found bilaterally in the thalamus, supporting the previous literature on thalamic pathology in EPM1. The observed putamenal involvement is a novel finding. Our results encourage further studies on the clinical applications, feasibility, reproducibility and reliability of 3D TA.

An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters

Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.

Texture analysis of X-ray radiographs is correlated with bone histomorphometry

Three-dimensional (3D) alteration of trabecular microarchitecture is a risk factor for vertebral fractures. Histomorphometry allows microarchitectural analysis of bone, but the necessity to perform a transiliac bone biopsy limits its use in large series of patients. X-Ray films are commonly used in routine practice and constitute a 2D projection of the trabecular architecture. In this study, blocks of trabecular dried bone were prepared from calf bone. They were selected from different parts of the femoral condyle to provide various patterns of trabecular disposition. All blocks (25 x 15 x 8 mm) were radiographed on a numeric X-ray system, with the same acquisition parameters. Texture analysis was carried out by several methods: (skeletonization, run lengths, and fractal techniques). Histological sections of the blocks were obtained and analyzed by 2D histomorphometric methods: trabecular bone volume (BV/TV), trabecular characteristics, and 2D descriptors of architecture (star volumes, strut analysis, fractal dimensions). Significant correlations were obtained for BV/TV with run length parameters measured on the X-ray image. The trabecular characteristics were found to be highly correlated with texture parameters describing the X-ray image (e.g., trabecular number [Tb.N]/axis, r = -0.82;/short run emphasis [SRE], r = -0.87; trabecular separation [Tb.Sp]/gray level nonuniformity [GLN], r = -0.82). Several histomorphometric parameters, independent of the "plate-and-rod" model, were also well correlated with texture descriptors. Although the relationships between the 2D and 3D trabecular architecture are not fully elucidated, the texture analysis of X-ray films might be a suitable approach to investigate the disorganization of bone in osteoporosis.

Method and apparatus for diagnosis of breast tumors

An apparatus for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient. A region of interest is located and defined on the ultrasonic image, including substantially all of the candidate tissue and excluding substantially all the normal tissue. The region of interest is digitized, generating an array of pixels intensity values. A first features is generated from the arrays of pixels corresponding to the angular second moment of the pixel intensity values. A second feature is generated from the array of pixels corresponding to the inverse contrast of the pixel intensity values. A third feature is generated from the array of pixels corresponding to the short run emphasis of the pixel intensity values. The first, second and third feature values are provided to a neural network. A set of trained weights are applied to the feature values, which generates a network output between 0 and 1, whereby the output values tend toward 1 when the candidate tissue is malignant and the output values tend toward 0 when the candidate tissue is benign.

El problema del atraso cambiario: consideraciones teóricas y prácticas

​This article considers the prospects for constructing a theoretical framework that allows the author understand the trend of the real exchange rate in Peru since 1989. After discarding the Purchasing Power Parity approach, because of its theoretical flaws and the portfolio approaches, because of their short-run emphasis, the author states the need for a real approach. Based on the Dornbusch, Fischer and Samuelson model he proposes an explanation in term of the interrelation between the sectors producing tradeables and non tradeables goods.