Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom

SUMMARY: Our aim was to evaluate changes in texture features based on variations in CT parameters on a phantom. Scans were performed with varying milliampere, kilovolt, section thickness, pitch, and acquisition mode. Forty-two texture features were extracted by using an in-house-developed Matlab program. Two-tailed t tests and false-detection analyses were performed with significant differences in texture features based on detector array configurations (Q values = 0.001–0.006), section thickness (Q values = 0.0002–0.001), and acquisition mode (Q values = 0.003–0.006). Variations in milliampere and kilovolt had no significant effect.

[1]  V. Goh,et al.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. , 2013, Radiology.

[2]  W. Shabana,et al.  Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis. , 2015, AJR. American journal of roentgenology.

[3]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[4]  Edson Amaro,et al.  Texture analysis of high resolution MRI allows discrimination between febrile and afebrile initial precipitating injury in mesial temporal sclerosis , 2012, Magnetic resonance in medicine.

[5]  Beatriz Paniagua,et al.  Validation of CBCT for the computation of textural biomarkers , 2015, Medical Imaging.

[6]  Samuel G Armato,et al.  Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy , 2014, Physics in medicine and biology.

[7]  M. M. Qureshi,et al.  Using Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinomas on CT , 2015, American Journal of Neuroradiology.

[8]  Sang Min Lee,et al.  Prognostic Value of Computed Tomography Texture Features in Non–Small Cell Lung Cancers Treated With Definitive Concomitant Chemoradiotherapy , 2015, Investigative radiology.

[9]  Arie Nakhmani,et al.  Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging. , 2014, Magnetic resonance imaging.

[10]  Siegfried Trattnig,et al.  Quantitative analysis of lumbar intervertebral disc abnormalities at 3.0 Tesla: value of T2 texture features and geometric parameters , 2012, NMR in biomedicine.

[11]  Alejandro Munoz del Rio,et al.  CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes , 2015, Abdominal Imaging.

[12]  Jayashree Kalpathy-Cramer,et al.  Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive. , 2014, Translational oncology.

[13]  P. J. Murray,et al.  MRI texture analysis of subchondral bone at the tibial plateau , 2016, European Radiology.

[14]  Milan Hájek,et al.  Texture analysis of human liver , 2002, Journal of magnetic resonance imaging : JMRI.

[15]  Balaji Ganeshan,et al.  Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. , 2009, Radiology.

[16]  Max Wintermark,et al.  Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project , 2014, BMC Medical Genomics.

[17]  Jinzhong Yang,et al.  Preliminary investigation into sources of uncertainty in quantitative imaging features , 2015, Comput. Medical Imaging Graph..

[18]  Baojun Li,et al.  Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. , 2016, European journal of radiology.

[19]  Lars E Olsson,et al.  A texture analysis approach to quantify ventilation changes in hyperpolarised 3He MRI of the rat lung in an asthma model , 2012, NMR in biomedicine.

[20]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[21]  O. Mawlawi,et al.  Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. , 2014, International journal of radiation oncology, biology, physics.

[22]  Balaji Ganeshan,et al.  Quantifying tumour heterogeneity with CT , 2013, Cancer imaging : the official publication of the International Cancer Imaging Society.

[23]  Takeshi Johkoh,et al.  Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade. , 2011, Radiology.

[24]  Dina Muin,et al.  Texture-based classification of different gastric tumors at contrast-enhanced CT. , 2013, European journal of radiology.

[25]  Nicola Schieda,et al.  Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? , 2015, Radiology.

[26]  J. Soto,et al.  Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI , 2015, Journal of magnetic resonance imaging : JMRI.

[27]  Xiaoou Tang,et al.  Texture information in run-length matrices , 1998, IEEE Trans. Image Process..