Literature DB >> 28341714

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

K Buch1, B Li1, M M Qureshi1,2, H Kuno1, S W Anderson1, O Sakai3,2,4.   

Abstract

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.
© 2017 by American Journal of Neuroradiology.

Mesh:

Year:  2017        PMID: 28341714      PMCID: PMC7960393          DOI: 10.3174/ajnr.A5139

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  25 in total

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Journal:  NMR Biomed       Date:  2011-12-09       Impact factor: 4.044

2.  Texture information in run-length matrices.

Authors:  X Tang
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3.  Preliminary investigation into sources of uncertainty in quantitative imaging features.

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Journal:  Comput Med Imaging Graph       Date:  2015-05-05       Impact factor: 4.790

4.  Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade.

Authors:  Kiminori Fujimoto; Tatsuyuki Tonan; Sanae Azuma; Masayoshi Kage; Osamu Nakashima; Takeshi Johkoh; Naofumi Hayabuchi; Koji Okuda; Takumi Kawaguchi; Michio Sata; Aliya Qayyum
Journal:  Radiology       Date:  2011-01-19       Impact factor: 11.105

5.  A texture analysis approach to quantify ventilation changes in hyperpolarised ³He MRI of the rat lung in an asthma model.

Authors:  Frank Risse; Jelena Pesic; Simon Young; Lars E Olsson
Journal:  NMR Biomed       Date:  2011-07-07       Impact factor: 4.044

6.  Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI.

Authors:  HeiShun Yu; Karen Buch; Baojun Li; Michael O'Brien; Jorge Soto; Hernan Jara; Stephan W Anderson
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7.  Prognostic value of computed tomography texture features in non-small cell lung cancers treated with definitive concomitant chemoradiotherapy.

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8.  Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive.

Authors:  Jayashree Kalpathy-Cramer; John Blake Freymann; Justin Stephen Kirby; Paul Eugene Kinahan; Fred William Prior
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

9.  Quantifying tumour heterogeneity with CT.

Authors:  Balaji Ganeshan; Kenneth A Miles
Journal:  Cancer Imaging       Date:  2013-03-26       Impact factor: 3.909

10.  Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project.

Authors:  Rivka R Colen; Mark Vangel; Jixin Wang; David A Gutman; Scott N Hwang; Max Wintermark; Rajan Jain; Manal Jilwan-Nicolas; James Y Chen; Prashant Raghavan; Chad A Holder; Daniel Rubin; Eric Huang; Justin Kirby; John Freymann; Carl C Jaffe; Adam Flanders; Pascal O Zinn
Journal:  BMC Med Genomics       Date:  2014-06-02       Impact factor: 3.063

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  22 in total

1.  CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma.

Authors:  Pritam Mukherjee; Murilo Cintra; Chao Huang; Mu Zhou; Shankuan Zhu; A Dimitrios Colevas; Nancy Fischbein; Olivier Gevaert
Journal:  Radiol Imaging Cancer       Date:  2020-05-15

2.  Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study.

Authors:  Mohammadhadi Khorrami; Kaustav Bera; Patrick Leo; Pranjal Vaidya; Pradnya Patil; Rajat Thawani; Priya Velu; Prabhakar Rajiah; Mehdi Alilou; Humberto Choi; Michael D Feldman; Robert C Gilkeson; Philip Linden; Pingfu Fu; Harvey Pass; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Lung Cancer       Date:  2020-02-26       Impact factor: 5.705

3.  CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy.

Authors:  H Kuno; M M Qureshi; M N Chapman; B Li; V C Andreu-Arasa; K Onoue; M T Truong; O Sakai
Journal:  AJNR Am J Neuroradiol       Date:  2017-10-12       Impact factor: 3.825

4.  Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study.

Authors:  Ying Li; Guanghua Tan; Mark Vangel; Jonathan Hall; Wenli Cai
Journal:  Quant Imaging Med Surg       Date:  2020-09

5.  Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.

Authors:  Mohammadhadi Khorrami; Kaustav Bera; Rajat Thawani; Prabhakar Rajiah; Amit Gupta; Pingfu Fu; Philip Linden; Nathan Pennell; Frank Jacono; Robert C Gilkeson; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Eur J Cancer       Date:  2021-03-17       Impact factor: 9.162

6.  Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features.

Authors:  Joseph J Foy; Mena Shenouda; Sahar Ramahi; Samuel Armato; Daniel Thomas Ginat
Journal:  J Med Imaging (Bellingham)       Date:  2020-12-30

7.  Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules.

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Review 8.  CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review.

Authors:  Mathilde Espinasse; Stéphanie Pitre-Champagnat; Benoit Charmettant; Francois Bidault; Andreas Volk; Corinne Balleyguier; Nathalie Lassau; Caroline Caramella
Journal:  Diagnostics (Basel)       Date:  2020-04-28

9.  Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

Authors:  Jonas Bianchi; Antônio Carlos de Oliveira Ruellas; João Roberto Gonçalves; Beatriz Paniagua; Juan Carlos Prieto; Martin Styner; Tengfei Li; Hongtu Zhu; James Sugai; William Giannobile; Erika Benavides; Fabiana Soki; Marilia Yatabe; Lawrence Ashman; David Walker; Reza Soroushmehr; Kayvan Najarian; Lucia Helena Soares Cevidanes
Journal:  Sci Rep       Date:  2020-05-15       Impact factor: 4.379

10.  Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model.

Authors:  Karen Buch; Hirofumi Kuno; Muhammad M Qureshi; Baojun Li; Osamu Sakai
Journal:  J Appl Clin Med Phys       Date:  2018-10-27       Impact factor: 2.102

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