Literature DB >> 26624973

Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm.

Justin Solomon1, Achille Mileto1, Rendon C Nelson1, Kingshuk Roy Choudhury1, Ehsan Samei1.   

Abstract

PURPOSE: To determine if radiation dose and reconstruction algorithm affect the computer-based extraction and analysis of quantitative imaging features in lung nodules, liver lesions, and renal stones at multi-detector row computed tomography (CT).
MATERIALS AND METHODS: Retrospective analysis of data from a prospective, multicenter, HIPAA-compliant, institutional review board-approved clinical trial was performed by extracting 23 quantitative imaging features (size, shape, attenuation, edge sharpness, pixel value distribution, and texture) of lesions on multi-detector row CT images of 20 adult patients (14 men, six women; mean age, 63 years; range, 38-72 years) referred for known or suspected focal liver lesions, lung nodules, or kidney stones. Data were acquired between September 2011 and April 2012. All multi-detector row CT scans were performed at two different radiation dose levels; images were reconstructed with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) algorithms. A linear mixed-effects model was used to assess the effect of radiation dose and reconstruction algorithm on extracted features.
RESULTS: Among the 23 imaging features assessed, radiation dose had a significant effect on five, three, and four of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Adaptive statistical iterative reconstruction had a significant effect on three, one, and one of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). MBIR reconstruction had a significant effect on nine, 11, and 15 of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Of note, the measured size of lung nodules and renal stones with MBIR was significantly different than those for the other two algorithms (P < .002 for all comparisons). Although lesion texture was significantly affected by the reconstruction algorithm used (average of 3.33 features affected by MBIR throughout lesion types; P < .002, for all comparisons), no significant effect of the radiation dose setting was observed for all but one of the texture features (P = .002-.998).
CONCLUSION: Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

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Year:  2015        PMID: 26624973     DOI: 10.1148/radiol.2015150892

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  37 in total

1.  Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging.

Authors:  Thomas Perrin; Abhishek Midya; Rikiya Yamashita; Jayasree Chakraborty; Tome Saidon; William R Jarnagin; Mithat Gonen; Amber L Simpson; Richard K G Do
Journal:  Abdom Radiol (NY)       Date:  2018-12

2.  Investigating the Robustness Neighborhood Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix Radiomic Features on Clinical Computed Tomography Systems Using Anthropomorphic Phantoms: Evidence From a Multivendor Study.

Authors:  Usman Mahmood; Aditya P Apte; Joseph O Deasy; C Ross Schmidtlein; Amita Shukla-Dave
Journal:  J Comput Assist Tomogr       Date:  2017 Nov/Dec       Impact factor: 1.826

3.  Design and fabrication of heterogeneous lung nodule phantoms for assessing the accuracy and variability of measured texture radiomics features in CT.

Authors:  Ehsan Samei; Jocelyn Hoye; Yuese Zheng; Justin B Solomon; Daniele Marin
Journal:  J Med Imaging (Bellingham)       Date:  2019-06-21

4.  Can virtual monochromatic images from dual-energy CT replace low-kVp images for abdominal contrast-enhanced CT in small- and medium-sized patients?

Authors:  Peijie Lv; Zhigang Zhou; Jie Liu; Yaru Chai; Huiping Zhao; Hua Guo; Daniele Marin; Jianbo Gao
Journal:  Eur Radiol       Date:  2018-11-30       Impact factor: 5.315

Review 5.  Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.

Authors:  Mario Silva; Gianluca Milanese; Valeria Seletti; Alarico Ariani; Nicola Sverzellati
Journal:  Br J Radiol       Date:  2018-01-12       Impact factor: 3.039

6.  Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study.

Authors:  Shaimaa Bakr; Sebastian Echegaray; Rajesh Shah; Aya Kamaya; John Louie; Sandy Napel; Nishita Kothary; Olivier Gevaert
Journal:  J Med Imaging (Bellingham)       Date:  2017-08-21

7.  Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra.

Authors:  Muhammad Shafiq-Ul-Hassan; Geoffrey G Zhang; Dylan C Hunt; Kujtim Latifi; Ghanim Ullah; Robert J Gillies; Eduardo G Moros
Journal:  J Med Imaging (Bellingham)       Date:  2017-12-14

8.  CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts.

Authors:  Su Joa Ahn; Jung Hoon Kim; Sang Min Lee; Sang Joon Park; Joon Koo Han
Journal:  Eur Radiol       Date:  2018-11-19       Impact factor: 5.315

9.  Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility.

Authors:  Abhishek Midya; Jayasree Chakraborty; Mithat Gönen; Richard K G Do; Amber L Simpson
Journal:  J Med Imaging (Bellingham)       Date:  2018-02-15

10.  Differentiating low and high grade mucoepidermoid carcinoma of the salivary glands using CT radiomics.

Authors:  Michael H Zhang; Adam Hasse; Timothy Carroll; Alexander T Pearson; Nicole A Cipriani; Daniel T Ginat
Journal:  Gland Surg       Date:  2021-05
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