Literature DB >> 33907724

A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction.

Yongfeng Gao1, Zhengrong Liang2, Hao Zhang3, Jie Yang4, John Ferretti1, Thomas Bilfinger5, Kavitha Yaddanapudi1, Mark Schweitzer1, Priya Bhattacharji6, William Moore6.   

Abstract

Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.

Entities:  

Keywords:  Lung cancer; low-dose computed tomography; nodule characterization; nodule localization; texture-enhanced image reconstruction; tissue texture

Year:  2019        PMID: 33907724      PMCID: PMC8075295          DOI: 10.1109/trpms.2019.2957459

Source DB:  PubMed          Journal:  IEEE Trans Radiat Plasma Med Sci        ISSN: 2469-7303


  35 in total

1.  A three-dimensional statistical approach to improved image quality for multislice helical CT.

Authors:  Jean-Baptiste Thibault; Ken D Sauer; Charles A Bouman; Jiang Hsieh
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

2.  Adaptive nonlocal means filtering based on local noise level for CT denoising.

Authors:  Zhoubo Li; Lifeng Yu; Joshua D Trzasko; David S Lake; Daniel J Blezek; Joel G Fletcher; Cynthia H McCollough; Armando Manduca
Journal:  Med Phys       Date:  2014-01       Impact factor: 4.071

3.  State of the Art: Iterative CT Reconstruction Techniques.

Authors:  Lucas L Geyer; U Joseph Schoepf; Felix G Meinel; John W Nance; Gorka Bastarrika; Jonathon A Leipsic; Narinder S Paul; Marco Rengo; Andrea Laghi; Carlo N De Cecco
Journal:  Radiology       Date:  2015-08       Impact factor: 11.105

4.  Task-based image quality evaluation of iterative reconstruction methods for low dose CT using computer simulations.

Authors:  Jingyan Xu; Matthew K Fuld; George S K Fung; Benjamin M W Tsui
Journal:  Phys Med Biol       Date:  2015-03-17       Impact factor: 3.609

5.  Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.

Authors:  Eunhee Kang; Won Chang; Jaejun Yoo; Jong Chul Ye
Journal:  IEEE Trans Med Imaging       Date:  2018-06       Impact factor: 10.048

6.  Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics.

Authors:  Tingting Zhao; John Hoffman; Michael McNitt-Gray; Dan Ruan
Journal:  Med Phys       Date:  2018-11-19       Impact factor: 4.071

7.  Deriving adaptive MRF coefficients from previous normal-dose CT scan for low-dose image reconstruction via penalized weighted least-squares minimization.

Authors:  Hao Zhang; Hao Han; Jing Wang; Jianhua Ma; Yan Liu; William Moore; Zhengrong Liang
Journal:  Med Phys       Date:  2014-04       Impact factor: 4.071

8.  Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?

Authors:  Xiaochuan Pan; Emil Y Sidky; Michael Vannier
Journal:  Inverse Probl       Date:  2009-01-01       Impact factor: 2.407

9.  Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography.

Authors:  Yifan Hu; Zhengrong Liang; Bowen Song; Hao Han; Perry J Pickhardt; Wei Zhu; Chaijie Duan; Hao Zhang; Matthew A Barish; Chris E Lascarides
Journal:  IEEE Trans Med Imaging       Date:  2016-01-18       Impact factor: 10.048

Review 10.  Quantitative imaging in cancer evolution and ecology.

Authors:  Robert A Gatenby; Olya Grove; Robert J Gillies
Journal:  Radiology       Date:  2013-10       Impact factor: 11.105

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  1 in total

1.  Machine learned texture prior from full-dose CT database via multi-modality feature selection for Bayesian reconstruction of low-dose CT.

Authors:  Yongfeng Gao; Jiaxing Tan; Yongyi Shi; Hao Zhang; Siming Lu; Amit Gupta; Haifang Li; Michael Reiter; Zhengrong Liang
Journal:  IEEE Trans Med Imaging       Date:  2021-12-30       Impact factor: 11.037

  1 in total

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