Literature DB >> 28374080

Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages.

André Euler1, Bram Stieltjes1, Zsolt Szucs-Farkas2, Reto Eichenberger1, Clemens Reisinger1, Anna Hirschmann1, Caroline Zaehringer1, Achim Kircher1, Matthias Streif1, Sabine Bucher1, David Buergler1, Luigia D'Errico1, Sebastién Kopp1, Markus Wilhelm1, Sebastian T Schindera3,4.   

Abstract

OBJECTIVES: To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages.
METHODS: A phantom with 45 hypoattenuating lesions was placed in two water containers and scanned at 70, 80, 100, and 120 kVp. The 120-kVp protocol served as reference, and the volume CT dose index (CTDIvol) was kept constant for all protocols. The datasets were reconstructed with MBIR and FBP. Image noise and contrast-to-noise-ratio (CNR) were assessed. Low-contrast lesion detectability was evaluated by 12 radiologists.
RESULTS: MBIR decreased the image noise by 24% and 27%, and increased the CNR by 30% and 29% for the medium and large phantoms, respectively. Lower tube voltages increased the CNR by 58%, 46%, and 16% at 70, 80, and 100 kVp, respectively, compared with 120 kVp in the medium phantom and by 9%, 18% and 12% in the large phantom. No significant difference in lesion detection rate was observed (medium: 79-82%; large: 57-65%; P > 0.37).
CONCLUSIONS: Although MBIR improved quantitative image quality compared with FBP, it did not result in increased low-contrast lesion detection in abdominal CT at different tube voltages in simulated medium and large patients. KEY POINTS: • MBIR improved quantitative image quality but not lesion detection compared with FBP. • Increased CNR by low tube voltages did not improve lesion detection. • Changes in image noise and CNR do not directly influence diagnostic accuracy.

Entities:  

Keywords:  Filtered back projection; Low-contrast detection; Model-based iterative reconstruction; Multidetector computed tomography; Radiological phantom

Mesh:

Year:  2017        PMID: 28374080     DOI: 10.1007/s00330-017-4825-9

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  38 in total

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2.  Image noise and liver lesion detection with MDCT: a phantom study.

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3.  A quantitative comparison of noise reduction across five commercial (hybrid and model-based) iterative reconstruction techniques: an anthropomorphic phantom study.

Authors:  Manuel Patino; Jorge M Fuentes; Koichi Hayano; Avinash R Kambadakone; Jennifer W Uyeda; Dushyant V Sahani
Journal:  AJR Am J Roentgenol       Date:  2015-02       Impact factor: 3.959

4.  Iterative reconstruction methods in two different MDCT scanners: physical metrics and 4-alternative forced-choice detectability experiments--a phantom approach.

Authors:  Frédéric A Miéville; François Gudinchet; Francis Brunelle; François O Bochud; Francis R Verdun
Journal:  Phys Med       Date:  2012-01-02       Impact factor: 2.685

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Journal:  Radiology       Date:  2005-08       Impact factor: 11.105

6.  Using the K-edge to improve contrast conspicuity and to lower radiation dose with a 16-MDCT: a phantom and human study.

Authors:  Sanjeeva P Kalva; Dushyant V Sahani; Peter F Hahn; Sanjay Saini
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Review 7.  Update on the role of imaging in management of metastatic colorectal cancer.

Authors:  Sree Harsha Tirumani; Kyung Won Kim; Mizuki Nishino; Stephanie A Howard; Katherine M Krajewski; Jyothi P Jagannathan; James M Cleary; Nikhil H Ramaiya; Atul B Shinagare
Journal:  Radiographics       Date:  2014 Nov-Dec       Impact factor: 5.333

8.  Detection of pancreatic tumors, image quality, and radiation dose during the pancreatic parenchymal phase: effect of a low-tube-voltage, high-tube-current CT technique--preliminary results.

Authors:  Daniele Marin; Rendon C Nelson; Huiman Barnhart; Sebastian T Schindera; Lisa M Ho; Tracy A Jaffe; Terry T Yoshizumi; Richard Youngblood; Ehsan Samei
Journal:  Radiology       Date:  2010-08       Impact factor: 11.105

9.  Observer Performance in the Detection and Classification of Malignant Hepatic Nodules and Masses with CT Image-Space Denoising and Iterative Reconstruction.

Authors:  Joel G Fletcher; Lifeng Yu; Zhoubo Li; Armando Manduca; Daniel J Blezek; David M Hough; Sudhakar K Venkatesh; Gregory C Brickner; Joseph C Cernigliaro; Amy K Hara; Jeff L Fidler; David S Lake; Maria Shiung; David Lewis; Shuai Leng; Kurt E Augustine; Rickey E Carter; David R Holmes; Cynthia H McCollough
Journal:  Radiology       Date:  2015-05-26       Impact factor: 11.105

10.  Comparison of Knowledge-based Iterative Model Reconstruction and Hybrid Reconstruction Techniques for Liver CT Evaluation of Hypervascular Hepatocellular Carcinoma.

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Journal:  J Comput Assist Tomogr       Date:  2016 Nov/Dec       Impact factor: 1.826

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  8 in total

1.  Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance.

Authors:  Gautier Laurent; Nicolas Villani; Gabriela Hossu; Aymeric Rauch; Alain Noël; Alain Blum; Pedro Augusto Gondim Teixeira
Journal:  Eur Radiol       Date:  2019-01-30       Impact factor: 5.315

2.  Can Realistic Liver Tissue Surrogates Accurately Quantify the Impact of Reduced-kV Imaging on Attenuation and Contrast of Parenchyma and Lesions?

Authors:  Andre Euler; Justin Solomon; Paul F FitzGerald; Ehsan Samei; Rendon C Nelson
Journal:  Acad Radiol       Date:  2018-09-28       Impact factor: 3.173

3.  Estimating detectability index in vivo: development and validation of an automated methodology.

Authors:  Taylor Brunton Smith; Justin Solomon; Ehsan Samei
Journal:  J Med Imaging (Bellingham)       Date:  2017-12-11

4.  Iterative reconstruction with multifrequency signal recognition technology to improve low-contrast detectability: A phantom study.

Authors:  Yoshinori Funama; Takashi Shirasaka; Taiga Goto; Yuko Aoki; Kana Tanaka; Ryo Yoshida
Journal:  Acta Radiol Open       Date:  2022-06-17

5.  Deep Learning-based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases.

Authors:  Yuko Nakamura; Toru Higaki; Fuminari Tatsugami; Jian Zhou; Zhou Yu; Naruomi Akino; Yuya Ito; Makoto Iida; Kazuo Awai
Journal:  Radiol Artif Intell       Date:  2019-10-09

6.  Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction.

Authors:  Bharti Kataria; Jonas Nilsson Althén; Örjan Smedby; Anders Persson; Hannibal Sökjer; Michael Sandborg
Journal:  Eur Radiol       Date:  2018-01-24       Impact factor: 5.315

7.  Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study.

Authors:  Takashi Shirasaka; Tsukasa Kojima; Yoshinori Funama; Yuki Sakai; Masatoshi Kondo; Ryoji Mikayama; Hiroshi Hamasaki; Toyoyuki Kato; Yasuhiro Ushijima; Yoshiki Asayama; Akihiro Nishie
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8.  Comparison of low-contrast detectability between uniform and anatomically realistic phantoms-influences on CT image quality assessment.

Authors:  Juliane Conzelmann; Ulrich Genske; Arthur Emig; Michael Scheel; Bernd Hamm; Paul Jahnke
Journal:  Eur Radiol       Date:  2021-09-02       Impact factor: 5.315

  8 in total

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