Literature DB >> 32416552

Ultra high-resolution computed tomography with 1024-matrix: Comparison with 512-matrix for the evaluation of pulmonary nodules.

Mitsuko Tsubamoto1, Akinori Hata2, Masahiro Yanagawa3, Osamu Honda3, Tomo Miyata3, Yuriko Yoshida3, Akiko Nakayama3, Noriko Kikuchi3, Ayumi Uranishi4, Shinsuke Tsukagoshi4, Yoshiyuki Watanabe2, Noriyuki Tomiyama3.   

Abstract

PURPOSE: To determine whether a 1024-matrix provides superior image quality for the evaluation of pulmonary nodules.
MATERIALS AND METHODS: Prospective evaluation conducted between December 2017 and April 2018, during which CT images showing lung nodules of more than 6 mm and less than 30 mmm were reconstructed with 2 different protocols: 0.5-mm thickness, 512 × 512 matrix, 34.5-cm field of view (FOV) (0.5-512 protocol); and 2-mm thickness, 1024 × 1024 matrix, 34.5-cm FOV (2-1024 protocol). Lung nodule characteristics such as margin, lobulation, pleural indentation, spiculation as well as peripheral vessels and bronchioles visibility and overall image quality were evaluated by three chest radiologists, using a 5-point scale. Image noise was evaluated by measuring the standard deviation in the region of interest for each image.
RESULTS: A total of 89 nodules were evaluated. The 2-1024 protocol performed significantly better for the subjective evaluation of pulmonary nodules (p = 0.006 ∼ p < 0.0001). However, image noise was significantly higher both subjectively and objectively (p = 0.036, p < 0.0001).
CONCLUSION: The use of a 2-1024 protocol does not increase the amount of images and allows better assessment of pulmonary nodules, despite noise increase.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  HRCT; Image reconstruction; Matrix size; Pulmonary nodule

Mesh:

Year:  2020        PMID: 32416552     DOI: 10.1016/j.ejrad.2020.109033

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  5 in total

1.  Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions.

Authors:  Ryo Matsukiyo; Yoshiharu Ohno; Takahiro Matsuyama; Hiroyuki Nagata; Hirona Kimata; Yuya Ito; Yukihiro Ogawa; Kazuhiro Murayama; Ryoichi Kato; Hiroshi Toyama
Journal:  Jpn J Radiol       Date:  2020-10-10       Impact factor: 2.374

2.  Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study.

Authors:  Yoshiharu Ohno; Naruomi Akino; Yasuko Fujisawa; Hirona Kimata; Yuya Ito; Kenji Fujii; Yumi Kataoka; Yoshihiro Ida; Yuka Oshima; Nayu Hamabuchi; Chika Shigemura; Ayumi Watanabe; Yuki Obama; Satomu Hanamatsu; Takahiro Ueda; Hirotaka Ikeda; Kazuhiro Murayama; Hiroshi Toyama
Journal:  Eur Radiol       Date:  2022-07-16       Impact factor: 7.034

3.  Novel Intraoperative Navigation Using Ultra-High-Resolution CT in Robot-Assisted Partial Nephrectomy.

Authors:  Kiyoshi Takahara; Yoshiharu Ohno; Kosuke Fukaya; Ryo Matsukiyo; Takuhisa Nukaya; Masashi Takenaka; Kenji Zennami; Manabu Ichino; Naohiko Fukami; Hitomi Sasaki; Mamoru Kusaka; Hiroshi Toyama; Makoto Sumitomo; Ryoichi Shiroki
Journal:  Cancers (Basel)       Date:  2022-04-18       Impact factor: 6.639

4.  A Pilot Study to Estimate the Impact of High Matrix Image Reconstruction on Chest Computed Tomography.

Authors:  Akitoshi Inoue; Tucker F Johnson; Benjamin A Voss; Yong S Lee; Shuai Leng; Chi Wan Koo; Brian D McCollough; Jayse M Weaver; Hao Gong; Rickey E Carter; Cynthia H McCollough; Joel G Fletcher
Journal:  J Clin Imaging Sci       Date:  2021-09-30

5.  The impact of the field of view (FOV) on image quality in MDCT angiography of the lower extremities.

Authors:  Nigar Salimova; Jan B Hinrichs; Marcel Gutberlet; Bernhard C Meyer; Frank K Wacker; Christian von Falck
Journal:  Eur Radiol       Date:  2021-12-13       Impact factor: 7.034

  5 in total

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