Literature DB >> 28089191

Object shape dependency of in-plane resolution for iterative reconstruction of computed tomography.

Tadanori Takata1, Katsuhiro Ichikawa2, Wataru Mitsui3, Hiroyuki Hayashi3, Kaori Minehiro3, Keita Sakuta3, Haruka Nunome3, Kousuke Matsubara2, Hiroki Kawashima2, Yukihiro Matsuura3, Toshifumi Gabata4.   

Abstract

The present study aimed to investigate whether the in-plane resolution property of iterative reconstruction (IR) of computed tomography (CT) data is object shape-dependent by testing columnar shapes with diameters of 3, 7, and 10cm (circular edge method) and a cubic shape with 5-cm side lengths (linear edge method). For each shape, objects were constructed of acrylic (contrast in Hounsfield units [ΔHU]=120) as well as a soft tissue equivalent material (ΔHU=50). For each shape, we measured the modulation transfer functions (MTFs) of IR and filtered back projection (FBP) using two multi-slice CT scanners at scan doses of 5 and 10mGy. In addition, we evaluated a thin metal wire using the conventional method at 10mGy. For FBP images, the MTF results of the tested objects and the wire method showed substantial agreement, thus demonstrating the validity of our analysis technique. For IR images, the MTF results of different shapes were nearly identical for each object contrast and dose combination, and we did not observe shape-dependent effects of the resolution properties of either tested IR. We conclude that both the circular edge method and linear edge method are equally useful for evaluating the resolution properties of IRs.
Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Keywords:  Computed tomography; Iterative reconstruction; Modulation transfer function; Spatial resolution

Mesh:

Year:  2017        PMID: 28089191     DOI: 10.1016/j.ejmp.2017.01.001

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  4 in total

1.  Tilted-wire method for measuring resolution properties of CT images under extremely low-contrast and high-noise conditions.

Authors:  Chiaki Tominaga; Hiroki Azumi; Mitsunori Goto; Masaaki Taura; Noriyasu Homma; Issei Mori
Journal:  Radiol Phys Technol       Date:  2018-02-23

2.  Quality evaluation of image-based iterative reconstruction for CT: Comparison with hybrid iterative reconstruction.

Authors:  Hiroki Kawashima; Katsuhiro Ichikawa; Kosuke Matsubara; Hiroji Nagata; Tadanori Takata; Satoshi Kobayashi
Journal:  J Appl Clin Med Phys       Date:  2019-05-02       Impact factor: 2.102

3.  Performance of clinically available deep learning image reconstruction in computed tomography: a phantom study.

Authors:  Hiroki Kawashima; Katsuhiro Ichikawa; Tadanori Takata; Wataru Mitsui; Hiroshi Ueta; Norihide Yoneda; Satoshi Kobayashi
Journal:  J Med Imaging (Bellingham)       Date:  2020-12-16

4.  A pitfall of using the circular-edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images.

Authors:  Akihiro Narita; Masaki Ohkubo
Journal:  J Appl Clin Med Phys       Date:  2020-01-20       Impact factor: 2.102

  4 in total

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