Literature DB >> 30460476

Evaluation of the depiction ability of similar subtraction images using digital chest radiographs of different patients.

Yoichiro Shimizu1, Junji Morishita2, Yusuke Matsunobu3, Yongsu Yoon2, Yasuo Sasaki4, Shigehiko Katsuragawa5, Hidetake Yabuuchi2.   

Abstract

The temporal subtraction (TS) technique requires the same patient's chest radiographs (CXRs) acquired on different dates, whereas the similar subtraction (SS) technique can be used in patients who have no previous CXR, using similar CXRs from different patients. This study aimed to examine the depiction ability of SS images with simulated nodules in comparison with that of TS images with 2- and 7-year acquisition intervals. One hundred patients were randomly selected from our image database. The most recently acquired images of the patients were used as target images for subtraction. The simulated nodule was superimposed on each target image to examine the usefulness of the SS technique. The most (Top 1) and ten most (Top 10) similar images for each target image were identified in the 24,254-image database using a template-matching technique, and used for the SS technique. SS and TS images were obtained using a previously developed nonlinear image-warping technique. The depiction ability of SS and TS images was evaluated using the contrast-to-noise ratio (CNR). The proportion of Top 1 SS images showing higher CNR than that of the TS images with 2- and 7-year acquisition intervals was 28% (28/100) and 33% (33/100), respectively. Moreover, the proportion of cases that had any of the Top 10 SS images with higher CNRs than those of TS images with 2- and 7-year acquisition intervals was 56% (56/100) and 72% (72/100), respectively. Our study indicates that the SS technique can potentially be used to detect lung nodules on CXRs.

Entities:  

Keywords:  Contrast-to-noise ratio; Digital chest radiograph; Similar subtraction technique; Temporal subtraction technique

Mesh:

Year:  2018        PMID: 30460476     DOI: 10.1007/s12194-018-0489-7

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  7 in total

1.  Iterative image warping technique for temporal subtraction of sequential chest radiographs to detect interval change.

Authors:  T Ishida; S Katsuragawa; K Nakamura; H MacMahon; K Doi
Journal:  Med Phys       Date:  1999-07       Impact factor: 4.071

2.  Application of temporal subtraction for detection of interval changes on chest radiographs: improvement of subtraction images using automated initial image matching.

Authors:  T Ishida; K Ashizawa; R Engelmann; S Katsuragawa; H MacMahon; K Doi
Journal:  J Digit Imaging       Date:  1999-05       Impact factor: 4.056

3.  An automated patient recognition method based on an image-matching technique using previous chest radiographs in the picture archiving and communication system environment.

Authors:  J Morishita; S Katsuragawa; K Kondo; K Doi
Journal:  Med Phys       Date:  2001-06       Impact factor: 4.071

4.  Digital image subtraction of temporally sequential chest images for detection of interval change.

Authors:  A Kano; K Doi; H MacMahon; D D Hassell; M L Giger
Journal:  Med Phys       Date:  1994-03       Impact factor: 4.071

5.  Clinical usefulness of temporal subtraction method in screening digital chest radiography with a mobile computed radiography system.

Authors:  Yasuo Sasaki; Katsumi Abe; Makiko Tabei; Shigehiko Katsuragawa; Atsuko Kurosaki; Shoji Matsuoka
Journal:  Radiol Phys Technol       Date:  2010-12-18

6.  Usefulness of computerized method for lung nodule detection on digital chest radiographs using similar subtraction images from different patients.

Authors:  Takatoshi Aoki; Nobuhiro Oda; Yoshiko Yamashita; Keiji Yamamoto; Yukunori Korogi
Journal:  Eur J Radiol       Date:  2011-03-05       Impact factor: 3.528

7.  Potential usefulness of biological fingerprints in chest radiographs for automated patient recognition and identification.

Authors:  Junji Morishita; Shigehiko Katsuragawa; Yasuo Sasaki; Kunio Doi
Journal:  Acad Radiol       Date:  2004-03       Impact factor: 3.173

  7 in total

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