Literature DB >> 16355033

Improvement of low-contrast detectability in low-dose hepatic multidetector computed tomography using a novel adaptive filter: evaluation with a computer-simulated liver including tumors.

Yoshinori Funama1, Kazuo Awai, Osamu Miyazaki, Yoshiharu Nakayama, Taiga Goto, Yasuo Omi, Toshiaki Shimonobo, Duo Liu, Yasuyuki Yamashita, Shinichi Hori.   

Abstract

PURPOSE: The purpose of this study was to investigate how much radiation dose can be reduced without loss of low-contrast detectability with a newly developed adaptive noise reduction filter in hepatic multidetector computed tomography (MDCT) scans by using a computer-simulated liver phantom.
MATERIALS AND METHODS: Simulated CT images, including liver and intrahepatic tumors, were mathematically constructed using a computer workstation to evaluate low-contrast detectability by the observer performance test. Milliampere second for construction of simulated images were 60, 80, 100, and 120 mAs (low dose) and 160 mAs (standard dose) at 120 kVp. Images with 60, 80, 100, and 120 mAs were postprocessed with the adaptive noise reduction filter. A total of 432 images were prepared and receiver operating characteristic (ROC) analysis was performed by 5 radiologists. The detectability of simulated tumor by radiologists was estimated with the area under the ROC curves (Az values). In addition, we visually evaluated CT images of 15 patients with chronic liver damage for graininess of the liver parenchyma, sharpness of the liver contour, conspicuity and marginal sharpness of the liver tumors, and overall image quality.
RESULTS: The mean Az value at 0.777 (60 mAs), 0.828 (80 mAs), and 0.844 (100 mAs) without filter was significantly lower than that of 160 mAs without filter (P < 0.001, 60 mAs; P = 0.010, 80 mAs; P = 0.040, 100 mAs). There was no statistical difference between the mean Az value at 80 mAs with and 160 mAs without the adaptive noise reduction filter (P = 0.220) and 100 mAs with and 160 mAs without the adaptive noise reduction filter (P = 0.979). In the visual evaluation of patient livers, there was no statistical difference in the graininess and sharpness of the liver, the conspicuity and marginal sharpness of the tumor, and the overall image quality between standard-dose and filtered low-dose images (Wilcoxon signed rank test, P > 0.05).
CONCLUSION: The radiation dose can be reduced by 50% without loss of nodule detectability by applying the adaptive noise reduction filter to simulated and patient liver images obtained at MDCT.

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Year:  2006        PMID: 16355033     DOI: 10.1097/01.rli.0000188026.20172.5d

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  17 in total

Review 1.  [Strategies for reducing the CT radiation dose].

Authors:  S T Schindera; C Nauer; R Treier; P Trueb; G von Allmen; P Vock; Z Szucs-Farkas
Journal:  Radiologe       Date:  2010-12       Impact factor: 0.635

2.  Method for reducing noise in X-ray images by averaging pixels based on the normalized difference with the relevant pixel.

Authors:  Masayuki Nishiki; Kunio Shiraishi; Takuya Sakaguchi; Kyojiro Nambu
Journal:  Radiol Phys Technol       Date:  2008-06-20

3.  Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering.

Authors:  Yang Chen; Fei Yu; Limin Luo; Christine Toumoulin
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2013

4.  An education and training programme for radiological institutes: impact on the reduction of the CT radiation dose.

Authors:  Sebastian T Schindera; Reto Treier; Gabriel von Allmen; Claude Nauer; Philipp R Trueb; Peter Vock; Zsolt Szucs-Farkas
Journal:  Eur Radiol       Date:  2011-05-31       Impact factor: 5.315

5.  Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT.

Authors:  K L Dobeli; S J Lewis; S R Meikle; D L Thiele; P C Brennan
Journal:  Br J Radiol       Date:  2013-02-07       Impact factor: 3.039

6.  Radiation dose reduction with application of non-linear adaptive filters for abdominal CT.

Authors:  Sarabjeet Singh; Mannudeep K Kalra; Mi Kim Sung; Anni Back; Michael A Blake
Journal:  World J Radiol       Date:  2012-01-28

7.  Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT.

Authors:  Akio Tamura; Eisuke Mukaida; Yoshitaka Ota; Iku Nakamura; Kazumasa Arakita; Kunihiro Yoshioka
Journal:  Quant Imaging Med Surg       Date:  2022-05

8.  Development of a noise reduction filter algorithm for pediatric body images in multidetector CT.

Authors:  Eiji Nishimaru; Katsuhiro Ichikawa; Izumi Okita; Yukihiro Tomoshige; Takehiro Kurokawa; Yuko Nakamura; Masayuki Suzuki
Journal:  J Digit Imaging       Date:  2009-06-18       Impact factor: 4.056

9.  Radiation dose reduction in hepatic multidetector computed tomography with a novel adaptive noise reduction filter.

Authors:  Yoshinori Funama; Kazuo Awai; Osamu Miyazaki; Taiga Goto; Yoshiharu Nakayama; Masamitchi Shimamura; Kumiko Hiraishi; Shinichi Hori; Yasuyuki Yamashita
Journal:  Radiat Med       Date:  2008-04

10.  Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection.

Authors:  Akio Tamura; Eisuke Mukaida; Yoshitaka Ota; Masayoshi Kamata; Shun Abe; Kunihiro Yoshioka
Journal:  Br J Radiol       Date:  2021-07-01       Impact factor: 3.039

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