Literature DB >> 31778233

Technical Note: Impact on central frequency and noise magnitude ratios by advanced CT image reconstruction techniques.

Tinsu Pan1, Akira Hasegawa2, Dershan Luo3, Carol C Wu4, Raghu Vikram4.   

Abstract

PURPOSE: We use central frequency ratio and noise magnitude ratio from noise power spectrum (NPS) to evaluate the noise reduction techniques of ASiR and ASiR-V of GE, SAFIRE and ADMIRE of Siemens, and PixelShine of AlgoMedica. ASiR, ASiR-V, SAFIRE and ADMIRE use a combination of image and projection data whereas PixelShine uses artificial intelligence neural network for noise reduction. METHODS AND MATERIALS: The homogeneous module of the ACR computed tomography (CT) phantom was scanned on a GE Revolution HD 64-slice CT for ASiR and ASiR-V, a Siemens Somatom Force for ADMIRE, and a Siemens Definition AS 64-slice for SAFIRE for NPS calculation. The baseline filtered back-projection (FBP) reconstructions were derived from the standard kernel on Revolution HD, Hr44f on Force and D40s on Definition AS. The central frequency ratio (CFR) indicates the degree of shift in the central frequency of NPS after noise reduction. A smaller CFR means a larger shift of the NPS curve, or a larger degree of image blurring. The noise magnitude ratio (NMR) indicates the amount of noise removed. A smaller NMR means a larger degree of noise reduction. An ideal noise reduction shall maintain a CFR close to 1 and a NMR close to 0.
RESULTS: The ideal noise reduction by increasing radiation exposure did not shift the central frequency when the image noise was reduced. PixelShine was the closest to the ideal noise reduction in CFR, and was followed by SAFIRE, ASiR-V, ADMIRE and ASiR, in sequence. Similarly, PixelShine had the smallest NMR, and was followed by SAFIRE, ASiR-V, ADMIRE and ASiR in sequence. Overall, PixelShine had the least central frequency shift for the same amount of noise reduction or the most noise reduction for the same amount of central frequency shift. For the same CFR, ASiR-V reduced more noise than ASiR; and SAFIRE reduced more noise than ADMIRE.
CONCLUSIONS: We introduced two new parameters of CFR and NMR from NPS to compare the reconstructions from different manufacturers. PixelShine had the least central frequency shift for the same amount of noise reduction or the most noise reduction for the same amount of central frequency shift. For the same central frequency shift, ASiR-V reduced more noise than ASiR, and SAFIRE reduced more noise than ADMIRE.
© 2019 American Association of Physicists in Medicine.

Entities:  

Keywords:  CT iterative reconstruction; CT noise reduction; artificial intelligence; deep learning; noise power spectrum

Mesh:

Year:  2019        PMID: 31778233     DOI: 10.1002/mp.13937

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  6 in total

Review 1.  Advanced CT techniques for assessing hepatocellular carcinoma.

Authors:  Yuko Nakamura; Toru Higaki; Yukiko Honda; Fuminari Tatsugami; Chihiro Tani; Wataru Fukumoto; Keigo Narita; Shota Kondo; Motonori Akagi; Kazuo Awai
Journal:  Radiol Med       Date:  2021-05-05       Impact factor: 3.469

2.  Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.

Authors:  Philip M Adamson; Vrunda Bhattbhatt; Sara Principi; Surabhi Beriwal; Linda S Strain; Michael Offe; Adam S Wang; Nghia-Jack Vo; Taly Gilat Schmidt; Petr Jordan
Journal:  Med Phys       Date:  2022-02-22       Impact factor: 4.071

3.  Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography.

Authors:  Andrea Steuwe; Marie Weber; Oliver Thomas Bethge; Christin Rademacher; Matthias Boschheidgen; Lino Morris Sawicki; Gerald Antoch; Joel Aissa
Journal:  Br J Radiol       Date:  2020-10-23       Impact factor: 3.039

4.  Noise reduction profile: A new method for evaluation of noise reduction techniques in CT.

Authors:  Akira Hasegawa; Toshihiro Ishihara; M Allan Thomas; Tinsu Pan
Journal:  Med Phys       Date:  2021-12-15       Impact factor: 4.506

5.  Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones.

Authors:  Andrea Steuwe; Birte Valentin; Oliver T Bethge; Alexandra Ljimani; Günter Niegisch; Gerald Antoch; Joel Aissa
Journal:  Diagnostics (Basel)       Date:  2022-07-05

6.  Improved precision of noise estimation in CT with a volume-based approach.

Authors:  Hendrik Joost Wisselink; Gert Jan Pelgrim; Mieneke Rook; Ivan Dudurych; Maarten van den Berge; Geertruida H de Bock; Rozemarijn Vliegenthart
Journal:  Eur Radiol Exp       Date:  2021-09-10
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.