Method for measuring noise-power spectrum independent of the effect of extracting the region of interest from a noise image.
Computed tomography
Image quality
Noise power spectrum
Region of interest
Journal
Radiological physics and technology
ISSN: 1865-0341
Titre abrégé: Radiol Phys Technol
Pays: Japan
ID NLM: 101467995
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
23
03
2023
accepted:
11
07
2023
revised:
11
07
2023
medline:
23
11
2023
pubmed:
29
7
2023
entrez:
29
7
2023
Statut:
ppublish
Résumé
This study aimed to evaluate the impact of region of interest (ROI) size on noise-power spectrum (NPS) measurement in computed tomography (CT) images and to propose a novel method for measuring NPS independent of ROI size. The NPS was measured using the conventional method with an ROI of size P × P pixels in a uniform region in the CT image; the NPS is referred to as NPS
Identifiants
pubmed: 37515623
doi: 10.1007/s12194-023-00733-2
pii: 10.1007/s12194-023-00733-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
471-477Subventions
Organisme : Japan Society for the Promotion of Science
ID : JP20K08046
Informations de copyright
© 2023. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics.
Références
Kijewski MF, Judy PF. The noise power spectrum of CT images. Phys Med Biol. 1987;32(5):565–75. https://doi.org/10.1088/0031-9155/32/5/003 .
doi: 10.1088/0031-9155/32/5/003
pubmed: 3588670
Siewerdsen JH, Cunningham IA, Jaffray DA. A framework for noise-power spectrum analysis of multidimensional images. Med Phys. 2002;29(11):2655–71. https://doi.org/10.1118/1.1513158 .
doi: 10.1118/1.1513158
pubmed: 12462733
Boedeker KL, Cooper VN, McNitt-Gray MF. Application of the noise power spectrum in modern diagnostic MDCT: Part I. Measurement of noise power spectra and noise equivalent quanta. Phys Med Biol. 2007;52(14):4027–46. https://doi.org/10.1088/0031-9155/52/14/002 . (Epub 2007 Jun 8).
doi: 10.1088/0031-9155/52/14/002
pubmed: 17664593
Solomon JB, Christianson O, Samei E. Quantitative comparison of noise texture across CT scanners from different manufacturers. Med Phys. 2012;39(10):6048–55. https://doi.org/10.1118/1.4752209 .
doi: 10.1118/1.4752209
pubmed: 23039643
Samei E, Bakalyar D, Boedeker KL, et al. Performance evaluation of computed tomography systems: summary of AAPM Task Group 233. Med Phys. 2019;46(11):e735–56. https://doi.org/10.1002/mp.13763 . (Epub 2019 Sep 11).
doi: 10.1002/mp.13763
pubmed: 31408540
Greffier J, Viry A, Barbotteau Y, et al. Phantom task-based image quality assessment of three generations of rapid kV-switching dual-energy CT systems on virtual monoenergetic images. Med Phys. 2022;49(4):2233–44. https://doi.org/10.1002/mp.15558 . (Epub 2022 Mar 7).
doi: 10.1002/mp.15558
pubmed: 35184293
Li K, Tang J, Chen GH. Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance. Med Phys. 2014;41(4):041906. https://doi.org/10.1118/1.4867863 .
doi: 10.1118/1.4867863
pubmed: 24694137
pmcid: 3978426
Greffier J, Barbotteau Y, Gardavaud F. iQMetrix-CT: New software for task-based image quality assessment of phantom CT images. Diagn Interv Imaging. 2022; S2211–5684(22)00111–5. https://doi.org/10.1016/j.diii.2022.05.007 . Online ahead of print.
Tao S, Rajendran K, Zhou W, et al. Noise reduction in CT image using prior knowledge aware iterative denoising. Phys Med Biol 2020;65(22):1–11. https://doi.org/10.1088/1361-6560/abc231 .
Racine D, Brat HG, Dufour B, et al. Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction. Eur J Radiol. 2021;141:109808. https://doi.org/10.1016/j.ejrad.2021.109808 . (Epub 2021 Jun 3).
doi: 10.1016/j.ejrad.2021.109808
pubmed: 34120010
Solomon J, Lyu P, Marin D, Samei E. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys. 2020;47(9):3961–71. https://doi.org/10.1002/mp.14319 . (Epub 2020 Jul 6).
doi: 10.1002/mp.14319
pubmed: 32506661
Greffier J, Frandon J, Hamard A, et al. Impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images. Phys Med. 2020;77:36–42. https://doi.org/10.1016/j.ejmp.2020.07.024 . (Epub 2020 Aug 6).
doi: 10.1016/j.ejmp.2020.07.024
pubmed: 32771702
Dobbins JT 3rd, Ergun DL, Rutz L, et al. DQE(f) of four generations of computed radiography acquisition devices. Med Phys. 1995;22(10):1581–93. https://doi.org/10.1118/1.597627 .
doi: 10.1118/1.597627
pubmed: 8551982
Jacob B, Harold LK, Richard LVM. Handbook of medical imaging, Volume 1. Physics and Psychophysics. SPIE; 2000. pp 190–196.
Li K, Garrett J, Ge Y, Chen GH. Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance. Med Phys. 2014;41(7):071911. https://doi.org/10.1118/1.4884038 .
doi: 10.1118/1.4884038
pubmed: 24989389
pmcid: 4106476
Kataria B, Nilsson Althén J, Smedby Ö, et al. Assessment of image quality in abdominal computed tomography: Effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose reduction. Eur J Radiol. 2020;122:108703. https://doi.org/10.1016/j.ejrad.2019.108703 .
doi: 10.1016/j.ejrad.2019.108703
pubmed: 31810641
Afadzi M, Lysvik EK, Andersen HK, Martinsen ACT. Ultra-low dose chest computed tomography: effect of iterative reconstruction levels on image quality. Eur J Radiol. 2019;114:62–8. https://doi.org/10.1016/j.ejrad.2019.02.021 . (Epub 2019 Feb 18).
doi: 10.1016/j.ejrad.2019.02.021
pubmed: 31005179
Tao S, Rajendran K, Zhou W, et al. Noise reduction in CT image using prior knowledge aware iterative denoising. Phys Med Biol. 2020;65(22):1–23. https://doi.org/10.1088/1361-6560/abc231 .
Leon S, Olguin E, Schaeffer C, et al. Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images. Phys Med Biol. 2021;66(12):1–17. https://doi.org/10.1088/1361-6560/ac0391 .
Nickoloff EL, Riley R. A simplified approach for modulation transfer function determinations in computed tomography. Med Phys. 1985;12(4):437–42. https://doi.org/10.1118/1.595706 .
doi: 10.1118/1.595706
pubmed: 4033588
Wagner RF, Brown DG, Pastel MS. Application of information theory to the assessment of computed tomography. Med Phys. 1979;6(2):83–94. https://doi.org/10.1118/1.594559 .
doi: 10.1118/1.594559
pubmed: 460068
Hanson KM. Detectability in computed tomographic images. Med Phys. 1979;6(5):441–51. https://doi.org/10.1118/1.594534 .
doi: 10.1118/1.594534
pubmed: 492079
Hoye J, Solomon J, Sauer TJ, Robins M, Samei E. Systematic analysis of bias and variability of morphologic features for lung lesions in computed tomography. J Med Imaging (Bellingham). 2019;6(1):013504. https://doi.org/10.1117/1.JMI.6.1.013504 . (Epub 2019 Mar 26).
doi: 10.1117/1.JMI.6.1.013504
pubmed: 30944842