Acceso abierto

Comparison of noise-power spectrum and modulation-transfer function for CT images reconstructed with iterative and deep learning image reconstructions: An initial experience study


Cite

Yim D, Lee S, Nam K, Lee D, Kim KD, Kim J S. Deep learning-based image reconstruction for few-view computed tomography. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2021;1011:166594. https://doi.org/10.1016/j.nima.2021.165594Search in Google Scholar

Anam C, Naufal A, Fujibuchi T, Matsubara K, Dougherty G. Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images. J Appl Clin Med Phys. 2022;23:e13719. https://doi.org/10.1002/acm2.13719Search in Google Scholar

Li K, Tang J, Chen GH. Statistical model based iterative reconstruction (MBIR) in clinical CT systems: experimental assessment of noise performance. Med Phys. 2014;41:041906. https://doi.org/10.1118/1.4867863Search in Google Scholar

Morsbach F, Desbiolles L, Raupach R, Leschka S, Schmidt B, Alkadhi H. Noise texture deviation: a measure for quantifying artefacts in computed tomography images with iterative reconstruction. Invest Radiol. 2017;52:87-94. https://doi.org/10.1097/RLI.0000000000000312Search in Google Scholar

Solomon J, Samei E. Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE. Med Phys. 2014;41:091908. https://doi.org/10.1118/1.4893497Search in Google Scholar

Andersen HK, Volgyes D, Martinsen ACT. Image quality with iterative reconstruction techniques in CT of the lungs: a phantom study. Eur J Radiol Open. 2018;5:35-40. https://doi.org/10.1016/j.ejro.2018.02.002Search in Google Scholar

Willemink MJ, Noël PB. The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur J Radiol. 2019;29:2185-2195. https://doi.org/10.1007/s00330-018-5810-7Search in Google Scholar

Mcleavy CM, et al. The future of CT: Deep learning reconstruction. Clin Radiol. 2021;76:407-415. https://doi.org/10.1016/j.crad.2021.01.010Search in Google Scholar

Szczykutowicz PT, Toia VG, Dhanantwari A, Nett B. A review of deep learning CT reconstruction: Concepts, limitations, and promise in clinical practice. Curr Radiol Rep. 2022;10:101-115. https://doi.org/10.1007/s40134-022-00399-5Search in Google Scholar

Kataria B, Nilsson AJ, Smedby Ö, Persson A, Sökjer H, Sandborg M. Image quality and potential dose reduction using advanced modeled iterative reconstruction (ADMIRE) in abdominal CT - A review. Radiat Prot Dosim. 2021;195:177-187. https://doi.org/10.1093/rpd/ncab020Search in Google Scholar

Greffier J, Frandon J, Larbi A, Om D, Beregi PJ, Perreira F. CT Iterative reconstruction algorithm: A task-based quality assessment. Eur Radiol. 2020;30:487-500. https://doi.org/10.1007/s00330-019-06359-6Search in Google Scholar

Greffier J, Franfond J, Si-Mohamed S, et al. Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data. Diagn Interv Imaging. 2022;103:21-22. https://doi.org/10.1016/j.diii.2021.08.001Search in Google Scholar

Greffier J, Hamard A, Pereira F, et al. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: A phantom study. Eur Radiol. 2020;30:3951-3959. https://doi.org/10.1007/s00330-020-06724-wSearch in Google Scholar

Benz DC, Benetos G, Rampidis G, et al. Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr. 2020;14:444-451. https://doi.org/10.1016/j.jcct.2020.01.002Search in Google Scholar

Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH. Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: Emphasis on image quality and noise. Korean J Radiol. 2021;22:131-138. https://doi.org/10.3348/kjr.2020.0116Search in Google Scholar

Wang H, Li LL, Shang J, Song J, Liu B. Application of deep learning image reconstruction in low-dose chest CT scan. Br J Radiol. 2022;95:20210380. https://doi.org/10.1259/bjr.20210380Search in Google Scholar

Zahro MU, Anam C, Budi WS, et al. Investigation of noise level and spatial resolution of CT images filtered with a selective mean filter and its comparison to an adaptive statistical iterative reconstruction. Iran J Med Phys. 2021;18:374-383. https://doi.org/10.22038/ijmp.2020.48813.1786Search in Google Scholar

Hsieh J, Liu E, Nett B, Tang J, Thibault BJ, Sahney S. A new era of image reconstruction: True FidelityTM Technical White Paper on Deep Learning Image Reconstruction. GE Healthcare. 2019. https://www.gehealthcare.com/-/jssmedia/040dd213fa89463287155151fdb01922.pdfSearch in Google Scholar

Szczykutowicz PT, Nett B, Cherkezyan L, et al. Protocol optimization considerations for implementing deep learning CT reconstruction. Am J Radiol. 2021;216:1668-1677. https://doi.org/10.2214/AJR.20.23397Search in Google Scholar

Anam C, Fujibuchi T, Haryanto F, et al. Automated MTF measurement in CT images with a simple wire phantom. Pol J Med Phys Eng. 2019;25:179-187. https://doi.org/10.2478/pjmpe-2019-0024Search in Google Scholar

Li G, Liu X, Dodge C T, Jensen CT, Rong XJ. A noise power spectrum study of a new model-based iterative reconstruction system: Veo 3.0. J Appl Clin Med Phys. 2016;17:428-439. https://doi.org/10.1120/jacmp.v17i5.6225Search in Google Scholar

Samei E, Bakalyar D, Boedeker KL, et al. Performance evaluation of computed tomography systems. Med Phys. 2019;46:735-756. https://doi.org/10.1002/mp.13763Search in Google Scholar

Hasegawa A, Ishihara T, Thomas MA, Pan T. Noise reduction profile: A new method for evaluation of noise reduction techniques in CT. Med Phys. 2022;49:186-200. https://doi.org/10.1002/mp.15382Search in Google Scholar

Anam C, Arif I, Haryanto F, et al. An improved method of automated noise measurement system in CT images. J Biomed Phys Eng. 2019;11:163-174. https://doi.org/10.31661%2Fjbpe.v0i0.1198Search in Google Scholar

Kayugawa A, Ohkubo M, Wada S. Accurate determination of CT point-spread-function with high precision. J Appl Clin Med Phys. 2013;14:3905. https://doi.org/10.1120/jacmp.v14i4.3905Search in Google Scholar

Anam C, Fujibuchi T, Budi WS, Haryanto F, Dougherty G. An algorithm for automated modulation transfer function measurement using an edge of a PMMA phantom: Impact of field of view on spatial resolution of CT images. J Appl Clin Med Phys. 2018;19:244-252. https://doi.org/10.1002/acm2.12476Search in Google Scholar

Anam C, Naufal A, Sutanto H, Adi K, Dougherty G. Impact of iterative bilateral filtering on the noise power spectrum of computed tomography images. Algorithms. 2022;15:374. https://doi.org/10.3390/a15100374Search in Google Scholar

ImQuest. https://deckard.duhs.duke.edu/~samei/tg233.htmlSearch in Google Scholar

Anam C, Naufal A, Fujibuchi T, Matsubara K, Dougherty G. Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images. J Appl Clin Med Phys. 2022;23:e13719. https://doi.org/10.1002/acm2.13719Search in Google Scholar

Verdun FR, Racine D, Ott JG, et al. Image quality in CT: From physical measurements to model observer. Phys Med. 2015;31:823-843. https://doi.org/10.1016/j.ejmp.2015.08.007Search in Google Scholar

Higaki T, Nakamura Y, Zhou J, et al. Deep learning reconstruction at CT: Phantom study of the image characteristic. Acad Radiol. 2020;27: 82-87. https://doi.org/10.1016/j.acra.2019.09.008Search in Google Scholar

Anam C, Naufal A, Sutanto H, Dougherty G. Computational phantoms for investigating impact of noise magnitude on modulation transfer function. Indonesian J Elec Eng Comp Sci. 2022;27:1428-1437. https://doi.org/10.11591/ijeecs.v27.i3.pp1428-1437Search in Google Scholar

Racine D, Becce F, Viry A, et al. Task-based characterization of deep learning image reconstruction and comparison with filtered back-projection and partial mode-based iterative reconstruction in abdominal CT: A phantom study. Phys Med. 2020;76:28-37. https://doi.org/10.1016/j.ejmp.2020.06.004Search in Google Scholar

Racine D, Brat HG, Dufour B, et al. Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compare to partial mode-based iterative reconstruction. Eur J Radiol. 2021;141:190808. https://doi.org/10.1016/j.ejrad.2021.109808Search in Google Scholar

Greffier J, Frandon J, Durand Q, et al. Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine examination: A phantom study. Diagn Interv Imaging. 2022;1-8. https://doi.org/10.1016/j.diii.2022.08.004Search in Google Scholar

Solomon J, Daniele M, Lyu P, Samei E. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys. 2020;47:3961-3970. https://doi.org/10.1002/mp.14319Search in Google Scholar

Papadakis EA, Damilakis J. Technical note: Quality assessment of virtual monochromatic spectral images on a dual energy CT scanner. Phys Med. 2021;82:114-121. https://doi.org/10.1016/j.ejmp.2021.01.079Search in Google Scholar

Sugisawa K, et al Technical note: Spatial resolution compensation by adjusting the reconstruction kernels for iterative reconstruction images of computed tomography. Phys Med. 2020;74:47-55. https://doi.org/10.1016/j.ejmp.2020.05.002Search in Google Scholar

eISSN:
1898-0309
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Medicine, Biomedical Engineering, Physics, Technical and Applied Physics, Medical Physics