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NTIS 바로가기한국방사선학회 논문지 = Journal of the Korean Society of Radiology, v.17 no.1, 2023년, pp.37 - 46
김가현 (을지대학교 보건과학대학 방사선학과) , 김지수 (을지대학교 보건과학대학 방사선학과) , 김찬들 (을지대학교 보건과학대학 방사선학과) , 이준표 (서울아산병원 영상의학과) , 홍주완 (을지대학교 보건과학대학 방사선학과) , 한동균 (을지대학교 보건과학대학 방사선학과)
This study aims to evaluate the usefulness of Deep Learning Image Reconstruction (TrueFidelity, TF), the image quality of existing Filtered Back Projection (FBP) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) were compared. Noise, CNR, and SSIM were measured by obtaining images with ...
A. Korn, M. Fenchel, B. Bender, S. Danz, T. K. Hauser, D. Ketelsen, T. Flohr, C. D. Claussen, M. Heuschmid, U. Ernemann, H. Brodoefel "Iterative Reconstruction in Head CT; Image Quality of Routine and Low-Dose Protocols in Comparision with Standard Filtered Back Projection", American journal of neuroradiology, Vol. 33, No. 2, pp. 218-224, 2012. https://doi.org/10.3174/ajnr.a2749?
L. L. Geyer, U. J. Schoeppf, F. G. Meinel, J. W. Nance, G. Bastarrika, J. A. Leipsic, N. S. Paul, M. Rengo, A. Laghi, C. N. D. Cecco, "State of the Art; Iterative CT Reconstruction Techniques", Radiology, Vol. 276, No. 2, pp. 339-357, 2015. https://doi.org/10.1148/radiol.2015132766?
O. Baskan, C. Erol, H. Ozbek, Y. Paksoy, "Effect of radiation dose reduction on image quality in adult head CT with noise-suppressing reconstruction system with a 256 slice MDCT", Journal of Applied Clinical Medical Physics, Vol. 16, No. 3, pp. 285-296, 2015. https://doi.org/10.1120/jacmp.v16i3.5360?
R. Singh, S. R. Digumarthy, V. V. Muse, A. R. Kambadakone, M. A. Black, A. Tabari, Y. Hoi, N. Akino, E. Angel, R. Madan, M. K. Kalra, "Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT", American Roentgen Ray Society, Vol. 214, No. 3, pp. 566-573, 2020. https://doi.org/10.2214/ajr.19.21809?
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. christe, S. Mougiakakou, "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolution Neural Network", IEEE transactions on medical imaging, Vol. 35, No. 3, pp. 1207-1216, 2016. https://doi.org/10.1109/TMI.2016.2535865?
G. Chartrand, P. M. Cheng, E. Vorontsov, M. Drozdzal, S. Turcotte, C. J. Pal, S. Kadoury, A. Tang, "Deep Learning; A Primer for Radiologists", Radiographics, Vol. 37, No. 7, pp. 2113-2131, 2017. https://doi.org/10.1148/rg.2017170077?
A. Hamidinekoo, E. Denton, A. Rampun, K. Honnor, R. Zwiggelaar, "Deep Learning in Mammography and Breast Histology, an Overview and Future Trends", Medical image analysis, Vol. 47, pp. 45-67, 2018. https://doi.org/10.1016/j.media.2018.03.006?
J. Hsieh, E. Liu, B. Nett, J. Tang, J. B. Thibault, S. Sahney, "A New Era of Image Reconstruction", TrueFidelityTM Technical White Paper on Deep Learning Image Reconstruction, 2018.?
C. H. McCollough, L. Yr, J. M. Kofler, S. Leng, Y. Zhang, Z. Li, R. E. Carter, "Degradation of CT Low-Contratst Spatial Resolution Due to the Use of Iterative Reconstruction and Reduced Dose Levels", Radiology, Vol. 276, No. 2, pp. 499-506, 2015. https://doi.org/10.1148/radiol.15142047?
J. E. Lee, S. Y. Choi, J. A. Hwang. S. H. Lim, M. H. Lee, B. H. Yi, J. G. Cha, "The Potential for Reduced Radiation Dose From Deep Learning-Based CT Image Reconstruction: A Comparison with Filtered Back Projection and Hybrid Iterative Reconstruction using a Phantom", Medicine(Baltimore), Vol. 100, No. 19, pp. 25814, 2021. https://doi.org/10.1097/md.0000000000025814?
C. T. Jensen, X. Liu, E. P. Tamm, A. G. Chandler, J. Sun, A. C. Morani, S. Javadi, N. A. W. Bartak, "Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction; Initial Experience", American Roentgen Ray Society, Vol. 215, No. 1, pp. 50-57, 2020. https://doi.org/10.2214/ajr.19.22332?
J. Greffier, A. Hamard, F. Pereira, C. Barrau, H. Pasquier, J. P. Beregi, J. Frandon, "Image Quality and Dose Reduction Opportunity of Deep Learning Image Reconstruction Algorithm for CT; A Phantom Study", European Society of Radiology, Vol. 30, No. 7, pp. 3951-3959, 2020. https://doi.org/10.1007/s00330-020-06724-w?
C. Frank, G. Zhang, P. Deak, F. Zanca, "Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study", Physica Media, Vol. 82, pp. 86-93, 2021. https://doi.org/10.1016/j.ejmp.2020.12.005?
Z. Alagic, J. D. Cardenas, K. Halldorsson, V. Grozman, S. Wallgren, C. Suzuki, J. Helmenkamp, S. K. Koskinen, "Deep learning versus iterative reconstruction algorithm for head CT in trauma", Emergency Radiology, Vol. 29, pp. 339-352, 2022. https://doi.org/10.1007/s10140-021-02012-2?
C. S. Ko, I. W. Cho, J. W. Kang, W. J. Jeong, H. Song, "Comparative Analysis and Usefulness by Quantitative Evaluation of Deep Learning Image Reconstruction and Adaptive Statistical Iterative Reconstruction-V in Aortic Vessels CT", Journal of Korean Society of Computed Tomographic Technology, Vol. 23, No. 2, pp 9-19, 2021.?
H. B. Shim, K. H. Lee, R. H. Kim, S. K. Park, J. N. Shim, "A Study on Image Quality and Dose Comparison of Abdominal CT with Deep Learning Iterative Reconstruction Method and Model-Based Iterative Reconstruction Method", Journal of Korean Society of Computed Tomographic, Vol. 23, No. 1, pp. 57-65, 2021.?
Y. Bie, S. Yang, Z. Li, K. Zhao, C. Zhang, H. Zhong, "Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography", Journal of Z-ray Science and Technology, Vol. 30, No. 3, pp. 409-418, 2022. https://doi.org/10.3233/xst-211105?
M. Akagi, Y. Nakamura, T. Higaki, K. Narita, Y. Honda, J. Zhou, Z. Yu, M. Akino, K. Awai, "Deep learning reconstruction improveds image quality of abdominal ultra-high-resolution CT", European Society of Radiology, Vol. 29, No. 11, pp. 6163-6171, 2019. https://doi.org/10.1007/s00330-019-06170-3
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