[1] |
Li X, Shu H, Zhang Y, et al. Low-dose CT with adaptive statistical iterative reconstruction for evaluation of urinary stone[J]. Oncotarget, 2018, 9(28): 20103-20111.
|
[2] |
Ren Z, Zhang X, Hu Z, et al. Reducing radiation dose and improving image quality in ct portal venography using 80 kv and adaptive statistical iterative reconstruction-v in slender patients[J]. Acad Radiol, 2020, 27(2): 233-243.
|
[3] |
Lee HN, Lee SM, Choe J, et al. Diagnostic performance of CT-guided percutaneous transthoracic core needle biopsy using low tube voltage (100 kVp): comparison with conventional tube voltage (120 kVp). Acta Radiol[J]. 2018, 59(4): 425-433.
|
[4] |
Goodenberger MH, Wagner-Bartak NA, Gupta S, et al. Computed tomography image quality evaluation of a new iterative reconstruction algorithm in the abdomen (adaptive statistical iterative reconstruction-v) a comparison with model-based iterative reconstruction, adaptive statistical iterative reconstruction, and filtered back projection reconstructions[J]. J Comput Assist Tomogr, 2018, 42(2): 184-190.
|
[5] |
Park C, Choo KS, Kim JH, et al. Image quality and radiation dose in ct venography using model-based iterative reconstruction at 80 kvp versus adaptive statistical iterative reconstruction-Vat 70 kVp[J]. Korean J Radiol, 2019, 20(7): 1167-1175.
|
[6] |
Goodenberger MH, Wagner-Bartak NA, Gupta S, et al. Computed tomography image quality evaluation of a new iterative reconstruction algorithm in the abdomen (adaptive statistical iterative reconstruction-v) a comparison with model-based iterative reconstruction, adaptive statistical iterative reconstruction, and filtered back projection reconstructions[J]. J Comput Assist Tomogr, 2018, 42(2): 184-190.
|
[7] |
Laurent G, Villani N, Hossu G, et al. Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance[J]. Eur Radiol, 2019, 29(8): 4016-4025.
|
[8] |
Jensen CT, Liu X, Tamm EP, et al. Image quality assessment of abdominal ct by use of new deep learning image reconstruction: initial experience[J]. AJR Am J Roentgenol, 2020, 215(1): 50-57.
|
[9] |
Hwang I, Cho JY, Kim SY, et al. Low tube voltage computed tomography urography using low-concentration contrast media: Comparison of image quality in conventional computed tomography urography[J]. Eur J Radiol, 2015, 84(12): 2454-2463.
|
[10] |
Kim SY, Cho JY, Lee J, et al. Low-Tube-Voltage CT urography using low-concentration-iodine contrast media and iterative reconstruction: a multi-institutional randomized controlled trial for comparison with conventional CT urography[J]. Korean J Radiol, 2018, 19(6): 1119-1129.
|
[11] |
Kwon H, Cho J, Oh J, et al. The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique[J]. Br J Radiol, 2015, 88(1054): 20150463.
|
[12] |
Arndt C, Güttler F, Heinrich A, et al. Deep learning ct image reconstruction in clinical practice[J]. Rofo, 2021, 193(3): 252-261.
|
[13] |
Willemink MJ, Noël PB. The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence[J]. Eur Radiol, 2019, 29(5): 2185-2195.
|
[14] |
Kim JH, Yoon HJ, Lee E, et al. Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise[J]. Korean J Radiol, 2021, 22(1): 131-138.
|
[15] |
王甜, 曹治婷, 韩秋丽. 低管电压联合低剂量对比剂在肺动脉CT成像中的应用[J]. 实用医学杂志, 2016, 32(15): 2532-2534.
|