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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

Application of Different Levels of Advanced Modeling Iterative Reconstruction in Brain CT Scanning

Author(s): Jun Xu, Xiaoli Hu, Youxin Zhang, Zhihan Xu, Hongying Wu* and Kun Luo*

Volume 18, Issue 13, 2022

Published on: 27 July, 2022

Article ID: e160522204835 Pages: 7

DOI: 10.2174/1573405618666220516121722

Price: $65

Abstract

Background: Advanced Modeling Iterative Reconstruction (ADMIRE) algorithm has five intensity levels; it is important to study which algorithm is better for brain CT scanning.

Objective: The aim of the study is to compare the influence of different strength levels of ADMIRE and traditional Filtered Back Projection (FBP) on image quality in brain CT scanning.

Methods: 60 patients were retrospectively selected, and the data from each of these patients’ brains were reconstructed by four different reconstruction methods (FBP, ADMIRE1, ADMIRE3, and ADMIRE5). A five-point Likert Scale was implemented to evaluate the subjective image quality. Image noise, CT value of brain tissue , signal-to-noise ratio (SNR) of gray white matter, contrast-to-noise ratio (CNR), and beam hardening artifact index (AI) of the posterior fossa, were measured for evaluating the objective image quality. Finally, the differences between the subjective and objective evaluations were compared.

Results: There were no statistical differences observed in CT values of gray matter and white matter between the four groups (all P >0.05). The image noise gradually decreased with the increase of ADMIRE algorithm level. The AI exhibited no statistical difference between the four groups (F =0.793, P =0.499), but it tended to decrease slightly with the increase of ADMIRE algorithm level. Compared to other groups (all p <0.001), the ADMIRE5 group demonstrated the best objective image quality. Nevertheless, the highest subjective score was observed in the ADMIRE3 group, which exhibited significant differences with other images (all P <0.001).

Conclusion: ADMIRE algorithm can clearly improve image quality, but it cannot significantly improve the linear sclerosis artifacts in the posterior cranial fossa. Based on the subjective evaluation of image quality, ADMIRE3 algorithm is recommended in brain CT scanning.

Keywords: Brain CT examination, ADMIRE, SNR, CNR, FBP, gray-white matter.

[1]
Örgel A, Bier G, Hennersdorf F, Richter H, Ernemann U, Hauser TK. Image Quality of CT Angiography of Supra-Aortic Arteries: Comparison Between Advanced Modelled Iterative Reconstruction (ADMIRE), Sinogram Affirmed Iterative Reconstruction (SAFIRE) and Filtered Back Projection (FBP) in One Patients’ Group. Clin Neuroradiol 2020; 30(1): 101-7.
[http://dx.doi.org/10.1007/s00062-018-0740-y] [PMID: 30397728]
[2]
Caruso D, De Cecco CN, Schoepf UJ, et al. Correction Factors for CT Coronary Artery Calcium Scoring Using Advanced Modeled Iterative Reconstruction Instead of Filtered Back Projection. Acad Radiol 2016; 23(12): 1480-9.
[http://dx.doi.org/10.1016/j.acra.2016.07.015] [PMID: 27614365]
[3]
Schaller F, Sedlmair M, Raupach R, Uder M, Lell M. Noise Reduction in Abdominal Computed Tomography Applying Iterative Reconstruction (ADMIRE). Acad Radiol 2016; 23(10): 1230-8.
[http://dx.doi.org/10.1016/j.acra.2016.05.016] [PMID: 27318787]
[4]
Moscariello A, Takx RA, Schoepf UJ, et al. Coronary CT angiography: image quality, diagnostic accuracy, and potential for radiation dose reduction using a novel iterative image reconstruction technique-comparison with traditional filtered back projection. Eur Radiol 2011; 21(10): 2130-8.
[http://dx.doi.org/10.1007/s00330-011-2164-9] [PMID: 21611758]
[5]
Scholtz JE, Kaup M, Hüsers K, et al. Advanced modeled iterative reconstruction in low-tube-voltage contrast-enhanced neck CT: Evaluation of objective and subjective image quality. AJNR Am J Neuroradiol 2016; 37(1): 143-50.
[http://dx.doi.org/10.3174/ajnr.A4502] [PMID: 26427836]
[6]
Kataria B, Althén JN, Smedby Ö, Persson A, Sökjer H, Sandborg M. Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction. Eur Radiol 2018; 28(6): 2464-73.
[http://dx.doi.org/10.1007/s00330-017-5113-4] [PMID: 29368163]
[7]
Cho HH, Lee SM, You SK. Pediatric head computed tomography with advanced modeled iterative reconstruction: focus on image quality and reduction of radiation dose. Pediatr Radiol 2020; 50(2): 242-51.
[http://dx.doi.org/10.1007/s00247-019-04532-z] [PMID: 31630218]
[8]
Hempel JM, Niklas Bongers M, Braun K, Ernemann U, Bier G. Noise reduction and image quality in ultra-high resolution computed tomography of the temporal bone using advanced modeled iterative reconstruction. Acta Radiol 2019; 60(9): 1135-43.
[http://dx.doi.org/10.1177/0284185118820699] [PMID: 30621442]
[9]
Hu Y, Pan S, Zhao X, Guo W, He M, Guo Q. Value and clinical application of orthopedic metal artifact reduction algorithm in CT scans after orthopedic metal implantation. Korean J Radiol 2017; 18(3): 526-35.
[http://dx.doi.org/10.3348/kjr.2017.18.3.526] [PMID: 28458605]
[10]
Kataria B, Althén JN. Smedby; Persson, A.; Sandborg, M. Image quality and potential dose reduction using advanced modeled iterative reconstruction (Admire) in abdominal Ct - A review. Radiat Prot Dosimetry 2021; 195: 1-11.
[http://dx.doi.org/10.1093/rpd/ncab020]
[11]
Barrett JF, Keat N. Artifacts in CT: recognition and avoidance. Radiographics 2004; 24(6): 1679-91.
[http://dx.doi.org/10.1148/rg.246045065] [PMID: 15537976]
[12]
Danad I, Fayad ZA, Willemink MJ, Min JK. New Applications of Cardiac Computed Tomography: Dual-Energy, Spectral, and Molecular CT Imaging. JACC Cardiovasc Imaging 2015; 8(6): 710-23.
[http://dx.doi.org/10.1016/j.jcmg.2015.03.005] [PMID: 26068288]
[13]
Neroladaki A, Martin SP, Bagetakos I, et al. Metallic artifact reduction by evaluation of the additional value of iterative reconstruction algorithms in hip prosthesis computed tomography imaging. Medicine (Baltimore) 2019; 98(6): e14341.
[http://dx.doi.org/10.1097/MD.0000000000014341] [PMID: 30732160]
[14]
Elbakri IA, Fessler JA. Statistical image reconstruction for polyenergetic X-ray computed tomography. IEEE Trans Med Imaging 2002; 21(2): 89-99.
[http://dx.doi.org/10.1109/42.993128] [PMID: 11929108]

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