Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
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Evaluation of Performance and No-reference-based Quality for CT Image with ADMIRE Iterative Reconstruction Parameters: A Pilot Study

ADMIRE 반복적 재구성 파라메터에 따른 CT 영상의 특성 및 무참조 기반 화질 평가: 선행연구

  • Bo-Min Park (Department of Radiological Science, Gachon University) ;
  • Yoo-Jin Seo (Department of Radiological Science, Gachon University) ;
  • Seong-Hyeon Kang (Department of Biomedical Engineering, Eulji University) ;
  • Jina Shim (Department of Diagnostic Radiology, Severance Hospital) ;
  • Hajin Kim (Department of Health Science, General School of Gachon University) ;
  • Sewon Lim (Department of Health Science, General School of Gachon University) ;
  • Youngjin Lee (Department of Radiological Science, Gachon University)
  • 박보민 (가천대학교 방사선학과) ;
  • 서유진 (가천대학교 방사선학과) ;
  • 강성현 (을지대학교 의료공학과) ;
  • 심지나 (세브란스병원 영상의학과) ;
  • 김하진 (가천대학교 일반대학원 보건과학과) ;
  • 임세원 (가천대학교 일반대학원 보건과학과) ;
  • 이영진 (가천대학교 방사선학과)
  • Received : 2024.04.03
  • Accepted : 2024.05.13
  • Published : 2024.06.30
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Abstract

Advanced modeled iterative reconstruction (ADMIRE) represents a repetitive reconstruction method that can adjust strength and kernel, each of which are known to affect computed tomography (CT) image quality. The aim of this study was to quantitatively analyze the noise and spatial resolution of CT images according to ADMIRE control factors. Patient images were obtained by applying ADMIRE strength 2 and 3, and kernel B40 and B59. For quantitative evaluations, the noise level, spatial resolution, and overall image quality were measured using coefficient of variation (COV), edge rise distance (ERD), and natural image quality evaluation (NIQE). The superior values for the average COV, ERD, and NIQE results were obtained for the ADMIRE reconstruction conditions of ADMIRE 2 + B40, ADMIRE 3 + B59, and ADMIRE3 + B59. NIQE, which represents the overall image quality based on no-reference, was about 6.04 when using ADMIRE 3 + B59, showing the best result among the reconstructed image acquisition conditions. The results of this study indicate that the ADMIRE strength and kernel chosen for use in ADMIRE reconstruction have a significant impact on CT image quality. This highlights the importance of adjusting to the control factors in consideration of the clinical environment.

Keywords

References

  1. Jeong HS, Kim IJ, Park SB, Park S, Oh Y, Lee WS, et al. Optimization of non-local means algorithm in low-dose computed tomographic image based on noise level and similarity evaluations. Journal of Radiological Science and Technology. 2024;47(1):39-48. http://dx.doi.org/10.17946/JRST.2024.47.1.39. 
  2. Kim JW, Seoung YH. Development of biopsy assist device on computed tomography using 3D printing technology. Journal of Radiological Science and Technology. 2023;46(2):151-7. http://dx.doi.org/10.17946/JRST.2023.46.2.151. 
  3. Yoon S, Yoo KH, Park SH, Kim H, Lee JH, Park J, et al. Low-dose abdominopelvic computed tomography in patients with lymphoma: An image quality and radiation dose reduction study. PLoS One. 2022;17(8):e0272356. https://doi.org/10.1371/journal.pone.0272356. 
  4. Kataria B, Althen JN, Smedby O, Persson A, Sokjer H, Sandborg M. Image quality and potential dose reduction using advanced modeled iterative reconstruction (admire) in abdominal CT: A review. Radiation Protection Dosimetry. 2021;195(3-4):177-87. https://doi.org/10.1093/rpd/ncab020. 
  5. Ellmann S, Kammerer F, Allmendinger T, Hammon M, Janka R, Lell M, et al. Advanced modeled iterative reconstruction (ADMIRE) facilitates radiation dose reduction in abdominal CT. Academic Radiology. 2018;25:1277-84. https://doi.org/10.1016/j.acra.2018.01.014. 
  6. Richards PJ, George J, Metelko M, Brown M. Spine computed tomography doses and cancer induction. Spine. 2010;35(4):430-3. DOI: 10.1097/BRS.0b013e3181cdde47. 
  7. Naziroglu RE, van Ravesteijn VF, van Vliet LJ, Streekstra GJ, Vos FM. Simulation of scanner- and patient-specific low-dose CT imaging from existing CT images. Physica Medica. 2017;36:12-23. http://dx.doi.org/10.1016/j.ejmp.2017.02.009. 
  8. Hsieh J, Nett B, Yu Z, Sauer K, Thibault JB, Bouman CA. Recent advances in CT image reconstruction. Current Radiology Reports. 2013;1:39-51. https://doi.org/10.1007/s40134-012-0003-7. 
  9. Padole A, Khawaja RDA, Kalra MK, Singh S. CT radiation dose and iterative reconstruction techniques. AJR. 2015;204:W384-92. DOI: 10.2214/AJR.14.13241. 
  10. Geyer LL, Schoepf UJ, Meinel FG, Nance JW, Bastarrika G, Leipsic JA, et al. State of the Art: Iterative CT reconstruction techniques. Radiology. 2015;276(2):339-57. https://doi.org/10.1148/radiol.2015132766. 
  11. Solomon J, Mileto A, Ramirez-Giraldo JC, Samei E. Diagnostic performance of an advanced modeled iterative reconstruction algorithm for low-contrast detectability with a third-generation dual-source multidetector CT scanner: Potential for radiation dose reduction in a multireader study. Radiology. 2015;275(3):735-45. https://doi.org/10.1148/radiol.15142005. 
  12. Anja O, Georg B, Florian H, Richter H, Ulrike E, Till-Karsten H. Image quality of CT angiography of supra-aortic arteries. Clinical Neuroradiology. 2020;30:101-7. https://doi.org/10.1007/s00062-018-0740-y. 
  13. Gordic S, Desbiolles L, Stolzmann P, Gantner L, Leschka S, Husarik DB, et al. Advanced modelled iterative reconstruction for abdominal CT: Qualitative and quantitative evaluation. Clinical Radiology. 2014;69:e497-504. http://dx.doi.org/10.1016/j.crad.2014.08.012. 
  14. Rompel O, Glockler M, Janka R, Dittrich S, Cesnjevar R, Lell MM, et al. Third-generation dual-source 70-kVp chest CT angiography with advanced iterative reconstruction in young children: Image quality and radiation dose reduction. Pediatric Radiology. 2016;46:462-72. DOI: 10.1007/s00247-015-3510-x. 
  15. Weiss KL, Cornelius RS, Greeley AL, Sun D, Chang I-YJ, Boyce WO, et al. Hybrid convolution kernel: Optimized CT of the head, neck, and spine. AJR. 2011;196:403-6. DOI: 10.2214/AJR.10.4425. 
  16. Gierada DS, Bierhals AJ, Choong CK, Bartel ST, Ritter JH, Das NA, et al. Effects of CT section thickness and reconstruction kernel on emphysema quantification: Relationship to the magnitude of the CT emphysema index. Academic Radiology. 2010;17:146-56. DOI: 10.1016/j.acra.2009.08.007. 
  17. Gordic S, Morsbach F, Schmidt B, Allmendinger T, Flohr T, Husarik D, et al. Ultralow-dose chest computed tomography for pulmonary nodule detection. Investigative Radiology. 2014;49(7): 465-73. DOI: 10.1097/RLI.0000000000000037. 
  18. Denzinger F, Wels M, Breininger K, Taubmann O, Muhlberg A, Allmendinger T, et al. How scan parameter choice affects deep learning‑based coronary artery disease assessment from computed tomography. Scientific Reports. 2023;13. DOI: https://doi.org/10.1038/s41598-023-29347-9. 
  19. Tatsugami F, Higaki T, Sakane H, Fukumoto W, Kaichi Y, Iida M, et al. Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Academic Radiology. 2017;24:975-81. DOI: http://dx.doi.org/10.1016/j.acra.2016.12.020. 
  20. Mittal A, Soundararajan R, Bovik AC. Making a "Completely Blind" image quality analyzer. IEEE Signal Processing Letters. 2013;20(3):209-12. DOI: 10.1109/LSP.2012.2227726. 
  21. Gordic S, Desbiolles L, Sedlmair M, Manka R, Plass A, Schmidt B, et al. Optimizing radiation dose by using advanced modelled iterative reconstruction in high-pitch coronary CT angiograph. European Radiology. 2016;26(2):459-68. DOI: 10.1007/s00330-015-3862-5. 
  22. Hayashi R, Miyazaki K, Takao S, Yokokawa K, Tanaka S, Matsuura T, et al. Real-time CT image generation based on voxel-by- voxel modeling of internal deformation by utilizing the displacement of fiducial markers. Medical Physics. 2021;48:5311-26. DOI: 10.1002/mp.15095. 
  23. Joemai RMS, Geleijns J. Assessment of structural similarity in CT using filtered backprojection and iterative reconstruction: A phantom study with 3D printed lung vessels. The British Journal of Radiology. 2017;90:20160519. DOI: 10.1259/bjr.20160519. 
  24. Shin JB, Yoon DK, Pak S, Kwon YH, Suh TS. Comparative performance analysis for abdominal phantom ROI detectability according to CT reconstruction algorithm: ADMIRE. Journal of Applied Clinical Medical Physics. 2020;21(1):136-43. DOI: 10.1002/acm2.12765. 
  25. Solomon JB, Christianson O, Samei E. Quantitative comparison of noise texture across CT scanners from different manufacturers. Medical Physics. 2012;39(10):6048-55. http://dx.doi.org/10.1118/1.4752209. 
  26. Lim S, Park M, Kim H, Kang SH, Kim K, Lee Y. Optimization of median modified wiener filter for improving lung segmentation performance in low-dose computed tomography images. Applied Sciences. 2023;13(19):10679. https://doi.org/10.3390/app131910679.