1. Expert Panel on Gastrointestinal Imaging, Kaur, H, Hindman, NM, et al. ACR appropriateness criteria® suspected liver metastases. J Am Coll Radiol 2017;14:S314–S325.
Google Scholar | Crossref | Medline2. de Ridder, J, de Wilt, JH, Simmer, F, et al. Incidence and origin of histologically confirmed liver metastases: an explorative case-study of 23,154 patients. Oncotarget 2016;7:55368–55376.
Google Scholar | Crossref | Medline3. Niekel, MC, Bipat, S, Stoker, J. Diagnostic imaging of colorectal liver metastases with CT, MR imaging, FDG PET, and/or FDG PET/CT: a meta-analysis of prospective studies including patients who have not previously undergone treatment. Radiology 2010;257:674–684.
Google Scholar | Crossref | Medline | ISI4. Bipat, S, van Leeuwen, MS, Comans, EF, et al. Colorectal liver metastases: CT, MR imaging, and PET for diagnosis–meta-analysis. Radiology 2005;237:123–131.
Google Scholar | Crossref | Medline | ISI5. Floriani, I, Torri, V, Rulli, E, et al. Performance of imaging modalities in diagnosis of liver metastases from colorectal cancer: a systematic review and meta-analysis. J Magn Reson Imaging 2010;31:19–31.
Google Scholar | Crossref | Medline | ISI6. Schulz, A, Viktil, E, Godt, JC, et al. Diagnostic performance of CT, MRI and PET/CT in patients with suspected colorectal liver metastases: the superiority of MRI. Acta Radiol 2016;57:1040–1048.
Google Scholar | SAGE Journals | ISI7. Krakora, GA, Coakley, FV, Williams, G, et al. Small hypoattenuating hepatic lesions at contrast-enhanced CT: prognostic importance in patients with breast cancer. Radiology 2004;233:667–673.
Google Scholar | Crossref | Medline8. Khalil, HI, Patterson, SA, Panicek, DM. Hepatic lesions deemed too small to characterize at CT: prevalence and importance in women with breast cancer. Radiology 2005;235:872–878.
Google Scholar | Crossref | Medline9. Jones, EC, Chezmar, JL, Nelson, RC, et al. The frequency and significance of small (less than or equal to 15 mm) hepatic lesions detected by CT. AJR Am J Roentgenol 1992;158:535–539.
Google Scholar | Crossref | Medline | ISI10. Schwartz, LH, Gandras, EJ, Colangelo, SM, et al. Prevalence and importance of small hepatic lesions found at CT in patients with cancer. Radiology 1999;210:71–74.
Google Scholar | Crossref | Medline | ISI11. Solomon, J, Marin, D, Roy Choudhury, K, et al. Effect of radiation dose reduction and reconstruction algorithm on image noise, contrast, resolution, and detectability of subtle hypoattenuating liver lesions at multidetector CT: filtered back projection versus a commercial model-based iterative reconstruction algorithm. Radiology 2017;284:777–787.
Google Scholar | Crossref | Medline12. Singh, S, Kalra, MK, Hsieh, J, et al. Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques. Radiology 2010; 257:373–383.
Google Scholar | Crossref | Medline | ISI13. Arapakis, I, Efstathopoulos, E, Tsitsia, V, et al. Using “iDose4” iterative reconstruction algorithm in adults’ chest-abdomen-pelvis CT examinations: effect on image quality in relation to patient radiation exposure. Br J Radiol 2014;87:20130613.
Google Scholar | Crossref | Medline14. Mello-Amoedo, CD, Martins, AN, Tachibana, A, et al. Comparison of radiation dose and image quality of abdominopelvic CT using iterative (AIDR 3D) and conventional reconstructions. AJR Am J Roentgenol 2018;210:127–133.
Google Scholar | Crossref | Medline15. Geyer, LL, Schoepf, UJ, Meinel, FG, et al. State of the Art: iterative CT reconstruction techniques. Radiology 2015;276:339–357.
Google Scholar | Crossref | Medline | ISI16. Mileto, A, Guimaraes, LS, McCollough, CH, et al. State of the Art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology 2019;293:491–503.
Google Scholar | Crossref | Medline17. Thibault, JB, Sauer, KD, Bouman, CA, et al. A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 2007;34:4526–4544.
Google Scholar | Crossref | Medline | ISI18. Pickhardt, PJ, Lubner, MG, Kim, DH, et al. Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging. AJR Am J Roentgenol 2012; 199:1266–1274.
Google Scholar | Crossref | Medline | ISI19. Mileto, A, Zamora, DA, Alessio, AM, et al. CT detectability of small low-contrast hypoattenuating focal lesions: iterative reconstructions versus filtered back projection. Radiology 2018;289:443–454.
Google Scholar | Crossref | Medline20. Millon, D, Vlassenbroek, A, Van Maanen, AG, et al. Low contrast detectability and spatial resolution with model-based iterative reconstructions of MDCT images: a phantom and cadaveric study. Eur Radiol 2017;27:927–937.
Google Scholar | Crossref | Medline21. Hara, HMH, Ogawa, Y, Chikaraishi, K, et al. A new iterative reconstruction CT technique of forward projected model-based IR solution (FIRST): evaluation of image quality at head CT using a cerebral stroke phantom model. ECR 2019; 2019, Vienna.
Google Scholar22. Hara, HMH, Ogawa, Y, Chikaraishi, K, et al. Low-energy X-ray CT technique in forward projected model-based iterative reconstruction to improve the Low contrast using cerebral stroke phantom model. ECR 2020; 2020, Vienna.
Google Scholar23. Toshiba's AIDR 3D receives FDA clearance . 2012. Available at: https://us.medical.canon/news/press-releases/2012/04/25/1277/ (last accessed 17 October 2019).
Google Scholar24. Irwan, RNS, Blum, A. AIDR 3D - Reduces Dose and Simultaneously Improves Image Quality (White Paper). Zoetermeer: Toshiba Medical Systems Europe BV, 2011.
Google Scholar25. Toshiba Medical's Fully Integrated Model-Based Iterative CT Reconstruction Receives FDA Clearance . 2016. Available at: https://us.medical.canon/news/press-releases/2016/11/28/2518/ (last accessed 23 October 2019).
Google Scholar26. Joemai, RMS , Geleijns J . Forward projected model-based Iterative Reconstruction SoluTion “FIRST” (White Paper). Toshiba Medical Systems Corporation, 2017. Available at: file:///C:/Users/Amreeta/Downloads/CT-WP-Forward-projected-model-based-Iterative-Reconstruction-SoluTion-FIRST.pdf.
Google Scholar27. Tabari, A, Ramandeep, S, Khera, RD, et al. Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv? Eur J Radiol Open 2019;6:225–230.
Google Scholar | Crossref | Medline28. Tatsugami, F, Higaki, T, Sakane, H, et al. Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Acad Radiol 2017;24:975–981.
Google Scholar | Crossref | Medline29. Maeda, E, Tomizawa, N, Kanno, S, et al. The feasibility of forward-projected model-based iterative reconstruction SoluTion (FIRST) for coronary 320-row computed tomography angiography: a pilot study. J Cardiovasc Comput Tomogr 2017;11:40–45.
Google Scholar | Crossref | Medline30. Ohno, Y, Yaguchi, A, Okazaki, T, et al. Comparative evaluation of newly developed model-based and commercially available hybrid-type iterative reconstruction methods and filter back projection method in terms of accuracy of computer-aided volumetry (CADv) for low-dose CT protocols in phantom study. Eur J Radiol 2016;85:1375–1382.
Google Scholar | Medline31. Fujita, M, Higaki, T, Awaya, Y, et al. Lung cancer screening with ultra-low dose CT using full iterative reconstruction. Jpn J Radiol 2017;35:179–189.
Google Scholar | Crossref | Medline32. Noel, PB, Engels, S, Kohler, T, et al. Evaluation of an iterative model-based CT reconstruction algorithm by intra-patient comparison of standard and ultra-low-dose examinations. Acta Radiol 2018;59:1225–1231.
Google Scholar | SAGE Journals | ISI33. Choi, SJ, Park, SH, Shim, YS, et al. Comparison of image quality and focal lesion detection in abdominopelvic CT: potential dose reduction using advanced modelled iterative reconstruction. Clin Imaging 2020;62:41–48.
Google Scholar | Crossref | Medline