{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:22:57Z","timestamp":1775607777541,"version":"3.50.1"},"reference-count":133,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Science and Technology Development Fund of Macao","award":["0021\/2022\/AGJ"],"award-info":[{"award-number":["0021\/2022\/AGJ"]}]},{"name":"Macao Polytechnic University Grant","award":["RP\/FCA-15\/2022"],"award-info":[{"award-number":["RP\/FCA-15\/2022"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s44443-025-00085-4","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T12:11:14Z","timestamp":1751371874000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Charting the path forward: CT image quality assessment - an in-depth review"],"prefix":"10.1007","volume":"37","author":[{"given":"Siyi","family":"Xun","sequence":"first","affiliation":[]},{"given":"Qiaoyu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaohong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Pu","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Guangtao","family":"Zhai","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Peter H. N.","family":"de With","sequence":"additional","affiliation":[]},{"given":"Mingxiang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"85_CR1","doi-asserted-by":"publisher","first-page":"6163","DOI":"10.1007\/s00330-019-06170-3","volume":"29","author":"M Akagi","year":"2019","unstructured":"Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Zhou J, Yu Z, Akino N, Awai K (2019) Deep learning reconstruction improves image quality of abdominal ultra-high-resolution ct. Eur Radiol 29:6163\u20136171","journal-title":"Eur Radiol"},{"issue":"1","key":"85_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-015-0214-1","volume":"2015","author":"Z Al-Ameen","year":"2015","unstructured":"Al-Ameen Z, Sulong G, Rehman A, Al-Dhelaan A, Saba T, Al-Rodhaan M (2015) An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization. EURASIP Journal on Advances in Signal Processing. 2015(1):1\u201312","journal-title":"EURASIP Journal on Advances in Signal Processing."},{"key":"85_CR3","doi-asserted-by":"crossref","unstructured":"Allert KD, DiBianca FA (2005) An automated image evaluation procedure for computed tomography systems. In: Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, vol. 5749, pp. 527\u2013537 . SPIE","DOI":"10.1117\/12.595744"},{"key":"85_CR4","unstructured":"Alvarez\u00a0Melis D, Jaakkola T (2018) Towards robust interpretability with self-explaining neural networks. Advances in neural information processing systems 31"},{"issue":"11","key":"85_CR5","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1001\/jama.2020.1039","volume":"323","author":"DC Angus","year":"2020","unstructured":"Angus DC (2020) Randomized clinical trials of artificial intelligence. JAMA 323(11):1043\u20131045","journal-title":"JAMA"},{"key":"85_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2022.102517","volume":"106","author":"P Barca","year":"2023","unstructured":"Barca P, Domenichelli S, Golfieri R, Pierotti L, Spagnoli L, Tomasi S, Strigari L (2023) Image quality evaluation of the precise image ct deep learning reconstruction algorithm compared to filtered back-projection and idose4: a phantom study at different dose levels. Physica Med 106:102517","journal-title":"Physica Med"},{"issue":"5","key":"85_CR7","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.jcct.2020.01.002","volume":"14","author":"DC Benz","year":"2020","unstructured":"Benz DC, Benetos G, Rampidis G, Von Felten E, Bakula A, Sustar A, Kudura K, Messerli M, Fuchs TA, Gebhard C et al (2020) Validation of deep-learning image reconstruction for coronary computed tomography angiography: impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr 14(5):444\u2013451","journal-title":"J Cardiovasc Comput Tomogr"},{"issue":"2","key":"85_CR8","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1002\/mp.16619","volume":"51","author":"M Bhattarai","year":"2024","unstructured":"Bhattarai M, Bache S, Abadi E, Samei E (2024) A systematic task-based image quality assessment of photon-counting and energy integrating ct as a function of reconstruction kernel and phantom size. Med Phys 51(2):1047\u20131060","journal-title":"Med Phys"},{"key":"85_CR9","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s11760-017-1166-8","volume":"12","author":"S Bianco","year":"2018","unstructured":"Bianco S, Celona L, Napoletano P, Schettini R (2018) On the use of deep learning for blind image quality assessment. SIViP 12:355\u2013362","journal-title":"SIViP"},{"issue":"2","key":"85_CR10","doi-asserted-by":"publisher","first-page":"229","DOI":"10.2217\/iim.12.13","volume":"4","author":"FE Boas","year":"2012","unstructured":"Boas FE, Fleischmann D et al (2012) Ct artifacts: causes and reduction techniques. Imaging Med. 4(2):229\u2013240","journal-title":"Imaging Med."},{"issue":"1","key":"85_CR11","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/TIP.2017.2760518","volume":"27","author":"S Bosse","year":"2017","unstructured":"Bosse S, Maniry D, M\u00fcller K-R, Wiegand T, Samek W (2017) Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans Image Process 27(1):206\u2013219","journal-title":"IEEE Trans Image Process"},{"key":"85_CR12","doi-asserted-by":"crossref","unstructured":"Bostrom N, Yudkowsky E (2018) The ethics of artificial intelligence. In: Artificial Intelligence Safety and Security, pp. 57\u201369. Chapman and Hall\/CRC, ???","DOI":"10.1201\/9781351251389-4"},{"key":"85_CR13","doi-asserted-by":"crossref","unstructured":"Cao R, Liu Y, Wen X, Liao C, Wang X, Gao Y, Tan T (2024) Reinvestigating the performance of artificial intelligence classification algorithms on covid-19 x-ray and ct images. Iscience 27(5)","DOI":"10.1016\/j.isci.2024.109712"},{"issue":"2133","key":"85_CR14","doi-asserted-by":"publisher","first-page":"20180080","DOI":"10.1098\/rsta.2018.0080","volume":"376","author":"C Cath","year":"2018","unstructured":"Cath C (2018) Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 376(2133):20180080","journal-title":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences."},{"key":"85_CR15","doi-asserted-by":"crossref","unstructured":"Chen C (2004) Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences 101(suppl_1):5303\u20135310","DOI":"10.1073\/pnas.0307513100"},{"issue":"3","key":"85_CR16","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1002\/asi.20317","volume":"57","author":"C Chen","year":"2006","unstructured":"Chen C (2006) Citespace ii: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inform Sci Technol 57(3):359\u2013377","journal-title":"J Am Soc Inform Sci Technol"},{"issue":"3","key":"85_CR17","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1002\/asi.21694","volume":"63","author":"C Chen","year":"2012","unstructured":"Chen C (2012) Predictive effects of structural variation on citation counts. J Am Soc Inform Sci Technol 63(3):431\u2013449","journal-title":"J Am Soc Inform Sci Technol"},{"key":"85_CR18","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","volume":"2","author":"X-W Chen","year":"2014","unstructured":"Chen X-W, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE access. 2:514\u2013525","journal-title":"IEEE access."},{"issue":"7","key":"85_CR19","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.1002\/asi.21309","volume":"61","author":"C Chen","year":"2010","unstructured":"Chen C, Ibekwe-SanJuan F, Hou J (2010) The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis. J Am Soc Inform Sci Technol 61(7):1386\u20131409","journal-title":"J Am Soc Inform Sci Technol"},{"key":"85_CR20","doi-asserted-by":"crossref","unstructured":"Chen Z, Hu B, Niu C, Chen T, Li Y, Shan H, Wang G (2023) Iqagpt: Image quality assessment with vision-language and chatgpt models. arXiv:2312.15663","DOI":"10.1186\/s42492-024-00171-w"},{"key":"85_CR21","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.bspc.2016.02.006","volume":"27","author":"LS Chow","year":"2016","unstructured":"Chow LS, Paramesran R (2016) Review of medical image quality assessment. Biomed Signal Process Control 27:145\u2013154","journal-title":"Biomed Signal Process Control"},{"key":"85_CR22","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.mri.2017.07.016","volume":"43","author":"LS Chow","year":"2017","unstructured":"Chow LS, Rajagopal H (2017) Modified-brisque as no reference image quality assessment for structural mr images. Magn Reson Imaging 43:74\u201387","journal-title":"Magn Reson Imaging"},{"key":"85_CR23","unstructured":"Cordeiro JVC, Laureano NK (2025) Bone quality assessment around dental implants in cone-beam ct images: effect of rotation mode and metal artefact reduction tool"},{"issue":"1","key":"85_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0217-0","volume":"6","author":"S Dash","year":"2019","unstructured":"Dash S, Shakyawar SK, Sharma M, Kaushik S (2019) Big data in healthcare: management, analysis and future prospects. Journal of big data. 6(1):1\u201325","journal-title":"Journal of big data."},{"key":"85_CR25","doi-asserted-by":"crossref","unstructured":"Das K, Jiang J, Rao J (2004) Mean squared error of empirical predictor","DOI":"10.1214\/009053604000000201"},{"issue":"2","key":"85_CR26","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.jcct.2007.12.017","volume":"2","author":"D Dey","year":"2008","unstructured":"Dey D, Lee CJ, Ohba M, Gutstein A, Slomka PJ, Cheng V, Suzuki Y, Suzuki S, Wolak A, Le Meunier L et al (2008) Image quality and artifacts in coronary ct angiography with dual-source ct: initial clinical experience. J Cardiovasc Comput Tomogr 2(2):105\u2013114","journal-title":"J Cardiovasc Comput Tomogr"},{"issue":"1","key":"85_CR27","doi-asserted-by":"publisher","first-page":"20160285","DOI":"10.1259\/dmfr.20160285","volume":"46","author":"J-P Dillenseger","year":"2017","unstructured":"Dillenseger J-P, Gros C-I, Sayeh A, Rasamimanana J, Lawniczak F, Leminor J-M, Matern J-F, Constantinesco A, Bornert F, Choquet P (2017) Image quality evaluation of small fov and large fov cbct devices for oral and maxillofacial radiology. Dentomaxillofacial Radiology. 46(1):20160285","journal-title":"Dentomaxillofacial Radiology."},{"key":"85_CR28","doi-asserted-by":"crossref","unstructured":"Duan J, Cai J, Zhi S, Mou X (2020) Blind ct image quality assessment model based on ct image statistics. In: The Fourth International Symposium on Image Computing and Digital Medicine, pp. 201\u2013205","DOI":"10.1145\/3451421.3451464"},{"issue":"4","key":"85_CR29","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1097\/RCT.0000000000001316","volume":"46","author":"E Fair","year":"2022","unstructured":"Fair E, Profio M, Kulkarni N, Laviolette PS, Barnes B, Bobholz S, Levenhagen M, Ausman R, Griffin MO, Duvnjak P et al (2022) Image quality evaluation in dual-energy ct of the chest, abdomen, and pelvis in obese patients with deep learning image reconstruction. J Comput Assist Tomogr 46(4):604\u2013611","journal-title":"J Comput Assist Tomogr"},{"issue":"9","key":"85_CR30","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1002\/mp.13058","volume":"45","author":"V Filippou","year":"2018","unstructured":"Filippou V, Tsoumpas C (2018) Recent advances on the development of phantoms using 3d printing for imaging with ct, mri, pet, spect, and ultrasound. Med Phys 45(9):740\u2013760","journal-title":"Med Phys"},{"issue":"8","key":"85_CR31","doi-asserted-by":"publisher","first-page":"2536","DOI":"10.1118\/1.1949787","volume":"32","author":"T Flohr","year":"2005","unstructured":"Flohr T, Stierstorfer K, Ulzheimer S, Bruder H, Primak A, McCollough C (2005) Image reconstruction and image quality evaluation for a 64-slice ct scanner with-flying focal spot. Med Phys 32(8):2536\u20132547","journal-title":"Med Phys"},{"key":"85_CR32","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.ejmp.2020.10.030","volume":"79","author":"T Flohr","year":"2020","unstructured":"Flohr T, Petersilka M, Henning A, Ulzheimer S, Ferda J, Schmidt B (2020) Photon-counting ct review. Physica Med 79:126\u2013136","journal-title":"Physica Med"},{"key":"85_CR33","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.ejmp.2020.12.005","volume":"81","author":"C Franck","year":"2021","unstructured":"Franck C, Zhang G, Deak P, Zanca F (2021) Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest ct: a phantom study. Physica Med 81:86\u201393","journal-title":"Physica Med"},{"issue":"5","key":"85_CR34","doi-asserted-by":"publisher","DOI":"10.1118\/1.4800795","volume":"40","author":"SN Friedman","year":"2013","unstructured":"Friedman SN, Fung GSK, Siewerdsen JH, Tsui BMW (2013) A simple approach to measure computed tomography (ct) modulation transfer function (mtf) and noise-power spectrum (nps) using the american college of radiology (acr) accreditation phantom a simple approach to measure computed tomography (ct) modulation transfer function (mtf) and noise-power spectrum (nps) using the american college of radiology (acr) accreditation phantom. Med Phys 40(5):051907","journal-title":"Med Phys"},{"key":"85_CR35","doi-asserted-by":"crossref","unstructured":"Gao Q, Li S, Zhu M, Li D, Bian Z, Lyu Q, Zeng D, Ma J (2019) Blind ct image quality assessment via deep learning framework. In: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS\/MIC), pp. 1\u20134. IEEE","DOI":"10.1109\/NSS\/MIC42101.2019.9059777"},{"key":"85_CR36","doi-asserted-by":"crossref","unstructured":"Gao R, Tang Y, Khan MS, Xu K, Paulson AB, Sullivan S, Huo Y, Deppen S, Massion PP, Sandler KLet al (2021) Cancer risk estimation combining lung screening ct with clinical data elements. Radiology: Artificial Intelligence 3(6):210032","DOI":"10.1148\/ryai.2021210032"},{"key":"85_CR37","unstructured":"Gao Q, Zhu M, Li D, Bian Z, Ma J (2021) Ct image quality assessment based on prior information of pre-restored images. Nan Fang yi ke da xue xue bao= Journal of Southern Medical University 41(2):230\u2013237"},{"key":"85_CR38","doi-asserted-by":"crossref","unstructured":"Gjesteby L, Yang Q, Xi Y, Zhou Y, Zhang J, Wang G (2017) Deep learning methods to guide ct image reconstruction and reduce metal artifacts. In: Medical Imaging 2017: Physics of Medical Imaging, vol. 10132, pp. 752\u2013758 . SPIE","DOI":"10.1117\/12.2254091"},{"key":"85_CR39","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s00330-019-06359-6","volume":"30","author":"J Greffier","year":"2020","unstructured":"Greffier J, Frandon J, Larbi A, Beregi J, Pereira F (2020) Ct iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol 30:487\u2013500","journal-title":"Eur Radiol"},{"key":"85_CR40","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s00330-019-06359-6","volume":"30","author":"J Greffier","year":"2020","unstructured":"Greffier J, Frandon J, Larbi A, Beregi J, Pereira F (2020) Ct iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol 30:487\u2013500","journal-title":"Eur Radiol"},{"key":"85_CR41","doi-asserted-by":"publisher","first-page":"3951","DOI":"10.1007\/s00330-020-06724-w","volume":"30","author":"J Greffier","year":"2020","unstructured":"Greffier J, Hamard A, Pereira F, Barrau C, Pasquier H, Beregi JP, Frandon J (2020) Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for ct: a phantom study. Eur Radiol 30:3951\u20133959","journal-title":"Eur Radiol"},{"issue":"10","key":"85_CR42","doi-asserted-by":"publisher","first-page":"5743","DOI":"10.1002\/mp.15180","volume":"48","author":"J Greffier","year":"2021","unstructured":"Greffier J, Dabli D, Frandon J, Hamard A, Belaouni A, Akessoul P, Fuamba Y, Le Roy J, Guiu B, Beregi J-P (2021) Comparison of two versions of a deep learning image reconstruction algorithm on ct image quality and dose reduction: A phantom study. Med Phys 48(10):5743\u20135755","journal-title":"Med Phys"},{"key":"85_CR43","doi-asserted-by":"publisher","first-page":"5324","DOI":"10.1007\/s00330-020-07671-2","volume":"31","author":"J Greffier","year":"2021","unstructured":"Greffier J, Si-Mohamed S, Dabli D, Forges H, Hamard A, Douek P, Beregi J, Frandon J (2021) Performance of four dual-energy ct platforms for abdominal imaging: a task-based image quality assessment based on phantom data. Eur Radiol 31:5324\u20135334","journal-title":"Eur Radiol"},{"issue":"8","key":"85_CR44","doi-asserted-by":"publisher","first-page":"5052","DOI":"10.1002\/mp.15807","volume":"49","author":"J Greffier","year":"2022","unstructured":"Greffier J, Si-Mohamed S, Frandon J, Loisy M, Oliveira F, Beregi JP, Dabli D (2022) Impact of an artificial intelligence deep-learning reconstruction algorithm for ct on image quality and potential dose reduction: A phantom study. Med Phys 49(8):5052\u20135063","journal-title":"Med Phys"},{"key":"85_CR45","doi-asserted-by":"crossref","unstructured":"Gross KA, Kupinski MA (2005) Spect image quality assessment and system parameter optimization for detection tasks. In: Frontiers in Optics, p. 2 . Optica Publishing Group","DOI":"10.1364\/FIO.2005.FThM2"},{"key":"85_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.radphyschem.2021.109692","volume":"188","author":"HH Harun","year":"2021","unstructured":"Harun HH, Karim MKA, Muhammad NA, Kechik MMA, Chew MT, Talib ZA (2021) Task-based assessment on various optimization protocols of computed tomography pulmonary angiography examination. Radiat Phys Chem 188:109692","journal-title":"Radiat Phys Chem"},{"issue":"2","key":"85_CR47","doi-asserted-by":"publisher","first-page":"113","DOI":"10.3233\/THC-191718","volume":"28","author":"N Hayashi","year":"2020","unstructured":"Hayashi N, Maruyama T, Sato Y, Watanabe H, Ogura T, Ogura A (2020) Evaluating medical images using deep convolutional neural networks: A simulated ct phantom image study. Technol Health Care 28(2):113\u2013120","journal-title":"Technol Health Care"},{"key":"85_CR48","doi-asserted-by":"crossref","unstructured":"He Y, Huang F, Jiang X, Nie Y, Wang M, Wang J, Chen H (2024) Foundation model for advancing healthcare: challenges, opportunities and future directions. IEEE Reviews in Biomedical Engineering","DOI":"10.1109\/RBME.2024.3496744"},{"key":"85_CR49","doi-asserted-by":"crossref","unstructured":"Hore A, Ziou D (2010) Image quality metrics: Psnr vs. ssim. In: 2010 20th International Conference on Pattern Recognition, pp. 2366\u20132369. IEEE","DOI":"10.1109\/ICPR.2010.579"},{"key":"85_CR50","doi-asserted-by":"publisher","unstructured":"Ieee guide for an architectural framework for explainable artificial intelligence. IEEE Std 2894-2024, 1\u201355 (2024) https:\/\/doi.org\/10.1109\/IEEESTD.2024.10659410","DOI":"10.1109\/IEEESTD.2024.10659410"},{"issue":"4","key":"85_CR51","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1097\/RCT.0000000000001353","volume":"46","author":"CT Jensen","year":"2022","unstructured":"Jensen CT (2022) Commentary on: Image quality evaluation in dual energy ct of the chest, abdomen and pelvis in obese patients with deep learning image reconstruction. J Comput Assist Tomogr 46(4):612\u2013613","journal-title":"J Comput Assist Tomogr"},{"issue":"1","key":"85_CR52","doi-asserted-by":"publisher","first-page":"50","DOI":"10.2214\/AJR.19.22332","volume":"215","author":"CT Jensen","year":"2020","unstructured":"Jensen CT, Liu X, Tamm EP, Chandler AG, Sun J, Morani AC, Javadi S, Wagner-Bartak NA (2020) Image quality assessment of abdominal ct by use of new deep learning image reconstruction: initial experience. Am J Roentgenol 215(1):50\u201357","journal-title":"Am J Roentgenol"},{"key":"85_CR53","unstructured":"Joy K, Sarma EG (2014) Recent developments in image quality assessment algorithms: A review. Journal of Theoretical & Applied Information Technology. 65(1)"},{"key":"85_CR54","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1097\/01.rct.0000120857.80935.bd","volume":"28","author":"MK Kalra","year":"2004","unstructured":"Kalra MK, Maher MM, D\u2019Souza R, Saini S (2004) Multidetector computed tomography technology: current status and emerging developments. J Comput Assist Tomogr 28:2\u20136","journal-title":"J Comput Assist Tomogr"},{"key":"85_CR55","doi-asserted-by":"crossref","unstructured":"Kang L, Ye P, Li Y, Doermann D (2014) Convolutional neural networks for no-reference image quality assessment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1733\u20131740","DOI":"10.1109\/CVPR.2014.224"},{"key":"85_CR56","doi-asserted-by":"publisher","first-page":"14154","DOI":"10.1109\/ACCESS.2023.3243466","volume":"11","author":"S Kastryulin","year":"2023","unstructured":"Kastryulin S, Zakirov J, Pezzotti N, Dylov DV (2023) Image quality assessment for magnetic resonance imaging. IEEE Access. 11:14154\u201314168","journal-title":"IEEE Access."},{"issue":"1","key":"85_CR57","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3348\/kjr.2020.0116","volume":"22","author":"JH Kim","year":"2021","unstructured":"Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH (2021) Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise. Korean J Radiol 22(1):131","journal-title":"Korean J Radiol"},{"issue":"1","key":"85_CR58","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1038\/s41598-024-52517-2","volume":"14","author":"A-C Klemenz","year":"2024","unstructured":"Klemenz A-C, Albrecht L, Manzke M, Dalmer A, B\u00f6ttcher B, Surov A, Weber M-A, Meinel FG (2024) Improved image quality in ct pulmonary angiography using deep learning-based image reconstruction. Sci Rep 14(1):2494","journal-title":"Sci Rep"},{"key":"85_CR59","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s40799-018-0276-8","volume":"43","author":"Y Lee","year":"2019","unstructured":"Lee Y (2019) Feasibility study for application of total-variation-based noise-removal algorithm with 450-kvp high-energy industrial computed-tomography imaging system for non-destructive testing. Exp Tech 43:117\u2013123","journal-title":"Exp Tech"},{"issue":"4","key":"85_CR60","volume":"3","author":"W Lee","year":"2022","unstructured":"Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H (2022) No-reference perceptual ct image quality assessment based on a self-supervised learning framework. Machine Learning: Science and Technology. 3(4):045033","journal-title":"Machine Learning: Science and Technology."},{"key":"85_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102344","volume":"77","author":"K Lei","year":"2022","unstructured":"Lei K, Syed AB, Zhu X, Pauly JM, Vasanawala SS (2022) Artifact-and content-specific quality assessment for mri with image rulers. Med Image Anal 77:102344","journal-title":"Med Image Anal"},{"issue":"3","key":"85_CR62","doi-asserted-by":"publisher","first-page":"649","DOI":"10.2214\/AJR.10.4285","volume":"195","author":"J Leipsic","year":"2010","unstructured":"Leipsic J, Labounty TM, Heilbron B, Min JK, Mancini GJ, Lin FY, Taylor C, Dunning A, Earls JP (2010) Adaptive statistical iterative reconstruction: assessment of image noise and image quality in coronary ct angiography. Am J Roentgenol 195(3):649\u2013654","journal-title":"Am J Roentgenol"},{"issue":"3","key":"85_CR63","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1148\/rg.2019180115","volume":"39","author":"S Leng","year":"2019","unstructured":"Leng S, Bruesewitz M, Tao S, Rajendran K, Halaweish AF, Campeau NG, Fletcher JG, McCollough CH (2019) Photon-counting detector ct: system design and clinical applications of an emerging technology. Radiographics 39(3):729\u2013743","journal-title":"Radiographics"},{"key":"85_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110221","volume":"149","author":"W Li","year":"2022","unstructured":"Li W, You Y, Zhong S, Shuai T, Liao K, Yu J, Zhao J, Li Z, Lu C (2022) Image quality assessment of artificial intelligence iterative reconstruction for low dose aortic cta: a feasibility study of 70 kvp and reduced contrast medium volume. Eur J Radiol 149:110221","journal-title":"Eur J Radiol"},{"key":"85_CR65","doi-asserted-by":"crossref","unstructured":"Li R, Dai G, Wang Z, Yu S, Xie Y (2018) Using signal-to-noise ratio to connect the quality assessment of natural and medical images. In: Tenth International Conference on Digital Image Processing (ICDIP 2018), vol. 10806, pp. 1327\u20131332 . SPIE","DOI":"10.1117\/12.2503084"},{"key":"85_CR66","unstructured":"Li S, He J, Wang Y, Liao Y, Zeng D, Bian Z, Ma J (2018) Blind ct image quality assessment via deep learning strategy: initial study. In: Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, vol. 10577, pp. 293\u2013297. SPIE"},{"key":"85_CR67","doi-asserted-by":"crossref","unstructured":"Lin Z, Zhang Z, Hu X, Gao Z, Yang X, Sun Y, Ni D, Tan T (2024) Uniusnet: A promptable framework for universal ultrasound disease prediction and tissue segmentation. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 3501\u20133504. IEEE","DOI":"10.1109\/BIBM62325.2024.10822429"},{"issue":"3","key":"85_CR68","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","volume":"16","author":"ZC Lipton","year":"2018","unstructured":"Lipton ZC (2018) The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue. 16(3):31\u201357","journal-title":"Queue."},{"issue":"11","key":"85_CR69","doi-asserted-by":"publisher","first-page":"3691","DOI":"10.1109\/TMI.2020.3002708","volume":"39","author":"S Liu","year":"2020","unstructured":"Liu S, Thung K-H, Lin W, Shen D, Yap P-T (2020) Hierarchical nonlocal residual networks for image quality assessment of pediatric diffusion mri with limited and noisy annotations. IEEE Trans Med Imaging 39(11):3691\u20133702","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"85_CR70","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad3cb0","volume":"69","author":"D Lustermans","year":"2024","unstructured":"Lustermans D, Fonseca GP, Taasti VT, Schoot A, Petit S, Elmpt W, Verhaegen F (2024) Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager. Physics in Medicine & Biology. 69(10):105018","journal-title":"Physics in Medicine & Biology."},{"key":"85_CR71","doi-asserted-by":"crossref","unstructured":"Mantiuk RK, Tomaszewska A, Mantiuk R (2012) Comparison of four subjective methods for image quality assessment. In: Computer Graphics Forum, vol. 31, pp. 2478\u20132491. Wiley Online Library","DOI":"10.1111\/j.1467-8659.2012.03188.x"},{"issue":"1","key":"85_CR72","doi-asserted-by":"publisher","first-page":"2329950","DOI":"10.2214\/AJR.23.29950","volume":"222","author":"AA Marth","year":"2024","unstructured":"Marth AA, Marcus RP, Feuerriegel GC, Nanz D, Sutter R (2024) Photon-counting detector ct versus energy-integrating detector ct of the lumbar spine: comparison of radiation dose and image quality. Am J Roentgenol 222(1):2329950","journal-title":"Am J Roentgenol"},{"issue":"4","key":"85_CR73","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TMI.2019.2930338","volume":"39","author":"A Mason","year":"2019","unstructured":"Mason A, Rioux J, Clarke SE, Costa A, Schmidt M, Keough V, Huynh T, Beyea S (2019) Comparison of objective image quality metrics to expert radiologists\u2019 scoring of diagnostic quality of mr images. IEEE Trans Med Imaging 39(4):1064\u20131072","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"85_CR74","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1016\/j.jacr.2019.01.026","volume":"16","author":"MA Mazurowski","year":"2019","unstructured":"Mazurowski MA (2019) Artificial intelligence may cause a significant disruption to the radiology workforce. J Am Coll Radiol 16(8):1077\u20131082","journal-title":"J Am Coll Radiol"},{"issue":"2","key":"85_CR75","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1148\/radiol.15142047","volume":"276","author":"CH McCollough","year":"2015","unstructured":"McCollough CH, Yu L, Kofler JM, Leng S, Zhang Y, Li Z, Carter RE (2015) Degradation of ct low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology 276(2):499\u2013506","journal-title":"Radiology"},{"key":"85_CR76","doi-asserted-by":"crossref","unstructured":"Naeemi MD, Ren J, Hollcroft N, Alessio AM, Roychowdhury S (2016) Application of big data analytics for automated estimation of ct image quality. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 3422\u20133431. IEEE","DOI":"10.1109\/BigData.2016.7841003"},{"issue":"12","key":"85_CR77","doi-asserted-by":"publisher","first-page":"8488","DOI":"10.1007\/s00330-023-09888-3","volume":"33","author":"Y Nagayama","year":"2023","unstructured":"Nagayama Y, Emoto T, Kato Y, Kidoh M, Oda S, Sakabe D, Funama Y, Nakaura T, Hayashi H, Takada S et al (2023) Improving image quality with super-resolution deep-learning-based reconstruction in coronary ct angiography. Eur Radiol 33(12):8488\u20138500","journal-title":"Eur Radiol"},{"key":"85_CR78","doi-asserted-by":"crossref","unstructured":"Nikolaev D, Buzmakov A, Chukalina M, Ivan Y, Gladkov A, Ingacheva A (2017) Ct image quality assessment based on morphometric analysis of artifacts. In: 2016 International Conference on Robotics and Machine Vision, vol. 10253, pp. 52\u201356. SPIE","DOI":"10.1117\/12.2266268"},{"key":"85_CR79","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1007\/s00330-012-2668-y","volume":"23","author":"PB No\u00ebl","year":"2013","unstructured":"No\u00ebl PB, Bendik E, M\u00fcnzel D, Schneider A, Goshen L, Gringauz A, Lamash Y, Vlassenbroek A, Fingerle AA, Rummeny EJ et al (2013) A method for improving iodine contrast enhancement in abdominal computed tomography: experimental study in a pig model. Eur Radiol 23:985\u2013990","journal-title":"Eur Radiol"},{"issue":"3","key":"85_CR80","doi-asserted-by":"publisher","first-page":"77","DOI":"10.5755\/j01.eie.25.3.23681","volume":"25","author":"K Okarma","year":"2019","unstructured":"Okarma K (2019) Current trends and advances in image quality assessment. Elektronika ir Elektrotechnika. 25(3):77\u201384","journal-title":"Elektronika ir Elektrotechnika."},{"issue":"1","key":"85_CR81","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/S0969-8043(98)00029-3","volume":"50","author":"M Oresegun","year":"1999","unstructured":"Oresegun M, LeHeron J, Maccia C, Padovani R, Vano E (1999) Radiation protection and quality assurance in diagnostic radiology-an iaea coordinated research project in asia and eastern europe. Appl Radiat Isot 50(1):271\u2013276","journal-title":"Appl Radiat Isot"},{"issue":"3","key":"85_CR82","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1002\/mrm.28201","volume":"84","author":"M Oszust","year":"2020","unstructured":"Oszust M, Pi\u00f3rkowski A, Obuchowicz R (2020) No-reference image quality assessment of magnetic resonance images with high-boost filtering and local features. Magn Reson Med 84(3):1648\u20131660","journal-title":"Magn Reson Med"},{"key":"85_CR83","doi-asserted-by":"crossref","unstructured":"Pal D, Patel B, Wang A et al (2021) Ssiqa: multi-task learning for non-reference ct image quality assessment with self-supervised noise level prediction. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1962\u20131965. IEEE","DOI":"10.1109\/ISBI48211.2021.9434044"},{"issue":"2","key":"85_CR84","doi-asserted-by":"publisher","first-page":"2330154","DOI":"10.2214\/AJR.23.30154","volume":"222","author":"P Pannenbecker","year":"2024","unstructured":"Pannenbecker P, Heidenreich JF, Grunz J-P, Huflage H, Gruschwitz P, Patzer TS, Feldle P, Bley TA, Petritsch B (2024) Image quality and radiation dose of ctpa with iodine maps: a prospective randomized study of high-pitch mode photon-counting detector ct versus energy-integrating detector ct. Am J Roentgenol 222(2):2330154","journal-title":"Am J Roentgenol"},{"key":"85_CR85","doi-asserted-by":"publisher","first-page":"2865","DOI":"10.1007\/s00330-021-08380-0","volume":"32","author":"S Park","year":"2022","unstructured":"Park S, Yoon JH, Joo I, Yu MH, Kim JH, Park J, Kim SW, Han S, Ahn C, Kim JH et al (2022) Image quality in liver ct: low-dose deep learning vs standard-dose model-based iterative reconstructions. Eur Radiol 32:2865\u20132874","journal-title":"Eur Radiol"},{"issue":"1","key":"85_CR86","doi-asserted-by":"publisher","first-page":"8226","DOI":"10.1038\/s41598-023-35367-2","volume":"13","author":"TS Patzer","year":"2023","unstructured":"Patzer TS, Kunz AS, Huflage H, Luetkens KS, Conrads N, Gruschwitz P, Pannenbecker P, Erg\u00fcn S, Bley TA, Grunz J-P (2023) Quantitative and qualitative image quality assessment in shoulder examinations with a first-generation photon-counting detector ct. Sci Rep 13(1):8226","journal-title":"Sci Rep"},{"issue":"5","key":"85_CR87","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1007\/s00259-022-06078-z","volume":"50","author":"C Qi","year":"2023","unstructured":"Qi C, Wang S, Yu H, Zhang Y, Hu P, Tan H, Shi Y, Shi H (2023) An artificial intelligence-driven image quality assessment system for whole-body [18f] fdg pet\/ct. Eur J Nucl Med Mol Imaging 50(5):1318\u20131328","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"85_CR88","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ejmp.2020.06.004","volume":"76","author":"D Racine","year":"2020","unstructured":"Racine D, Becce F, Viry A, Monnin P, Thomsen B, Verdun FR, Rotzinger DC (2020) Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal ct: a phantom study. Physica Med 76:28\u201337","journal-title":"Physica Med"},{"key":"85_CR89","doi-asserted-by":"crossref","unstructured":"Rasoolzadeh N, Zhang T, Gao Y, Dijk JM, Yang Q, Tan T, Mann RM (2024) Multimodal breast mri language-image pretraining (mlip): An exploration of a breast mri foundation model. In: Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, pp. 42\u201353. Springer","DOI":"10.1007\/978-3-031-77789-9_5"},{"key":"85_CR90","doi-asserted-by":"crossref","unstructured":"Ray PP (2023) Chatgpt: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems","DOI":"10.1016\/j.iotcps.2023.04.003"},{"key":"85_CR91","first-page":"33","volume":"61","author":"R Reisenhofer","year":"2018","unstructured":"Reisenhofer R, Bosse S, Kutyniok G, Wiegand T (2018) A haar wavelet-based perceptual similarity index for image quality assessment. Signal Processing: Image Communication. 61:33\u201343","journal-title":"Signal Processing: Image Communication."},{"key":"85_CR92","doi-asserted-by":"crossref","unstructured":"Richard S, Husarik DB, Yadava G, Murphy SN, Samei E (2012) Towards task-based assessment of ct performance: system and object mtf across different reconstruction algorithms. Medical physics. 39(7Part1), 4115\u20134122","DOI":"10.1118\/1.4725171"},{"issue":"2","key":"85_CR93","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1120\/jacmp.v16i2.4972","volume":"16","author":"AMA Roa","year":"2015","unstructured":"Roa AMA, Andersen HK, Martinsen ACT (2015) Ct image quality over time: comparison of image quality for six different ct scanners over a six-year period. J Appl Clin Med Phys 16(2):350\u2013365","journal-title":"J Appl Clin Med Phys"},{"issue":"2S","key":"85_CR94","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1148\/radiol.14141356","volume":"273","author":"GD Rubin","year":"2014","unstructured":"Rubin GD (2014) Computed tomography: revolutionizing the practice of medicine for 40 years. Radiology 273(2S):45\u201374","journal-title":"Radiology"},{"issue":"2","key":"85_CR95","doi-asserted-by":"publisher","first-page":"14270","DOI":"10.1002\/acm2.14270","volume":"25","author":"RT Sadia","year":"2024","unstructured":"Sadia RT, Chen J, Zhang J (2024) Ct image denoising methods for image quality improvement and radiation dose reduction. J Appl Clin Med Phys 25(2):14270","journal-title":"J Appl Clin Med Phys"},{"issue":"2","key":"85_CR96","first-page":"68","volume":"8","author":"A Saiyeda","year":"2017","unstructured":"Saiyeda A, Mir MA (2017) Cloud computing for deep learning analytics: A survey of current trends and challenges. Int J Adv Res Comput Sci 8(2):68","journal-title":"Int J Adv Res Comput Sci"},{"key":"85_CR97","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105111","volume":"140","author":"Z Salahuddin","year":"2022","unstructured":"Salahuddin Z, Woodruff HC, Chatterjee A, Lambin P (2022) Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Comput Biol Med 140:105111","journal-title":"Comput Biol Med"},{"issue":"1","key":"85_CR98","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1118\/1.4903899","volume":"42","author":"E Samei","year":"2015","unstructured":"Samei E, Richard S (2015) Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology. Med Phys 42(1):314\u2013323","journal-title":"Med Phys"},{"issue":"11","key":"85_CR99","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1002\/mp.13763","volume":"46","author":"E Samei","year":"2019","unstructured":"Samei E, Bakalyar D, Boedeker KL, Brady S, Fan J, Leng S, Myers KJ, Popescu LM, Ramirez Giraldo JC, Ranallo F et al (2019) Performance evaluation of computed tomography systems: summary of aapm task group 233. Med Phys 46(11):735\u2013756","journal-title":"Med Phys"},{"key":"85_CR100","doi-asserted-by":"crossref","unstructured":"Sheikh HR, Bovik AC (2005) A visual information fidelity approach to video quality assessment. In: The First International Workshop on Video Processing and Quality Metrics for Consumer Electronics, vol. 7, pp. 2117\u20132128. sn","DOI":"10.1109\/TIP.2005.859389"},{"issue":"3","key":"85_CR101","first-page":"1043","volume":"14","author":"KS Sim","year":"2018","unstructured":"Sim KS, Chung S, Zheng Y (2018) Contrast enhancement brain infarction images using sigmoidal eliminating extreme level weight distributed histogram equalization. Int. J. Innov. Comput. Inf. Control (IJICIC) 14(3):1043\u20131056","journal-title":"Int. J. Innov. Comput. Inf. Control (IJICIC)"},{"issue":"3","key":"85_CR102","doi-asserted-by":"publisher","first-page":"566","DOI":"10.2214\/AJR.19.21809","volume":"214","author":"R Singh","year":"2020","unstructured":"Singh R, Digumarthy SR, Muse VV, Kambadakone AR, Blake MA, Tabari A, Hoi Y, Akino N, Angel E, Madan R et al (2020) Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal ct. Am J Roentgenol 214(3):566\u2013573","journal-title":"Am J Roentgenol"},{"key":"85_CR103","doi-asserted-by":"crossref","unstructured":"Soares E, Barrett H, Krupinski E (1993) Noise properties of attenuation correction methods for spect. In: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, pp. 1409\u20131413. IEEE","DOI":"10.1109\/NSSMIC.1993.373520"},{"issue":"9","key":"85_CR104","doi-asserted-by":"publisher","first-page":"3961","DOI":"10.1002\/mp.14319","volume":"47","author":"J Solomon","year":"2020","unstructured":"Solomon J, Lyu P, Marin D, Samei E (2020) Noise and spatial resolution properties of a commercially available deep learning-based ct reconstruction algorithm. Med Phys 47(9):3961\u20133971","journal-title":"Med Phys"},{"key":"85_CR105","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2024.110081","volume":"108","author":"MV Spampinato","year":"2024","unstructured":"Spampinato MV, Rodgers J, McGill LJ, Schoepf UJ, O\u2019Doherty J (2024) Image quality of photon-counting detector ct virtual monoenergetic and polyenergetic reconstructions for head and neck ct angiography. Clin Imaging 108:110081","journal-title":"Clin Imaging"},{"issue":"6","key":"85_CR106","doi-asserted-by":"publisher","first-page":"160","DOI":"10.3390\/jimaging8060160","volume":"8","author":"I Stepien","year":"2022","unstructured":"Stepien I, Oszust M (2022) A brief survey on no-reference image quality assessment methods for magnetic resonance images. Journal of Imaging. 8(6):160","journal-title":"Journal of Imaging."},{"issue":"2","key":"85_CR107","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s00530-014-0446-1","volume":"22","author":"RC Streijl","year":"2016","unstructured":"Streijl RC, Winkler S, Hands DS (2016) Mean opinion score (mos) revisited: methods and applications, limitations and alternatives. Multimedia Syst 22(2):213\u2013227","journal-title":"Multimedia Syst"},{"issue":"1\u20134","key":"85_CR108","first-page":"101","volume":"57","author":"O Suleiman","year":"1995","unstructured":"Suleiman O, Conway B, Rueter F, McCrohan J, Slayton R, Antonsen R (1995) The united states experience in patient dose and image quality. Radiat Prot Dosimetry 57(1\u20134):101\u2013104","journal-title":"Radiat Prot Dosimetry"},{"key":"85_CR109","unstructured":"Sun K, Xue S, Sun F, Sun H, Luo Y, Wang L, Wang S, Guo N, Liu L, Zhao T et al (2024) Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions. arXiv preprint arXiv:2412.02621"},{"issue":"10","key":"85_CR110","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.3174\/ajnr.A8350","volume":"45","author":"A T\u00f3th","year":"2024","unstructured":"T\u00f3th A, Chetta JA, Yazdani M, Matheus MG, O\u2019Doherty J, Tipnis SV, Spampinato MV (2024) Neurovascular imaging with ultra-high-resolution photon-counting ct: Preliminary findings on image-quality evaluation. Am J Neuroradiol 45(10):1450\u20131457","journal-title":"Am J Neuroradiol"},{"issue":"7","key":"85_CR111","doi-asserted-by":"publisher","DOI":"10.1118\/1.4881148","volume":"41","author":"J Vaishnav","year":"2014","unstructured":"Vaishnav J, Jung W, Popescu L, Zeng R, Myers K (2014) Objective assessment of image quality and dose reduction in ct iterative reconstruction. Med Phys 41(7):071904","journal-title":"Med Phys"},{"issue":"3","key":"85_CR112","doi-asserted-by":"publisher","first-page":"545","DOI":"10.2214\/AJR.12.9424","volume":"200","author":"V Vardhanabhuti","year":"2013","unstructured":"Vardhanabhuti V, Loader RJ, Mitchell GR, Riordan RD, Roobottom CA (2013) Image quality assessment of standard-and low-dose chest ct using filtered back projection, adaptive statistical iterative reconstruction, and novel model-based iterative reconstruction algorithms. Am J Roentgenol 200(3):545\u2013552","journal-title":"Am J Roentgenol"},{"issue":"8","key":"85_CR113","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1016\/j.ejmp.2015.08.007","volume":"31","author":"F Verdun","year":"2015","unstructured":"Verdun F, Racine D, Ott J, Tapiovaara M, Toroi P, Bochud F, Veldkamp W, Schegerer A, Bouwman R, Giron IH et al (2015) Image quality in ct: From physical measurements to model observers. Physica Med 31(8):823\u2013843","journal-title":"Physica Med"},{"key":"85_CR114","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.ejmp.2018.04.006","volume":"48","author":"A Viry","year":"2018","unstructured":"Viry A, Aberle C, Racine D, Knebel J-F, Schindera ST, Schmidt S, Becce F, Verdun FR (2018) Effects of various generations of iterative ct reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: a quantitative task-based phantom study. Physica Med 48:111\u2013118","journal-title":"Physica Med"},{"issue":"2","key":"85_CR115","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s00330-023-10087-3","volume":"34","author":"J Wang","year":"2024","unstructured":"Wang J, Sui X, Zhao R, Du H, Wang J, Wang Y, Qin R, Lu X, Ma Z, Xu Y et al (2024) Value of deep learning reconstruction of chest low-dose ct for image quality improvement and lung parenchyma assessment on lung window. Eur Radiol 34(2):1053\u20131064","journal-title":"Eur Radiol"},{"key":"85_CR116","unstructured":"Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol. 2, pp. 1398\u20131402. Ieee"},{"key":"85_CR117","doi-asserted-by":"crossref","unstructured":"Wang G, Sun C, Liu Y, Yang H (2022) Optimization of ct image quality assessment metric based on genetic algorithm. In: 2022 IEEE 10th International Conference on Computer Science and Network Technology (ICCSNT), pp. 14\u201318. IEEE","DOI":"10.1109\/ICCSNT56096.2022.9972914"},{"key":"85_CR118","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ejmp.2020.06.015","volume":"76","author":"S Watanabe","year":"2020","unstructured":"Watanabe S, Ichikawa K, Kawashima H, Kono Y, Kosaka H, Yamada K, Ishii K (2020) Image quality comparison of a nonlinear image-based noise reduction technique with a hybrid-type iterative reconstruction for pediatric computed tomography. Physica Med 76:100\u2013108","journal-title":"Physica Med"},{"issue":"1","key":"85_CR119","doi-asserted-by":"publisher","first-page":"101","DOI":"10.5114\/jcb.2021.103593","volume":"13","author":"S Wilby","year":"2021","unstructured":"Wilby S, Palmer A, Polak W, Bucchi A (2021) A review of brachytherapy physical phantoms developed over the last 20 years: clinical purpose and future requirements. Journal of Contemporary Brachytherapy. 13(1):101\u2013115","journal-title":"Journal of Contemporary Brachytherapy."},{"issue":"2","key":"85_CR120","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1148\/radiol.2018172656","volume":"289","author":"MJ Willemink","year":"2018","unstructured":"Willemink MJ, Persson M, Pourmorteza A, Pelc NJ, Fleischmann D (2018) Photon-counting ct: technical principles and clinical prospects. Radiology 289(2):293\u2013312","journal-title":"Radiology"},{"issue":"3","key":"85_CR121","doi-asserted-by":"publisher","DOI":"10.1118\/1.4791645","volume":"40","author":"JM Wilson","year":"2013","unstructured":"Wilson JM, Christianson OI, Richard S, Samei E (2013) A methodology for image quality evaluation of advanced ct systems. Med Phys 40(3):031908","journal-title":"Med Phys"},{"key":"85_CR122","doi-asserted-by":"crossref","unstructured":"Xia L, Jebbari H, Deforges O, Zhang L, L\u00e9v\u00eaque L, Outtas M (2023) Denoised ct images quality assessment through covid-19 pneumonia detection task. In: 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), pp. 183\u2013188. IEEE","DOI":"10.1109\/QoMEX58391.2023.10178603"},{"issue":"8","key":"85_CR123","doi-asserted-by":"publisher","first-page":"4932","DOI":"10.1118\/1.4736805","volume":"39","author":"J Xu","year":"2012","unstructured":"Xu J, Reh D, Carey J, Mahesh M, Siewerdsen J (2012) Technical assessment of a cone-beam ct scanner for otolaryngology imaging: image quality, dose, and technique protocols. Med Phys 39(8):4932\u20134942","journal-title":"Med Phys"},{"issue":"4","key":"85_CR124","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1007\/s00330-022-09233-0","volume":"33","author":"K Yang","year":"2023","unstructured":"Yang K, Cao J, Pisuchpen N, Kambadakone A, Gupta R, Marschall T, Li X, Liu B (2023) Ct image quality evaluation in the age of deep learning: trade-off between functionality and fidelity. Eur Radiol 33(4):2439\u20132449","journal-title":"Eur Radiol"},{"key":"85_CR125","first-page":"205","volume":"32","author":"F Yin","year":"2015","unstructured":"Yin F, Ji Z, Zhou J, Zhang H (2015) Subjective assessment and perception model of pet\/ct image quality. 32:205\u2013212","journal-title":"Subjective assessment and perception model of pet\/ct image quality."},{"issue":"4","key":"85_CR126","doi-asserted-by":"publisher","DOI":"10.1118\/1.4794498","volume":"40","author":"L Yu","year":"2013","unstructured":"Yu L, Leng S, Chen L, Kofler JM, Carter RE, McCollough CH (2013) Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized hotelling observer: Impact of radiation dose and reconstruction algorithms. Med Phys 40(4):041908","journal-title":"Med Phys"},{"issue":"3","key":"85_CR127","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.cpet.2009.03.002","volume":"3","author":"H Zaidi","year":"2008","unstructured":"Zaidi H, Montandon M-L, Alavi A (2008) The clinical role of fusion imaging using pet, ct, and mr imaging. PET clinics. 3(3):275\u2013291","journal-title":"PET clinics."},{"key":"85_CR128","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102996","volume":"91","author":"S Zhang","year":"2024","unstructured":"Zhang S, Metaxas D (2024) On the challenges and perspectives of foundation models for medical image analysis. Med Image Anal 91:102996","journal-title":"Med Image Anal"},{"issue":"10","key":"85_CR129","doi-asserted-by":"publisher","first-page":"4270","DOI":"10.1109\/TIP.2014.2346028","volume":"23","author":"L Zhang","year":"2014","unstructured":"Zhang L, Shen Y, Li H (2014) Vsi: A visual saliency-induced index for perceptual image quality assessment. IEEE Trans Image Process 23(10):4270\u20134281","journal-title":"IEEE Trans Image Process"},{"key":"85_CR130","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.114303","volume":"227","author":"B Zhang","year":"2024","unstructured":"Zhang B, Zhang Y, Wang B, He X, Zhang F, Zhang X (2024) Denoising swin transformer and perceptual peak signal-to-noise ratio for low-dose ct image denoising. Measurement 227:114303","journal-title":"Measurement"},{"key":"85_CR131","doi-asserted-by":"crossref","unstructured":"Zhang Z, Han L, Zhang T, Lin Z, Gao Q, Tong T, Sun Y, Tan T (2024) Unimrisegnet: Universal 3d network for various organs and cancers segmentation on multi-sequence mri. IEEE Journal of Biomedical and Health Informatics","DOI":"10.1109\/JBHI.2024.3504603"},{"key":"85_CR132","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595","DOI":"10.1109\/CVPR.2018.00068"},{"issue":"1","key":"85_CR133","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1166\/jmihi.2017.2019","volume":"7","author":"Y Zhu","year":"2017","unstructured":"Zhu Y, Ding Y (2017) Auto-optimized paralleled sinogram noise reduction method based on relative quality assessment for low-dose x-ray computed tomography. Journal of Medical Imaging and Health Informatics. 7(1):278\u2013282","journal-title":"Journal of Medical Imaging and Health Informatics."}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00085-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00085-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00085-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T01:34:27Z","timestamp":1757208867000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00085-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":133,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["85"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00085-4","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"15 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"92"}}