The widespread use of CT has raised concerns about potential radiation risks, motivating diverse strategies to reduce the radiation dose associated with CT. CT manufacturers have developed alternative reconstruction algorithms intended to improve image quality on dose-optimized CT studies, mainly through noise and artifact reduction. Iterative reconstruction techniques take unique approaches to noise reduction and provide distinct strength levels or settings.
BackgroundMost of the knowledge about the mechanisms of multidrug resistance in lung cancer has been achieved through the use of cell lines isolated from tumours cultivated either in suspensions of isolated cells or in monolayers and following exposition to different cytostatic agents. However, tumour cell lines growing as multicellular tumour spheroids (MTS) frequently develop multicellular resistance in a drug-independent form. The aim of this study was to characterize the phenotypic and functional differences between two human NSCLC cell lines (INER-37 and INER-51) grown as traditional monolayer cultures versus as MTS.MethodsAfter 72 hours treatment with anticancer drugs, chemosensitivity in monolayers and tumour spheroids cultures was assessed using MTT assay. Reverse transcription-polymerase chain reaction was employed to detect the mRNAs of multidrug resistance-related genes. The expression of P-gp was analyzed by immunohistochemical staining and cell cycle profiles were analyzed using FACS.ResultsThe results indicate that when grown as MTS each lung cancer cell line had different morphologies as well as and abrogation of cell proliferation with decrease of the G2/M phase. Also, MTS acquired multicellular resistance to several chemotherapeutic agents in only a few days of culture which were accomplished by significant changes in the expression of MDR-related genes.ConclusionOverall, the MTS culture changed the cellular response to drugs nevertheless each of the cell lines studied seems to implement different mechanisms to acquire multicellular resistance.
OBJECTIVE. The objective of our study was to compare the performance of three hybrid iterative reconstruction techniques (IRTs) (ASiR, iDose4, SAFIRE) and their respective strengths for image noise reduction on low-dose CT examinations using filtered back projection (FBP) as the standard reference. Also, we compared the performance of these three hybrid IRTs with two model-based IRTs (Veo and IMR) for image noise reduction on low-dose examinations. MATERIALS AND METHODS. An anthropomorphic abdomen phantom was scanned at 100 and 120 kVp and different tube current-exposure time products (25-100 mAs) on three CT systems (for ASiR and Veo, Discovery CT750 HD; for iDose4 and IMR, Brilliance iCT; and for SAFIRE, Somatom Definition Flash). Images were reconstructed using FBP and using IRTs at various strengths. Nine noise measurements (mean ROI size, 423 mm(2)) on extracolonic fat for the different strengths of IRTs were recorded and compared with FBP using ANOVA. Radiation dose, which was measured as the volume CT dose index and dose-length product, was also compared. RESULTS. There were no significant differences in radiation dose and image noise among the scanners when FBP was used (p > 0.05). Gradual image noise reduction was observed with each increasing increment of hybrid IRT strength, with a maximum noise suppression of approximately 50% (48.2-53.9%). Similar noise reduction was achieved on the scanners by applying specific hybrid IRT strengths. Maximum noise reduction was higher on model-based IRTs (68.3-81.1%) than hybrid IRTs (48.2-53.9%) (p < 0.05). CONCLUSION. When constant scanning parameters are used, radiation dose and image noise on FBP are similar for CT scanners made by different manufacturers. Significant image noise reduction is achieved on low-dose CT examinations rendered with IRTs. The image noise on various scanners can be matched by applying specific hybrid IRT strengths. Model-based IRTs attain substantially higher noise reduction than hybrid IRTs irrespective of the radiation dose.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.