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Woolloongabba, Australia

Schick D.,Biomedical Technology Services | Pratap J.,Princess Alexandra Hospital
British Journal of Radiology | Year: 2015

Objective: This study evaluated the radiation dose and image quality implications of dual-energy CT (DECT) use, compared with kilovoltage-optimized single-source/single-energy CT (SECT) on a dual-source Siemens Somatom Definition Flash CT scanner (Siemens Healthcare, Forcheim, Germany). Methods: With equalized radiation dose (volumetric CT dose index), image noise (standard deviation of CT number) and signal-difference-to-noise ratio (SDNR) were measured and compared across three techniques: 100, 120 and 100/140kVp (dual energy). Noise in a 30-cm-diameter water phantom and SDNR within unenhanced soft-tissue regions of a small adult (50kg/165cm) anthropomorphic phantom were utilized for the assessment. Results: Water phantom image noise decreased with DECT compared with the lower noise SECT setting of 120 kVp (p50.046). A decrease in SDNR within the anthropomorphic phantom was demonstrated at 120kVp compared with the SECT kilovoltage-optimized setting of 100 kVp (p50.001). A further decrease in SDNR was observed for the DECT technique when compared with 120 kVp (p50.01). Conclusion: On the Siemens Somatom Definition Flash system (Siemens Healthcare), and for equalized radiation dose conditions, image quality expressed as SDNR of unenhanced soft tissue may be compromised for DECT when compared with kilovoltage-optimized SECT, particularly for smaller patients. Advances in knowledge: DECT on a dual-source CT scanner may require a radiation dose increase to maintain unenhanced soft-tissue contrast detectability, particularly for smaller patients. © 2015 The Authors. Published by the British Institute of Radiology. Source

Wallace A.B.,Medical Physics Section | Goergen S.K.,Monash Medical Center | Schick D.,Biomedical Technology Services | Soblusky T.,Skills Development Center
Journal of the American College of Radiology | Year: 2010

Purpose: The aims of this study were to collect data relating to radiation dose delivered by multidetector CT scanning at 10 hospitals and private practices in Queensland, Australia, and to test methods for dose optimization training, including audit feedback and didactic, face-to-face, small-group teaching of optimization techniques. Methods: Ten hospital-based public and private sector radiology practices, with one CT scanner per site, volunteered for the project. Data were collected for a variety of common adult and pediatric CT scanning protocols, including tube current-time product, pitch, collimation, tube voltage, the use of dose modulation, and scan length. A one-day feedback and optimization training workshop was conducted for participating practices and was attended by the radiologist and medical imaging technologist responsible for the project at each site. Data were deidentified for the workshop presentation. During the feedback workshop, a detailed analysis and discussion of factors contributing to dose for higher dosing practices for each protocol occurred. The postoptimization training data collection phase allowed changes to median and spread of doses to be measured. Results: During the baseline survey period, data for 1,208 scans were collected, and data from 1,153 scans were collected for the postoptimization dose survey for the 4 adult protocols (noncontrast brain CT, CT pulmonary angiography , CT lumbar spine, and CT urography). A mean decrease in effective dose was achieved with all scan protocols. Average reductions of 46% for brain CT, 28% for CT pulmonary angiography, 29% for CT lumbar spine, and 24% CT urography were calculated. It proved impossible to collect valid pediatric data from most sites, because of the small numbers of children presenting for multidetector CT, and phantom data were acquired during the preoptimization and postoptimization phase. Substantial phantom dose reductions were demonstrated at all sites. Conclusion: Audit feedback and small-group teaching about optimization enabled clinically meaningful dose reduction for a variety of common adult scans. However, access to medical radiation physicists, assistance with time-consuming data collection, and technical support from a medical imaging technologist were costly and critical to the success of the program. Copyright © 2010 American College of Radiology. Source

Burns C.L.,Level 2 Industries | Burns C.L.,University of Queensland | Keir B.,Biomedical Technology Services | Ward E.C.,University of Queensland | And 5 more authors.
Dysphagia | Year: 2015

High-quality fluoroscopy images are required for accurate interpretation of videofluoroscopic swallow studies (VFSS) by speech pathologists and radiologists. Consequently, integral to developing any system to conduct VFSS remotely via telepractice is ensuring that the quality of the VFSS images transferred via the telepractice system is optimized. This study evaluates the extent of change observed in image quality when videofluoroscopic images are transmitted from a digital fluoroscopy system to (a) current clinical equipment (KayPentax Digital Swallowing Workstation, and b) four different telepractice system configurations. The telepractice system configurations consisted of either a local C20 or C60 Cisco TelePresence System (codec unit) connected to the digital fluoroscopy system and linked to a second remote C20 or C60 Cisco TelePresence System via a network running at speeds of either 2, 4 or 6 megabits per second (Mbit/s). Image quality was tested using the NEMA XR 21 Phantom, and results demonstrated some loss in spatial resolution, low contrast detectability and temporal resolution for all transferred images when compared to the fluoroscopy source. When using higher capacity codec units and/or the highest bandwidths to support data transmission, image quality transmitted through the telepractice system was found to be comparable if not better than the current clinical system. This study confirms that telepractice systems can be designed to support fluoroscopy image transfer and highlights important considerations when developing telepractice systems for VFSS analysis to ensure high-quality radiological image reproduction. © 2015, Springer Science+Business Media New York. Source

Dobeli K.L.,University of Sydney | Lewis S.J.,University of Sydney | Meikle S.R.,University of Sydney | Thiele D.L.,Biomedical Technology Services | Brennan P.C.,University of Sydney
Journal of Digital Imaging | Year: 2013

This study aimed to determine if phantom-based methodologies for optimization of hepatic lesion detection with computed tomography (CT) require randomization of lesion placement and inclusion of normal images. A phantom containing fixed opacities of varying size (diameters, 2.4, 4.8, and 9.5 mm) was scanned at various exposure and slice thickness settings. Two image sets were compared: All images in the first image set contained opacities with known location; the second image set contained images with opacities in random locations. Following Institutional Review Board approval, nine experienced observers scored opacity visualization using a 4-point confidence scale. Comparisons between image sets were performed using Spearman, Kappa, and Wilcoxon techniques. Observer scores demonstrated strong correlation between both approaches when all opacity sizes were combined (r = 0.92, p < 0.0001), for the 9.5 mm opacity (r = 0.96, p < 0.0001) and for the 2.4 mm opacity (r = 0.64, p < 0.05). There was no significant correlation for the 4.8 mm opacity. A significantly higher sensitivity score for the known compared with the unknown location was found for the 9.5 mm opacity and 4.8 mm opacity for a single slice thickness and exposure condition (p < 0.05). Phantom-based optimization of CT hepatic examinations requires randomized lesion location when investigating challenging conditions; however, a standard phantom with fixed lesion location is suitable for the optimization of routine liver protocols. The development of more sophisticated phantoms or methods than those currently available is indicated for the optimization of CT protocols for diagnostic tasks involving the detection of subtle change. © 2013 Society for Imaging Informatics in Medicine. Source

Dobeli K.L.,University of Sydney | Lewis S.J.,University of Sydney | Meikle S.R.,University of Sydney | Thiele D.L.,Biomedical Technology Services | Brennan P.C.,University of Sydney
British Journal of Radiology | Year: 2013

Objective: To compare the doseoptimisation potential of a smoothing filtered backprojection (FBP) and a hybrid FBP/iterative algorithm to that of a standard FBP algorithm at three slice thicknesses for hepatic lesion detection with multidetector CT. Methods: A liver phantom containing a 9.5-mm opacity with a density of 10HU below background was scanned at 125, 100, 75, 50 and 25mAs. Data were reconstructed with standard FBP (B), smoothing FBP (A) and hybrid FBP/iterative (iDose4) algorithms at 5-, 3- and 1-mm collimation. 10 observers marked opacities using a four-point confidence scale. Jackknife alternative freeresponse receiver operating characteristic figure of merit (FOM), sensitivity and noise were calculated. Results: Compared with the 125-mAs/5-mm setting for each algorithm, significant reductions in FOM (p,0.05) and sensitivity (p,0.05) were found for all three algorithms for all exposures at 1-mm thickness and for all slice thicknesses at 25mAs, with the exception of the 25-mAs/5-mm setting for the B algorithm. Sensitivity was also significantly reduced for all exposures at 3-mm thickness for the A algorithm (p,0.05). Noise for the A and iDose4 algorithms was approximately 13% and 21% lower, respectively, than for the B algorithm. Conclusion: Superior performance for hepatic lesion detection was not shown with either a smoothing FBP algorithm or a hybrid FBP/iterative algorithm compared with a standard FBP technique, even though noise reduction with thinner slices was demonstrated with the alternative approaches. Advances in knowledge: Reductions in image noise with non-standard CT algorithms do not necessarily translate to an improvement in low-contrast object detection. © 2013 The Authors. Source

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