Optimal Tube Potential for Radiation Dose Reduction in Pediatric CT: Principles, Clinical Implementations, and Pitfalls

To compare the radiation dose and image noise of nonenhanced CT scans performed at 80, 100, and 120 kVp with tube current modulation (TCM) we used anthropomorphic phantoms of newborn, 1‐year‐old, and 5‐year‐old children. The noise index was set at 12. The image noise in the center of the phantoms at the level of the chest and abdomen was measured within a circumscribed region of interest. We measured the doses in individual tissues or organs with radio‐photoluminescence glass dosimeters for each phantom. Various tissues or organs were assigned and the radiation dose was calculated based on the international commission on radiological protection definition. With TCM the respective radiation dose at tube voltages of 80, 100, and 120 was 29.71, 31.60, and 33.79 mGy for the newborn, 32.00, 36.79, and 39.48 mGy for the 1‐year‐old, and 32.78, 38.11, and 40.85 mGy for the 5‐year‐old phantom. There were no significant differences in the radiation dose among the tube voltages and phantoms (P > 0.05). Our comparison of the radiation dose using anthropomorphic phantoms of young children showed that the radiation dose of nonenhanced CT performed at different tube voltages with TCM was not significantly different.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Relationship between the radiation doses at nonenhanced CT studies using different tube voltages and automatic tube current modulation during anthropomorphic phantoms of young children

Masuda

Funama

Kiguchi

et al. 2017

“…To determine how the image contrast changes with beam energy, we have relied on the CT literature 11 , 16 , 17 . For example, if the CNR is desired to be kept constant but the kV is changed from 100 to 80, the contrast would increase by a factor of 1.32 (282 HU at 80 kV divided by 213 HU at 100 kV for iodine in a 25 cm phantom), according to Yu et al (11) For the example change of 100 to 80 kV, the noise needs to increase by a factor of 1.32 to keep the CNR constant (for iodine contrast imaging) and therefore the mA would need to change by

{(\frac{1}{1.32})}^{2} = 0.57

. To determine how the image noise changes with kV, we performed phantom experiments to measure what mA changes were needed to maintain the same image noise at different kV settings.…”

Section: Methodsmentioning

confidence: 99%

“…However, there are very few studies detailing how to ensure that protocol changes within a single scanner, or protocol customizations from one scanner platform to another that will allow the AEC to function within the mA limits of the scanner 9 , 10 , 11 . Inter‐ and intra scanner protocol changes and customizations are complicated by the nonlinear and “black box” nature of the AEC systems on today's commercial CT systems.…”

Section: Introductionmentioning

confidence: 99%

CT protocol management: simplifying the process by using a master protocol concept

Szczykutowicz

Bour

Rubert

et al. 2015

This article explains a method for creating CT protocols for a wide range of patient body sizes and clinical indications, using detailed tube current information from a small set of commonly used protocols. Analytical expressions were created relating CT technical acquisition parameters which can be used to create new CT protocols on a given scanner or customize protocols from one scanner to another. Plots of mA as a function of patient size for specific anatomical regions were generated and used to identify the tube output needs for patients as a function of size for a single master protocol. Tube output data were obtained from the DICOM header of clinical images from our PACS and patient size was measured from CT localizer radiographs under IRB approval. This master protocol was then used to create 11 additional master protocols. The 12 master protocols were further combined to create 39 single and multiphase clinical protocols. Radiologist acceptance rate of exams scanned using the clinical protocols was monitored for 12,857 patients to analyze the effectiveness of the presented protocol management methods using a two‐tailed Fisher's exact test. A single routine adult abdominal protocol was used as the master protocol to create 11 additional master abdominal protocols of varying dose and beam energy. Situations in which the maximum tube current would have been exceeded are presented, and the trade‐offs between increasing the effective tube output via 1) decreasing pitch, 2) increasing the scan time, or 3) increasing the kV are discussed. Out of 12 master protocols customized across three different scanners, only one had a statistically significant acceptance rate that differed from the scanner it was customized from. The difference, however, was only 1% and was judged to be negligible. All other master protocols differed in acceptance rate insignificantly between scanners. The methodology described in this paper allows a small set of master protocols to be adapted among different clinical indications on a single scanner and among different CT scanners.PACS number: 87.57.Q

Automatic calculation of patient size metrics in computed tomography: What level of computational accuracy do we need?

Sarmento

Mendes

Gouvêa

2017

ObjectivesTo compare the effectiveness of two different patient size metrics based on water equivalent diameter (D w), the mid‐scan water equivalent diameter D w_c, and the mean (average) water equivalent diameter in the imaged region, D w_ave, for automatic detection of accidental changes in computed tomography (CT) acquisition protocols.MethodsPatient biometric data (height and weight) were available from a previous survey for 80 adult chest examinations, and 119 adult single‐acquisition chest–abdomen–pelvis (CAP) examinations for two 16 slice scanners (GE LightSpeed and Toshiba Aquilion RXL) equipped with automatic tube current modulation (ATCM). D w_c and D w_ave were calculated from the archived CT images. Size‐specific dose estimates (SSDE) were obtained from volume CT dose index (CTDI vol), using the conversion factors for a patient diameter of D w_c.Results CTDI vol and SSDE correlate better with D w_ave than with D w_c. R‐squared values of linear fits to CTDI vol of CAP examinations were 0.81–0.89 for D w_c and 0.93–0.94 for D w_ave (SSDE: 0.69–080 for D w_c, 0.87–0.92 for D w_ave). Percentage differences between D w_c and D w_ave were −4 ± 4% for chest and +5 ± 4% for CAP examinations (in % of D w_ave). However, small D w variations translated as larger variations in CTDI vol for these ATCM systems (e.g., a 24% increase in D w doubled CTDI vol). The dependence of CTDI vol on D w_ave was similar for chest and CAP examinations performed with similar ATCM parameters, while use of D w_c resulted in a clear separation of the same data according to examination type. Maximum D w variation in the imaged region was 5.6 ± 1.6 cm for chest and 6.5 ± 1.4 cm for CAP examinations.Conclusions D w_ave is a better metric than D w_c for binning similar‐sized patients in dose comparison studies, despite the additional computational effort required for its calculation Therefore, when implementing automatic determination of D w for SSDE calculations, automatic calculation of D w_ave should be considered.