Size Specific Dose Estimate (SSDE) for Head CT The Report of AAPM Task Group 293

Boone, John M.; Strauss, Keith J.; Hernandez, Andrew M.; Hardy, Anthony; Applegate, Kimberly E.; Artz, Nathan S.; Brady, Samuel L.; Cody, Dianna D.; Kasraie, Nima; McCollough, Cynthia H.; McNitt‐Gray, Michael F.

doi:10.37206/185

Cited by 11 publications

(3 citation statements)

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“…Very recently, the AAPM task group No. 293 has published a new report about SSDEs for head CT, following the report about SSDEs for body CT examinations [27,28]. However, CT protocols of the neck include parts of the head and the body, and are thus neither full head nor body CT examinations.…”

Section: Discussionmentioning

confidence: 99%

Development of size-specific institutional diagnostic reference levels for computed tomography protocols in neck imaging

et al. 2019

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Purpose: To develop size-specific institutional diagnostic reference levels (DRLs) for computed tomography (CT) protocols used in neck CT imaging (cervical spine CT, cervical CT angiography (CTA) and cervical staging CT) and to compare institutional to national DRLs. Materials and methods: Cervical CT examinations (spine, n = 609; CTA, n = 505 and staging CT, n = 184) performed between 01/2016 and 06/2017 were included in this retrospective study. For each region and examination, the volumetric CT dose index (CTDIvol) and dose-length product (DLP) were determined and binned into size bins according to patient water-equivalent diameter (dw). Linear regression analysis was performed to calculate size-specific institutional DRLs for CTDIvol and DLP, applying the 75th percentile as the upper limit for institutional DRLs. The mean institutional CTDIvol and DLP were compared to national DRLs (CTDIvol 20 mGy for cervical spine CT (DLP 300 mGycm) and cervical CTA (DLP 600 mGycm), and CTDIvol 15 mGy for cervical staging CT (DLP 330 mGycm)). Results: The mean CTDIvol and DLP (±standard deviation) were 15.2 ± 4.1 mGy and 181.5 ± 88.3 mGycm for cervical spine CT; 8.1 ± 4.3 mGy and 280.2 ± 164.3 mGycm for cervical CTA; 8.6 ± 1.9 mGy and 162.8 ± 85.0 mGycm for cervical staging CT. For all CT protocols, there was a linear increase in CTDIvol and DLP with increasing dw. For the CTDIvol, size-specific institutional DRLs increased with dw from 14 to 29 mGy for cervical spine CT, from 5 to 17 mGy for cervical CTA and from 8 to 13 mGy for cervical staging CT. For the DLP, size-specific institutional DRLs increased with dw from 130 to 510 mGycm for cervical spine CT, from 140 to 640 mGycm for cervical CTA and from 140 to 320 mGycm for cervical staging CT. Institutional DRLs were lower than national DRLs by 81% and 67% for cervical spine CT (dw = 17.8 cm), 43% and 51% for cervical CTA (dw = 19.5 cm) and 59% and 53% for cervical staging CT (dw = 18.8 cm) for CTDIvol and DLP, respectively. Conclusion: Size-specific institutional DRLs were generated for neck CT examinations. The mean institutional CTDIvol and DLP values were well below national DRLs.

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Section: Discussionmentioning

confidence: 99%

Development of size-specific institutional diagnostic reference levels for computed tomography protocols in neck imaging

et al. 2019

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“…This range of SSDE levels is consistent with published diagnostic reference levels (DRLs) for abdomen and pelvis CT examinations with contrast. 26 If CTDI vol,16 values and head CTDI vol,16 -to-SSDE conversation factors from AAPM Report 293 27 were used instead, albeit with the body bowtie filter as was used in the acquisitions, the resulting SSDE levels range from 9 mGy to 18 mGy which encompasses the published DRL of 16.5 mGy SSDE (converted from 18 mGy CTDI vol published in Table 4 of reference 26) for neck CT examinations with contrast. 26 A water equivalent diameter (D w ) of 18.6 cm was used for all SSDE calculations.…”

Section: Phantom Imaging and Reconstructionmentioning

confidence: 99%

Performance of high‐resolution CT for detection and discrimination tasks related to stenotic lesions – A phantom study using model observers

et al. 2023

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Background Accurate detection and grading of atheromatous stenotic lesions within the cardiac, renal, and intracranial vasculature is imperative for early recognition of disease and guiding treatment strategies. Purpose In this work, a stenotic lesion phantom was used to compare high resolution and normal resolution modes on the same CT scanner in terms of detection and size discrimination performance. Materials and Methods The phantom is comprised of three acrylic cylinders (each 15.0 cm in diameter and 1.3 cm thick) with a matching array of holes in each module. The outer two modules contain holes that are slightly larger than the corresponding hole in the central module to simulate stenotic narrowing in vasculature. The stack of modules was submerged in an iodine solution simulating contrast‐enhanced stenotic lesions with a range of lumen diameters (1.32–10.08 mm) and stenosis severity (0%, 50%, 60%, 70%, and 80%). The phantom was imaged on the Canon Aquilion Precision high‐resolution CT scanner in high‐resolution (HR) mode (0.25 mm × 0.50 mm detector element size) and normal‐resolution (NR) mode (0.50 mm × 0.50 mm) using 120 kV and two dose levels (14 and 21 mGy SSDE) with 30 repeat scans acquired for each combination. Filtered back‐projection (FBP) and a hybrid‐iterative reconstruction (AIDR) were used with the FC18 kernel, as well as a deep learning algorithm (AiCE) which is only available for HR. A non‐prewhitening model observer with an eye filter was implemented to quantify performance for detection and size discrimination tasks in the axial plane. Results Detection performance improved with increasing diameter, dose, and for AIDR in comparison to FBP for a fixed resolution mode. Performance in the HR mode was generally higher than NR for the smaller lumen diameters (1–5 mm) with decreasing differences as the diameter increased. Performance in NR mode surpassed HR mode for lumen diameters greater than ∼4 mm and ∼5 mm for 14 mGy and 21 mGy, respectively. AiCE provided consistently higher detection performance compared with AIDR‐FC18 (48% higher for a 6 mm lumen diameter). Discrimination performance increased with increasing nominal diameter, dose, and for larger differences in stenosis severity. When comparing discrimination performance in HR to NR modes, the largest relative differences occur at the smallest nominal diameters and smallest differences in stenosis severity. The AiCE reconstruction algorithm produced the highest overall discrimination performance values, and these were significantly higher than AIDR‐FC18 for nominal diameters of 7.14 and 10.08 mm. Conclusions HR mode outperforms NR for detection up to a specific diameter and the results improve with AiCE and for higher dose levels. For the task of size discrimination, HR mode consistently outperforms NR if AIDR‐FC18 is used for dose levels of at least 21 mGy, and the results improve with AiCE and for the smallest differences in stenosis severity investigated (50% vs. 60%). High‐resolution CT appears to be beneficial for detecting s...

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“…To accurately predict the patient-specific organ dose from brain CT examinations, it is necessary to consider patient-specific features, such as geometric size, geometric shape, anatomical features, and tissue properties. Size-specific dose estimate (SSDE) is widely used to calculate CT radiation doses (Boone et al 2019, Abuhaimed and Martin 2020, Gharbi et al 2020, Hardy et al 2021. Several existing studies have adopted SSDE to assess radiation hazards from head CT scans and convert SSDE to organ doses (Honorio da Silva et al 2021, Shohji et al 2021, Zunaide et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm

Shao,

Lin,

et al. 2024

Phys. Med. Biol.

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Objectives:This study aims to develop a method for predicting patient-specific head organ doses by training a support vector regression (SVR) model based on radiomics features and GPU-calculated reference doses.Methods:In this study, 237 patients who underwent brain CT scans were selected, and their CT data were transferred to an autosegmentation software to segment head regions of interest (ROIs). Subsequently, radiomics features were extracted from the CT data and ROIs, and the benchmark organ doses were computed using fast GPU-accelerated Monte Carlo (MI) simulations. The SVR organ dose prediction model was then trained using the radiomics features and benchmark doses. For the predicted organ doses, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated. The robustness of organ dose prediction was verified by changing the patient samples on the training and test sets randomly.Results:For all head organs, the maximal difference between the reference and predicted dose was less than 1 mGy. For the brain, the organ dose was predicted with an absolute error of 1.3%, and the R2 reached up to 0.88. For the eyes and lens, the organ doses predicted by SVR achieved an RRMSE of less than 13%, the MAPE ranged from 4.5% to 5.5%, and the R2 values were more than 0.7.Conclusions:Patient-specific head organ doses from CT examinations can be predicted within one second with high accuracy, speed, and robustness by training an SVR using radiomics features.

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Size Specific Dose Estimate (SSDE) for Head CT The Report of AAPM Task Group 293

Cited by 11 publications

References 0 publications

Development of size-specific institutional diagnostic reference levels for computed tomography protocols in neck imaging

Development of size-specific institutional diagnostic reference levels for computed tomography protocols in neck imaging

Performance of high‐resolution CT for detection and discrimination tasks related to stenotic lesions – A phantom study using model observers

Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm

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