METHODS: A user-friendly software was developed to accurately predict the individual size-specific dose estimation of paediatric patients undergoing computed tomography (CT) scans of the head, thorax, and abdomen. The software includes a calculation equation developed based on a novel SSDE prediction equation that used a population's pre-determined percentage difference between volume-weighted computed tomography dose index (CTDIvol) and SSDE with age. American Association of Physicists in Medicine (AAPM RPT 204) method (manual) and segmentation-based SSDE calculators (indoseCT and XXautocalc) were used to assess the proposed software predictions comparatively.
RESULTS: The results of this study show that the automated equation-based calculation of SSDE and the manual and segmentation-based calculation of SSDE are in good agreement for patients. The differences between the automated equation-based calculation of SSDE and the manual and segmentation-based calculation are less than 3%.
CONCLUSION: This study validated an accurate SSDE calculator that allows users to enter key input values and calculate SSDE.
IMPLICATION FOR PRACTICE: The automated equation-based SSDE software (PESSD) seems a promising tool for estimating individualised CT doses during CT scans.
METHODS: A total of 54 patients (8-79 years) with intracranial haemorrhage who underwent both CT examination and six-vessel cerebral angiography were studied over a 2-year period. Cerebral angiography was repeated within 6 weeks if the first angiogram was negative.
RESULTS: Angiography detected vascular lesions in 50% of cases (aneurysm 38.9% and arteriovenous malformation, AVM, 11.1%). In the aneurysm group, angiographic yield was 34.3% whereas in the AVM group, it was 37.9%. Subarachnoid haemorrhage (SAH) combined with other types of haemorrhage (such as intracerebral haemorrhage, ICH) was not significantly correlated with the likelihood of finding a vascular lesion, both aneurysm and AVM (p = 0.157). Age less than 50 years had significant correlation (p = 0.021) in the AVM group as well as in the aneurysm group (p < 0.001). A history of hypertension was associated with both aneurysm (p = 0.039) and AVM (p = 0.008). No patients with deep intracerebral haematoma had vascular lesions. The presence of an intravascular haemorrhage (IVH) had significant correlation with aneurysm (p = 0.008) but not AVM. There was no significant difference in mean age between patients with and without a vascular lesion (p = 0.134).
CONCLUSION: Cerebral angiography is justified in patients with ICH accompanied by pure SAH (p = 0.001). Other factors associated with finding a vascular lesion were a history of hypertension and the presence of IVH. Diagnostic cerebral angiography is indicated for patients with ICH and SAH and IVH with a history of hypertension, regardless of age.
MATERIALS AND METHODS: A MATLAB platform was used to develop software of algorithms based on image segmentation techniques to automate the calculation of patient size and SSDE. The algorithm was used to automatically estimate the individual size and SSDE of four CT dose index phantoms and 80 CT images of pediatric patients comprising head, thorax, and abdomen scans. For validation, the American Association of Physicists in Medicine (AAPM) manual methods were used to determine the patient's size and SSDE for the same subjects. The accuracy of the proposed algorithm in size and SSDE calculation was evaluated for agreement with the AAPM's estimations (manual) using Bland-Altman's agreement and Pearson's correlation coefficient. The normalized error, system bias, and limits of agreement (LOA) between methods were derived.
RESULTS: The results demonstrated good agreement and accuracy between the automated and AAPM's patient size estimations with an error rate of 1.9% and 0.27% on the patient and phantoms study, respectively. A 1% percentage difference was found between the automated and manual (AAPM) SSDE estimates. A strong degree of correlation was seen with a narrow LOA between methods for clinical study (r > 0.9771) and phantom study (r > 0.9999).
CONCLUSION: The proposed automated algorithm provides an accurate estimation of patient size and SSDE with negligible error after validation.