Influence of CT parameters on STL model accuracy

Purpose - Additive manufactured (AM) skull models are increasingly used to plan complex surgical cases and design custom implants. The accuracy of such constructs depends on the standard tessellation language (STL) model, which is commonly obtained from computed tomography (CT) data. The aims of this study were to assess the image quality and the accuracy of STL models acquired using different CT scanners and acquisition parameters. Design/methodology/approach - Images of three dry human skulls were acquired using two multi-detector row computed tomography (MDCT) scanners, a dual energy computed tomography (DECT) scanner and one cone beam computed tomography (CBCT) scanner. Different scanning protocols were used on each scanner. All images were ranked according to their image quality and converted into STL models. The STL models were compared to gold standard models. Findings - Image quality differed between the MDCT, DECT and CBCT scanners. Images acquired using low-dose MDCT protocols were preferred over images acquired using routine protocols. All CT-based STL models demonstrated non-uniform geometrical deviations of up to +0.9 mm. The largest deviations were observed in CBCT-derived STL models. Practical implications - While patient-specific AM constructs can be fabricated with great accuracy using AM technologies, their design is more challenging because it is dictated by the correctness of the STL model. Inaccurate STL models can lead to ill-fitting implants that can cause complications after surgery. Originality/value - This paper suggests that CT imaging technologies and their acquisition parameters affect the accuracy of medical AM constructs

Implementation of the YEARS algorithm to optimise pulmonary embolism diagnostic workup in the emergency department

Background Excessive use of CT pulmonary angiography (CTPA) to investigate pulmonary embolism (PE) in the emergency department (ED) contributes to adverse patient outcomes. Non-invasive D-dimer testing, in the context of a clinical algorithm, may help decrease unnecessary imaging but this has not been widely implemented in Canadian EDs.Aim To improve the diagnostic yield of CTPA for PE by 5% (absolute) within 12 months of implementing the YEARS algorithm.Measures and design Single centre study of all ED patients >18 years investigated for PE with D-dimer and/or CTPA between February 2021 and January 2022. Primary and secondary outcomes were the diagnostic yield of CTPA and frequency of CTPA ordered compared with baseline. Process measures included the percentage of D-dimer tests ordered with CTPA and CTPAs ordered with D-dimers <500 µg/L Fibrinogen Equivalent Units (FEU). The balancing measure was the number of PEs identified on CTPA within 30 days of index visit. Multidisciplinary stakeholders developed plan- do-study-act cycles based on the YEARS algorithm.Results Over 12 months, 2695 patients were investigated for PE, of which 942 had a CTPA. Compared with baseline, the CTPA yield increased by 2.9% (12.6% vs 15.5%, 95% CI −0.06% to 5.9%) and the proportion of patients that underwent CTPA decreased by 11.4% (46.4% vs 35%, 95% CI −14.1% to −8.8%). The percentage of CTPAs ordered with a D-dimer increased by 26.3% (30.7% vs 57%, 95% CI 22.2% 30.3%) and there were two missed PE (2/2695, 0.07%).Impact Implementing the YEARS criteria may safely improve the diagnostic yield of CTPAs and reduce the number of CTPAs completed without an associated increase in missed clinically significant PEs. This project provides a model for optimising the use of CTPA in the ED