Diagnostic reference levels for chest computed tomography in children as a function of patient size.

BACKGROUND: Radiation exposures from computed tomography (CT) in children are inadequately studied. Diagnostic reference levels (DRLs) can help optimise radiation doses. OBJECTIVE: To determine local DRLs for paediatric chest CT performed mainly on modern dual-source, multi-slice CT scanners as a function of patient size. MATERIALS AND METHODS: Five hundred thirty-eight chest CT scans in 345 children under 15 years (y) of age (median age: 8 y, interquartile range [IQR]: 4-13 y) performed on four different CT scanners (38% on third-generation and 43% on second-generation dual-source CT) between November 2013 and December 2020 were retrospectively analysed. Examinations were grouped by water-equivalent diameter as a measure of patient size. DRLs for volume CT dose index (CTDIvol) and dose-length product (DLP) were determined for six different patient sizes and compared to national and European DRLs. RESULTS: The DRLs for CTDIvol and DLP are determined for each patient size group as a function of water-equivalent diameter as follows: (I) < 13 cm (n = 22; median: age 7 months): 0.4 mGy, 7 mGy·cm; (II) 13 cm to less than 17 cm (n = 151; median: age 3 y): 1.2 mGy, 25 mGy·cm; (III) 17 cm to less than 21 cm (n = 211; median: age 8 y): 1.7 mGy, 44 mGy·cm; (IV) 21 cm to less than 25 cm (n = 97; median: age 14 y): 3.0 mGy, 88 mGy·cm; (V) 25 cm to less than 29 cm (n = 42; median: age 14 y): 4.5 mGy, 135 mGy·cm; (VI) ≥ 29 cm (n = 15; median: age 14 y): 8.0 mGy, 241 mGy·cm. Compared with corresponding age and weight groups, our size-based DRLs for DLP are 54% to 71% lower than national and 23% to 85% lower than European DRLs. CONCLUSION: We developed DRLs for paediatric chest CT as a function of patient size with substantially lower values than national and European DRLs. Precise knowledge of size-based DRLs may assist other institutions in further dose optimisation in children.

Impact of CT and MRI in the diagnostic workup of malignant triton tumour-a monocentric analysis and review of the literature.

OBJECTIVES: Malignant triton tumours (MTTs) are rare but aggressive subtypes of malignant peripheral nerve sheath tumours (MPNSTs) with a high recurrence rate and 5-year survival of 14%. Systematic imaging data on MTTs are scarce and mainly based on single case reports. Therefore, we aimed to identify typical CT and MRI features to improve early diagnosis rates of this uncommon entity. METHODS: A systematic review on literature published until December 2022 on imaging characteristics of MTTs was performed. Based on that, we conducted a retrospective, monocentric analysis of patients with histopathologically proven MTTs from our department. Explorative data analysis was performed. RESULTS: Initially, 29 studies on 34 patients (31.42 ± 22.6 years, 12 female) were evaluated: Literature described primary MTTs as huge, lobulated tumours (108 ± 99.3 mm) with central necrosis (56% [19/34]), low T1w (81% [17/21]), high T2w signal (90% [19/21]) and inhomogeneous enhancement on MRI (54% [7/13]). Analysis of 16 patients (48.9 ± 13.8 years; 9 female) from our institution revealed comparable results: primary MTTs showed large, lobulated masses (118 mm ± 64.9) with necrotic areas (92% [11/12]). MRI revealed low T1w (100% [7/7]), high T2w signal (100% [7/7]) and inhomogeneous enhancement (86% [6/7]). Local recurrences and soft-tissue metastases mimicked these features, while nonsoft-tissue metastases appeared unspecific. CONCLUSIONS: MTTs show characteristic features on CT and MRI. However, these do not allow a reliable differentiation between MTTs and other MPNSTs based on imaging alone. Therefore, additional histopathological analysis is required. ADVANCES IN KNOWLEDGE: This largest published systematic analysis on MTT imaging revealed typical but unspecific imaging features that do not allow a reliable, imaging-based differentiation between MTTs and other MPNSTs. Hence, additional histopathological analysis remains essential.

Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.

Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast chest and low-dose abdominal CTs. In this retrospective study, non-contrast chest CTs of patients suspected of COVID-19 pneumonia and low-dose abdominal CTs suspected of urolithiasis were analysed. All images were reconstructed using filtered back-projection (FBP) and were post-processed using an artificial intelligence (AI)-based commercial software (PixelShine (PS)). Additional iterative reconstruction (IR) was performed for abdominal CTs. Objective and subjective image quality were evaluated. AI-based post-processing led to an overall significant noise reduction independent of the protocol (chest or abdomen) while maintaining image information (max. difference in SNR 2.59 ± 2.9 and CNR 15.92 ± 8.9, p < 0.001). Post-processing of FBP-reconstructed abdominal images was even superior to IR alone (max. difference in SNR 0.76 ± 0.5, p ≤ 0.001). Subjective assessments verified these results, partly suggesting benefits, especially in soft-tissue imaging (p < 0.001). All in all, the deep learning-based denoising-which was non-inferior to IR-offers an opportunity for image quality improvement especially in institutions using older scanners without IR availability. Further studies are necessary to evaluate potential effects on dose reduction benefits.

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.

In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.

Radiation Dose Aspects of Hepatic Artery Infusion Chemotherapy in Uveal Melanoma Patients with Liver Metastases.

PURPOSE: In uveal melanoma patients, liver metastases can be treated by hepatic artery infusion chemotherapy (HAIC). During this procedure, melphalan or, less frequently, fotemustine is infused into the hepatic artery or the hepatic lobe arteries in regularly repeated interventions to achieve local tumor control. The aim of this study was to investigate the radiation exposure of HAIC. MATERIAL AND METHODS: In this retrospective study, dose data from 841 procedures in 140 patients (mean age 65.3 ± 9.9 years, 74 female) who underwent HAIC between 06/2017 and 10/2021 at one of three different angiography systems were analyzed. RESULTS: In the overall population, dose area product (DAP) (median (IQR)) was 1773 cGy·cm2 (884-3688). DAP was significantly higher in the first intervention, where a complete diagnostic workup of the vasculature was performed, than in follow-up interventions: 5765 cGy·cm2 (3160-8804) versus 1502 cGy·cm2 (807-2712) (p < 0.0001). DAP also increased significantly with the number of infusion positions (median, (IQR)): one position 1301 cGy·cm2 (633-2717), two positions 1985 cGy·cm2 (1118-4074), three positions 6407 cGy·cm2 (2616-11590) (p < 0.0001). CONCLUSION: In uveal melanoma patients with liver metastases undergoing HAIC, radiation exposure is significantly higher both at the first intervention compared to follow-up interventions, but also with increasing number of infusion positions.