Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality.

This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels. In the phantom study, the spatial resolution of the two datasets proved to be comparable; conversely, AIDR-3D reconstructions showed a broader noise pattern. In the human study, median image noise was lower with AiCE compared to AIDR-3D (4.7 vs. 5.3, p < 0.001, median 19.6% noise reduction), whereas AIDR-3D yielded a lower artifact index than AiCE (7.5 vs. 8.4, p < 0.001). AiCE also showed higher median CNRs at the cortical (2.5 vs. 1.8, p < 0.001) and thalamic levels (2.8 vs. 1.7, p < 0.001). These results highlight how image quality improvements granted by deep learning-based (AiCE) and iterative (AIDR-3D) image reconstruction algorithms vary according to different brain areas.

Analysis of a multicentre cloud-based CT dosimetric database: preliminary results.

BACKGROUND: To manage and analyse dosimetric data provided by computed tomography (CT) scanners from four Italian hospitals. METHODS: A radiation dose index monitoring (RDIM) software was used to collect anonymised exams stored in a cloud server. Since hospitals use different names for the same procedure, digital imaging and communications in medicine (DICOM) tags more appropriate to describe exams were selected and associated to study common names (SCNs) from a radiology playbook according to scan region and use of contrast media. Retrospective analysis was carried out to describe population and to evaluate dosimetric indexes and inaccuracies associated with SCNs. RESULTS: More than 400 procedures were clustered into 95 SCNs, but 78% of exams on adults were described with only 10 SCNs. Median values of dose-length product (DLP) and volumetric CT dose index (CTDIvol) for three analysed SCNs were in agreement with those previously published. The percentage of inaccuracies does not heavily affect the dosimetric analysis on the whole cloud, since variations in median values reached at most 8%. CONCLUSIONS: Implementation of a cloud-based RDIM software and related issues were described, showing the strength of the chosen playbook-based clustering and its usefulness for homogeneous data analysis. This approach may allow for optimisation actions, accurate assessment of the risk associated with radiation exposure, comparison of different facilities, and, last but not least, collection of information for the implementation of the 2013/59 Euratom Directive.

Analysis and reduction of thermal dose errors in MRgFUS treatment.

ExAblate 2000 MRgFUS system (InSightec) installed in Ospedale Maggiore Niguarda Ca' Granda (Milano, Italy) is currently used to treat uterine fibroids. Through the magnetic resonance thermometry (PRF method), it is possible to monitor the temperature in the target in real-time and compute the treated region calculating the thermal dose. The purpose of this work is to investigate the errors in the temperature measurements and their effect on thermal dose. A low pass filtering of temperature maps is proposed to reduce the errors and therefore to improve the reliability of the treated regions calculated. The PRF method was studied through a calibration experiment on ex vivo pig muscle. The outcome resulted to be a very good linearity (p value 0.03) between phase and temperature in the range of interest, and an α value of -0.0109 ± 0.0002 ppm/°C. Temperature statistical uncertainty was evaluated by analyzing the temperature readout variability in specific gel provided by InSightec for daily quality assurance control. It resulted to be 1.89 ± 0.32 °C. A Monte Carlo simulation of the MRI temperature measurement and thermal dose calculations in our specific conditions of geometry and statistical uncertainty revealed that a low-pass filtering process on each temperature map can strongly reduce systematic errors in thermal dose evaluations (1.11 overestimation factor instead of 2.62 without filter); consequently the systematic errors on the size of the predicted ablated area are reduced as well.