A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms.

PURPOSE: To characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose4 iterative reconstruction for brain computed tomography (CT) phantom images. METHODS: Catphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDIvol): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose4, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother). Image quality was assessed by evaluating CT numbers, image histograms, noise, image non-uniformity (NU), noise power spectrum, target transfer function, and detectability index. RESULTS: The five PI levels did not significantly affect the mean CT number. For a given CTDIvol using Sharper-to-Smoother levels, the spatial resolution for all the investigated materials and the detectability index increased while the noise magnitude decreased, slightly affecting noise texture. For a fixed PI level increasing the CTDIvol the detectability index increased, the noise magnitude decreased. From 29 mGy, NU values converged within 1 Hounsfield Unit from each other without a substantial improvement at higher CTDIvol values. CONCLUSIONS: The improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDIvol.

Proton computed tomography using a 1D silicon diode array.

PURPOSE: Proton radiography (PR) and proton computed tomography (PCT) can be used to measure proton stopping power directly. However, practical and cost effective proton imaging detectors are not widely available. In this study, the authors investigated the feasibility of proton imaging using a silicon diode array. METHODS: A one-dimensional silicon diode detector array (1DSDA) was aligned with the central axis (CAX) of the proton beam. Polymethyl methacrylate (PMMA) slabs were used to find the correspondence between the water equivalent thickness (WET) and 1DSDA channel number. Two-dimensional proton radiographs were obtained by translation and rotation of a phantom relative to CAX while the proton nozzle and 1DSDA were kept stationary. A PCT image of one slice of the phantom was reconstructed using filtered backprojection. RESULTS: PR and PCT images of the PMMA cube were successfully acquired using the 1DSDA. The WET of the phantom was measured using PR data. The resolution and maximum error in WET measurement are 2.0 and 1.5 mm, respectively. Structures down to 2.0 mm in size could be resolved completely. Reconstruction of a PCT image showed very good agreement with simulation. Limitations in spatial resolution are attributed to limited spatial sampling, beam collimation, and proton scatter. CONCLUSIONS: The results demonstrate the feasibility of using silicon diode arrays for proton imaging. Such a device can potentially offer fast image acquisition and high spatial and energy resolution for PR and PCT.