Validation of synthesized normal‐resolution image data generated from high‐resolution acquisitions on a commercial CT scanner
Authors: Hernandez Andrew M. , Shin Daniel W. , Abbey Craig K. , Seibert J. Anthony , Akino Naruomi , Goto Takahiro , Vaishnav Jay Y. , Boedeker Kirsten L. , Boone John M. 
Published: July 2020

Purpose

To validate a normal‐resolution (NR) simulation (NRsim) algorithm that uses high‐resolution (HR) or super‐high resolution (SHR) acquisitions on a commercial HR computed tomography (CT) scanner by comparing image quality between NRsim‐generated images and actual NR images. NRsim is intended to allow direct comparison between normal‐resolution CT and HR/SHR reconstructions in clinical investigations, without repeating exams.

Methods

The Aquilion Precision CT (Canon Medical Systems Corporation) HR CT scanner has three resolution modes resulting from detector binning in the channel (x‐y) and row (z) directions. For NR, each detector element is 0.5 mm × 0.5 mm along the channel and row directions, 0.25 mm × 0.5 mm for HR, and 0.25 mm × 0.25 mm for SHR. The NRsim algorithm simulates NR acquisitions from HR or SHR acquisitions (termed NRHR and NRSHR, respectively) by downsampling the pre‐log raw data in the channel direction for the HR acquisitions and in the channel and row direction for the SHR acquisition. The downsampled data are then reconstructed using the same process as NR. The axial modulation transfer function (MTF), slice sensitivity profile (SSP), and CT number accuracy were measured using the Catphan 600 phantom, and the three‐dimensional noise power spectrum (NPS) was measured in water‐equivalent phantoms for standard protocols across a range of size‐specific dose estimates (SSDE): head (6.2–29.8 mGy), lung (2.2–18.2 mGy), and body (5.6–19.4 mGy). The MTF and NPS measurements were combined to estimate low‐contrast detectability (LCD) using a non‐prewhitening model observer with an eye filter for a 5‐mm disk with 10 HU contrast. All metrics were compared for NR, NRHR, and NRSHR images reconstructed using filtered back projection (FBP) and an iterative reconstruction algorithm (AIDR3D). We chose a 15% error threshold as a reasonable definition of success for NRsim when compared against actual NR based on published studies showing that a just‐noticeable difference in image noise level for human observers is typically <15%.

Results

The axial MTF and SSPs for NRsim were in good agreement with NR demonstrated by a maximum difference of 5.1% for the MTF at 10% and 50% across materials (air, Teflon, LDPE, and polystyrene) and a maximum SSP difference of 2.2%. Noise magnitude differences were within 15% across the SSDE levels with the exception of below 4.5 mGy for the lung protocol with FBP. The relative RMSE of normalized NPS comparisons were all <15%. Differences in CT numbers for NRsim reconstructions were within 2 HU of NR. LCD for NRsim was within 15% of NR with the exception of NRSHR for the lung protocol SSDE levels below 3.7 mGy with FBP.

Conclusions

NRsim, an algorithm for simulating NR acquisitions using HR and SHR raw data, was introduced and shown to generate images with spatial resolution, noise, HU accuracy, and LCD largely equivalent to scans acquired using an actual NR acquisition. At SSDE levels below ~5 mGy for the lung protocol, differences in noise magnitude and LCD for NRSHR were >15% which defines a region where NRsim degrades due to contributions from electronic noise.