Use of a channelized Hotelling observer to assess CT image quality and optimize dose reduction for iteratively reconstructed images

Abstract

The use of iterative reconstruction (IR) algorithms in CT generally decreases image noise and enables dose reduction. However, the amount of dose reduction possible using IR without sacrificing diagnostic performance is difficult to assess with conventional image quality metrics. Through this investigation, achievable dose reduction using a commercially available IR algorithm without loss of low contrast spatial resolution was determined with a channelized Hotelling observer (CHO) model and used to optimize a clinical abdomen/pelvis exam protocol. A phantom containing 21 low contrast disks-three different contrast levels and seven different diameters-was imaged at different dose levels. Images were created with filtered backprojection (FBP) and IR. The CHO was tasked with detecting the low contrast disks. CHO performance indicated dose could be reduced by 22% to 25% without compromising low contrast detectability (as compared to full-dose FBP images) whereas 50% or more dose reduction significantly reduced detection performance. Importantly, default settings for the scanner and protocol investigated reduced dose by upward of 75%. Subsequently, CHO-based protocol changes to the default protocol yielded images of higher quality and doses more consistent with values from a larger, dose-optimized scanner fleet. CHO assessment provided objective data to successfully optimize a clinical CT acquisition protocol.

Keywords: channelized Hotelling observer; computed tomography; iterative reconstruction; low contrast resolution; radiation dose.

Fig. 1

An ensemble average image of the phantom’s center showing the 21 disk objects evaluated with the CHO and the three rectangular objects used to confirm object contrast levels. The subtle rings are not visible in individual images due to masking by noise, and are apparent only in this ensemble average image.

Fig. 2

Sample object-present images for the surrogate small patient size at each dose setting and reconstruction method.

Fig. 3

(a) Center image of phantom with evaluated objects outlined in red. (b–d) Normalized AUC values (relative to full-dose FBP image) for (b) small, (c), medium, and (d) large patient size as a function of ${\mathrm{CTDI}}_{\text{vol}}$ relative to full-dose acquisition. Error bars represent the standard deviation of AUC values. ^†Indicates 77% ${\mathrm{CTDI}}_{\text{vol}}$ for the small and medium size settings. *Indicates 22% ${\mathrm{CTDI}}_{\text{vol}}$ for the medium size setting.

Fig. 4

Plots of image noise as function of ${\mathrm{CTDI}}_{\text{vol}}$ relative to the full-dose acquisition setting for (a) small, (b) medium, and (c) large patient size classes. Error bars represent the standard deviation of image noise values. ^†Indicates 77% ${\mathrm{CTDI}}_{\text{vol}}$ for the small and medium size settings. *Indicates 22% ${\mathrm{CTDI}}_{\text{vol}}$ for the medium size setting.

Fig. 5

Average ${\mathrm{CTDI}}_{\text{vol}}$ values from routine abdomen/pelvis exams performed over a 1-year period prior to CHO-based protocol changes and over a 6-month period after the CHO-based protocol changes were implemented. Exam data were pulled from our main clinical site (non-Toshiba scanners) and our fleet of Toshiba scanners.

Fig. 6

Example images from four serial exams of a single patient obtained before and after the protocol intervention. Notably, two small potential cysts (denoted by the blue arrows) were identified for follow-up in the exam only after protocol changes were made to improve low contrast detectability.