Objective Task-Based Assessment of Low-Contrast Detectability in Iterative Reconstruction

Abstract

Evaluating image quality by using receiver operating characteristic studies is time consuming and difficult to implement. This work assesses a new iterative algorithm using a channelised Hotelling observer (CHO). For this purpose, an anthropomorphic abdomen phantom with spheres of various sizes and contrasts was scanned at 3 volume computed tomography dose index (CTDIvol) levels on a GE Revolution CT. Images were reconstructed using the iterative reconstruction method adaptive statistical iterative reconstruction-V (ASIR-V) at ASIR-V 0, 50 and 70 % and assessed by applying a CHO with dense difference of Gaussian and internal noise. Both CHO and human observers (HO) were compared based on a four-alternative forced-choice experiment, using the percentage correct as a figure of merit. The results showed accordance between CHO and HO. Moreover, an improvement in the low-contrast detection was observed when switching from ASIR-V 0 to 50 %. The results underpin the finding that ASIR-V allows dose reduction.

Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: A quantitative task-based phantom study

“…This is the case when comparing different CT systems, or when comparing various image reconstruction algorithms on the same CT scanner. Our results confirmed the findings of previous human subjective analyses of statistical IR (ASiR and SA-FIRE) [26,27] and partial model-based IR (ASiR-V and ADMIRE) [25,28] using an objective methodology based on a task-specific concept.…”

“…Other investigators have also used CT phantoms to assess the potential improvement of LCD with various IR algorithms. Subjective analyses with human readers (ROC, 4AFC experiment) have indicated that IR techniques (SAFIRE, ASiR, and AIDR) do not statistically improve LCD [9,[23][24][25] or only slightly improve LCD at the same dose level with various IR algorithms [26][27][28]. These researchers have also applied quantitative image quality parameters (noise and CNR) to validate their use.…”

“…Other studies have also applied task-based image quality metrics to LCD assessment using Fourier transform metrics (noise power spectrum and modulation transfer function) to compute a detectability index [30][31][32] or by directly computing mathematical model observers [16,28,33]. To compute Fourier transform metrics, imaging systems must be linear and shift invariant [34].…”

Objective comparison of high‐contrast spatial resolution and low‐contrast detectability for various clinical protocols on multiple CT scanners

“…6 Task-oriented image quality criteria can then be used as surrogates for the assessment of actual clinical image quality. [7][8][9][10] They will necessarily be simple in comparison to the clinical situations, but will make it possible, for example, to predict the ability to detect simple structures of high and low contrast within homogeneous backgrounds. 9,10,11 This represents the most basic task that can be considered a surrogate for measuring clinical image quality.…”

Assessing the Usefulness of the Quasi-Ideal Observer for Quality Control in Fluoroscopy

“…A channelized Hotelling model observer has previously been used to assess the low-contrast detectability in images of an anthropomorphic phantom in computed tomography (5,6) . These studies showed a positive correlation and agreement with human observers in terms of percent correct in 4AFC experiments, and the mathematical model observer was shown to be less time consuming than human observers when performing receiver operating characteristics (ROC) studies.…”