Assessment of the dose reduction potential of a model‐based iterative reconstruction algorithm using a task‐based performance metrology. Issue 1 (24th December 2014)

• Record Type: Journal Article
• Authors: Samei, Ehsan; Richard, Samuel
• Is Part Of: Medical physics. Volume 42:Issue 1(2015)
• Page Start: 314
• Page End: 323
• Publication Date: 2014-12-24
• Subjects: computerised tomography -- edge detection -- feature extraction -- image denoising -- image reconstruction -- image resolution -- image texture -- iterative methods -- medical image processing -- optical transfer function -- phantoms -- statistical analysis; Computed tomography -- Numerical approximation and analysis -- Spatial resolution -- Reconstruction -- Probability theory, stochastic processes, and statistics -- Noise; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image -- Analysis of texture; computed tomography -- image quality -- radiation dose -- detectability -- iterative reconstruction; Medical image noise -- Medical image reconstruction -- Computed tomography -- Modulation transfer functions -- Dosimetry -- Image detection systems -- Image reconstruction -- Image quality assessment; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4903899
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction potential against each other. The purpose of this study was to evaluate and compare the task‐based imaging performance of CT systems to enable the assessment of the dose performance of a model‐based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique. Methods: The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64‐slice CT scanner (GE Discovery CT750 HD, Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task‐based modulation transfer function (MTF) using a circular‐edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise‐power spectrum (NPS) measured from the uniform section of the phantom. The task‐based MTF and NPS were combined with a task function to yield a task‐based estimate of imaging performance, the detectability index ( d ′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the d ′ was compared with that of ASIR and FBP to assess its dose reduction potential. Results: Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low‐pass quality compared to the typical midpass noise found in FBP‐based CT images. At comparable dose, the d ′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR indicated a 46%–84% dose reduction potential, depending on task, without compromising the modeled detection performance. Conclusions: The presented methodology based on ACR phantom measurements extends current possibilities for the assessment of CT image quality under the complex resolution and noise characteristics exhibited with statistical and iterative reconstruction algorithms. The findings further suggest that MBIR can potentially make better use of the projections data to reduce CT dose by approximately a factor of 2. Alternatively, if the dose held unchanged, it can improve image quality by different levels for different tasks.