Comparison of low‐contrast detectability between two CT reconstruction algorithms using voxel‐based 3D printed textured phantoms. Issue 12 (11th November 2016)

• Record Type: Journal Article
• Authors: Solomon, Justin; Ba, Alexandre; Bochud, François; Samei, Ehsan
• Is Part Of: Medical physics. Volume 43:Issue 12(2016)
• Page Start: 6497
• Page End: 6506
• Publication Date: 2016-11-11
• Subjects: computerised tomography -- dosimetry -- feature extraction -- genetic algorithms -- image reconstruction -- image segmentation -- image texture -- iterative methods -- liver -- medical image processing -- phantoms -- regression analysis; Computed tomography -- Reconstruction -- Dosimetry/exposure assessment -- Segmentation -- Probability theory, stochastic processes, and statistics -- Numerical optimization; 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 -- Analysis of texture -- Scintigraphy; computed tomography -- texture -- quantum noise -- image quality -- iterative reconstruction -- 3D printing; Computed tomography -- Medical image noise -- 3D printing -- Medical image reconstruction -- Medical image contrast -- Image reconstruction -- Liver -- Materials properties; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4967478
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To use novel voxel‐based 3D printed textured phantoms in order to compare low‐contrast detectability between two reconstruction algorithms, FBP (filtered‐backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE. Methods: Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray‐level co‐occurrence and run‐length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A‐C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low‐contrast spherical signals (6 mm diameter at nominal contrast levels of ∼3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel‐based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels) as a function of dose were constructed for each reconstruction algorithm and background texture. FBP and SAFIRE were compared for each background type to determine the improvement in detectability at a given dose, and the reduced dose at which SAFIRE had equivalent performance compared to FBP at 100% dose. Results: Detectability increased with increasing radiation dose ( P = 2.7 × 10 −59 ) and contrast level ( P = 2.2 × 10 −86 ) and was higher in the uniform phantom compared to the textured phantoms ( P = 6.9 × 10 −51 ). Overall, SAFIRE had higher d ′ compared to FBP ( P = 0.02). The estimated dose reduction potential of SAFIRE was found to be 8%, 10%, 27%, and 8% for Texture‐A, Texture‐B, Texture‐C and uniform phantoms. Conclusions: In all background types, detectability was higher with SAFIRE compared to FBP. However, the relative improvement observed from SAFIRE was highly dependent on the complexity of the background texture. Iterative algorithms such as SAFIRE should be assessed in the most realistic context possible.