Comparison of low‐contrast detectability between two CT reconstruction algorithms using voxel‐based 3D printed textured phantoms

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. C

Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound

“…The resolution is expressed in dots per inch (dpi) or micrometers (lm) and assessed by comparing the dimensions of the produced physical phantom with the original dimensions provided to the printer. Among the papers used, 10 have assessed the resolution or accuracy of the printer by quantitative (numerical) comparison, [26][27][28][29][30][31][32][33][34][35] 10 by qualitative (figural) comparison, 22,[36][37][38][39][40][41][42][43][44] and 14 by both quantitative and qualitative comparison. 10,21,[45][46][47][48][49][50][51][52][53][54][55][56] Sixteen of the research articles do not include a verification of the printers' resolutions.…”

Three‐dimensional printing CT‐derived objects with controllable radiopacity

“…To compare CT reconstruction algorithms, Solomon et al developed a PolyJet 3D-printed liver tissue phantom based on a texture model with parameters derived from CT images. 16 They were able to achieve specific HU values using digital dithering of the available printer materials within a voxel, via custom software. The range of resulting HU values for the phantom was approximately 5 to 20 HU (at 120 kVp).…”

Anatomically Informed 3D Printed CT phantoms: The First Step of a Pipeline To Identify Robust Quantitative Radiomic Features

“…As a result, the morphological detail of tumors seen on CT scans may be preserved , while the heterogenous contrast differences of tumors may be reproduced with some degree of precision [11,20,21]. Similar to Bader et al [20], a graphical overview of the methods used are shown in Figure 1.…”

“…These To prepare the volumes for 3D printing, the voxel Hounsfield unit (HU) values were normalized to range from 0 to 1. The new fractional intensity values were used to dictate the proportion of resin material that would be deposited in any given voxel [11]. To obtain the gray scale intensity gradients seen in CT scans, interpolation of each volume was necessary.…”

“…These unrealistic representations of human tissue may over or understate the uncertainty of quantitative radiomic features extracted from tumors seen on CT scans [10]. For example, the heterogeneity of tissue surrounding a lesion is known to influence the local noise and resolution properties of the identified structures observed on CT scans, especially when iterative reconstruction algorithms are used [11][12][13]. Uniform phantoms are incapable of capturing the degree to which the unique attributes of each CT scanner influence quantitative radiomic features [7,10,[14][15][16].…”