Rationale and Objectives
This study aimed to evaluate the correlation of quantitative measurements with visual grading regression (VGR) and receiver operating characteristics (ROC) analysis in computed tomography (CT) images reconstructed with iterative reconstruction.
Materials and Methods
CT scans on a liver phantom were performed on CT scanners from GE, Philips, and Toshiba at three dose levels. Images were reconstructed with filtered back projection (FBP) and hybrid iterative techniques (ASiR, iDose, and AIDR 3D of different strengths). Images were visually assessed by five readers using a four- and five-grade ordinal scale for liver low contrast lesions and for 10 image quality criteria. The results were analyzed with ROC and VGR. Standard deviation, signal-to-noise ratios, and contrast-to-noise ratios were measured in the images.
Results
All data were compared to FBP. The results of the quantitative measurements were improved for all algorithms. ROC analysis showed improved lesion detection with ASiR and AIDR and decreased lesion detection with iDose. VGR found improved noise properties for all algorithms, increased sharpness with iDose and AIDR, and decreased artifacts from the spine with AIDR, whereas iDose increased the artifacts from the spine. The contrast in the spine decreased with ASiR and iDose.
Conclusions
Improved quantitative measurements in images reconstructed with iterative reconstruction compared to FBP are not equivalent to improved diagnostic image accuracy.
Introduction
Iterative reconstruction algorithms decrease image noise in computed tomography (CT) images compared to filtered back projection (FBP) . In FBP, the image noise is inversely proportional to the square of the radiation dose, but with iterative reconstruction, this relationship is changed. Some iterative algorithms change the image texture, which is shown by the different shape of the noise power spectrum . The shape of the noise power spectrum can be dose-dependent , and thereby influence the relationship between noise and low contrast resolution. Studies have shown that regardless of vendors’ claims of dose reduction because of use of iterative reconstruction, low contrast resolution does not benefit from the same improvement as noise .
In addition to noise, spatial resolution may influence the visibility of small low-contrast objects. Some iterative reconstruction algorithms improve spatial resolution , but there are also studies that show that the spatial resolution can be degraded . Iterative reconstruction can reduce artifacts such as metal artifacts, beam hardening artifacts, and scattering artifacts ; however, they can also introduce phenomena perceived by viewers as artifacts, like an artificial or blotchy appearance .
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Materials and Methods
Phantom
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Image Acquisition and Reconstruction
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TABLE 1
Scan Parameters
CT Model Reconstruction Method Tube Voltage (kV) mAs Rotation Time (s) Pitch Collimation Reconstructed Slice Thickness (mm) Reconstruction Filter Reconstructed Field of View (mm) CTDI (mGy) GE
LightSpeed VCT FBP 120 70, 140, and 210 0.7 0.98 64 × 0.625 3 Std 350 5.1, 10.2, and 15.2 ASiR 30 % ASiR 40 % ASiR 50 % Philips
Brilliance 64 FBP 120 89, 163, and 180 0.74/0.94/0.74 1 64 × 0.625 3 B/C/B 350 5.8, 10.6, and 11.8 iDose 1 iDose 3 iDose 6 Toshiba
Aquilion One FBP 120 45, 85, and 130 0.5 0.81 80 × 0.5 3 FC19 350 5.2, 9.9, and 15.2 AIDR 3D mild AIDR 3D std AIDR 3D strong
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Quantitative Measurements
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VGR Analysis
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ROC Analysis
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Statistical Analysis
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Results
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TABLE 2
VGR Results
GE LightSpeed VCT Philips Brilliance 64 Toshiba Aquilion One Algorithm Strength (%) Coefficient (Conf. Int.)P Value R McF 2 Algorithm Strength Coefficient (Conf. Int.)P Value R McF 2 Algorithm Strength Coefficient (Conf. Int.)P Value R McF 2 Noise ASiR 30 −0.5 (−1.6 to 0.7) .42 0.51 iDose 1 − 1.6 ( − 2.5 to − 0.7)<.01 0.37 AIDR 3D Mild − 3.0 ( − 4.5 to − 1.5)<.01 0.51 40 −0.5 (−1.6 to 0.7) .43 3 − 2.4 ( − 3.4 to − 1.5)<.01 Std − 2.1 ( − 3.5 to − 0.6)<.01 50 − 1.8 ( − 3.0 to − 0.6)<.01 6 − 4.8 ( − 6.0 to − 3.5)<.01 Strong − 5.2 ( − 6.9 to − 3.4)<.01 Contrast resolution liver 30 1.0 (−1.1 to 3.0) .36 0.55 1 −1.9 (−3.9 to 0.1) .07 0.46 Mild −1.3 (−3.5 to 1.0) .28 0.49 40 1.0 (−1.1 to 3.0) .35 3 −1.9 (−3.9 to 0.1) .06 Std −1.7 (−4.0 to 0.7) .18 50 −1.3 (−3.2 to 0.7) .20 6 − 3.0 ( − 5.2 to − 0.9)<.01 Strong −1.3 (−3.5 to 1.0) .28 Sharpness 30 0.3 (−0.5 to 1.1) .44 0.40 1 − 1.4 ( − 2.2 to − 0.6)<.01 0.25 Mild − 1.6 ( − 2.4 to − 0.8)<.01 0.31 401.0 (0.1 to 1.8).03 3 − 0.8 ( − 1.6 to 0.0).05 Std − 1.0 ( − 1.8 to − 0.1).02 50 0.6 (−0.2 to 1.4) .14 6 0.6 (−0.2 to 1.4) .16 Strong − 1.0 ( − 1.8 to − 0.1).03 Artifact spine 30 0.1 (−1.3 to 1.5) .88 0.03 1 −0.8 (−2.3 to 0.8) .31 0.13 Mild − 3.8 ( − 6.0 to − 1.6)<.01 0.25 40 0.1 (−1.2 to 1.5) .88 3 0.3 (−1.2 to 1.7) .72 Std − 4.2 ( − 6.4 to − 1.9)<.01 50 −0.9 (−2.3 to 0.5) .23 61.7 (0.1–3.3).04 Strong − 3.9 ( − 6.1 to − 1.7)<.01 Contrast spine 302.2 (0.2 to 4.1).03 0.32 1 −0.5 (−2.0 to 0.9) .47 0.20 Mild −0.8 (−2.2 to 0.6) .25 0.11 40 1.6 (−0.4 to 3.5) .12 3 −0.1 (−1.5 to 1.4) .92 Std 0.3 (−1.1 to 1.6) .71 50 1.4 (−0.5 to 3.4) .14 61.6 (0.0–3.2).05 Strong −0.4 (−1.8 to 1.1) .61
Regression coefficient estimates (95% confidence intervals) for effect of each reconstruction algorithm, P Value and McFadden coefficient of determination, R McF 2 for ASiR, iDose, and AIDR 3D for the criteria evaluated: noise, low contrast resolution in the liver, sharpness, artifacts from spine, and contrast in spine.
Boldface denotes significant ( P < .05) differences from FBP. Because higher image quality was coded with lower numbers, a negative coefficient denotes an improvement in image quality.
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TABLE 3
The Area Under the Curve (AUC) and the Difference (Diff) Between Iterative Reconstruction Algorithm and FBP with 95 % Confidence Intervals for Filtered Back Projection (FBP), ASiR, iDose, and AIDR 3D at 5 mGy, 10 mGy, and 15 mGy
GE LightSpeed VCT Philips Brilliance 64 Toshiba Aquilion One AUC 95% CI ± Diff. 95% CI ± AUC 95% CI ± Diff. 95% CI ± AUC 95% CI ± Diff. 95% CI ± 5 mGy ASiR FBP 0.74 0.06 iDose FBP 0.82 0.04 AIDR 3D FBP 0.67 0.04 30% 0.70 0.03 −0.037 0.058 1 0.79 0.07 −0.035 0.046 Mild 0.68 0.10 0.012 0.100 40% 0.73 0.06 −0.007 0.046 3 0.80 0.03 −0.021 0.028 Std 0.71 0.08 0.042 0.082 50% 0.72 0.06 −0.023 0.084 60.760.03 − 0.0680.032 Strong0.750.080.0830.076 10 mGy FBP 0.79 0.03 FBP 0.83 0.02 FBP 0.75 0.08 30%0.840.030.0560.051 1 0.82 0.01 −0.006 0.011 Mild0.810.030.0650.063 40% 0.83 0.03 0.041 0.048 3 0.82 0.02 −0.007 0.022 Std0.830.020.0790.063 50%0.830.020.0430.030 6 0.82 0.02 −0.006 0.021 Strong0.820.020.0750.074 15 mGy FBP 0.92 0.03 FBP 0.92 0.04 FBP 0.75 0.09 30% 0.90 0.03 −0.020 0.047 1 0.89 0.02 −0.015 0.019 Mild0.810.080.0630.033 40% 0.92 0.01 −0.003 0.034 30.850.01 − 0.0570.041 Std0.840.100.0960.015 50% 0.91 0.04 −0.004 0.037 60.870.04 − 0.0350.022 Strong0.840.040.0880.062
Boldface denotes significant differences from FBP (confidence intervals of difference not containing zero).
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TABLE 4
Significant Improvements in Standard Deviation (SD), Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), Visual Grading Score from VGR, or Area Under the Curve (AUC) from ROC Analysis with Iterative Reconstruction Compared to Filtered Back Projection
Measured Increase VGR ROC SD SNR CNR 5 mGy 10 mGy 15 mGy 5 mGy 10 mGy 15 mGy 5 mGy 10 mGy 15 mGy Noise Contrast Res. Liver Sharpness Artifact Spine Contrast Spine 5 mGy 10 mGy 15 mGy GE LightSpeed VCT ASiR 30 % \* \* [ \* ] \* ASiR 40 % \* † \* † [ \* ] ASiR 50 % \* ‡ † † † \* Philips Brilliance 64 iDose 1 † \* † ‡ iDose 3 ‡ \* \* \* ‡ \* [ \* ] iDose 6 ‡ † \* \* † † ‡ ‡ † [ \* ] [ \* ] [ \* ] [ \* ] Toshiba Aquilion One AIDR 3D mild ‡ \* \* \* ‡ ‡ † \* \* AIDR 3D standard ‡ \* \* \* \* † \* ‡ \* \* AIDR 3D strong ‡ \* \* \* † ‡ \* † ‡ \* \* \*
Asterisks within [] denote results that are significantly worse than filtered back projection.
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Discussion
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Conclusion
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