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Adaptive Iterative Dose Reduction 3D Integrated with Automatic Tube Current Modulation for CT Coronary Artery Calcium Quantification

Rationale and Objectives

We aimed to evaluate integrated adaptive iterative dose reduction 3D (AIDR 3D) algorithm in automatic tube current modulation (ATCM) for the quantification of coronary artery calcium score (CACS) and cardiac risk stratification.

Materials and Methods

A thoracic phantom with calcium inserts of known densities was scanned with filtered back projection (FBP) and AIDR 3D algorithms in small- and medium-sized phantoms. Twenty-four patients underwent two consecutive scans of CACS with FBP and AIDR 3D algorithms. The absolute Agatston score, Agatston score risk, volume score, and Agatston score percentile-based risk were compared, and concordance coefficients and agreement plots were made.

Results

Agatston and volume scores were significantly different between the phantom sizes ( P < .01). There were no significant differences in the Agatston scores between FBP and AIDR 3D for the medium phantoms ( P = .25). In the patients, there were no significant differences in Agatston and volume scores between FBP and AIDR 3D ( P = .06 and P = .09, respectively). The correlation coefficients of Agatston and volume scores with AIDR 3D were excellent compared to those of FBP. There were no significant differences in Agatston score risk and Agatston score percentile-based risk between FBP and AIDR 3D ( P = .74 and P = 1, respectively). There was mean dose reduction of 57.8% ± 18.6% for AIDR 3D.

Conclusion

The absolute Agatston score differed between FBP and AIDR 3D reconstructions. However, the cardiac risk categorizations of the two methods were comparable. An integrated AIDR 3D algorithm with automatic tube current modulation enables radiation dose savings at a consistent noise level without sacrificing CACS.

Introduction

To date, coronary artery disease (CAD) is a major cause of death and disability worldwide . An early and accurate diagnosis of CAD should prompt aggressive treatment and lead to the prevention of heart attacks and unfavorable outcomes . Because the extent of coronary artery calcium (CAC), which is a pathognomonic hallmark of CAD, is strongly correlated with the degree of atherosclerosis, CAC scanning has emerged to assess the risk for CAD by using the CAC score (CACS) . CAC scanning serves as a noninvasive and convenient diagnostic tool and provides excellent diagnostic value for detecting calcified plaque in patients with CAD, particularly in the intermediate-risk cohort . However, CAC scanning can be associated with higher radiation exposure using the filtered back projection (FBP) reconstruction method, which can result in an effective dose range of 1.2–2.0 mSv . Based on the principle of “as low as reasonably achievable,” radiation exposure should be kept at an acceptable minimum through the optimization of the CAC scanning without losing imaging quality.

Currently, several approaches to reduce radiation exposure have been developed, including the tube current and voltage reduction, the prospective scanning with electrocardiogram modulation, and new reconstruction methods . However, tube current and voltage reductions will lead to increased image noise, which is a reliable index for image quality. Because it is image quality that constrains the dose, maintaining a reasonable image noise level for CAC scanning across different patient sizes is important. The consensus report by McCollough et al. established an image noise target on the basis of three different patient sizes that are categorized by the lateral chest width on computed tomography (CT) .

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Materials and Methods

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Figure 1, Flow diagram of the study design. ATCM, automatic tube current modulation; AIDR, adaptive iterative dose reduction; FBP, filtered back projection; HU, Hounsfield unit.

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Cardiac Phantom

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Figure 2, Images of the anthropomorphic cardio CT phantom (lateral dimension 30 cm) and medium-size extension ring (lateral dimension 35 cm). Images are not to the same scale. CT, computed tomography.

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Patient Cohort

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CT Protocol

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Image Reconstruction

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Quantitative Analysis

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Radiation Dose Estimation

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Statistical Analysis

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Results

Cardiac Phantom

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TABLE 1

Agatston Score, Volume Score, and Image Noise for Phantom

Size Parameter Reconstruction Algorithm FBP (mA) AIDR 3D (SD) 150 200 300 400 500 24 23 22 21 20 19 18 17 16 Small Agatston score \* 765 761 761 761 754 783 789 794 795 781 781 764 771 766 Volume score \* 657 646 653 661 662 693 709 715 705 694 688 689 707 686 Image noise (HU) \* 15.4 12.3 10.1 9.5 7.8 21.8 21.5 19.9 22.7 18.9 17.9 19.7 16.3 17.5 Medium Agatston score † 728 719 717 717 716 715 722 714 718 713 717 710 712 708 Volume score \* 703 681 675 676 680 654 656 643 646 636 662 646 645 639 Image noise (HU) † 29.9 24.1 20.0 17.0 14.1 22.3 20.1 19.5 20.1 19.1 18.4 18.2 17.6 16.9

Abbreviations: AIDR 3D, adaptive iterative dose reduction 3D; FBP, filtered back projection; HU, Hounsfield unit; SD, standard deviation of noise.

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Figure 3, Scatter plot of Agatston and volume scores for small and medium phantoms with FBP reconstruction. FBP, filtered back projection; SSDE, size-specific dose estimate.

Figure 4, Scatter plot of Agatston and volume scores for small and medium phantoms with AIDR 3D reconstruction. AIDR, adaptive iterative dose reduction; SSDE, size-specific dose estimate.

Figure 5, Scatter plot of image noise for small and medium phantoms with FBP and AIDR 3D reconstructions. AIDR, adaptive iterative dose reduction; FBP, filtered back projection; HU, Hounsfield unit; SSDE, size-specific dose estimate.

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TABLE 2

Radiation Dose for Phantom

Size Parameter Reconstruction Algorithm FBP (mA) AIDR 3D (SD) 150 200 300 400 500 24 23 22 21 20 19 18 17 16 Small CTDI vol (mGy) 4.1 5.5 8.8 11.7 16.1 0.7 0.7 0.7 0.8 0.8 1.0 1.1 1.2 1.4 SSDE (mGy) 6.2 8.3 13.2 17.6 24.2 1.1 1.1 1.1 1.2 1.2 1.2 1.7 1.8 2.1 Medium CTDI vol (mGy) 4.1 5.5 8.8 11.7 16.1 2.5 2.7 3.0 3.3 3.5 4.1 4.4 4.9 5.5 SSDE (mGy) 4.8 6.4 10.2 13.6 18.7 2.9 3.1 3.5 3.8 4.1 4.8 5.1 5.7 6.4

Abbreviations: AIDR 3D, adaptive iterative dose reduction 3D; CTDI vol , volume CT dose index; FBP, filtered back projection; SD, standard deviation of noise; SSDE, size-specific dose estimate.

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Patient Cohort

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TABLE 3

Patient Characteristics

Patients (n = 24) Sex (M:F) 21:3 Age (y) 66 ± 10 Heart rate (bpm) 63 ± 9 Chest dimension (cm) 34.3 ± 2.3 Body mass index (kg/m 2 ) 26.2 ± 2.9

Data presented as mean and standard deviation.

TABLE 4

Agatston Score, Volume Score, Agatston Score Risk Category, Agatston Score Percentile-based Risk Category, Image Noise, and Radiation Dose for Patients

Reconstruction Algorithm FBP AIDR 3D_P_ Value Agatston score 258 (139, 896) 226 (138, 993) .06 Volume score 271 (123, 796) 268 (117, 783) .09 Agatston score risk category (0/1/2/3/4) 4 (0/3/2/9/10) 4 (0/4/1/9/10) .92 Agatston score percentile-based risk category (1/2/3/4/5/6) 6 (0/0/2/5/7/10) 6 (0/0/2/5/7/10) 1 Image noise (HU) 16.5 ± 4.1 16.9 ± 1.9 .63 SSDE (mGy) 13.9 ± 1.2 5.7 ± 2.2 <.01 Effective dose (mSV) 2.1 ± 0.3 0.9 ± 0.4 <.01

Abbreviations: AIDR 3D, adaptive iterative dose reduction 3D; FBP, filtered back projection; HU, Hounsfield unit; SSDE, size-specific dose estimate.

Agatston and volume scores presented as median and interquartile range in parenthesis. Agatston score risk and Agatston score percentile-based risk categories presented as mode and frequency of each category in parenthesis. Image noise, SSDE, and effective dose presented as mean and standard deviation.

Figure 6, Bland-Altman plots of Agatston scores for patient cohort (mean difference 22.8, upper limit 140.1, lower limit −94.8). AIDR, adaptive iterative dose reduction; FBP, filtered back projection.

Figure 7, Bland-Altman plots of volume scores for patient cohort (mean difference 11.6, upper limit 75.8, lower limit −52.7). AIDR, adaptive iterative dose reduction; FBP, filtered back projection.

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Discussion

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Acknowledgments

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