Evaluation of an integrated 3D‐printed phantom for coronary CT angiography using iterative reconstruction algorithm

Abstract Introduction 3D‐printed imaging phantoms are now increasingly available and used for computed tomography (CT) dose optimisation study and image quality analysis. The aim of this study was to evaluate the integrated 3D‐printed cardiac insert phantom when evaluating iterative reconstruction (IR) algorithm in coronary CT angiography (CCTA) protocols. Methods The 3D‐printed cardiac insert phantom was positioned into a chest phantom and scanned with a 16‐slice CT scanner. Acquisitions were performed with CCTA protocols using 120 kVp at four different tube currents, 300, 200, 100 and 50 mA (protocols A, B, C and D, respectively). The image data sets were reconstructed with a filtered back projection (FBP) and three different IR algorithm strengths. The image quality metrics of image noise, signal–noise ratio (SNR) and contrast–noise ratio (CNR) were calculated for each protocol. Results Decrease in dose levels has significantly increased the image noise, compared to FBP of protocol A (P < 0.001). As a result, the SNR and CNR were significantly decreased (P < 0.001). For FBP, the highest noise with poor SNR and CNR was protocol D with 19.0 ± 1.6 HU, 18.9 ± 2.5 and 25.1 ± 3.6, respectively. For IR algorithm, the highest strength (AIDR3Dstrong) yielded the lowest noise with excellent SNR and CNR. Conclusions The use of IR algorithm and increasing its strengths have reduced noise significantly and thus increased the SNR and CNR when compared to FBP. Therefore, this integrated 3D‐printed phantom approach could be used for dose optimisation study and image quality analysis in CCTA protocols.


Introduction
Coronary CT angiography (CCTA) has emerged as one of the most practical diagnostic imaging modality for investigating coronary artery disease. A minimal invasive procedure and less motion artefacts produce a reliable alternative to the invasive coronary angiography. 1,2 Recently, the number of CCTA scans being requested by the radiologists and cardiologists has increased drastically, mainly due to the improved spatial and temporal resolution. 3 However, radiation dose associated with CCTA has raised serious concerns in the literature. The radiation dose carries a risk of developing malignancy to the patients. 4,5 Therefore, dose reduction strategies must be implemented to reduce the radiation dose as low as reasonably achievable.
Reconstruction algorithms are one of many strategies to reduce dose in CCTA. Currently, filtered back projection (FBP) is the mostly used image reconstruction algorithm. However, FBP results in images that can be deteriorated by both electronic and quantum noise. 6 Iterative reconstruction (IR) algorithms allow using lowdose CT protocols while maintaining the image quality. It uses statistical noise models to optimise the image quality of the final image. 7,8 This requires multiple steps, and with every step, noise is reduced according to the specific statistical model of the IR algorithms. IR algorithms can be represented by various strengths to determine the This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
power of noise reduction. Therefore, CT image quality should be assessed to characterise the performance of IR algorithms.
Imaging phantoms are widely used as a tool for assessing the performance of IR algorithms for dose reduction in CCTA scans. Many phantoms, such as the Catphan â (The Phantom Laboratory, Salem, NY) phantom, used in the previous studies [9][10][11] to provide a good first-order approximation of image quality. However, it is possible that such phantoms are not fully adequate to assess the impact of IR algorithms due to their current shape, the complexity of IR algorithms and the different types of patient body habitus, which can influence the radiation dose during CCTA scans. On the other hand, anthropomorphic phantom, such as the Lungman Phantom (Kyoto Kagaku, Japan), provides very similar shape to patients' anatomy. Therefore, a combination of this phantom with three-dimensional (3D)-printed cardiac insert would be an appropriate simulation of heart scanning.
In our previous work, 12 this 3D-printed phantom has only been validated for Hounsfield unit (HU) values but it has not been validated for image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR). Therefore, in this current study, these characteristics of objective analysis will be used to evaluate the image quality especially for CCTA scans.

Materials and methods
The 3D-printed cardiac insert phantom is a similar shape and size to the cardiac insert of an anthropomorphic chest phantom (Lungman N-01, Kyoto Kagaku Co., Ltd., Kyoto, Japan) and filled with different attenuating materials (Fig. 1A). The phantom's filling materials were composed of a jelly (27.24 AE 2.67 HU), water (À6.83 AE 3.09 HU), oil (À93.73 AE 4.35 HU) and air (À996.77 AE 2.35 HU). Cylindrical structures simulating the coronary vessels and ascending aorta were filled with contrast media (354.33 AE 3.21 HU) (Fig. 1).

Acquisition protocols
The 3D-printed cardiac insert phantom was placed in the anthropomorphic chest phantom and scanned using a 16slice CT scanner (Alexion, Toshiba Medical Systems Co Ltd., Otowara, Japan) (Fig. 1C, D). The baseline exposure factors were based on the study centre's standard CCTA protocols of 120 kVp and 300 mA (Protocol A). Additional acquisitions were made at 200 (Protocol B), 100 (Protocol C) and 50 mA (Protocol D). The volume of CT dose index (CTDI vol ) for the 4 protocols was 19.2 mGy, 11.6 mGy, 5.8 mGy and 2.9 mGy, representing dose reductions of 40%, 70% and 85%, respectively. These dose reductions were chosen based on the previous work 6 . The detector collimation was 1 x 16 mm, the display field of view (DFOV) was 350 mm, and gantry rotation time was 0.75 s. Data acquisitions of the phantom were repeated thirty times for each exposure settings.

Reconstruction settings
Protocol A was reconstructed only with the FBP, as the standard algorithm. Protocols B, C and D were reconstructed with the FBP and the IR algorithm of adaptive iterative dose reduction three-dimensional (AIDR3D) (Toshiba Medical Systems Co Ltd., Otowara, Japan). The AIDR3D is the manufacturer's commercial hybrid IR algorithm, which combines reconstruction in the raw data and image space. The iterations are executed in image space only, where the edge preservation and the smoothing are performed. The corrected image was blended with the initial image from the raw data to keep the noise granularity. 13 The AIDR3D has three different strengths: mild, standard and strong. Table 1 shows the imaging parameters. Note: The IR algorithm is referred as the AIDR3D in the text and the figures.

Image quality and dose reduction
A region of interest (ROI) was placed in the centre of contrast-enhanced region that simulates the contrast filled ascending aorta for each slice of axial CT images. The size of ROI was adjusted to the maximum allowed area within that region. The measurements were made from 15-slices at four dose levels, resulting in 15 9 4 = 60 slices. As the acquisitions were repeated thirty times, the total of images measured was 60 9 30 = 1,800 slices for each reconstruction. Image noise was quantified as standard deviation (SD) of attenuation values within the ROI. The SNR and CNR were calculated using equations 1 and 2, respectively. 14,15 The SNR was calculated by dividing the mean attenuation values (HU) by the corresponding SD (Equation 1).
The CNR was calculated as the difference between the two mean HU values (A and B) divided by the SD of the first material (A) (Equation 2). A pair of contrasts was measured (the contrast media (A) and the oil (B)) to simulate the ascending aorta and the fat.

Results
The resulting CT images of the associated 3D-printed cardiac insert and the anthropomorphic chest phantom are shown in Figure 2. Table 2 presents the image noise, SNR and CNR values. The FBP image noise (HU values) for protocols B, C and D were significantly higher than protocol A with the highest for protocol D (P < 0.001 for all). For protocols B, C and D, the AIDR3D strong yielded the lowest image noise (9.8 AE 1.1, 12.4 AE 0.7 and 15.5 AE 1.2 HU, respectively) and the highest noise reduction (15%, 16% and 18%, respectively) when compared to the FBP. In contrast, the AIDR3D mild showed the highest image noise (11.0 AE 1.2, 14.1 AE 0.9 and 18.0 AE 1.4, respectively) but the lowest noise reduction (<5%).

Image noise, SNR and CNR
The SNR values of protocol A was the highest when compared to the other three protocols. For FBP, the SNR was significantly reduced for protocols B, C and D, (12%-47%) when compared to the protocol A (P < 0.001 for all). For protocols B, C and D, the AIDR3D strong yielded the highest SNR while AIDR3D mild showed the lowest compared to FBP and the highest SNR percentage differences (11%, 30% and 44%, respectively) when compared to AIDR3D standard and AIDR3D strong .
The CNR values of AIDR3D in protocols B, C and D were significantly lower than the FBP in protocol A (P < 0.001 for all). Of these, the lowest CNR was measured in the protocol D. For FBP, the highest CNR

Discussion
An organ-specific 3D-printed phantom, integrated with an anthropomorphic phantom, could be used for dose optimisation in CT examinations. In this study, the resulting images of an integrated 3D-printed cardiac insert phantom and a chest phantom were analysed. The objective image analysis was performed to evaluate the IR algorithm at different strengths and dose levels in CCTA protocols.
The image noise, SNR and CNR are commonly used for the evaluation of IR algorithms. 16 Although these objective noise characteristics of the CT image are just one metric of image quality, but it is likely that any changes made on them would affect the visualisation or measurement in a clinical task. [17][18][19] For example, the visualisation of low contrast liver lesions is noise dependant, so reduction in noise would result in clearer visibility of the lesion. For CCTA, the image noise is usually measured in the centre of the ascending aorta as it is the area of the highest density of contrast-enhanced region. [20][21][22] In this study, the ROI was placed at the centre of the largest diameter of the contrast-enhanced region to simulate the ascending aorta and measure the image noise, SNR and CNR. In line with previous studies, 15,23-28 the results of 3D-printed cardiac insert phantom images show that IR algorithm has significantly less image noise as compared to FBP. In addition, increasing the strength from AIDR3Dmild to AIDR3Dstrong has resulted in a range of noise reduction with improved measures of SNR and CNR. The results of this phantom study could infer dose reduction potential if performed in clinical settings. This is especially indicated by the image quality of IR algorithm at the reduced dose of 11.6 mGy (CTDIvol), which was similar to FBP at 19.2 mGy (CTDIvol). The similar results may be seen in paediatric patients. The integrated 3Dprinted phantom and chest phantom can be reproduced to allow smaller heart size and chest region. As such, the results would aid in development of dose optimised protocols for a department and thus reducing the risks associated with the radiation received by all types of patients. This opens up the potential of creating sizespecific phantoms, normal variant-specific phantoms and pathology-specific phantoms for optimisation. Researchers could use this 3D printing methodology to investigate its effects on radiation dose of rare normal variants such as situs inversus or dextra cardia.
Our results using the integrated 3D-printed cardiac insert phantom suffer several limitations. First, the image quality metrics were objective measures of noise only. However, in real patients, the subjective measurements of image quality are also important for CT images with lesions. We aim to include subjective measurements in future studies using cylindrical contrast-enhanced region of varying diameters to simulate lesion detectability. Second, the 3D-printed cardiac insert involves no physiological motion such as breathing, heartbeat or peristalsis. We recognised that motion contributes a significant impact on the image quality; however, the aim was to investigate the influence of post-acquisition factor of image reconstruction. As such, the motion factor was excluded. Also, the ECG-gating method used in the recent studies [29][30][31] has shown that images used for reconstruction are effectively still during the acquisitions. Further work plans to improve this design by introducing features to simulate the heart movement during a cardiac cycle. Third, the scanning was performed with the use of 16-slice CT scanner and single type of IR algorithm. Therefore, the results may not apply to more advanced CT scanners and different IR algorithms. However, in the future work, this study will be conducted using 64-slice CT scanners or above and multiple types of IR algorithm for comparison. Last, the physical geometry of our 3Dprinted cardiac insert phantom is not a patient-specific model. The phantom has smooth surface with less complexity than in the real heart. These cardiac phantom features could produce different measurement of image quality metrics as shape and size may affect the image noise. However, in future study, the work will focus on developing patient-specific cardiac insert to allow personalised 3D-printed models.
In summary, using IR algorithm and increasing its strengths have reduced noise significantly and thus increased the SNR and CNR when compared to FBP. Therefore, this integrated 3D-printed cardiac insert and chest phantoms approach has enabled image quality analysis and could be used for dose optimisation in CCTA protocols.

Ethics
No human or animal testing is required for this study.