Brain multidetector-row Computed Tomography Angiography (CTA) is a suitable noninvasive imaging modality frequently used in cases of vascular diseases including aneurysm, vessel dissection, vascular malformations, stroke, and tumors [1]. The technical improvements with high spatial and temporal resolution enabled CTA to produce comparable image quality to conventional angiography and evaluate cerebrovascular diseases, especially intracranial aneurysms [2]. However, lowering the tube voltage and currents results in a reduction of radiation exposure, which causes for degradation of image quality on CTA and affects its diagnostic accuracy. Especially the detection of small diameter vessels by CTA still presents a challenge. To visualize the intracranial vessels clearly and accurately, there should be a great deal to improve the image quality by developing image reconstruction algorithms.

Initially, hybrid iterative reconstruction (Hybrid IR) and model-based iterative reconstruction (MBIR) were effective until deep learning image reconstruction appeared on the stage. Several studies [3, 4, 5] reported that MBIR improves the delineation of small vascular structures with high image quality and spatial resolution compared to Hybrid IR. However, long reconstruction time limits its routine use in clinical practice. Clinical studies have confirmed that the low radiation dose used during hybrid iterative reconstruction and deep learning reconstruction (DLR) implies patient safety while improving image quality; additionally, lower image noise, higher contrast-to-noise ratio (CNR), and lower blooming artifacts were observed [6, 7, 8, 9].

Advancements in developing deep learning reconstruction have led to better outcomes by low radiation exposure and excellent imaging quality. The Food and Drug Administration has approved two artificial intelligent image reconstruction algorithms for clinical purposes: Advanced Intelligent Clear-IQ Engine (AiCE, Canon Medical Systems Corporation, Otawara, Japan) [10] and TrueFidelity${}^{TM}$ system (GE Healthcare, Waukesha, WI) [11]. By utilizing deep convolutional neural networks, AiCE learns differentiation between the signal and the noise with the target images of MBIR images (FIRST, forward projected model-based iterative reconstruction, Canon Medical Systems Corporation, Otawara, Japan) (Fig. 1). On the other hand, the TrueFidelity${}^{TM}$ system is trained with high-dose filtered-back projection (FBP) images [12].

Fig. 1.

Overview of deep learning reconstruction. The vast learning capacities of the deep convolutional neural network allow for differentiation between the signal and the noise. Consequently, deep learning reconstruction (DLR) reduces artifacts and noise separately from the signal. AiCE, a DLR used in this study, was trained to use advanced model-based iterative reconstruction (MBIR) target images.

Several studies have utilized DLR in the abdominal, chest, and brain CT imaging and cardiopulmonary CTA and found better image quality than other image reconstruction algorithms [13, 14, 15, 16, 17, 18]. No studies so far have investigated the application of AiCE to brain CTA protocols. Therefore, we propose to evaluate the ability of a commercialized deep learning reconstruction technique (AiCE) to depict intracranial vessels on the brain CTA and compare the image quality with other reconstruction algorithms (FBP and Hybrid IR) in terms of objective and subjective measures.

We evaluated the image quality of brain CTA using different image reconstruction algorithms, i.e., FBP, Hybrid IR, and DLR. We found that compared to Hybrid IR and FBP, DLR significantly improved objective and subjective imaging quality by reducing image noise, blooming artifacts and improving SNR and CNR. Compared to Hybrid IR and FBP, DLR has the advantage of maintaining higher image quality while minimizing the cumulative radiation exposure with low tube voltage and current [15, 21]. Recently, the fast speed reconstruction and radiation dose reduction have increased the interest in the use of DLR. Previous studies have investigated the importance of radiation dose reduction by DLR [15, 18]. The vast learning capacities of the deep convolutional neural network allow for differentiation between the signal and the noise, and consequently, DLR reduces artifacts and noise separately from the signal. AiCE, a commercialized DLR used here, was trained to the target images of advanced model-based iterative reconstruction (MBIR); additionally, it overcomes the limitations of MBIR with fast reconstruction time and improved spatial resolution and image quality [14, 22]. The reconstruction time is up to 3.5 images per sec for MBIR [15]. The reconstruction times by Hybrid IR and DLR were about 25 images per sec and 43 images per sec, which was similar to [15].

The DLR significantly reduced image noise and improved the attenuation for bilateral ICA segments, bilateral vertebral arteries, bilateral M1 segments of the MCA, and basilar apex, which led to significant improvement in the SNR and CNR. We found a 33.68% and 44.70% reduction in noise when using DLR compared to Hybrid IR and FBP. Additionally, the CNR was improved by 44.86% and 54.43% when using DLR compared to Hybrid IR and FBP, respectively. The DLR algorithm allowed an average increase in the SNR of 46.22% and 55.37% compared to Hybrid IR and FBP. No study has investigated the use of DLR in brain CTA despite previous studies investigating DLR in other body sections [13, 15, 16, 18]. Our results are consistent with previous studies investigating CT imaging of other organs [5, 13, 14, 15, 16, 17] despite the difference in location. The current and previous studies demonstrated the improved imaging quality when using DLR compared to Hybrid IR, MBIR, and FBP. We believe this improved image quality with DLR may be due to advanced MBIR images used for training DLR images. There was no significant difference in the attenuation of the right and left cavernous segments of the ICA among all image reconstruction methods. We consider that this phenomenon was caused by beam hardening artifacts in the ICA segments near the surrounding bone.

Kim et al. [16], investigating the commercialization of DLR, TrueFidelity${}^{TM}$, found improved image noise compared to Hybrid IR in Brain CT. Compared to previous work [23, 24], we assessed DLR’s performance with more ROI’s in various cerebral arteries. Lenfant et al. [15] reported that the use of AiCE allowed an approximately 25% noise reduction and 20% for both SNR and CNR improvement compared to AIDR 3D in pulmonary CTA. In our work, greater SNR and CNR improvements were observed in the posterior cranial fossa, including the basilar artery and the bilateral vertebral arteries. This indicates an advantage of DLR in diagnosing posterior fossa infarction, aneurysm, and dissection of these arteries. Increased diagnostic value of DLR is deemed to result from CNR, SNR, and noise reduction improvements. Further work is required to investigate the diagnostic accuracy of DLR in the posterior fossa.

Subjectively, improvements were observed in image quality when using DLR compared to FBP and Hybrid IR. According to observer 2, DLR images showed significantly better overall image quality than FBP and Hybrid IR; however, no significant difference was observed between Hybrid IR and DLR. The unfamiliar visual appearance of image features in DLR images may have affected this result, as reported previously [16, 25]. The high kVp tube voltage and variable filter kernels are the most effective approach to overcome the blooming artifacts. Unfortunately, images at high kVp tube voltage suffernlmstringname at the cost of increased radiation dose. Therefore, the image reconstruction algorithms and kernel settings are viable options to reduce the stent-related blooming artifact without exposure to high radiation doses. The reduction of blooming artifacts with DLR improved the visualization of intracranial vessels in the regions containing a surgical clip or coil. Therefore, DLR should have advantages in assessing the follow-up of aneurysms treated with flow diverters, stents, and clipping due to lower blooming artifacts. The use of DLR resulted in sharper images compared to FBP and Hybrid IR when considering analysis of 10–90% ERD and perimeter. However, hybrid iterative reconstruction reduces the blooming artifact, which is strongly related to the kernel strength [26, 27]. The result of 10–90% ERD and perimeter of the coiled aneurysm and surgical clip in FBP was similar to Hybrid IR that may be affected by reconstruction kernels used in our paper (FC23 kernel for both FBP and Hybrid IR). Therefore, the optimized reconstruction kernel is required for the reduction of blooming artifacts.

As seen in Fig. 5, the cortical segments of large vessels and the clinoid and supraclinoid segments of the ICA were well-demonstrated. The vessel completeness was the highest in the DLR, which are impacted less by noise. The use of DLR has increased the spatial resolution [28, 29]. It has been shown to significantly enhance small cortical branches of brain arteries, resulting in markedly improved vascular visualization. In addition, increasing the volume or injection rate of contrast media could enhance the small peripheral vessels. Fig. 6 illustrates that the excellent vessel delineation wall and sharp vessels were more evident in DLR than in Hybrid IR and FBP. The improvement of the spatial resolution can decrease errors in the evaluation of small vessel and carotid stenosis. Thus, the deep learning image reconstruction may be especially helpful for delineating major terminal branches, e.g., frontobasal and frontopolar arteries, and accurately estimating the grade of carotid artery stenosis. Further studies need to be done to establish these qualities of DLR as benefits in detecting vascular abnormalities, including arteriovenous fistula, abnormalities of the collateral vessels, and distal aneurysms. The DLR resulted in sharper images and reduced the blooming artifact caused by surgical clips and coiled aneurysm compared to FBP and Hybrid IR without increasing the tube voltage that leads to high radiation dose for patients. The size of the surgical clip and coiled aneurysm appeared larger than its actual size due to blooming artifacts in the FBP and Hybrid IR than DLR. The visualization of intracranial vessels in the regions containing a surgical clip or coils could be evaluated accurately with DLR.

This work has several limitations. First, this was a retrospective study that only assessed a relatively small number of patients. Second, pathologic findings were not sufficiently studied. Third, we did not compare the radiation exposure in each reconstruction method in our study. In addition, image reconstructions in this study were provided by one CT.

Furthermore, the evaluation of image quality of brain CTA with deep learning reconstruction by other vendors is needed. Lastly, the analysis of 10–90% ERD and perimeter in the surgical clip and coiled aneurysm was evaluated for only two patients and limited to coils and surgical clip types. Despite these limitations, improving vascular enhancement provides better visualization of small branches in the brain arteries without increasing the overall iodine dose delivery. As mentioned above, the image quality is improved in the deep learning reconstruction compared to filtered-back projection and hybrid iterative reconstruction.

The deep learning reconstruction method generally improves the image quality of brain CTA in terms of objective measurement and subjective grading compared with filtered back projection and hybrid iterative reconstruction methods. Especially, deep learning reconstruction is deemed advantageous for better depicting small vessels than FBP and Hybrid IR.

CTA, Computed Tomography Angiography; IR, Iterative Reconstruction; MBIR, Model-Based Iterative Reconstruction; DLR, Deep Learning Reconstruction; CNR, Contrast-to-noise ratio; FBP, Filtered-Back Projection; AiCE, Advanced Intelligent Clear-IQ Engine; SNR, Signal-to-noise ratio; ROI, Regions of interest; ICA, Internal carotid artery; MCA, Middle cerebral artery.

CO, JKR and SKK designed the research study. CO, HJS, DHH and SKK performed the research. HJS, DHH and JWS provided help and advice on the research. CO and JKR wrote the manuscript. CO and JKR analyzed the data. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.

The institutional review board of the Inje University Seoul Paik Hospital approved this retrospective study, IRB File No. 2021-06-008.

Not applicable.

This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project number: 1711139017).

The co-authors (Jae-Kyun Ryu, Seonkyu Kim) are employees of Canon Medical Systems Korea, Seoul, Korea that is the subsidiary in Korea of Canon Medical Systems Corporation, Otawara-si, Japan. The authors declare the paper has no conflicts of interest with the company.