Image Quality of CT Angiography of Supra-Aortic Arteries : Comparison Between Advanced Modelled Iterative Reconstruction (ADMIRE), Sinogram Affirmed Iterative Reconstruction (SAFIRE) and Filtered Back Projection (FBP) in One Patients' Group

Radiation dose reduction in pediatric computed tomography (CT) using deep convolutional neural network denoising

AIM

We evaluated the quality of noncontrast chest computed tomography (CT) for pediatric patients at two dose levels with and without denoising using a deep convolutional neural network (CNN).

MATERIALS AND METHODS

Forty children underwent noncontrast chest CTs for "chronic cough" using a routine dose (RD) protocol. Images were reconstructed using iterative reconstruction (IR). A validated noise insertion method was used to simulate 20% dose (TD) data for each case. A deep CNN model was trained and validated on 10 cases and then applied to the remaining 30 cases. Three certificate of qualification (CAQ)-certified pediatric radiologists evaluated 30 cases under 4 conditions: (1) RD + IR; (2) RD + CNN; (3) TD + IR; and (4) TD + CNN. Likert scales were used to score subjective image quality (1-5, 5 = excellent) and subjective noise artifact (1-4, 4 = no noise). Images were reviewed for specific findings.

RESULTS

For the 30 patients evaluated (14 female, mean age: 10.8 years, range: 0.17-17), the mean effective dose was 0.46 ± 0.21 mSv for the original RD exam, with an effective dose of 0.09 mSv for the TD exam. Both RD + CNN (3.6 ± 1.1, p < 0.001) and TD + CNN (3.4 ± 0.9, p = 0.023) had higher image quality than RD + IR (3.1 ± 0.9). Both RD + CNN (3.2 ± 0.9, p-value = <0.001) and TD + CNN (2.9 ± 0.6, p-value = 0.001) showed significantly lower subjective noise artifact scores than RD + IR (2.7 ± 0.7). There was excellent intrareader (RD + IR-RD + CNN: mean κ = 0.96, RD + IR-TD + CNN = 0.96, RD + IR-TD + IR = 0.98) and moderate inter-reader reliability (RD + IR: mean κ = 0.55, RD + CNN = 0.50, TD + CNN = 0.54, TD + IR = 0.57) on all 4 image reconstructions.

CONCLUSION

CNN denoising outperforms IR as a means of radiation dose reduction in pediatric CT.

Iterative Reconstruction: State-of-the-Art and Future Perspectives

ABSTRACT

Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.

Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance

Objectives

To evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V).

Methods

Carotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups.

Results

The image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000–0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000–0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000–0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05).

Conclusions

Compared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction.

Application of Different Levels of Advanced Modeling Iterative Reconstruction in Brain CT Scanning

BACKGROUND

Advanced Modeling Iterative Reconstruction (ADMIRE) algorithm has five intensity levels; it is important to study which algorithm is better for brain CT scanning.

OBJECTIVE

The aim of the study is to compare the influence of different strength levels of ADMIRE and traditional Filtered Back Projection (FBP) on image quality in brain CT scanning.

METHODS

60 patients were retrospectively selected, and the data from each of these patients' brains were reconstructed by four different reconstruction methods (FBP, ADMIRE1, ADMIRE3, and ADMIRE5). A five-point Likert Scale was implemented to evaluate the subjective image quality. Image noise, CT value of brain tissue , signal-to-noise ratio (SNR) of gray white matter, contrast-to-noise ratio (CNR), and beam hardening artifact index (AI) of the posterior fossa, were measured for evaluating the objective image quality. Finally, the differences between the subjective and objective evaluations were compared.

RESULTS

There were no statistical differences observed in CT values of gray matter and white matter between the four groups (all P >0.05). The image noise gradually decreased with the increase of ADMIRE algorithm level. The AI exhibited no statistical difference between the four groups (F =0.793, P =0.499), but it tended to decrease slightly with the increase of ADMIRE algorithm level. Compared to other groups (all p <0.001), the ADMIRE5 group demonstrated the best objective image quality. Nevertheless, the highest subjective score was observed in the ADMIRE3 group, which exhibited significant differences with other images (all P <0.001).

CONCLUSION

ADMIRE algorithm can clearly improve image quality, but it cannot significantly improve the linear sclerosis artifacts in the posterior cranial fossa. Based on the subjective evaluation of image quality, ADMIRE3 algorithm is recommended in brain CT scanning.

Coronary Artery Magnetic Resonance Angiography Combined with Computed Tomography Angiography in Diagnosis of Coronary Heart Disease by Reconstruction Algorithm

This research aimed at discussing the diagnosis effect of coronary artery magnetic resonance angiography (MRA) combined with computed tomography (CT) angiography (CTA) based on the back-projection filter reconstruction (BPFR) algorithm in coronary heart disease (CHD), and its role in the diagnosis of coronary artery disease (CAD). Sixty patients with CHD were selected and randomly rolled into group A (undergone MRA examination), group B (undergone CTA examination), and group C (undergone MRA + CTA), with 20 cases in each group. Taking the diagnostic results of coronary angiography as the gold standard, the MRA and CTA images were reconstructed using a BPFR algorithm, and a filter function was added to solve the problem of image sharpness. In addition, the iterative reconstruction algorithm and the Fourier transform analysis method were introduced. As a result, the image clarity and resolution obtained by the BPFR algorithm were better than those obtained by the Fourier transform analytical method and the iterative reconstruction algorithm. The accuracy of group C for the diagnosis of mild coronary stenosis, moderate stenosis, and severe stenosis was 94.02%, 96.13%, and 98.01%, respectively, which was significantly higher than that of group B (87.5%, 90.2%, and 88.4%) and group C (83.4%, 89.1%, and 91.5%) (P < 0.05). The sensitivity and specificity for the diagnosis of noncalcified plaque in group C were 87.9% and 89.2%, respectively, and the sensitivity and specificity for the diagnosis of calcified plaque were 84.5% and 78.4%, respectively, which were significantly higher than those in groups B and C (P < 0.05). In summary, the BPFR algorithm had good denoising and artifact removal effects on coronary MRA and CTA images. The combined detection of reconstructed MRA and CTA images had a high diagnostic value for CHD.

Determine Cumulative Radiation Dose and Lifetime Cancer Risk in Marfan Syndrome Patients Who Underwent Computed Tomography Angiography of the Aorta in Northeast Thailand: A 5-Year Retrospective Cohort Study

Objective: To evaluate computed tomography angiography (CTA) data focusing on radiation dose parameters in Thais with Marfan syndrome (MFS) and estimate the distribution of cumulative radiation exposure from CTA surveillance and the risk of cancers. Methods: Between 1st January 2015 and 31st December 2020, we retrospectively evaluated the cumulative CTA radiation doses of MFS patients who underwent CTA at Khon Kaen University Hospital, a leading teaching hospital and advanced tertiary care institution in northeastern Thailand. We utilized the Radiation Risk Assessment Tool (RadRAT) established at the National Cancer Institute in Bethesda, Maryland, to evaluate the risk of cancer-related CTA radiation. Results: The study recruited 29 adult MFS patients who had CTA of the aorta during a 5-year study period with 89 CTA studies. The mean cumulative CTDI vol is 21.5 ± 14.68 mGy, mean cumulative DLP is 682.2 ± 466.7 mGy.cm, the mean baseline future risk for all cancer is 26,134 ± 7601 per 100,000, and the excess lifetime risk for all cancer is 2080.3 ± 1330 per 100,000. The excess lifetime risk of radiation-induced cancer associated with the CTA surveillance study is significantly lower than the risk of aortic dissection or rupture and lower than the baseline future cancer risk. Conclusions: We attempted to quantify the radiation-induced cancer risk from CTA surveillance imaging performed for MFS patients in this study, with all patients receiving a low-risk cumulative radiation dose (less than 1 Gy) and all patients having a low excessive lifetime risk of cancer as a result of CTA. The risk–benefit decision must be made at the point of care, and it entails balancing the benefits of surveillance imaging in anticipating rupture and providing practical, safe treatment, therefore avoiding morbidity and mortality.

CT Image Features under Reconstruction Algorithm in Analysis of the Effect of Probiotics Combined with Ursodeoxycholic Acid in Treatment of Intrahepatic Cholestasis of Pregnancy

This research was to explore the adoption value of computed tomography (CT) images based on adaptive statistical iterative reconstruction (ASIR) algorithm in the evaluation of probiotics combined with ursodeoxycholic acid in the treatment of intrahepatic cholestasis of pregnancy (ICP). A total of 82 patients with ICP were selected as the research subjects and they were randomly rolled into experimental group (380 mg probiotics enteric-soluble capsule twice a day, combined with 90 mg ursodeoxycholic acid soft capsule three times a day) and control group (90 mg ursodeoxycholic acid soft capsule three times a day), with 41 cases in each. The treatment course was four months. The ASIR algorithm was constructed and applied to the CT image analysis and diagnosis of ICP patients. The effects of filtering back projection (FBP) reconstruction and ASIR algorithm on CT image quality, denoising degree, and artifacts of ICP patients were compared. Moreover, blood indicator levels of ICP patients before and after treatment were assessed. The results showed that the SD values of liver and gallbladder (20.77 Hu and 27.58 Hu) in the reconstructed image of the ASIR algorithm were significantly lower than those of the FBP algorithm (40.58 Hu and 45.63 Hu) (P < 0.05). The SNR values of the liver and gallbladder (3.68 and 2.05) of the reconstructed image were significantly higher than those of the FBP algorithm (1.91 and 1.19) (P < 0.05). The overall image quality after ASIR reconstruction (3.92 points) was significantly better than that of the FBP algorithm (2.36 points), and the image noise score (3.21 points) reconstructed by the FBP algorithm was higher than that by the ASIR algorithm (1.83 points). The artifact score of FBP reconstructed image (4.47 points) was greatly higher than that of ASIR algorithm (2.26 points) (P < 0.05). Before treatment, there was no remarkable difference in the indexes between the two groups of patients (P > 0.05). After treatment, the γ-glutamyltransferase (γ-GT) and alkaline phosphatase (ALP) levels (327.55 U/L and 778.15 μmol/L) of the experimental group of ICP patients were higher than those of the control group (248.63 U/L and 668.43 μmol/L), with substantial difference between the two groups (P < 0.05). The blood ammonia (BA) level (151.09 μmol/L) of the experimental group was lower than that of the control group (178.46 μmol/L), and the difference between the two groups was remarkable (P < 0.05). To sum up, the CT image denoising degree based on ASIR algorithm was high, with few artifacts and good overall quality. Probiotics combined with ursodeoxycholic acid in the treatment of ICP can effectively improve the liver function and intestinal flora of patients, which was of great significance in the clinical diagnosis and treatment of the disease.

Impact of increasing levels of adaptive statistical iterative reconstruction on image quality in oil-based postmortem CT angiography in coronary arteries

Introduction

Postmortem multi-detector computed tomography (PMCT) has become an important part in forensic imaging. Modern reconstruction techniques such as iterative reconstruction (IR) are frequently used in postmortem CT angiography (PMCTA). The image quality of PMCTA depends on the strength of IR. For this purpose, we aimed to investigate the impact of different advanced IR levels on the objective and subjective PMCTA image quality.

Material and methods

We retrospectively analyzed the coronary arteries of 27 human cadavers undergoing whole-body postmortem CT angiography between July 2017 and March 2018 in a single center. Iterative reconstructions of the coronary arteries were processed in five different level settings (0%; 30%; 50%; 70%; 100%) by using an adaptive statistical IR method. We evaluated the objective (contrast-to-noise ratio (CNR)) and subjective image quality in several anatomical locations.

Results

Our results demonstrate that the increasing levels of an IR technique have relevant impact on the image quality in PMCTA scans in forensic postmortem examinations. Higher levels of IR have led to a significant reduction of image noise and therefore to a significant improvement of objective image quality (+ 70%). However, subjective image quality is inferior at higher levels of IR due to plasticized image appearance.

Conclusion

Objective image quality in PMCTA progressively improves with increasing level of IR with the best CNR at the highest IR level. However, subjective image quality is best at low to medium levels of IR. To obtain a “classic” image appearance with optimal image quality, PMCTAs should be reconstructed at medium levels of IR.

Comparison of quantitative image quality of cardiac computed tomography between raw-data-based and model-based iterative reconstruction algorithms with an emphasis on image sharpness

BACKGROUND

Image sharpness is commonly degraded on cardiac CT images reconstructed using iterative reconstruction algorithms.

OBJECTIVE

To compare the image quality of cardiac CT between raw-data-based and model-based iterative reconstruction algorithms developed by the same CT vendor in children and young adults with congenital heart disease.

MATERIALS AND METHODS

In 29 patients with congenital heart disease, we reconstructed 39 cardiac CT datasets using raw-data-based and model-based iterative reconstruction algorithms. We performed quantitative analysis of image sharpness using distance_25-75% and angle_25-75% on a line density profile across an edge of the descending thoracic aorta in addition to CT attenuation, image noise, signal-to-noise ratio and contrast-to-noise ratio. We compared these quantitative image-quality measures between the two algorithms.

RESULTS

CT attenuation did not show significant differences between the algorithms (P>0.05) except in the aorta. Image noise was small but significantly higher in the model-based algorithm than in the raw-data-based algorithm (4.8±2.3 Hounsfield units [HU] vs. 4.7±2.1 HU, P<0.014). Signal-to-noise ratio (110.2±50.9 vs. 108.4±50.4, P=0.050) and contrast-to-noise ratio (91.0±45.7 vs. 89.6±45.1, P=0.063) showed marginal significance between the two algorithms. The model-based algorithm showed a significantly smaller distance_25-75% (1.4±0.4 mm vs. 1.6±0.3 mm, P<0.001) and a significantly higher angle_25-75% (77.0±4.3° vs. 74.1±5.7°, P<0.001) than the raw-data-based algorithm.

CONCLUSION

Compared with the raw-data-based algorithm, the model-based iterative reconstruction algorithm demonstrated better image sharpness and higher image noise on cardiac CT in patients with congenital heart disease.

Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study

OBJECTIVES

This study aimed to evaluate the image quality of 7 iterative reconstruction (IR) algorithms in comparison to filtered back-projection (FBP) algorithm.

METHODS

An anthropomorphic chest phantom was scanned on 4 computed tomography scanners and reconstructed with FBP and IR algorithms. Image quality of anatomical details-large/medium-sized pulmonary vessels, small pulmonary vessels, thoracic wall, and small and large lesions-was scored. Furthermore, general impression of noise, image contrast, and artifacts were evaluated. Visual grading regression was used to analyze the data. Standard deviations were measured, and the noise power spectrum was calculated.

RESULTS

Iterative reconstruction algorithms showed significantly better results when compared with FBP for these criteria (regression coefficients/P values in parentheses): vessels (FIRST: -1.8/0.05, AIDR Enhanced: <-2.3/0.01, Veo: <-0.1/0.03, ADMIRE: <-2.1/0.04), lesions (FIRST: <-2.6/0.01, AIDR Enhanced: <-1.9/0.03, IMR1: <-2.7/0.01, Veo: <-2.4/0.02, ADMIRE: -2.3/0.02), image noise (FIRST: <-3.2/0.004, AIDR Enhanced: <-3.5/0.002, IMR1: <-6.1/0.001, iDose: <-2.3/0.02, Veo: <-3.4/0.002, ADMIRE: <-3.5/0.02), image contrast (FIRST: -2.3/0.01, AIDR Enhanced: -2.5/0.01, IMR1: -3.7/0.001, iDose: -2.1/0.02), and artifacts (FIRST: <-3.8/0.004, AIDR Enhanced: <-2.7/0.02, IMR1: <-2.6/0.02, iDose: -2.1/0.04, Veo: -2.6/0.02). The iDose algorithm was the only IR algorithm that maintained the noise frequencies.

CONCLUSIONS

Iterative reconstruction algorithms performed differently on all evaluated criteria, showing the importance of careful implementation of algorithms for diagnostic purposes.