Improving image quality with deep learning image reconstruction in double-low-dose head CT angiography compared with standard dose and adaptive statistical iterative reconstruction

Low-dose and low-contrast computed tomography pulmonary angiography in pediatric with pulmonary embolism: a prospective study

OBJECTIVE

We evaluated the feasibility of reducing contrast agent and radiation dose in pediatric computed tomography pulmonary angiography (CTPA) while ensuring image quality.

MATERIALS AND METHODS

In this prospective study, two readers assessed the computed tomography (CT) image quality (using a 5-point scale (1: undiagnosable and 5: excellent) and objective evaluation criteria (measuring CT and noise values of the left atrium and pulmonary trunk) of 116 patients who underwent pulmonary artery computed tomography angiography (CTA) from January 2023 to April 2024. independent sample t-test and Chi-square test were used to analyze and evaluate group differences.

RESULT

Fifty-eight participants were enrolled in the study group (mean age, 6.86 years ± 2.74, 30 males) and fifty-eight participants were enrolled in the control group (mean age, 6.71 years ± 2.59, 22 males). The radiation dose was significantly decreased in the study group (study group, 3.01 ± 0.24 mGy, control group 3.77 ± 1.06 mGy, p < 0.001). Overall quality was higher in control group, but displaying ability of pulmonary artery trunk and branch was higher in study group (p < 0.001).

CONCLUSION

This study proved that a low-dose, low-contrast CTPA strategy could reduce radiation dosage by 50% and contrast agent by 20% while maintaining a satisfying image quality.

The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection

Computed tomography pulmonary angiography (CTPA) is an essential diagnostic tool for identifying pulmonary embolism (PE). The integration of AI has significantly advanced CTPA-based PE detection, enhancing diagnostic accuracy and efficiency. This review investigates the growing role of AI in the diagnosis of pulmonary embolism using CTPA imaging. The review examines the capabilities of AI algorithms, particularly deep learning models, in analyzing CTPA images for PE detection. It assesses their sensitivity and specificity compared to human radiologists. AI systems, using large datasets and complex neural networks, demonstrate remarkable proficiency in identifying subtle signs of PE, aiding clinicians in timely and accurate diagnosis. In addition, AI-powered CTPA analysis shows promise in risk stratification, prognosis prediction, and treatment optimization for PE patients. Automated image interpretation and quantitative analysis facilitate rapid triage of suspected cases, enabling prompt intervention and reducing diagnostic delays. Despite these advancements, several limitations remain, including algorithm bias, interpretability issues, and the necessity for rigorous validation, which hinder widespread adoption in clinical practice. Furthermore, integrating AI into existing healthcare systems requires careful consideration of regulatory, ethical, and legal implications. In conclusion, AI-driven CTPA-based PE detection presents unprecedented opportunities to enhance diagnostic precision and efficiency. However, addressing the associated limitations is critical for safe and effective implementation in routine clinical practice. Successful utilization of AI in revolutionizing PE care necessitates close collaboration among researchers, medical professionals, and regulatory organizations.

Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis

BACKGROUND

Artificial intelligence (AI) presents a promising approach to balancing high image quality with reduced radiation exposure in computed tomography (CT) imaging.

OBJECTIVE

This meta-analysis evaluates the effectiveness of AI in enhancing CT image quality and lowering radiation doses.

METHODS

A thorough literature search was performed across several databases, including PubMed, Embase, Web of Science, Science Direct, and Cochrane Library, with the final update in 2024. We included studies that compared AI-based interventions to conventional CT techniques. The quality of these studies was assessed using the Newcastle-Ottawa Scale. Random effect models were used to pool results, and heterogeneity was measured using the I² statistic. Primary outcomes included image quality, CT dose index, and diagnostic accuracy.

RESULTS

This meta-analysis incorporated 5 clinical validation studies published between 2022 and 2024, totaling 929 participants. Results indicated that AI-based interventions significantly improved image quality (mean difference 0.70, 95% CI 0.43-0.96; P<.001) and showed a positive trend in reducing the CT dose index, though not statistically significant (mean difference 0.47, 95% CI -0.21 to 1.15; P=.18). AI also enhanced image analysis efficiency (odds ratio 1.57, 95% CI 1.08-2.27; P=.02) and demonstrated high accuracy and sensitivity in detecting intracranial aneurysms, with low-dose CT using AI reconstruction showing noninferiority for liver lesion detection.

CONCLUSIONS

The findings suggest that AI-based interventions can significantly enhance CT imaging practices by improving image quality and potentially reducing radiation doses, which may lead to better diagnostic accuracy and patient safety. However, these results should be interpreted with caution due to the limited number of studies and the variability in AI algorithms. Further research is needed to clarify AI's impact on radiation reduction and to establish clinical standards.

Double low-dose computed tomography (CT) angiography of craniocervical arteries using a test bolus of diluted contrast medium and a personalized contrast protocol

AIM

To prospectively assess the value of a test bolus of diluted contrast medium (CM) combined with a personalized contrast protocol in craniocervical computed tomography angiography (cc-CTA) with low radiation and CM doses.

MATERIALS AND METHODS

Eighty-six consecutive subjects were divided into two groups at random (43 in each one): group A: 100/Sn140 kVp, filtered back-projection reconstruction, iopromide (370 mgI/ml) 50 ml; group B: 80/Sn140 kVp, iterative reconstruction, iodixanol (270 mgI/ml). In group B, the test bolus contained 27 ml of diluted CM, a personalized protocol with low-concentration CM was used for angiography, and the test bolus injection duration in angiography remained the same. Artery values over 200 Hounsfield units were considered significant.

RESULTS

Image quality for all cases was found to be diagnostic. No significant differences were found in the arterial densities of the ascending aorta or basilar artery between the groups. The values of the common carotid artery, internal carotid artery, and middle cerebral artery in group B were significantly lower. The effective dose and average iodine uptake were significantly lower in group B.

CONCLUSION

With double-low-dose cc-CTA, test bolus scanning based on diluted CM combined with a personalized contrast protocol can yield diagnostic-quality images and significantly reduce the radiation and CM doses.

Improving intracranial aneurysms image quality and diagnostic confidence with deep learning reconstruction in craniocervical CT angiography

BACKGROUND

The diagnostic impact of deep learning computed tomography (CT) reconstruction on intracranial aneurysm (IA) remains unclear.

PURPOSE

To quantify the image quality and diagnostic confidence on IA in craniocervical CT angiography (CTA) reconstructed with DEep Learning Trained Algorithm (DELTA) compared to the routine hybrid iterative reconstruction (HIR).

MATERIAL AND METHODS

A total of 60 patients who underwent craniocervical CTA and were diagnosed with IA were retrospectively enrolled. Images were reconstructed with DELTA and HIR, where the image quality was first compared in noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Next, two radiologists independently graded the noise appearance, arterial sharpness, small vessel visibility, conspicuity of calcifications that may present in arteries, and overall image quality, each with a 5-point Likert scale. The diagnostic confidence on IAs of various sizes was also graded.

RESULTS

Significantly lower noise and higher SNR and CNR were found on DELTA than on HIR images (all P < 0.05). All five subjective metrics were scored higher by both readers on the DELTA images (all P < 0.05), with good to excellent inter-observer agreement (κ = 0.77-0.93). DELTA images were rated with higher diagnostic confidence on IAs compared to HIR (P < 0.001), particularly for those with size ≤3 mm, which were scored 4.5 ± 0.6 versus 3.4 ± 0.8 and 4.4 ± 0.7 versus 3.5 ± 0.8 by two readers, respectively.

CONCLUSION

The DELTA shows potential for improving the image quality and the associated confidence in diagnosing IA that may be worth consideration for routine craniocervical CTA applications.

Application of deep learning image reconstruction-high algorithm in one-stop coronary and carotid-cerebrovascular CT angiography with low radiation and contrast doses

Background

For patients with suspected simultaneous coronary and cerebrovascular atherosclerosis, conventional single-site computed tomography angiography (CTA) for both sites can result in nonnegligible radiation and contrast agent dose. The purpose of this study was to validate the feasibility of one-stop coronary and carotid-cerebrovascular CTA (C&CC-CTA) with a "double-low" (low radiation and contrast) dose protocol reconstructed with deep learning image reconstruction with high setting (DLIR-H) algorithm.

Methods

From February 2018 to January 2019, 60 patients referred to C&CC-CTA simultaneously in West China Hospital were recruited in this prospective cohort study. By random assignment, patients were divided into two groups: double-low dose group (n=30) used 80 kVp and 24 mgI/kg/s contrast dose with images reconstructed using DLIR-H; and routine-dose group (n=30) used 100 kVp and 32 mgI/kg/s contrast dose with images reconstructed using 50% adaptive statistical iterative reconstruction-V (ASIR-V50%). Radiation and contrast doses, subjective image quality score, CT attenuation values, noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured and compared between the groups.

Results

The DLIR-H group used 30% less contrast dose (35.80±4.85 vs. 51.13±6.91 mL) and 48% less overall radiation dose (1.00±0.09 vs. 1.91±0.42 mSv) than the ASIR-V50% group (both P<0.001). There was no statistically significant difference on subjective quality score between the two groups (C-CTA: 4.38±0.67 vs. 4.17±0.81, P=0.337 and CC-CTA: 4.18±0.87 vs. 4.08±0.79, P=0.604). For coronary CTA, lower background noise (18.93±1.43 vs. 22.86±3.75 HU) was reached in DLIR-H group, and SNR and CNR at all assessed branches were significantly increased compared to ASIR-V50% group (all P<0.05), except SNR of left anterior descending (P>0.05). For carotid-cerebrovascular CTA, DLIR-H group was comparable in background noise (19.25±1.42 vs. 20.23±2.40 HU), SNR and CNR at all assessed branches with ASIR-V50% group (all P>0.05).

Conclusions

The "double-low" dose one-stop C&CC-CTA with DLIR-H obtained higher image quality compared with the routine-dose protocol with ASIR-V50% while achieving 48% and 30% reduction in radiation and contrast dose, respectively.