Comparative study of the image quality of twin beam dual energy and single energy carotid CT angiography

Fully automated image quality assessment based on deep learning for carotid computed tomography angiography: A multicenter study

PURPOSE

To develop and evaluate the performance of fully automated model based on deep learning and multiple logistics regression algorithm for image quality assessment (IQA) of carotid computed tomography angiography (CTA) images.

METHODS

This study retrospectively collected 840 carotid CTA images from four tertiary hospitals. Three radiologists independently assessed the image quality using a 3-point Likert scale, based on the degree of noise, vessel enhancement, arterial vessel contrast, vessel edge sharpness, and overall diagnostic acceptability. An automated assessment model was developed using a training dataset consisting of 600 carotid CTA images. The assessment steps included: (i) selection of objective representative slices; (ii) use of 3D Res U-net approach to extract objective indices from the representative slices and (iii) use of single objective index and multiple indices combinedly to develop logistic regression models for IQA. In the internal and external test datasets (n = 240), the performance of models was evaluated using sensitivity, specificity, precision, F-score, accuracy, the area under the receiver operating characteristic curve (AUC), and the IQA results of models was compared with radiologists' consensus.

RESULTS

The representative slices were determined based on the same length model. The performance of multi-index model was excellent in internal and external test datasets with AUCs of 0.98 and 0.97. And the consistency between model and radiologists achieved 91.8% (95% CI: 87.0-96.5) and 92.6% (95 % CI: 86.9-98.4) in internal and external test datasets respectively.

CONCLUSION

The fully automated multi-index model showed equivalent performance to the subjective perceptions of radiologists with greater efficiency for IQA.

Virtual Monoenergetic Images Facilitate Better Identification of the Arc of Riolan During Splenic Flexure Takedown

OBJECTIVE

This study aimed to investigate whether virtual monoenergetic images (VMIs) can aid radiologists and surgeons in better identifying the arc of Riolan (AOR) and to determine the optimal kilo electron volt (keV) level.

METHODS

Thirty-three patients were included. Conventional images (CIs) and VMI (40-100 keV) were reconstructed using arterial phase spectral-based images. The computed tomography (CT) attenuation and noise of the AOR, the CT attenuation of the erector spinal muscle, and the background noise on VMI and CI were measured, respectively. The signal-to-noise ratio, contrast-to-noise ratio (CNR), and signal intensity ratio were calculated. The image quality of the AOR was evaluated according to a 4-point Likert grade.

RESULTS

The CT attenuation, noise, CNR, and signal intensity ratio of the AOR were significantly higher in VMI at 40 and 50 keV compared with CI ( P < 0.001); VMI at 40 keV was significantly higher than 50 keV ( P < 0.05). No significant difference in signal-to-noise ratio, background noise, and CT attenuation of the spinal erector muscle was observed between VMI and CI ( P > 0.05). virtual monoenergetic image at 40 keV produced the best subjective scores.

CONCLUSIONS

Virtual monoenergetic image at 40 keV makes it easier to observe the AOR with optimized subjective and objective image quality. This may prompt radiologists and surgeons to actively search for it and encourage surgeons to preserve it during splenic flexure takedown.

Imaging the Vulnerable Carotid Plaque with CT: Caveats to Consider. Comment on Wang et al. Identification Markers of Carotid Vulnerable Plaques: An Update. Biomolecules 2022, 12, 1192

We read with great interest the review by Wang et al. entitled "Identification Markers of Carotid Vulnerable Plaques: An Update", recently published in Biomolecules [...].

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.

Photon Counting CT Angiography of the Head and Neck: Image Quality Assessment of Polyenergetic and Virtual Monoenergetic Reconstructions

Background: The purpose of the present study was the evaluation of the image quality of polyenergetic and monoenergetic reconstructions (PERs and MERs) of CT angiographies (CTAs) of the head and neck acquired with the novel photon counting CT (PCCT) method in clinical routine. Methods: Thirty-seven patients were enrolled in this retrospective study. Quantitative image parameters of the extracranial, intracranial and cerebral arteries were evaluated for the PER and MER (40–120 keV). Additionally, two radiologists rated the perceived image quality. Results: The mean CTDI_vol used in the PCCT was 8.31 ± 1.19 mGy. The highest signal within the vessels was detected in the 40 keV MER, whereas the lowest noise was detected in the 115 keV MER. The most favorable contrast-to-noise-ratio (CNR) and signal-to-noise-ratio (SNR) were detected in the PER and low keV MER. In the qualitative image analysis, the PER was superior to the MER in all rated criteria. For MER, 60–65 keV was rated as best image quality. Conclusion: Overall, PCCT offers excellent image quality for CTAs of the head and neck. At the current state, the PER of the PCCT seems to be the most favorable reconstruction for diagnostic reporting.