CT angiography with 15 mL contrast material injection on time-resolved imaging for endovascular abdominal aortic aneurysm repair

Evaluation of Image Quality of Temporal Maximum Intensity Projection and Average Intensity Projection of Adaptive 4D-Spiral CT Scans: A Phantom Study

Adaptive four-dimensional (4D) spiral computed tomography (CT) scans facilitate the acquisition of volume perfusion data for organs or long-range vessels; however, optimizing image quality and reducing noise while minimizing radiation doses remains challenging. Thus, image-processing techniques such as temporal maximum intensity projection (MIP) and average intensity projection (AIP) are crucial in this context. This ex vivo study aimed to compare the image noise, spatial resolution, and measurements of temporal MIP and AIP images generated from low radiation dose 4D CT scans data with those of conventional CT images using phantoms. Three phantoms were scanned with equivalent radiation doses using single helical and adaptive 10-phase 4D spiral scans using a third-generation dual-source CT scanner. Temporal MIP and AIP images of 4D CT scans were generated by summing varying numbers of phases, incorporating automatic motion correction with non-rigid registration and noise reduction algorithm. The CT values and image noise of the temporal MIP and AIP images were compared to conventional CT images. The task transfer function (TTF) was calculated using static phantoms. Vessel diameters of the phantoms for each image dataset were evaluated using motion phantoms. Temporal AIP images showed comparable CT values with those of the reference image. In contrast, the CT values of the temporal MIP images were significantly higher than those of the reference images (p<0.01). The image noise of temporal AIP images with six or more phases was equal to or lower than that of the reference images. In contrast, temporal MIP images exhibited consistently high noise levels regardless of the number of summed phases. The TTF of temporal AIP images was comparable to that of the reference CT images. However, the TTF of temporal MIP images gradually decreased as the number of summed phases increased. No significant differences were observed in vessel diameter measurements among the three groups or with varying numbers of summed phases (p>0.05). In conclusion, temporal MIP and AIP images generated from low radiation dose 4D CT scans could effectively reduce noise while preserving measurement reliability in the motion phantom, achieving performance comparable to conventional CT images.

Contrast enhancement boost improves the image quality of CT angiography derived from 80-kVp cerebral CT perfusion data

RATIONALE AND OBJECTIVE

To investigate the impact of the contrast enhancement boost (CE-boost) technique on the image quality of CT angiography (CTA) derived from 80-kVp cerebral CT perfusion (CTP) data, and to compare it with conventional CTApeak as well as other currently employed methods for enhancing CTA images, such as CTAtMIP and CTAtAve extracted from CTP.

MATERIALS AND METHODS

The data of forty-seven patients who underwent CTP at 80 kVp were retrospectively collected. Four sets of images: CTApeak, CTAtMIP, CTAtAve, and CE-boost images. The CTApeak image represents the arterial phase at its peak value, captured as a single time point. CTAtMIP and CTAtAve are 4D CTA images that provide maximum density projection and average images from the three most prominent time points. CE-boost is a postprocessing technique used to enhance contrast in the arterial phase at its peak value. We compared the average CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the internal carotid artery (ICA) and basilar artery (BA) among the four groups. Image quality was evaluated using a 5-point scale.

RESULTS

The CE-boost demonstrated and CNR in the ICA and BA (all p < 0.001). Compared with the other three CTA reconstructed images, the CE-boost images had the best subjective image quality, with the highest scores of 4.77 ± 0.43 and 4.87 ± 0.34 for each reader (all p < 0.001).

CONCLUSION

Compared with other currently used techniques,CE-boost enhances the image quality of CTA derived from 80-kVp CTP data, leading to improved visualization of intracranial arteries.

Deep Learning-based Post Hoc CT Denoising for Myocardial Delayed Enhancement

Background To improve myocardial delayed enhancement (MDE) CT, a deep learning (DL)-based post hoc denoising method supervised with averaged MDE CT data was developed. Purpose To assess the image quality of denoised MDE CT images and evaluate their diagnostic performance by using late gadolinium enhancement (LGE) MRI as a reference. Materials and methods MDE CT data obtained by averaging three acquisitions with a single breath hold 5 minutes after the contrast material injection in patients from July 2020 to October 2021 were retrospectively reviewed. Preaveraged images obtained in 100 patients as inputs and averaged images as ground truths were used to supervise a residual dense network (RDN). The original single-shot image, standard averaged image, RDN-denoised original (DL_original) image, and RDN-denoised averaged (DL_ave) image of holdout cases were compared. In 40 patients, the CT value and image noise in the left ventricular cavity and myocardium were assessed. The segmental presence of MDE in the remaining 40 patients who underwent reference LGE MRI was evaluated. The sensitivity, specificity, and accuracy of each type of CT image and the improvement in accuracy achieved with the RDN were assessed using odds ratios (ORs) estimated with the generalized estimation equation. Results Overall, 180 patients (median age, 66 years [IQR, 53-74 years]; 107 men) were included. The RDN reduced image noise to 28% of the original level while maintaining equivalence in the CT values (P < .001 for all). The sensitivity, specificity, and accuracy of the original images were 77.9%, 84.4%, and 82.3%, of the averaged images were 89.7%, 87.9%, and 88.5%, of the DL_original images were 93.1%, 87.5%, and 89.3%, and of the DL_ave images were 95.1%, 93.1%, and 93.8%, respectively. DL_original images showed improved accuracy compared with the original images (OR, 1.8 [95% CI: 1.2, 2.9]; P = .011) and DL_ave images showed improved accuracy compared with the averaged images (OR, 2.0 [95% CI: 1.2, 3.5]; P = .009). Conclusion The proposed denoising network supervised with averaged CT images reduced image noise and improved the diagnostic performance for myocardial delayed enhancement CT. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.

Contrast-induced nephropathy in non-cardiac vascular procedures, a narrative review: Part 1

Contrast-induced nephropathy (CIN) is an important complication of iodinated contrast medium (CM) administration, which is associated with both short- and long-term adverse outcomes (e.g., cardiorenal events, longer hospital stay, and mortality). CIN has been mainly studied in relation to cardiac procedures, but it can also occur following non-cardiac vascular interventions. This is Part 1 of a narrative review summarizing the available literature on CIN after non-cardiac vascular diagnostic or therapeutic procedures for aortic aneurysm and carotid stenosis. We discuss the definition, pathophysiology, incidence, risk factors, biomarkers, and consequences of CIN in these settings, as well as preventive strategies and alternatives to limit iodinated CM use. Physicians and vascular surgeons should be aware of CM-related adverse events and the potential strategies to avoid them. Clearly, more research in this important field is required.