Radiation dose reduction and image quality improvement with ultra-high resolution temporal bone CT using deep learning-based reconstruction: An anatomical study

PURPOSE

The purpose of this study was to evaluate the achievable radiation dose reduction of an ultra-high resolution computed tomography (UHR-CT) scanner using deep learning reconstruction (DLR) while maintaining temporal bone image quality equal to or better than high-resolution CT (HR-CT).

MATERIALS AND METHODS

UHR-CT acquisitions were performed with variable tube voltages and currents at eight different dose levels (volumic CT dose index [CTDIvol] range: 4.6-79 mGy), 10242 matrix, and 0.25 mm slice thickness and reconstructed using DLR and hybrid iterative reconstruction (HIR) algorithms. HR-CT images were acquired using a standard protocol (120 kV/220 mAs; CTDI vol, 54.2 mGy, 5122 matrix, and 0.5 mm slice thickness). Two radiologists rated the image quality of seven structures using a five point confidence scale on six cadaveric temporal bone CTs. A global image quality score was obtained for each CT protocol by summing the image quality scores of all structures.

RESULTS

With DLR, UHR-CT at 120 kV/220 mAs (CTDIvol, 50.9 mGy) and 140 kV/220 mAs (CTDIvol, 79 mGy) received the highest global image quality scores (4.88 ± 0.32 [standard deviation (SD)] [range: 4-5] and 4.85 ± 0.35 [range: 4-5], respectively; P = 0.31), while HR-CT at 120 kV/220 mAs and UHR-CT at 120 kV/20 mAs received the lowest (i.e., 3.14 ± 0.75 [SD] [range: 2-5] and 2.97 ± 0.86 [SD] [range: 1-5], respectively; P = 0.14). All the DLR protocols had better image quality scores than HR-CT with HIR.

CONCLUSION

UHR-CT with DLR can be performed with up to a tenfold reduction in radiation dose compared to HR-CT with HIR while maintaining or improving image quality.

Ultra-high-resolution CT of the temporal bone: Comparison between deep learning reconstruction and hybrid and model-based iterative reconstruction

PURPOSE

The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction algorithm compared to simulated conventional CT.

MATERIALS AND METHODS

CT acquisitions were performed with a Mercury 4.0 phantom. Images were acquired with a 1024 × 1024 matrix and a 0.25 mm slice thickness and reconstructed using DLR, model-based, and hybrid iterative reconstruction algorithms. To simulate conventional CT, images were also reconstructed with a 512 × 512 matrix and a 0.5 mm slice thickness. Spatial resolution, noise power spectrum, and objective high-contrast detectability were compared. Three radiologists evaluated the clinical acceptability of these algorithms by assessing the thickness and image quality of the stapes footplate and superstructure elements, as well as the image quality of the chorda tympani nerve bony and tympanic segments using a 5-point confidence scale on 13 temporal bone CT examinations reconstructed with the four algorithms.

RESULTS

UHR-CT provided higher spatial resolution than simulated conventional CT at the penalty of higher noise. DLR and model-based iterative reconstruction provided better noise reduction than hybrid iterative reconstruction, and DLR had the highest detectability index, regardless of the dose level. All stapedial structure thicknesses were thinner using UHR-CT by comparison with conventional simulated CT (P < 0.009). DLR showed the best visualization scores compared to the other reconstruction algorithms (P < 0.032).

CONCLUSION

UHR-CT with DLR results in less noise than UHR-CT with hybrid iterative reconstruction and significantly improves stapes and tympanic chorda tympani nerve depiction compared to simulated conventional CT and UHR-CT with iterative reconstruction.

Deep Learning-Based Reconstruction vs. Iterative Reconstruction for Quality of Low-Dose Head-and-Neck CT Angiography with Different Tube-Voltage Protocols in Emergency-Department Patients

Objective: To compare the image quality of computed tomography angiography of the supra-aortic arteries (CTSA) at different tube voltages in low doses settings with deep learning-based image reconstruction (DLR) vs. hybrid iterative reconstruction (H-IR). Methods: We retrospectively reviewed 102 patients who underwent CTSA systematically reconstructed with both DLR and H-IR. We assessed the image quality both quantitatively and qualitatively at 11 arterial segmental levels and 3 regional levels. Radiation-dose parameters were recorded and the effective dose was calculated. Eighty-six patients were eligible for analysis Of these patients, 27 were imaged with 120 kVp, 30 with 100 kVp, and 29 with 80 kVp. Results: The effective dose in 120 kVp, 100 kVp and 80 kVp was 1.5 ± 0.4 mSv, 1.1 ± 0.3 mSv and 0.68 ± 0.1 mSv, respectively (p < 0.01). Comparing 80 kVp + DLR vs. 120 and 100 kVp + H-IR CT scans, the mean overall arterial attenuation was about 64% and 34% higher (625.9 ± 118.5 HU vs. 382.3 ± 98.6 HU and 468 ± 118.5 HU; p < 0.01) without a significant difference in terms of image noise (17.7 ± 4.9 HU vs. 17.5 ± 5.2; p = 0.7 and 18.1 ± 5.4; p = 0.3) and signal-to-ratio increased by 59% and 33%, respectively (37.9 ± 12.3 vs. 23.8 ± 9.7 and 28.4 ± 12.5). This protocol also provided superior image quality in terms of qualitative parameters, compared to standard-kVp protocols with H-IR. Highest subjective image-quality grades for vascular segments close to the aorta were obtained with the 100 kVp + DLR protocol. Conclusions: DLR significantly reduced image noise and improved the overall image quality of CTSA with both low and standard tube voltages and at all vascular segments. CT that was acquired with 80 kVp and reconstructed with DLR yielded better overall image quality compared to higher kVp values with H-IR, while reducing the radiation dose by half, but it has limitations for arteries that are close to the aortic arch.

Radiation dose reduction with the wide-volume scan mode for interstitial lung diseases

OBJECTIVES

The wide-volume mode, available on wide-area detector row CTs, has the advantage of reducing exposure time and radiation dose. It is infrequently used for lung diseases. The purpose of this study is to compare image quality and radiation dose of wide-volume chest CT to those of standard helical CT in the setting of interstitial lung diseases.

METHODS

Retrospective monocentric study including 50 consecutive patients referred for follow-up or screening of interstitial lung diseases, requiring prone scan, acquired with the wide-volume mode, in addition to the routine supine scan, acquired with the helical mode. The optimal collimation in wide-volume mode (320 × 0.5mm or 240 × 0.5mm) was chosen according to the length of the thorax. Wide-volume acquisitions were compared to helical acquisitions for radiation dose (CTDI_vol, DLP) and image quality, including analysis of normal structures, lesions, overall image quality, and artifacts (Wilcoxon signed-rank test).

RESULTS

Median CTDI_vol and DLP with wide volumes (3.1 mGy and 94.6 mGy·cm) were significantly reduced (p < 0.0001) as compared to helical mode (3.7mGy and 122.1 mGy·cm), leading to a median 21% and 32% relative reduction of CTDI_vol and DLP, respectively. Image noise and quality were not significantly different between the two modes. Misalignment artifact at the junction of two volumes was occasionally seen in the wide-volume scans and, when present, did not impair the diagnostic quality in the majority of cases.

CONCLUSIONS

Wide-volume mode allows 32% radiation dose reduction compared to the standard helical mode and could be used routinely for diagnosis and follow-up of interstitial lung diseases.

KEY POINTS

• Retrospective monocentric study showed that wide-volume scan mode reduces radiation dose by 32% in comparison to helical mode for chest CT in the setting of interstitial lung diseases. • Mild misalignment may be observed at the junction between volumes with the wide-volume mode, without decrease of image quality in the majority of cases and without impairing diagnostic quality. • Wide-volume mode could be used routinely for the diagnosis and follow-up of interstitial lung diseases.

Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality

Background

To assess the radiation dose and image quality of cardiac computed tomography angiography (CCTA) in an acute stroke imaging protocol using a deep learning reconstruction (DLR) method compared to a hybrid iterative reconstruction algorithm.

Methods

Retrospective analysis of 296 consecutive patients admitted to the emergency department for stroke suspicion. All patients underwent a stroke CT imaging protocol including a non-enhanced brain CT, a brain perfusion CT imaging if necessary, a CT angiography (CTA) of the supra-aortic vessels, a CCTA and a post-contrast brain CT. The CCTA was performed with a prospectively ECG-gated volume acquisition. Among all CT scans performed, 143 were reconstructed with an iterative reconstruction algorithm (AIDR 3D, adaptive iterative dose reduction three dimensional) and 146 with a DLR algorithm (AiCE, advanced intelligent clear-IQ engine). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality (IQ) scored from 1 to 4 were assessed. Dose-length product (DLP), volume CT dose index (CTDIvol) and effective dose (ED) were obtained.

Results

The radiation dose was significantly lower with AiCE than with AIDR 3D (DLP =106.4±50.0 vs. 176.1±37.1 mGy·cm, CTDIvol =6.9±3.2 vs. 11.5±2.2 mGy, and ED =1.5±0.7 vs. 2.5±0.5 mSv) (P<0.001). The median SNR and CNR were higher [9.9 (IQR, 8.1-12.3); and 12.6 (IQR, 10.5-15.5), respectively], with AiCE than with AIDR 3D [6.5 (IQR, 5.2-8.5); and 8.4 (IQR, 6.7-11.0), respectively] (P<0.001). SNR and CNR were increased by 51% and 49%, respectively, with AiCE compared to AIDR 3D. The image quality was significantly better with AiCE (mean IQ score =3.4±0.7) than with AIDR 3D (mean IQ score =3±0.9) (P<0.001).

Conclusions

The use of a DLR algorithm for cardiac CTA in an acute stroke imaging protocol reduced the radiation dose by about 40% and improved the image quality by about 50% compared to an iterative reconstruction algorithm.

Volume variation may be a relevant metric in the study of aneurysm pulsatility: a study using ECG-gated 4D-CTA (PULSAN)

​Background and purpose

Intracranial aneurysms are a frequently occurring disease, with an estimated prevalence of 2–5% in the general population. They usually remain silent until rupture occurs, with a mortality rate of 35–50% and a high rate of morbidity, including long-term disability. However, preventative treatments have their own risk of complications and morbi-mortality rates, including stroke and hemorrhage. ECG-gated four-dimensional CT angiography (4D-CTA) allows the acquisition of time-resolved three-dimensional reconstructions. The aim of our study was to evaluate different intracranial aneurysm metrics over the cardiac cycle using ECG-gated 4D-CTA.

​Materials and methods

ECG-gated 4D-CTA datasets were acquired in patients presenting with intracranial aneurysms. Seven aneurysm metrics, including aneurysm height, aneurysm length, ostium width, aspect ratio, ostium area, volume, and volume-to-ostium ratio, were analysed over different cardiac phases. Intra-reader agreement, inter-reader agreement, and inter-cycle agreement were calculated through the intraclass correlation coefficient.

​Results

Twenty-one aneurysms from 11 patients were considered for inclusion. Post-processing failed for three aneurysms, and 18 aneurysms were finally analysed. There was good intra-reader agreement for each metric (ICC >0.9). Agreements among three consecutive cardiac cycles were calculated for six aneurysms and were especially good for the volume metric (ICC >0.9). Volume variation appears to be the most relevant metric and seems especially perceptible for aneurysms larger than 5 mm.

​Conclusions

Quantification of aneurysm volume changes during the cardiac cycle seems quantitatively possible and reproducible, especially for aneurysms larger than 5 mm. Further studies need to be conducted to validate this parameter for intracranial aneurysm assessment.