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.

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 (CTDIvol, DLP) and image quality, including analysis of normal structures, lesions, overall image quality, and artifacts (Wilcoxon signed-rank test). RESULTS: Median CTDIvol 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 CTDIvol 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.

Deep Learning Versus Iterative Reconstruction for CT Pulmonary Angiography in the Emergency Setting: Improved Image Quality and Reduced Radiation Dose.

To compare image quality and the radiation dose of computed tomography pulmonary angiography (CTPA) subjected to the first deep learning-based image reconstruction (DLR) (50%) algorithm, with images subjected to the hybrid-iterative reconstruction (IR) technique (50%). One hundred forty patients who underwent CTPA for suspected pulmonary embolism (PE) between 2018 and 2019 were retrospectively reviewed. Image quality was assessed quantitatively (image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) and qualitatively (on a 5-point scale). Radiation dose parameters (CT dose index, CTDIvol; and dose-length product, DLP) were also recorded. Ninety-three patients were finally analyzed, 48 with hybrid-IR and 45 with DLR images. The image noise was significantly lower and the SNR (24.4 ± 5.9 vs. 20.7 ± 6.1) and CNR (21.8 ± 5.8 vs. 18.6 ± 6.0) were significantly higher on DLR than hybrid-IR images (p < 0.01). DLR images received a significantly higher score than hybrid-IR images for image quality, with both soft (4.4 ± 0.7 vs. 3.8 ± 0.8) and lung (4.1 ± 0.7 vs. 3.6 ± 0.9) filters (p < 0.01). No difference in diagnostic confidence level for PE between both techniques was found. CTDIvol (4.8 ± 1.4 vs. 4.0 ± 1.2 mGy) and DLP (157.9 ± 44.9 vs. 130.8 ± 41.2 mGy∙cm) were lower on DLR than hybrid-IR images. DLR both significantly improved the image quality and reduced the radiation dose of CTPA examinations as compared to the hybrid-IR technique.

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.