Abstract
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.
Highlights
Acute pulmonary embolism (PE) is the third most frequent cardiovascular disease, after acute myocardial infarction and stroke, causing approximately 37,000 deaths in Europe and 60,000–100,000 deaths in the USA each year [1,2]
Our initial experience in the clinical setting, and vendor recommendation, we intended to on these estimations, our initial experience in the clinical setting, and vendor recommendation, we reduce the radiation dose by an estimated 20% in the AiCE group by increasing the noise index settings, intended to reduce the radiation dose by an estimated 20% in the AiCE group by increasing the noise expecting a significant dose reduction while still improving the overall image quality
The present study is the first to evaluate the effect of deep learning-based image reconstruction (DLR) on the image quality and radiation dose of Computed tomography pulmonary angiography (CTPA) in the emergency setting
Summary
Acute pulmonary embolism (PE) is the third most frequent cardiovascular disease, after acute myocardial infarction and stroke, causing approximately 37,000 deaths in Europe and 60,000–100,000 deaths in the USA each year [1,2]. Computed tomography pulmonary angiography (CTPA) is the first-choice diagnostic imaging modality for acute PE due to its wide availability and its minimal invasiveness [2,3,4]. Approximately 2% of all emergency department patients undergo pulmonary. Approximately 2% of all emergency department patients undergo. CTPA for suspected [5]. TheHowever, increasedthe useincreased of CT scans and pulmonary. CTPA forPEsuspected PE [5]. CT scans situations in emergency the stochastic low-level radiation-related carcinogenesis raise concerns about long-term radiation situations and the stochastic low-level radiation-related carcinogenesis raise concerns about longexposure [6,7,8,9].
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