Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT
| العنوان: | Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT |
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| المؤلفون: | Akio Tamura, Eisuke Mukaida, Yoshitaka Ota, Iku Nakamura, Kazumasa Arakita, Kunihiro Yoshioka |
| المصدر: | Quant Imaging Med Surg |
| بيانات النشر: | AME Publishing Company, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Brief Report, Radiology, Nuclear Medicine and imaging |
| الوصف: | We aimed to compare the radiation dose and image quality of a low-dose abdominal computed tomography (CT) protocol reconstructed with deep learning reconstruction (DLR) with those of a routine-dose protocol reconstructed with hybrid-iterative reconstruction. This retrospective study enrolled 71 patients [61 men; average age, 71.9 years; mean body mass index (BMI), 24.3 kg/m(2)] who underwent both low-dose abdominal CT with DLR [advanced intelligent clear-IQ engine (AiCE)] and routine-dose abdominal CT with hybrid-iterative reconstruction [adaptive iterative dose reduction 3D (AIDR 3D)]. Radiation dose parameters included volume CT dose index (CTDIvol), effective dose (ED), and size-specific dose estimate (SSDE). Mean image noise and contrast-to-noise ratio (CNR) were calculated. Image noise was measured in the hepatic parenchyma and bilateral erector spinae muscles. Moreover, subjective assessment of perceived image quality and diagnostic acceptability was performed. The low-dose protocol helped reduce the CTDIvol by 44.3%, ED by 43.7%, and SSDE by 44.9%. Moreover, the noise was significantly lower and CNR significantly higher with the low-dose protocol than with the normal-dose protocol (P |
| تدمد: | 2223-4306 2223-4292 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::266e040128906f818cb0eaadb0a0b2f4 https://doi.org/10.21037/qims-21-1216 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....266e040128906f818cb0eaadb0a0b2f4 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22234306 22234292 |
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