Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction.

Radiology
Samuel L BradyJonathan R Dillman

Abstract

Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation dose reduction has yet to be fully investigated. Purpose To investigate a DLR algorithm's dose reduction and image quality improvement for pediatric CT. Materials and Methods DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrospective study by using data from CT examinations of pediatric patients (February to December 2018). A comparison of object detectability for 15 objects (diameter, 0.5-10 mm) at four contrast difference levels (50, 150, 250, and 350 HU) was performed by using a non-prewhitening-matched mathematical observer model with eye filter (d'NPWE), task transfer function, and noise power spectrum analysis. Object detectability was assessed by using area under the curve analysis. Three pediatric radiologists performed an observer study to assess anatomic structures with low object-to-background signal and contrast to noise in the azygos vein, right hepatic vein, common bile duct, and superior mesenteric artery. Observers rated from 1 to 10 (worst to best) for...Continue Reading

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Citations

Apr 13, 2021·Pediatric Radiology·Jonathan R DillmanLili He
May 7, 2021·La Radiologia medica·Yuko NakamuraKazuo Awai
May 15, 2021·The British Journal of Radiology·Jarrel SeahMeng Law
Oct 2, 2021·Radiographics : a Review Publication of the Radiological Society of North America, Inc·Yasunori NagayamaToshinori Hirai
Oct 5, 2021·Abdominal Radiology·Samjhana ThapaliyaAndrew T Trout
Jul 8, 2021·AJR. American Journal of Roentgenology·Samjhana ThapaliyaAndrew T Trout

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