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Detectability of Small Low-Attenuation Lesions With Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study

Giuseppe V. Toia, D Zamora, Michael Singleton, Arthur Liu, Edward Tan, Shuai Leng, William P. Shuman, Kalpana M. Kanal, Achille Mileto

2022American Journal of Roentgenology18 citationsDOIOpen Access PDF

Abstract

DLIR has substantial potential to preserve contrast-dependent spatial resolution for the detection of hypoattenuating lesions at decreased radiation levels in a phantom model, addressing a major shortcoming of current IR techniques.

Topics & Concepts

Imaging phantomMedicineNuclear medicineIterative reconstructionImage qualityContrast (vision)Image resolutionArtificial intelligenceRadiologyComputer scienceImage (mathematics)Radiation Dose and ImagingAdvanced X-ray and CT ImagingAdvanced Radiotherapy Techniques
Detectability of Small Low-Attenuation Lesions With Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study | Litcius