Deep-Learning-Based Image Denoising in Imaging of Urolithiasis: Assessment of Image Quality and Comparison to State-of-the-Art Iterative Reconstructions

This study aimed to compare the image quality and diagnostic accuracy of deep-learning-based image denoising reconstructions (DLIDs) to established iterative reconstructed algorithms in low-dose computed tomography (LDCT) of patients with suspected urolithiasis. LDCTs (CTDIvol, 2 mGy) of 76 patients...

Ful tanımlama

Kaydedildi:

Detaylı Bibliyografya
Asıl Yazarlar:	Robert Terzis, Robert Peter Reimer, Christian Nelles, Erkan Celik, Liliana Caldeira, Axel Heidenreich, Enno Storz, David Maintz, David Zopfs, Nils Große Hokamp
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	MDPI AG 2023-08-01
Seri Bilgileri:	Diagnostics
Konular:	deep learning diagnostic imaging image enhancement kidney calculi tomography X-ray computed
Online Erişim:	https://www.mdpi.com/2075-4418/13/17/2821
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

Deep-Learning-Based Image Denoising in Imaging of Urolithiasis: Assessment of Image Quality and Comparison to State-of-the-Art Iterative Reconstructions

Benzer Materyaller