A clinically oriented overview of Deep Learning Reconstruction and Deep Iterative Reconstruction is provided, emphasizing their coexistence and differentiated roles on a 320-row CT scanner platform, offering radiologists insights for clinical practice as well as inspirations for future research.
Low-dose computed tomography (LDCT) is essential for reducing potential radiation-associated risks, but increased noise and artifacts can compromise diagnostic confidence. This review examines how AI is reshaping LDCT by improving reconstruction, denoising, protocol optimization, and workflow integration. It outlines t...
Comparing image quality and diagnostic performance among conventional sequences and an automated accelerated deep learning-based reconstruction (DLR) knee algorithm method in patients with knee pain revealed no significant differences in overall diagnosis, image quality, and artifacts.
Rola Husain, Stephen Belmustakov, Etan Dayan et al.· Diagnostics· 0 citations
Electron density provides a significantly better CNR for liver metastases with electron density derived from dual-energy computed tomography compared with both conventional and VMI at 40 keV reconstructions, in both the unenhanced and portal-dominant phases.
C. Chabrier, Maxime Leroy, Pr Olivier Ernst· 0 citations
BACKGROUND
Deep-learning applications in nuclear medicine are expanding, but many departments lack dedicated AI engineering support, creating a gap between interest and practical implementation. The aim of this study was to evaluate the feasibility of a physician-led, incremental workflow for developing reproducible de...
B. Duchaj, L. Noskovicova, I. Jaksic et al.· Nuclear medicine review. Cen...· 0 citations
We evaluated the feasibility of deep learning-based image reconstruction (DLIR) using GE TrueFidelity at high strength for coronary calcium scoring, compared with filtered back projection (FBP) and iterative reconstruction (IR; adaptive statistical iterative reconstruction-V with 80% blending), using a cardiac phantom...
Ji Won Kim, Tae Hoon Kim, Seung Hyun Lee et al.· PLoS ONE· 0 citations
Purpose: To compare image quality between deep learning reconstruction and hybrid iterative reconstruction for conventional and virtual monoenergetic images derived from the same spectral CT examinations. Methods and Materials: This retrospective, post hoc secondary analysis of a multi-institutional, controlled, blinde...
M. Nazarian, A. Perkins, E. Wong et al.· medRxiv· 0 citations
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