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Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study

Published Online: 2025-08-27

Authors

Sauter, Andreas Philipp

Thalhammer, Johannes https://orcid.org/0000-0001-5198-8571
Meurer, Felix

Dorosti, Tina

Sasse, Daniel

Ritter, Jessica

Leonhardt, Yannik

Pfeiffer, Franz

Schaff, Florian

Pfeiffer, Daniela
Funding

Funding for this research was provided by:

Federal Ministry of Education and Research (BMBF) and the Free State of Bavaria (GRK2274)

Technical University of Munich–Institute for Advanced Study

Technische Universität München
License Information

Text and Data Mining valid from 2025-08-27

Version of Record valid from 2025-08-27
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Article History

Received: 22 April 2025

Revised: 18 July 2025

Accepted: 29 July 2025

First Online: 27 August 2025

Declarations

:

: The authors declare no competing interests.

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