Work

Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction

Year: 2018

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-030-00129-2_12

Authors: Andreas Hauptmann, Ben Cox, Felix Lucka, Nam Huynh, Marta Betcke, Paul Beard, Simon Arridge

Institutions: University College London, Centrum Wiskunde & Informatica

Language: English

Topic: Photoacoustic and Ultrasonic Imaging

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Nvidia, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Engineering and Physical Sciences Research Council

Awards: G1703262157, Medical image computing for next-generation healthcare technology, Simulations and Inversions in Photoacoustic Tomography for High Resolution Quantitative Biomedical Imaging, Mathematics and Algorithms for 3D Imaging of Dynamic Processes, G807679557, Dynamic High Resolution Photoacoustic Tomography System

OpenAlex ID: W2855209531

FWCI: 9.885

Cites: 16

Cited by: 38

Related to: 10