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Conference 12031 > Paper 12031-54
Paper 12031-54

Implicit neural representation for dynamic imaging

22 February 2022 • 3:40 PM - 4:00 PM PST | Town & Country A

Abstract

Dynamic imaging plays a fundamental role in studying various biological phenomena but faces two challenges: data incompleteness and computational burden. This contribution investigates the use of implicit neural representations (INRs) to address both. Using INRs, a dynamic image reconstruction method is proposed, where a continuous mapping from spatiotemporal coordinates to a scalar value representing the object is learned directly from measurement data. As such, it is fundamentally different from other learning methods requiring large datasets of training images. The feasibility of the proposed framework is illustrated with an application to dynamic image reconstruction from undersampled circular Radon transform data.

Presenter

Luke Lozenski
Washington Univ. in St. Louis (United States)
Presenter/Author
Luke Lozenski
Washington Univ. in St. Louis (United States)
Author
Univ. of Illinois (United States)
Author
Washington Univ. in St. Louis (United States)