Virtual DSA for Learning Contrast Agent Dynamics in Projection Space

Maul N, Birkhold A, Thies M, Vysotskaya N, Wagner F, Pfaff L, Kowarschik M, Maier A (2025)

Publication Type: Conference contribution

Publication year: 2025

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 15408 LNCS

Pages Range: 51-60

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Marrakesh, MAR

ISBN: 9783031811005

DOI: 10.1007/978-3-031-81101-2_6

Abstract

Digital Subtraction Angiography (DSA) is a well-established imaging modality supporting treatment and diagnosis of vascular pathologies. Various clinical DSA acquisition protocols exist that provide qualitative blood flow information for vascular diseases. Velocity quantification algorithms primarily rely on tracking a contrast agent (CA) bolus through the vasculature. However, the true CA velocity is fast compared to the frame rate of angiographic images. Therefore, high blood velocities pose a challenge for these methods, as the bolus may flow through long vessel segments between two subsequent DSA frames. This problem can be mitigated by increasing the temporal resolution, or equivalently, the projection image frame rate. We propose a simulation-informed neural network approach to synthetically double the projection frame rate without additional patient dose. We evaluate the quality of the synthesized projection images and show the impact on bolus tracking algorithms. Synthesized projection images can be predicted with a mean absolute percentage error of 2.5±0.8 % in the inflow phase. Further, the synthesized projections qualitatively capture bolus dynamics more accurately compared to linear interpolation. Conceptually, our method allows extension to predicting multiple intermediate projection frames, which can be a valuable tool toward accurate quantitative vascular flow estimation.

Authors with CRIS profile

Involved external institutions

Siemens AG Germany (DE)

How to cite

APA:

Maul, N., Birkhold, A., Thies, M., Vysotskaya, N., Wagner, F., Pfaff, L.,... Maier, A. (2025). Virtual DSA for Learning Contrast Agent Dynamics in Projection Space. In Ruisheng Su, Danny Ruijters, Ezequiel de la Rosa, Leonhard Rist, Ewout Heylen, Frank te Nijenhuis, Theo van Walsum, Markus D. Schirmer, Richard McKinley, Roland Wiest, Susanne Wegener (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 51-60). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.

MLA:

Maul, Noah, et al. "Virtual DSA for Learning Contrast Agent Dynamics in Projection Space." Proceedings of the 4th International Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th International Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, Marrakesh, MAR Ed. Ruisheng Su, Danny Ruijters, Ezequiel de la Rosa, Leonhard Rist, Ewout Heylen, Frank te Nijenhuis, Theo van Walsum, Markus D. Schirmer, Richard McKinley, Roland Wiest, Susanne Wegener, Springer Science and Business Media Deutschland GmbH, 2025. 51-60.

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