collaborators

6 papers

physics.med-ph20269 cited

Fully three-dimensional sound speed-corrected multi-wavelength photoacoustic breast tomography

M. Dantuma, F. Lucka, S. C. Kruitwagen +21

Photoacoustic tomography is a contrast agent-free imaging technique capable of visualizing blood vessels and tumor-associated vascularization in breast tissue. While sophisticated…

eess.IV2026

Towards robust quantitative photoacoustic tomography via learned iterative methods

Anssi Manninen, Janek Gröhl, Felix Lucka +1

Photoacoustic tomography (PAT) is a medical imaging modality that can provide high-resolution tissue images based on the optical absorption. Classical reconstruction methods for qu…

physics.med-ph2026

Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction

Tianyuan Wang, Daniël M. Pelt, Felix Lucka +2

Traditional X-ray computed tomography (CT) scanning strategies typically select projection angles uniformly and allocate dose equally. In practice, however, CT scans often need to…

eess.IV2025

Exploring Out-of-distribution Detection for Sparse-view Computed Tomography with Diffusion Models

Ezgi Demircan-Tureyen, Felix Lucka, Tristan van Leeuwen

Recent works demonstrate the effectiveness of diffusion models as unsupervised solvers for inverse imaging problems. Sparse-view computed tomography (CT) has greatly benefited from…

eess.IV2025

Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning

Tianyuan Wang, Felix Lucka, Tristan van Leeuwen

In X-ray Computed Tomography (CT), projections from many angles are acquired and used for 3D reconstruction. To make CT suitable for in-line quality control, reducing the number of…

eess.IV2024

Benchmarking learned algorithms for computed tomography image reconstruction tasks

Maximilian B. Kiss, Ander Biguri, Zakhar Shumaylov +4

Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image recons…