| 2025 |
17. November |
14:30 |
at SR 127.11, IDea_Lab |
Sascha Beutler: From Motion Estimation to Active Correction in Intravital Fluorescence MicroscopyAbstract: Physiological motion from respiration and cardiac cycles poses significant challenges for fluorescence microscopy of living tissues. Since even small motions can move the cell out of the focal plane, it is particularly difficult to observe the same single cell over time. In this talk, I will first describe the nature of data produced by fluorescence microscopes, discuss key considerations for in vivo measurements, and explain which system parameters we can control to actively correct for motion during and in between image acquisition. I will then present a motion correction approach that leverages the periodicity of physiological motion, specifically under the assumption that a cylindrical structure, such as a blood vessel, is being observed. Finally, I will provide an overview of our research involving shape spaces, outlining how this infinite-dimensional geometric framework relates to the motion problem in microscopy and how we aim to integrate these methods to improve motion correction in the future. |
| 2025 |
14. November |
15:00 |
at Stremayrgasse 16, BMT 03 094, TU Graz |
Richard Huber: The L2-Optimal Discretization of Tomographic Projection OperatorsAbstract: Tomographic inverse problems remain a cornerstone of medical investigations, allowing the visualization of patients' interior features. While the infinite-dimensional operators modeling the measurement process (e.g., the Radon transform) are well understood, in practice, one can only observe finitely many measurements and employ finitely many computations in reconstruction. Thus, proper discretization of these operators is crucial. Different discretization approaches show distinct strengths regarding the approximation quality of the forward- or backward projections. Hence, it is common to employ distinct discretization frameworks for the two said operators, creating a non-adjoint pair of operators. Using such unmatched projection pairs in iterative methods can be problematic, as theoretical convergence guarantees of many iterative methods are based on matched operators. We present a novel theoretical framework for designing an $L^2$-optimal discretization of the forward projection. Curiously, the adjoint of said optimal discretization is the optimal discretization for the backprojection, yielding a matched discretization framework for which both the forward and backward discretization (being the optimal choices) converge, thus eliminating the need for unmatched operator pairs. In the parallel beam case, this optimal discretization is the well-known strip model for discretization, while in the fanbeam case, a novel weighted strip model is optimal. |
| 2025 |
20. October |
14:30 |
at SR 127.11, IDea_Lab |
Muhamed Kuric: The Gaussian Latent Machine: Efficient Prior and Posterior Sampling for Inverse ProblemsAbstract: We consider the problem of sampling from a product-of-experts-type model that encompasses many standard prior and posterior distributions commonly found in Bayesian imaging. We show that this model can be easily lifted into a novel latent variable model, which we refer to as a Gaussian latent machine. This leads to a general sampling approach that unifies and generalizes many existing sampling algorithms in the literature. Most notably, it yields a highly efficient and effective two-block Gibbs sampling approach in the general case, while also specializing to direct sampling algorithms in particular cases. Finally, we present detailed numerical experiments that demonstrate the efficiency and effectiveness of our proposed sampling approach across a wide range of prior and posterior sampling problems from Bayesian imaging. |
