Research project In general, most bio-imaging (imaging resulting in images that represent actual biological quantities, e.g., perfusion) is limited by noise, resolution, motion artifacts etc., but at the same time heavily oversampled with respect to the information relevant for the actual purpose.
This project aims to develop new statistical and computational methodology for intelligent data sampling and uncertainty analysis of MRI and PET measurements. More specifically, statistical spatiotemporal models to characterise stochastic noise in parametric imaging based on MRI and PET will be developed, and intelligent data sampling based on Compressed Sensing will be investigated. The focus will be on the statistical and computational challenges of uncertainty analysis and error versus speed optimisation for high-dimensional data.
Holger Rauhut (Department of Mathematics, RWTH Aachen University, Germany)
Ida Häggström (Chalmers University of Technology)
Zhiyong Zhou (former Postdoc)
Fekadu Bayisa (former PhD student)
Jianfeng Wang (former PhD student)
In general, most bio-imaging (imaging resulting in images that represent actual biological quantities, e.g., perfusion) is limited by
noise, resolution, motion artifacts etc., but at the same time heavily oversampled with respect to the information relevant for the
actual purpose.
The purpose of this project is to develop new statistical and computational methodology for intelligent data sampling and uncertainty analysis of MRI and PET measurements. More specific, statistical spatiotemporal models to characterize stochastic noise in parametric imaging based on MRI and PET will be developed and, for the same techniques, intelligent data sampling based on Compressed Sensing will be investigated. Focus will be on the statistical and computational challenges arising from uncertainty analysis and error versus speed optimization for high-dimensional data.
This project should contribute to the general understanding of optimised data sampling in bio-imaging and to efficient noise reduction for improved quality of the estimated parametric images. When applied in therapy response imaging this project should result in significantly shorter imaging time and more reliable quantitative information which are two important steps in bringing bio-imaging towards a more widespread clinical use.
