31 research outputs found
On Krylov Methods for Large Scale CBCT Reconstruction
Krylov subspace methods are a powerful family of iterative solvers for linear
systems of equations, which are commonly used for inverse problems due to their
intrinsic regularization properties. Moreover, these methods are naturally
suited to solve large-scale problems, as they only require matrix-vector
products with the system matrix (and its adjoint) to compute approximate
solutions, and they display a very fast convergence. Even if this class of
methods has been widely researched and studied in the numerical linear algebra
community, its use in applied medical physics and applied engineering is still
very limited. e.g. in realistic large-scale Computed Tomography (CT) problems,
and more specifically in Cone Beam CT (CBCT). This work attempts to breach this
gap by providing a general framework for the most relevant Krylov subspace
methods applied to 3D CT problems, including the most well-known Krylov solvers
for non-square systems (CGLS, LSQR, LSMR), possibly in combination with
Tikhonov regularization, and methods that incorporate total variation (TV)
regularization. This is provided within an open source framework: the
Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox, with the idea
of promoting accessibility and reproducibility of the results for the
algorithms presented. Finally, numerical results in synthetic and real-world 3D
CT applications (medical CBCT and {\mu}-CT datasets) are provided to showcase
and compare the different Krylov subspace methods presented in the paper, as
well as their suitability for different kinds of problems.Comment: submitte
Systematic Evaluation of the Impact of Involuntary Motion in Whole Body Dynamic PET
Involuntary patient motion can happen in dynamic whole body (DWB) PET due to long scanning times, which may cause inaccurate quantification of tissue parameters. To quantify the impact on Patlak parameters, we simulated dynamic data using patient-derived motion fields, systematically introducing the motion at different passes of the dynamic scan, both inter and intra-frame. Estimated parameters are compared against the ground truth. Results show that errors can be large, even for small motion. Caution is advised when quantitatively evaluating DWB-PET images, if any motion has been detected