Nikolas Löw | Searcharxiv

arXiv2023

Simulation Study on Super-Resolution for Coded Aperture Gamma Imaging

Coded Aperture Imaging (CAI) has been proposed as an alternative collimation technique in nuclear imaging. To maximize spatial resolution small pinholes in the coded aperture mask are required. However, a high-resolution detector is needed to correctly sample the point spread function (PSF) to keep the Nyquist-Shannon sampling theorem satisfied. The disadvantage of smaller pixels, though, is the resulting higher Poisson noise. Thus, the aim of this paper was to investigate if sufficiently accurate CAI reconstruction is achievable with a detector which undersamples the PSF. With the Monte Carlo simulation framework TOPAS a test image with multiple spheres of different diameter was simulated based on the setup of an experimental gamma camera from previous work. Additionally, measured phantom data were acquired. The captured detector images were converted to low-resolution images of different pixel sizes according to the super-resolution factor $k$. Multiple analytical reconstruction methods and a Machine Learning approach were compared based on the contrast-to-noise ratio (CNR). We show, that all reconstruction methods are able to reconstruct both the test image and the measured phantom data for $k \leq 7$. With a synthetic high-resolution PSF and upsampling the simulated low-resolution detector image by bilinear interpolation the CNR can be kept approximately constant. Results of this simulation study and additional validation on measured phantom data indicate that an undersampling detector can be combined with small aperture holes. However, further experiments need to be conducted.

physics.med-ph↗

arXiv2023

Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks

Point cloud registration is a fundamental problem in computer vision that aims to estimate the transformation between corresponding sets of points. Non-rigid registration, in particular, involves addressing challenges including various levels of deformation, noise, outliers, and data incompleteness. This paper introduces Robust-DefReg, a robust non-rigid point cloud registration method based on graph convolutional networks (GCNNs). Robust-DefReg is a coarse-to-fine registration approach within an end-to-end pipeline, leveraging the advantages of both coarse and fine methods. The method learns global features to find correspondences between source and target point clouds, to enable appropriate initial alignment, and subsequently fine registration. The simultaneous achievement of high accuracy and robustness across all challenges is reported less frequently in existing studies, making it a key objective of the Robust-DefReg method. The proposed method achieves high accuracy in large deformations while maintaining computational efficiency. This method possesses three primary attributes: high accuracy, robustness to different challenges, and computational efficiency. The experimental results show that the proposed Robust-DefReg holds significant potential as a foundational architecture for future investigations in non-rigid point cloud registration. The source code of Robust-DefReg is available.

cs.CV↗