@mastersthesis{MTMT:34738092, title = {Isotropic 3D sampling and signal reconstruction}, url = {https://m2.mtmt.hu/api/publication/34738092}, author = {Rácz, Gergely Ferenc}, publisher = {Budapest University of Technology and Economics}, unique-id = {34738092}, year = {2022}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835} } @inproceedings{MTMT:31205325, title = {Dynamic PET Reconstruction from Sinogram Data using Neural Networks}, url = {https://m2.mtmt.hu/api/publication/31205325}, author = {Rácz, Gergely Ferenc and Kacsó, Ágota Enikő and Tóth, Márton József and Szirmay-Kalos, László}, booktitle = {Proceedings of the Workshop on the Advances of Information Technology 2020 : WAIT 2020}, unique-id = {31205325}, keywords = {artificial neural network; positron emission tomography; CNN}, year = {2020}, pages = {99-104}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835; Szirmay-Kalos, László/0000-0002-8523-2315} } @inproceedings{MTMT:30717085, title = {Fast and accurate initial parameter fitting in Positron Emission Tomography using Neural Networks}, url = {https://m2.mtmt.hu/api/publication/30717085}, author = {Rácz, Gergely Ferenc and Kacsó, Ágota Enikő and Tóth, Márton József and Tóth, Balázs György}, booktitle = {Proceedings of the Workshop on the Advances of Information Technology: WAIT 2019}, unique-id = {30717085}, year = {2019}, pages = {65-71}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835} } @inproceedings{MTMT:30371042, title = {Controlling TV Regularization with Deep Learning}, url = {https://m2.mtmt.hu/api/publication/30371042}, author = {Tóth, Balázs György and Tóth, Márton József and Kacsó, Ágota Enikő and Rácz, Gergely Ferenc and Szirmay-Kalos, László}, booktitle = {2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018}, doi = {10.1109/NSSMIC.2018.8824545}, unique-id = {30371042}, year = {2018}, pages = {1-5}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835; Szirmay-Kalos, László/0000-0002-8523-2315} } @inproceedings{MTMT:3381208, title = {Enhanced PET Reconstruction with Neural Networks}, url = {https://m2.mtmt.hu/api/publication/3381208}, author = {Rácz, Gergely Ferenc and Kacsó, Ágota Enikő and Tóth, Márton József and Tóth, Balázs György}, booktitle = {IX. magyar számítógépes grafika és geometria konferencia, GRAFGEO 2018}, unique-id = {3381208}, year = {2018}, pages = {125-131}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835} } @article{MTMT:3368783, title = {Cosine-Weighted B-Spline Interpolation on the Face-Centered Cubic Lattice}, url = {https://m2.mtmt.hu/api/publication/3368783}, author = {Rácz, Gergely Ferenc and Csébfalvi, Balázs}, doi = {10.1111/cgf.13437}, journal-iso = {COMPUT GRAPH FORUM}, journal = {COMPUTER GRAPHICS FORUM}, volume = {37}, unique-id = {3368783}, issn = {0167-7055}, abstract = {Cosine-Weighted B-spline (CWB) interpolation [Cse13] has been originally proposed for volumetric data sampled on the Body-Centered Cubic (BCC) lattice. The BCC lattice is well known to be optimal for sampling isotropically band-limited signals above the Nyquist limit. However, the Face-Centered Cubic (FCC) lattice has been recently proven to be optimal for low-rate sampling. The CWB interpolation is a state-of-the-art technique on the BCC lattice, which outperforms, for example, the previously proposed box-spline interpolation in terms of both efficiency and visual quality. In this paper, we show that CWB interpolation can be adapted to the FCC lattice as well, and results in similarly isotropic signal reconstructions as on the BCC lattice.}, year = {2018}, eissn = {1467-8659}, pages = {503-511}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835} } @inproceedings{MTMT:3330835, title = {PET Image Denoising using a Deep Neural Network}, url = {https://m2.mtmt.hu/api/publication/3330835}, author = {Rácz, Gergely Ferenc and Kacsó, Ágota Enikő and Tóth, Márton József and Tóth, Balázs György}, booktitle = {Proceedings of the Workshop on the Advances of Information Technology}, unique-id = {3330835}, abstract = {Neural networks are used in a wide range of image processing applications. They are able to solve complex tasks, e.g., image classification, segmentation and filtering, with good results in a short time. In this paper, we focus on PET (positron-emission tomography) image denoising with deep convolutional neural networks. Inspired by the idea of encoder-decoder networks, here we propose a simple framework with hierarchical topology of convolu- tional and deconvolutional layers and direct connections between the corresponding hierarchy levels. Our network takes advantage of the a priori structural information included in CT images to better restore details. We show that our method is able to considerably reduce the noise in PET reconstructions. For the sake of completeness, we also analyze the PSNR and SSIM image quality metrics for different experimental settings.}, year = {2018}, pages = {110-116}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835} } @CONFERENCE{MTMT:3184353, title = {Optimális BCC rácson mintavételezett 3D jelek folytonos rekonstrukciójának elemzése a frekvenciatartományban}, url = {https://m2.mtmt.hu/api/publication/3184353}, author = {Csébfalvi, Balázs and Rácz, Gergely Ferenc}, booktitle = {KÉPAF 2017: Képfeldolgozók és Alakfelismerők Társaságának 11. országos konferenciája}, unique-id = {3184353}, year = {2017}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835} } @inproceedings{MTMT:3184326, title = {Designing Interpolation Filters for Isotropic Tomographic Reconstruction}, url = {https://m2.mtmt.hu/api/publication/3184326}, author = {Rácz, Gergely Ferenc and Csébfalvi, Balázs}, booktitle = {Proceedings of the Workshop on the Advances of Information Technology: WAIT 2017}, unique-id = {3184326}, year = {2017}, pages = {74-81}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835} } @article{MTMT:3120829, title = {Retailoring Box Splines to Lattices for Highly Isotropic Volume Representations}, url = {https://m2.mtmt.hu/api/publication/3120829}, author = {Csébfalvi, Balázs and Rácz, Gergely Ferenc}, doi = {10.1111/cgf.12917}, journal-iso = {COMPUT GRAPH FORUM}, journal = {COMPUTER GRAPHICS FORUM}, volume = {35}, unique-id = {3120829}, issn = {0167-7055}, abstract = {3D box splines are defined by convolving a 1D box function with itself along different directions. In volume visualization, box splines are mainly used as reconstruction kernels that are easy to adapt to various sampling lattices, such as the Cartesian Cubic (CC), Body-Centered Cubic (BCC), and Face-Centered Cubic (FCC) lattices. The usual way of tailoring a box spline to a specific lattice is to span the box spline by exactly those principal directions that span the lattice itself. However, in this case, the preferred directions of the box spline and the lattice are the same, amplifying the anisotropic effects of each other. This leads to an anisotropic volume representation with strongly preferred directions. Therefore, in this paper, we retailor box splines to lattices such that the sets of vectors that span the box spline and the lattice are disjoint sets. As the preferred directions of the box spline and the lattice compensate each other, a more isotropic volume representation can be achieved. We demonstrate this by comparing different combinations of box splines and lattices concerning their anisotropic behavior in tomographic reconstruction and volume visualization.}, keywords = {RECONSTRUCTION; BCC; CENTERED-CUBIC LATTICE; INTERPOLATORS}, year = {2016}, eissn = {1467-8659}, pages = {411-420}, orcid-numbers = {Rácz, Gergely Ferenc/0000-0002-8280-5835} }