| CNN-based superresolution reconstruction of 3D MR images using thick-slice scans J Jurek, M Kociński, A Materka, M Elgalal, A Majos Biocybernetics and Biomedical Engineering 40 (1), 111-125, 2020 | 41 | 2020 |
| Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate-and high-risk prostate cancer patients A Losnegård, LAR Reisæter, OJ Halvorsen, J Jurek, J Assmus, JB Arnes, ... Acta Radiologica 61 (11), 1570-1579, 2020 | 35 | 2020 |
| Supervised denoising of diffusion-weighted magnetic resonance images using a convolutional neural network and transfer learning J Jurek, A Materka, K Ludwisiak, A Majos, K Gorczewski, K Cepuch, ... Biocybernetics and Biomedical Engineering 43 (1), 206-232, 2023 | 8 | 2023 |
| CNN-Based Quantification of Blood Vessels Lumen in 3D Images A Materka, J Jurek, M Kocinski, A Klepaczko International Conference on Computational Science, 653-661, 2023 | 3 | 2023 |
| Super-resolution Reconstruction of Three-dimensional Magnetic Resonance Images Using Deep and Transfer Learning J Jurek | 3 | 2020 |
| Reconstruction of high-resolution t2W mr images of the prostate using maximum a posteriori approach and Markov random field regularization J Jurek, M Kociński, A Materka, A Losnegård, L Reisæter, OJ Halvorsen, ... 2017 Signal Processing: Algorithms, Architectures, Arrangements, and …, 2017 | 3 | 2017 |
| Phase Correction and Noise-to-Noise Denoising of Diffusion Magnetic Resonance Images Using Neural Networks J Jurek, A Materka, K Ludwisiak, A Majos, F Szczepankiewicz International Conference on Computational Science, 638-652, 2023 | 2 | 2023 |
| Dictionary-based through-plane interpolation of prostate cancer t2-weighted mr images J Jurek, M Kociński, A Materka, A Losnegård, L Reisæter, OJ Halvorsen, ... 2018 Signal Processing: Algorithms, Architectures, Arrangements, and …, 2018 | 2 | 2018 |
| Rule-based data-driven approach for computer aided diagnosis of the peripheral zone prostate cancer from multiparametric MRI: Proof of concept J Jurek, M Kociński, A Materka, A Losnegård, L Reisæter, OJ Halvorsen, ... 2017 Signal Processing: Algorithms, Architectures, Arrangements, and …, 2017 | 2 | 2017 |
| Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images A Materka, J Jurek Sensors 24 (3), 846, 2024 | | 2024 |
| Modeling Blood Vessels Lumen from 3D Images with the Use of Deep Learning and B-Splines A Materka, J Jurek Preprints, 2023 | | 2023 |
| Improving the Resolution and SNR of Diffusion Magnetic Resonance Images From a Low-Field Scanner J Jurek, K Ludwisiak, A Materka, F Szczepankiewicz Polish Conference on Biocybernetics and Biomedical Engineering, 147-160, 2023 | | 2023 |
| On the effect of DCE MRI slice thickness and noise on estimated pharmacokinetic biomarkers–a simulation study J Jurek, L Reisæter, M Kociński, A Materka International Conference on Computer Vision and Graphics, 72-86, 2020 | | 2020 |
| CRF-Based Clustering of Pharmacokinetic Curves from Dynamic Contrast-Enhanced MR Images J Jurek, M Pelesz, A Losnegård, L Reisæter, A Wojciechowski, ... 2018 Signal Processing: Algorithms, Architectures, Arrangements, and …, 2018 | | 2018 |
| materka. p. lodz. pl/dydaktyka. html A Materka, J Jurek | | |
| Phantom-based simulation of diffusion-weighted magnetic resonance images J Jurek, E Andersen, J Monssen, LAR Reisæter, M Kocinski, A Materka | | |
| Super-resolution Reconstruction of Prostate Cancer MRI using deep and transfer learning J Jurek, LA Reisæter, M Kocinski, A Losnegard, A Materka, A Lundervold | | |
