| A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise C Millard, M Chiew IEEE transactions on computational imaging 9, 707-720, 2023 | 41* | 2023 |
| Approximate message passing with a colored aliasing model for variable density Fourier sampled images C Millard, AT Hess, B Mailhé, J Tanner IEEE Open Journal of Signal Processing 1, 146-158, 2020 | 20 | 2020 |
| An approximate message passing algorithm for rapid parameter-free compressed sensing MRI C Millard, AT Hess, B Mailhe, J Tanner 2020 IEEE International Conference on Image Processing (ICIP), 91-95, 2020 | 13 | 2020 |
| Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDU C Millard, M Chiew Bioengineering 11 (12), 1305, 2024 | 5* | 2024 |
| Deep plug-and-play multi-coil compressed sensing MRI with matched aliasing: The denoising-P-VDAMP algorithm C Millard, A Hess, J Tanner, B Mailhe Proc. Annu. Meeting ISMRM, 1-9, 2022 | 3 | 2022 |
| Tuning-free multi-coil compressed sensing MRI with parallel variable density approximate message passing (P-VDAMP) C Millard, M Chiew, J Tanner, AT Hess, B Mailhe arXiv preprint arXiv:2203.04180, 2022 | 2 | 2022 |
| Approximate message passing for compressed sensing magnetic resonance imaging C Millard University of Oxford, 2021 | 2 | 2021 |
| Reconstruction with magnetic resonance compressed sensing B Mailhe, C Millard, MS Nadar US Patent 12,086,908, 2024 | 1 | 2024 |
| Image reconstruction using a colored noise model with magnetic resonance compressed sensing C Millard, B Mailhe, MS Nadar US Patent 11,035,919, 2021 | 1 | 2021 |
| Near-optimal tuning-free multicoil compressed sensing MRI with Parallel Variable Density Approximate Message Passing C Millard, AT Hess, J Tanner, B Mailhe 2021 ISMRM annual meeting, 2021 | 1 | 2021 |
| Joint Multi-Contrast Image Reconstruction with Self-Supervised Learning BT Kadota, C Millard, M Chiew | | |
| Simultaneous self-supervised reconstruction and denoising for low SNR, sub-sampled training data with Robust SSDU C Millard, M Chiew | | |
| Using Noisier2Noise to choose the sampling mask partition of Self-Supervised Learning via Data Undersampling (SSDU) C Millard, M Chiew | | |
| A self-supervised method for recovering clean images from noisy, sub-sampled training examples C Millard Northern Lights Deep Learning Conference Abstracts 2024, 0 | | |
| Versatile Parameter-Free Compressed Sensing MRI with Approximate Message Passing C Millard, AT Hess, B Mailhé, J Tanner | | |
Mark ChiewAssociate Professor, University of TorontoZweryfikowany adres z utoronto.ca