no code implementations • 1 Apr 2024 • Jun Lyu, Chen Qin, Shuo Wang, Fanwen Wang, Yan Li, Zi Wang, Kunyuan Guo, Cheng Ouyang, Michael Tänzer, Meng Liu, Longyu Sun, Mengting Sun, Qin Li, Zhang Shi, Sha Hua, Hao Li, Zhensen Chen, Zhenlin Zhang, Bingyu Xin, Dimitris N. Metaxas, George Yiasemis, Jonas Teuwen, Liping Zhang, Weitian Chen, Yidong Zhao, Qian Tao, Yanwei Pang, Xiaohan Liu, Artem Razumov, Dmitry V. Dylov, Quan Dou, Kang Yan, Yuyang Xue, Yuning Du, Julia Dietlmeier, Carles Garcia-Cabrera, Ziad Al-Haj Hemidi, Nora Vogt, Ziqiang Xu, Yajing Zhang, Ying-Hua Chu, Weibo Chen, Wenjia Bai, Xiahai Zhuang, Jing Qin, Lianmin Wu, Guang Yang, Xiaobo Qu, He Wang, Chengyan Wang
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
no code implementations • 15 Mar 2024 • George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen
Accelerating dynamic MRI is essential for enhancing clinical applications, such as adaptive radiotherapy, and improving patient comfort.
no code implementations • 27 Nov 2023 • George Yiasemis, Nikita Moriakov, Clara I. Sánchez, Jan-Jakob Sonke, Jonas Teuwen
In this paper, we introduce JSSL (Joint Supervised and Self-supervised Learning), a novel training approach for deep learning-based MRI reconstruction algorithms aimed at enhancing reconstruction quality in scenarios where target dataset(s) containing fully sampled k-space measurements are unavailable.
no code implementations • 10 Oct 2023 • George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
In this work, inspired by related work in accelerated MRI reconstruction, we present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of dynamic cardiac imaging.
no code implementations • 18 Sep 2023 • George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
In this study, we propose vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems), a novel DL-based method for solving ill-posed inverse problems arising in MI.
no code implementations • 20 Jan 2023 • George Yiasemis, Clara I. Sánchez, Jan-Jakob Sonke, Jonas Teuwen
This work investigates the impact of the $k$-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models.
3 code implementations • CVPR 2022 • George Yiasemis, Jan-Jakob Sonke, Clarisa Sánchez, Jonas Teuwen
Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours.
1 code implementation • 17 Aug 2021 • George Yiasemis, Chaoping Zhang, Clara I. Sánchez, Jan-Jakob Sonke, Jonas Teuwen
In spite of its extensive adaptation in almost every medical diagnostic and examinatorial application, Magnetic Resonance Imaging (MRI) is still a slow imaging modality which limits its use for dynamic imaging.
1 code implementation • 10 Nov 2020 • Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos, Nikita Moriakov, Mattha Caan, George Yiasemis, Lívia Rodrigues, Alexandre Lopes, Hélio Pedrini, Letícia Rittner, Maik Dannecker, Viktor Studenyak, Fabian Gröger, Devendra Vyas, Shahrooz Faghih-Roohi, Amrit Kumar Jethi, Jaya Chandra Raju, Mohanasankar Sivaprakasam, Mike Lasby, Nikita Nogovitsyn, Wallace Loos, Richard Frayne, Roberto Souza
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process.