Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations

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.

Experimental Design MRI Reconstruction

ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging

1 code implementation • 30 Apr 2024 • Dimitrios Karkalousos, Ivana Išgum, Henk A. Marquering, Matthan W. A. Caan

We benchmark 25 DL models on eight publicly available datasets to present distinct applications of ATOMMIC on accelerated MRI reconstruction, image segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and image segmentation utilizing MTL.

Benchmarking Image Segmentation +3

i-RIM applied to the fastMRI challenge

1 code implementation • 20 Oct 2019 • Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan Caan, Max Welling

We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018).

Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine

1 code implementation • 14 Dec 2020 • Dimitrios Karkalousos, Kai Lønning, Hanneke E. Hulst, Serge O. Dumoulin, Jan-Jakob Sonke, Frans M. Vos, Matthan W. A. Caan

The IndRNN is an efficient recurrent unit, reducing inference time by 68\% compared to CS, whereas maintaining performance.

MRI Reconstruction

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

3 code implementations • 9 Dec 2020 • Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh, Sunwoo Kim, Geunu Jeong, Jingyu Ko, Yohan Jun, Hyungseob Shin, Dosik Hwang, Mahmoud Mostapha, Simon Arberet, Dominik Nickel, Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck, Jonas Teuwen, Dimitrios Karkalousos, Chaoping Zhang, Anuroop Sriram, Zhengnan Huang, Nafissa Yakubova, Yvonne Lui, Florian Knoll

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.

BIG-bench Machine Learning MRI Reconstruction +1