22 citations · 65 across the 9 of their papers we have counts for
9 papers
Deep Learning-based Intraoperative MRI Reconstruction
Jon André Ottesen, Tryggve Storas, Svein Are Sirirud Vatnehol +8
Purpose: To evaluate the quality of deep learning reconstruction for prospectively accelerated intraoperative magnetic resonance imaging (iMRI) during resective brain tumor surgery…
A Densely Interconnected Network for Deep Learning Accelerated MRI
Jon Andre Ottesen, Matthan W. A. Caan, Inge Rasmus Groote +1
Objective: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and Methods: A cascading deep learning…
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction
D. Karkalousos, S. Noteboom, H. E. Hulst +2
Machine Learning methods can learn how to reconstruct Magnetic Resonance Images and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physi…
Recurrent Inference Machines as inverse problem solvers for MR relaxometry
E. R. Sabidussi, S. Klein, M. W. A. Caan +5
In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T1 and T2 mapping. The RIM is a neural network framework that learns an iterative inference proc…
Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine
Dimitrios Karkalousos, Kai Lønning, Hanneke E. Hulst +4
Objective: To allow efficient learning using the Recurrent Inference Machine (RIM) for image reconstruction whereas not being strictly dependent on the training data distribution s…
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos +20
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific communi…
i-RIM applied to the fastMRI challenge
Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen +4
We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018). Our approach builds on recent advances in invertible learning to infer models as pres…
A scale space based algorithm for automated segmentation of single shot tagged MRI of shearing deformation
André M. J. Sprengers, Matthan W. A. Caan, Kevin M. Moerman +3
Object This study proposes a scale space based algorithm for automated segmentation of single-shot tagged images of modest SNR. Furthermore the algorithm was designed for analysis…
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
James H Cole, Rudra PK Poudel, Dimosthenis Tsagkrasoulis +4
Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people and deviations from healthy brain ageing have been associated with cogniti…