83 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of MinnesotaUS11 papers
- Bernstein Center for Computational Neuroscience BerlinDE1 paper
- Beth Israel Deaconess Medical CenterUS1 paper
- Charité - Universitätsmedizin BerlinDE1 paper
- Cognitive Neuroimaging LabFR1 paper
- Columbia UniversityUS1 paper
- Copenhagen University HospitalDK1 paper
- Desert Research InstituteUS1 paper
- Einstein Center for Neurosciences BerlinDE1 paper
- Freie Universität BerlinDE1 paper
- Georgia Institute of TechnologyUS1 paper
- Hamad Medical CorporationQA1 paper
7 papers · 1 filter
Machine-agnostic Automated Lumbar MRI Segmentation using a Cascaded Model Based on Generative Neurons
Promit Basak, Rusab Sarmun, Saidul Kabir +5
Automated lumbar spine segmentation is very crucial for modern diagnosis systems. In this study, we introduce a novel machine-agnostic approach for segmenting lumbar vertebrae and…
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement
Mehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung +1
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with…
Anisotropic field-of-views in radial imaging
Peder E. Z. Larson, Paul T. Gurney, Dwight G. Nishimura
Radial imaging techniques, such as projection-reconstruction (PR), are used in magnetic resonance imaging (MRI) for dynamic imaging, angiography, and short-imaging. They are robust…
Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMR
Burhaneddin Yaman, Chetan Shenoy, Zilin Deng +4
Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard for diagnosis of myocardial scar. 3D isotropic LGE CMR provides improved coverage and resolution compar…
Improved Supervised Training of Physics-Guided Deep Learning Image Reconstruction with Multi-Masking
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller +1
Physics-guided deep learning (PG-DL) via algorithm unrolling has received significant interest for improved image reconstruction, including MRI applications. These methods unroll a…
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI
Burhaneddin Yaman, Hongyi Gu, Seyed Amir Hossein Hosseini +5
Self-supervised learning has shown great promise due to its capability to train deep learning MRI reconstruction methods without fully-sampled data. Current self-supervised learnin…
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks
Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller +1
Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularize…