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
16 papers
High-quality Nano-patterning of Oxide Interfaces Using Transferred Gold Mask
Qing Xiao, Yanling Liu, Changjian Ma +7
Complex oxide interfaces, such as and based heterostructures, host rich correlated phenomena with strong potential for advanced device applicat…
A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision
Alessandro T. Gifford, Radoslaw M. Cichy, Thomas Naselaris +1
Now published in Nature Communications DOI: https://doi.org/10.1038/s41467-026-69345-9 Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are boosting comp…
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…
Enabling Ice Core Science on Mars and Ocean Worlds
Alexander G. Chipps, Cassius B. Tunis, Nathan Chellman +3
Ice deposits on Earth provide an extended record of volcanism, planetary climate, and life. On Mars, such a record may extend as far back as tens to hundreds of millions of years (…
Superconducting magnet designs and MRI accessibility: a review
Marina Manso Jimeno, John Thomas Vaughan, Sairam Geethanath
Presently, Magnetic Resonance Imaging (MRI) magnets must deliver excellent magnetic field (B0) uniformity to achieve optimum image quality. Long magnets can satisfy the homogeneity…
The risk of bias in denoising methods
Kendrick Kay
Experimental datasets are growing rapidly in size, scope, and detail, but the value of these datasets is limited by unwanted measurement noise. It is therefore tempting to apply an…
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…
A Self-Decoupled 32 Channel Receive Array for Human Brain Magnetic Resonance Imaging at 10.5T
Nader Tavaf, Russell L. Lagore, Steve Jungst +8
Purpose: Receive array layout, noise mitigation and B0 field strength are crucial contributors to signal-to-noise ratio (SNR) and parallel imaging performance. Here, we investigate…
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…
Bayesian Spatial Models for Voxel-wise Prostate Cancer Classification Using Multi-parametric MRI Data
Jin Jin, Lin Zhang, Ethan Leng +2
Multi-parametric magnetic resonance imaging (mpMRI) plays an increasingly important role in the diagnosis of prostate cancer. Various computer-aided detection algorithms have been…
Transcranial Focused Ultrasound for BOLD fMRI Signal Modulation in Humans
Leo Ai, Jerel K. Mueller, Andrea Grant +2
Transcranial focused ultrasound (tFUS) is an emerging form of non-surgical human neuromodulation that confers advantages over existing electro and electromagnetic technologies by p…
Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data
Søren F. V. Nielsen, Kristoffer H. Madsen, Rasmus Røge +2
Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic res…