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A 3D-UNET for Gibbs artifact removal from quantitative susceptibility maps

Author:Iyad Ba Gari  Shruti Gadewar  Xingyu Wei  Piyush Maiti  Joshua Boyd  Neda Jahanshad  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1767  

Room Session:Concurrent 1  

Institution:Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC  

ArtifactID: identifying artifacts in low field MRI using deep learning

Author:Marina Manso Jimeno  Keerthi Sravan Ravi  John Thomas Vaughan Jr.  Dotun Oyekunle  Godwin Ogbole  Sairam Geethanath  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1764  

Room Session:Concurrent 1  

Institution:Columbia Magnetic Resonance Research Center (CMRRC)  Columbia University  University College Hospital  

Artificial Intelligence based smart MRI: Towards development of automated workflow for reduction of repeat and reject of scans

Author:PhD, Raghu Prasad  PhD, Harikrishna Rai  Sanket Mali  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1765  

Room Session:Concurrent 1  

Institution:GE Healthcare  

Breath-hold 3D MRCP at 1.5T using Fast 3D mode and a deep learning-based noise-reduction technique

Author:Taku Tajima  Hiroyuki Akai  Koichiro Yasaka  Rintaro Miyo  Masaaki Akahane  Naoki Yoshioka  Shigeru Kiryu  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1775  

Room Session:Concurrent 1  

Institution:International University of Health and Welfare Mita Hospital  International University of Health and Welfare Narita Hospital  The Institute of Medical Science, The University of Tokyo  

Enhancing the Reconstruction quality of Physics-Guided Deep Learning via Holdout Multi-Masking

Author:Burhaneddin Yaman  Seyed Amir Hossein Hosseini  Steen Moeller  Mehmet Akçakaya  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1781  

Room Session:Concurrent 1  

Institution:Center for Magnetic Resonance Research  University of Minnesota  

ENSURE: Ensemble Stein's Unbiased Risk Estimator for Unsupervised Learning

Author:Hemant Kumar Aggarwal  Aniket Pramanik  Mathews Jacob  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1774  

Room Session:Concurrent 1  

Institution:University of Iowa  

Evaluation of Super Resolution Network for Universal Resolution Improvement

Author:Zechen Zhou  Yi Wang  Johannes Peeters  Peter Börnert  Chun Yuan  Marcel Breeuwer  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1777  

Room Session:Concurrent 1  

Institution:MR Clinical Science, Philips Healthcare  MR Clinical Science, Philips Healthcare North America  Philips Research Hamburg  Philips Research North America  Vascular Imaging Lab, University of Washington  

Extending Scan-specific Artifact Reduction in K-space (SPARK) to Advanced Encoding and Reconstruction Schemes

Author:Yamin Arefeen  Onur Beker  Heng Yu  Elfar Adalsteinsson  Berkin Bilgic  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1768  

Room Session:Concurrent 1  

Institution:Athinoula A. Martinos Center for Biomedical Imaging  École Polytechnique Fédérale de Lausanne  Massachusetts Institute of Technology  Tsinghua University  Harvard Medical School  Harvard-MIT Health Sciences and Technology  Institute for Medical Engineering and Science  

Feasibility of Super Resolution Speech RT-MRI using Deep Learning

Author:Prakash Kumar  Yongwan Lim  Krishna Nayak  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1782  

Room Session:Concurrent 1  

Institution:University of Southern California  

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

Author:Chompunuch Sarasaen  Soumick Chatterjee  Fatima Saad  Mario Breitkopf  Andreas Nürnberger  Oliver Speck  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1778  

Room Session:Concurrent 1  

Institution:Center for Behavioral Brain Sciences  Otto von Guericke University  Leibniz Institute for Neurobiology  German Center for Neurodegenerative Disease  

Higher Resolution with Improved Image Quality without Increased Scan Time: Is it possible with MRI Deep Learning Reconstruction?

Author:Hung Do  Carly Lockard  Dawn Berkeley  Brian Tymkiw  Nathan Dulude  Scott Tashman  Garry Gold  Erin Kelly  Charles Ho  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1776  

Room Session:Concurrent 1  

Institution:Canon Medical Systems USA, Inc.  Stanford University  Steadman Philippon Research Institute  The Steadman Clinic  

Improving Deep Learning MRI Super-Resolution for Quantitative Susceptibility Mapping

Author:Antoine Moevus  Mathieu Dehaes  Benjamin De Leener  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1769  

Room Session:Concurrent 1  

Institution:Polytechnique Montréal  Université de Montréal  Ste-Justine Hospital University Centre  University of Montreal  

Learn to Better Regularize in Constrained Reconstruction

Author:Yue Guan  Yudu Li  Xi Peng  Yao Li  Yiping Du  Zhi-Pei Liang  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1772  

Room Session:Concurrent 1  

Institution:Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign  Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University  Mayo Clinic  Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign  

A Marriage of Subspace Modeling with Deep Learning to Enable High-Resolution Dynamic Deuterium MR Spectroscopic Imaging

Author:Yudu Li  Yibo Zhao  Rong Guo  Tao Wang  Yi Zhang  Mathew Chrostek  Walter Low  Xiao-Hong Zhu  Wei Chen  Zhi-Pei Liang  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1770  

Room Session:Concurrent 1  

Institution:University of Illinois at Urbana-Champaign  University of Minnesota  

MRI super-resolution reconstruction: A patient-specific and dataset-free deep learning approach

Author:Yao Sui  Onur Afacan  Ali Gholipour  Simon Warfield  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1779  

Room Session:Concurrent 1  

Institution:Harvard Medical School  Boston Children's Hospital  

Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising using Limited Data

Author:Arjun Desai  Batu Ozturkler  Christopher Sandino  Brian Hargreaves  John Pauly  Akshay Chaudhari  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1773  

Room Session:Concurrent 1  

Institution:Stanford University  

Optical Flow-based Data Augmentation and its Application in Deep Learning Super Resolution

Author:Yu He  Fangfang Tang  Jin Jin  Feng Liu  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1783  

Room Session:Concurrent 1  

Institution:The University of Queensland  Siemens Healthcare  

Removing structured noise from dynamic arterial spin labeling images

Author:Yanchen Guo  Zongpai Zhang  Shichun Chen  Lijun Yin  David Alsop  Weiying Dai  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1771  

Room Session:Concurrent 1  

Institution:Beth Israel Deaconess Medical Center & Harvard Medical School  State University of New York at Binghamton  

An self-supervised deep learning based super-resolution method for quantitative MRI

Author:Muzi Guo  Yuanyuan Liu  Yuxin Yang  Dong Liang  Hairong Zheng  Yanjie Zhu  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1780  

Room Session:Concurrent 1  

Institution:Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences  University of Chinese Academy of Sciences  

Uncertainty estimation for DL-based motion artifact correction in 3D brain MRI

Author:Karsten Sommer  Jochen Keupp  Christophe Schuelke  Oliver Lips  Tim Nielsen  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Data Acquisition & Image Reconstruction  

Session Name:Machine Learning: Removing Artifacts & Improving Resolution  

Program Number:1766  

Room Session:Concurrent 1  

Institution:Philips Research