On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations

(3) ON INSTABILITIES OF CONVENTIONAL MULTI-COIL MRI RECONSTRUCTION TO SMALL ADVERSARIAL PERTURBATIONS Chi Zhang, Jinghan Jia, Burhaneddin Yaman, Steen Moeller, Sijia Liu, Mingyi Hong, and Mehmet Akçakaya Electrical and Computer Engineering, and Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States University of Florida, Gainesville, FL, United States MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, United States

[1]  Francesco Renna,et al.  On instabilities of deep learning in image reconstruction and the potential costs of AI , 2019, Proceedings of the National Academy of Sciences.

[2]  Lars Kasper,et al.  CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge , 2020, Magnetic Resonance in Medicine.

[3]  Bo Li,et al.  Improving Robustness of Deep-Learning-Based Image Reconstruction , 2020, ICML.

[4]  Steen Moeller,et al.  Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms , 2020, IEEE Journal of Selected Topics in Signal Processing.

[5]  Jan MacDonald,et al.  Solving Inverse Problems With Deep Neural Networks - Robustness Included? , 2020, ArXiv.

[6]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[7]  Steen Moeller,et al.  Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues , 2019, IEEE Signal Processing Magazine.

[8]  Leslie Ying,et al.  Accelerating magnetic resonance imaging via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[9]  Daniel Rueckert,et al.  A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[10]  Thomas Pock,et al.  Learning a variational network for reconstruction of accelerated MRI data , 2017, Magnetic resonance in medicine.

[11]  Seyed-Mohsen Moosavi-Dezfooli,et al.  DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Michael Elad,et al.  ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA , 2014, Magnetic resonance in medicine.

[13]  Mathews Jacob,et al.  MoDL: Model-Based Deep Learning Architecture for Inverse Problems , 2017, IEEE Transactions on Medical Imaging.

[14]  P. Boesiger,et al.  Advances in sensitivity encoding with arbitrary k‐space trajectories , 2001, Magnetic resonance in medicine.

[15]  Seyed-Mohsen Moosavi-Dezfooli,et al.  Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

[17]  Steen Moeller,et al.  Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data , 2019, Magnetic resonance in medicine.

[18]  Michael Rabbat,et al.  fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. , 2020, Radiology. Artificial intelligence.

[19]  Michael G. Rabbat,et al.  Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge , 2020, Magnetic resonance in medicine.