PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction

19 June 2023

Papers citing "PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction"

6 / 6 papers shown

Title
PINNing Cerebral Blood Flow: Analysis of Perfusion MRI in Infants using Physics-Informed Neural Networks C. Galazis Ching-En Chiu Tomoki Arichi Anil A. Bharath Marta Varela 27 0 0 11 Oct 2024
PINNs for Medical Image Analysis: A Survey C. Banerjee Kien Nguyen Olivier Salvado Truyen Tran Clinton Fookes AI4CE 29 1 0 02 Aug 2024
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps F. Zimmermann A. Kofler 32 1 0 27 Sep 2023
Cramér-Rao bound-informed training of neural networks for quantitative MRI Xiaoxia Zhang Quentin Duchemin Kangning Liu Sebastian Flassbeck Cem Gultekin C. Fernandez‐Granda Jakob Assländer 24 19 0 22 Sep 2021
An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction A. Kofler Markus Haltmeier T. Schaeffter C. Kolbitsch 3DV 22 24 0 01 Feb 2021
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 221 1,399 0 04 Dec 2018