Respiratory Motion Prediction Using Fusion-Based Multi-Rate Kalman Filtering and Real-Time Golden-Angle Radial MRI

2020 ◽  
Vol 67 (6) ◽  
pp. 1727-1738 ◽  
Author(s):  
Xinzhou Li ◽  
Yu-Hsiu Lee ◽  
Samantha Mikaiel ◽  
James Simonelli ◽  
Tsu-Chin Tsao ◽  
...  
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Respiratory Motion ◽  
Motion Prediction ◽  
Golden Angle ◽  
Radial Mri ◽  
Golden Angle Radial

A multiple model approach to respiratory motion prediction for real-time IGRT

2008 ◽  
Vol 53 (6) ◽  
pp. 1651-1663 ◽  
Author(s):  
Devi Putra ◽  
Olivier C L Haas ◽  
John A Mills ◽  
Keith J Burnham
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Motion Prediction ◽  
Multiple Model ◽  
Model Approach

scholarly journals Feasibility of real‐time cardiac MRI in mice using tiny golden angle radial sparse

2020 ◽  
Vol 33 (7) ◽  
Author(s):  
Hao Li ◽  
Patrick Metze ◽  
Alireza Abaei ◽  
Wolfgang Rottbauer ◽  
Steffen Just ◽  
...  
Keyword(s):  
Real Time ◽  
Cardiac Mri ◽  
Golden Angle ◽  
Golden Angle Radial

scholarly journals Real-time exercise stress cardiac MRI with Fourier-series reconstruction from golden-angle radial data

2021 ◽  
Vol 75 ◽  
pp. 89-99
Author(s):  
Yu Y. Li ◽  
Pengyue Zhang ◽  
Shams Rashid ◽  
Yang J. Cheng ◽  
Wenhui Li ◽  
...  
Keyword(s):  
Fourier Series ◽  
Real Time ◽  
Cardiac Mri ◽  
Exercise Stress ◽  
Golden Angle ◽  
Golden Angle Radial

scholarly journals Dynamic Imaging of the Eye, Optic Nerve, and Extraocular Muscles With Golden Angle Radial MRI

2017 ◽  
Vol 58 (10) ◽  
pp. 4390 ◽  
Author(s):  
Saikat Sengupta ◽  
David S. Smith ◽  
Alex K. Smith ◽  
E. Brian Welch ◽  
Seth A. Smith
Keyword(s):  
Optic Nerve ◽  
Extraocular Muscles ◽  
Dynamic Imaging ◽  
Golden Angle ◽  
Radial Mri ◽  
Golden Angle Radial

scholarly journals PO-0925: Respiratory motion prediction filters for real time tumor tracking during radiation treatment

2015 ◽  
Vol 115 ◽  
pp. S481-S482
Author(s):  
A. Jöhl ◽  
M. Schmid Daners ◽  
S. Ehrbar ◽  
M. Guckenberger ◽  
S. Klöck ◽  
...  
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Radiation Treatment ◽  
Motion Prediction ◽  
Tumor Tracking

scholarly journals Towards real-time respiratory motion prediction based on long short-term memory neural networks

2019 ◽  
Vol 64 (8) ◽  
pp. 085010 ◽  
Author(s):  
Hui Lin ◽  
Chengyu Shi ◽  
Brian Wang ◽  
Maria F Chan ◽  
Xiaoli Tang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Respiratory Motion ◽  
Short Term Memory ◽  
Motion Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Photonic Reservoir Computing for Real-Time Respiratory Motion Prediction

2021 ◽  
Author(s):  
Zhizhuo Liang ◽  
Meng Zhang ◽  
Chengyu Shi ◽  
Z. Rena Huang
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Absolute Error ◽  
Motion Prediction ◽  
Reservoir Computing ◽  
Data Set ◽  
Real Time Processing ◽  
The Real ◽  
Look Ahead ◽  
Clinical Radiotherapy

Abstract The application of reservoir computing (RC) is for the first time studied in a class of forecasting tasks in which signals are under random physical perturbations, meaning that the data-baring waveform distortions are versatile, and the process is not repeatable. Tumor movement caused by respiratory motion is such a problem and real-time prediction of tumor motion is required by the clinical radiotherapy. In this work, a true-time delay (TTD) respiration monitor based on photonic RC with adjustable nodes connection is developed specifically for this task. A breathing data set from a total of 76 patients with breathing speeds ranging from 3 to 20 breath per minute (BPM) are studied. A double-sliding window technology is demonstrated to enable the real-time establishment of an individually trained model for each patient and the real-time processing of live-streamed tumor position data. Motion prediction of look-ahead times of 66.6 ms, 166.6 ms and 333 ms are investigated. With a 333 ms look-ahead time, the real-time RC model achieves an average normalized mean square error (NMSE) of 0.0246, an average mean absolute error (MAE) of 0.338 mm, an average therapeutic beam exposure efficiency of 94.14% for an absolute error (AE) < 1mm and 99.89% for AE < 3mm. This study demonstrates that real-time RC is an efficient computing framework for high precision respiratory motion prediction.

scholarly journals Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications

2016 ◽  
Vol 38 (8) ◽  
pp. 749-757 ◽  
Author(s):  
Sivanagaraja Tatinati ◽  
Kianoush Nazarpour ◽  
Wei Tech Ang ◽  
Kalyana C. Veluvolu
Keyword(s):  
Real Time ◽  
Respiratory Motion ◽  
Adaptive Radiotherapy ◽  
Motion Prediction
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