PMID- 37029184
OWN - NLM
STAT- MEDLINE
DCOM- 20230411
LR  - 20230420
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Apr 7
TI  - Real-time respiratory motion prediction using photonic reservoir computing.
PG  - 5718
LID - 10.1038/s41598-023-31296-2 [doi]
LID - 5718
AB  - Respiration induced motion is a well-recognized challenge in many clinical 
      practices including upper body imaging, lung tumor motion tracking and radiation 
      therapy. In this work, we present a recurrent neural network algorithm that was 
      implemented in a photonic delay-line reservoir computer (RC) for real-time 
      respiratory motion prediction. The respiratory motion signals are quasi-periodic 
      waveforms subject to a variety of non-linear distortions. In this work, we 
      demonstrated for the first time that RC can be effective in predicting short to 
      medium range of respiratory motions within practical timescales. A double-sliding 
      window technology is explored to enable the real-time establishment of an 
      individually trained model for each patient and the real-time processing of 
      live-streamed respiratory motion data. A breathing dataset from a total of 76 
      patients with breathing speeds ranging from 3 to 20 breaths per minute (BPM) is 
      studied. Motion prediction of look-ahead times of 66.6, 166.6, 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 0.025, an average mean absolute 
      error (MAE) of 0.34 mm, an average root mean square error (RMSE) of 0.45 mm, an 
      average therapeutic beam efficiency (TBE) of 94.14% for an absolute error 
      (AE) < 1 mm, and 99.89% for AE < 3 mm. This study demonstrates that real-time RC 
      is an efficient computing framework for high precision respiratory motion 
      prediction.
CI  - (c) 2023. The Author(s).
FAU - Liang, Zhizhuo
AU  - Liang Z
AD  - Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
FAU - Zhang, Meng
AU  - Zhang M
AD  - Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
FAU - Shi, Chengyu
AU  - Shi C
AD  - City of Hope Medical Center, Duarte, CA, 91010, USA.
FAU - Huang, Z Rena
AU  - Huang ZR
AD  - Rensselaer Polytechnic Institute, Troy, NY, 12180, USA. huangz3@rpi.edu.
LA  - eng
PT  - Journal Article
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20230407
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - Motion
MH  - *Respiration
MH  - Algorithms
MH  - Neural Networks, Computer
MH  - *Lung Neoplasms/radiotherapy
MH  - Movement
PMC - PMC10082218
COIS- The authors declare no competing interests.
EDAT- 2023/04/08 06:00
MHDA- 2023/04/11 06:42
PMCR- 2023/04/07
CRDT- 2023/04/07 23:19
PHST- 2021/09/26 00:00 [received]
PHST- 2023/03/09 00:00 [accepted]
PHST- 2023/04/11 06:42 [medline]
PHST- 2023/04/07 23:19 [entrez]
PHST- 2023/04/08 06:00 [pubmed]
PHST- 2023/04/07 00:00 [pmc-release]
AID - 10.1038/s41598-023-31296-2 [pii]
AID - 31296 [pii]
AID - 10.1038/s41598-023-31296-2 [doi]
PST - epublish
SO  - Sci Rep. 2023 Apr 7;13(1):5718. doi: 10.1038/s41598-023-31296-2.