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Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.

Publication ,  Journal Article
Pham, J; Harris, W; Sun, W; Yang, Z; Yin, F-F; Ren, L
Published in: Phys Med Biol
August 21, 2019

To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking. One phase of a prior 4D-MRI is set as the prior phase, MRIprior. Principal component analysis (PCA) is used to extract three major respiratory deformation modes from the DFMs generated between the prior and remaining phases. VC-MRI at each time-step is considered a deformation of MRIprior, where the DFM is represented as a weighted linear combination of the PCA components. The PCA weightings are solved by minimizing the differences between on-board 2D cine MRI and its corresponding VC-MRI slice. The PCA weightings solved during the initial training period are used to train an ADMLP-NN to predict PCA weightings ahead of time during the prediction period. The predicted PCA weightings are used to build predicted 3D DFM and ultimately, predicted VC-MRIs for 4D target tracking. The method was evaluated using a 4D computerized phantom (XCAT) with patient breathing curves and MRI data from a real liver cancer patient. Effects of breathing amplitude change and ADMLP-NN parameter variations were assessed. The accuracy of the PCA curve prediction was evaluated. The predicted real-time 3D tumor was evaluated against the ground-truth using volume dice coefficient (VDC), center-of-mass-shift (COMS), and target tracking errors. For the XCAT study, the average VDC and COMS for the predicted tumor were 0.92  ±  0.02 and 1.06  ±  0.40 mm, respectively, across all predicted time-steps. The correlation coefficients between predicted and actual PCA curves generated through VC-MRI estimation for the 1st/2nd principal components were 0.98/0.89 and 0.99/0.57 in the SI and AP directions, respectively. The optimal number of input neurons, hidden neurons, and MLP-NN for ADMLP-NN PCA weighting coefficient prediction were determined to be 7, 4, and 10, respectively. The optimal cost function threshold was determined to be 0.05. PCA weighting coefficient and VC-MRI accuracy was reduced for increased prediction-step size. Accurate PCA weighting coefficient prediction correlated with accurate VC-MRI prediction. For the patient study, the predicted 4D tumor tracking errors in superior-inferior, anterior-posterior and lateral directions were 0.50  ±  0.47 mm, 0.40  ±  0.55 mm, and 0.28  ±  0.12 mm, respectively. Preliminary studies demonstrated the feasibility to use VC-MRI and artificial neural networks to predict real-time 3D DFMs of the tumor for 4D target tracking.

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Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

August 21, 2019

Volume

64

Issue

16

Start / End Page

165016

Location

England

Related Subject Headings

  • Time Factors
  • Respiration
  • Principal Component Analysis
  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Magnetic Resonance Imaging, Cine
  • Liver Neoplasms
  • Imaging, Three-Dimensional
  • Humans
 

Citation

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Pham, J., Harris, W., Sun, W., Yang, Z., Yin, F.-F., & Ren, L. (2019). Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study. Phys Med Biol, 64(16), 165016. https://doi.org/10.1088/1361-6560/ab359a
Pham, Jonathan, Wendy Harris, Wenzheng Sun, Zi Yang, Fang-Fang Yin, and Lei Ren. “Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.Phys Med Biol 64, no. 16 (August 21, 2019): 165016. https://doi.org/10.1088/1361-6560/ab359a.
Pham, Jonathan, et al. “Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.Phys Med Biol, vol. 64, no. 16, Aug. 2019, p. 165016. Pubmed, doi:10.1088/1361-6560/ab359a.
Journal cover image

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

August 21, 2019

Volume

64

Issue

16

Start / End Page

165016

Location

England

Related Subject Headings

  • Time Factors
  • Respiration
  • Principal Component Analysis
  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Magnetic Resonance Imaging, Cine
  • Liver Neoplasms
  • Imaging, Three-Dimensional
  • Humans