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Publication Detail
Respiratory motion models built using MR-derived signals and different amounts of MR image data from multi-slice acquisitions
  • Publication Type:
    Conference
  • Authors:
    Tran EH, Eiben B, Wetscherek A, Oelfke U, Meedt G, Hawkes D, McClelland J
  • Publisher:
    ICCR
  • Publication date:
    20/06/2019
  • Published proceedings:
    Proceedings of the 19th International Conference on the Use of Computers in Radiation Therapy
  • Name of conference:
    ICCR, 19th International Conference on the Use of Computers in Radiation Therapy
  • Conference place:
    Montreal, Canada
  • Conference start date:
    17/06/2019
  • Conference finish date:
    20/06/2019
Abstract
MR-Linacs provide 2D cine-MR images capturing respiratory motion before and during radiotherapy treatment. Surrogate-driven respiratory motion models can estimate the 3D motion of tumour and organs-at-risk with high spatio-temporal resolution using surrogate signals extracted from 2D cine-MR images. Our motion modelling framework fits the model to unsorted 2D images producing a correspondence model and a motion-compensated super-resolution reconstruction (MCSR). This study investigates the effect of the training data size used to build the respiratory motion models, since long acquisition and processing times limit their application for MR-guided radiotherapy. Four volunteers were scanned on a 1.5T MR scanner with a 3-minute interleaved multi-slice acquisition of 2x2x10mm3 surrogate and motion images, repeated 10 times: sagittal surrogate images from a fixed location, sagittal and coronal motion images covering the thorax. Two surrogate signals were generated by applying principal component analysis to the deformation fields obtained from registering the surrogate images. For each volunteer we built motion models using data from 1, 3, 5, and 10 repetitions, generating a 2x2x2mm3 MCSR. We reconstructed super-resolution images without motion compensation (no-MCSR) to show the improvement obtained with the models. Visual assessment showed plausible estimated respiratory motion with breath-to-breath variations. We computed intensity profiles along the boundary between diaphragm and lung to assess the image quality of the super-resolution reconstructions. We calculated the mean absolute difference (MAD) between the training motion images and the corresponding model-simulated images, averaged over all images and volunteers. The MCSRs presented sharper intensity profiles than no-MCSRs indicating successful motion compensation. MAD increases with the number of repetitions and without motion compensation (for MCSRs/no-MCSRs: 2.10/2.40 with 1 repetition, 2.33/2.56 with 10 repetitions). Computational times to build the models without GPU implementation ranged from ~30 (1 repetition) to ~380 minutes (10 repetitions). Promising results indicated the feasibility of short acquisition and processing times.
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