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Objective: For real-time markerless tumour tracking in stereotactic lungradiotherapy, we propose a different approach which uses patient-specific deeplearning (DL) using a personalized data generation strategy, avoiding the needfor collection of a large patient data set. We validated our strategy withdigital phantom simulation and epoxy phantom studies.  Methods: We developed lung tumour tracking for radiotherapy using aconvolutional neural network trained for each phantom's lesion by usingmultiple digitally reconstructed radiographs (DRRs) generated from eachphantom's treatment planning 4D-CT. We trained tumour-bone differentiationusing large numbers of training DRRs generated with various projectiongeometries to simulate tumour motion. We solved the problem of using DRRs fortraining and X-ray images for tracking by using the training DRRs with randomcontrast transformation and random noise addition.  Results: We defined adequate tracking accuracy as the % frames satisfying < 1mm tracking error of the isocentre. In the simulation study, we achieved 100%tracking accuracy in 3-cm spherical and 1.5 x 2.25 x 3-cm ovoid masses. In thephantom study, we achieved 100% and 94.7% tracking accuracy in 3- and 2-cmspherical masses, respectively. This required 32.5 ms/frame (30.8 fps)real-time processing.  Conclusions: We proved the potential feasibility of a real-time markerlesstumour tracking framework for stereotactic lung radiotherapy based onpatient-specific DL with personalized data generation with digital phantom andepoxy phantom studies.  Advances in Knowledge: Using DL with personalized data generation is anefficient strategy for real-time lung tumour tracking.

the british journal of radiology/british journal of radiology

Real-time markerless tumour tracking with patient-specific deep learning  using a personalized data generation strategy: Proof of concept by phantom  study