of the movement in three dimensions of that target volume. as soon as the target volume that you want to radiate goes outside of that preset window, you can see that radiation beam is automatically turned off. are we going to be solely relying 100% on the ai when it comes to the treatment? or is there some sort of safety mechanism that we have if something goes wrong? yeah, that s a great question. there are always trained operators sitting at this control desk. they re the radiographers. they can be the physicists depending on the complexity of the treatment. so you don t100% rely on it, but what s so cool is that from an efficiency point of view, i showed you the set up in the other room where we set the patient up. you can automate really large parts of the workflow, and that s our intention in the future. what is the actual impact in terms of treatment times with this technology? well, one of the most exciting impacts is the confidence it gives to the clinician and the people deliv
imaging to get soft tissue, crystal clear vision of anatomy in real time. what i d like to show you is what we re doing with that information, so here you can see a lung volume. so the dark area here is the lung. what the mri imaging does is in real time, it takes soft tissue images and reconstruct them in three dimensions. so you re looking here at movement in all dimensions in and out, side to side, backwards and forwards. the machine learning algorithm, and where this really comes into play is as a training phase whereby the patient is set up on the system and the algorithm is watching the delineated target volume. that s the red bit here that the doctors created in the treatment plan. yeah. that s where you want your radiation to deposit all of its dose. you want to avoid the healthy tissue, so the template that s been generated by the algorithm using machine learning is delineated in blue as i ve said, and what your seeing along here at the bottom is a little trace
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Brain metastases treatment with single isocenter radiosurgery including challenges with determining planning target volume (PTV) margins and resulting consequences were assessed by UCLA researchers.