Transcript
Hello, everyone. As mentioned, I'm going to talk you through the new features and the accuracy of the ExacTrac Dynamic, and these are my disclosures. But first, a little about me. I started working as medical physicist since graduation from Lund University in Sweden. And I gained work experience in radiotherapy from both Sweden and Denmark where I work right now in the Radiotherapy Department. As you can see, it's kind of in front of the main building, on the underground, of course, where we have one ViewRay MRI Linac. We have 12 Varian linacs with 5 of them with the current ExacTrac 6.5 and we're planning to have ExacTrac Dynamic in 4 of our New TrueBeams. One is already installed for our testing and we will have two adaptive Varian "Halcyoms" if this will be the final name of the product.
So, for today's talk, I will be focusing on the new features of the ExacTrac Dynamic, the accuracy between the Cone Beam CT and the ExacTrac Dynamic x-ray, the accuracy between the ExacTrac Dynamic and the surface camera and the x-ray, and the total treatment time for pure Cone Beam CT and pure ExacTrac Dynamic workflow. And of course, all these tests have been performed with static phantom since this product is not CE-labeled yet. And once the system is accepted for clinical use, we will be doing real patient testing.
So, starting with the system, as you maybe know, the system consists of two image-guided tools. The surface camera, which you can see here, and the x-ray, and here are the flat panels for the x-ray. Now, starting with the features, the three main features I'm gonna focus on is the surface camera's filter view, the live tracking with the surface camera and the x-ray, and the size of the x-ray's field of view. Starting with the surface camera, it consists of two cameras, the 3D camera and the thermal camera. And together, they aim to give a 4D surface information.
And for the first step in the workflow, which is the prepositioning, we're just using the 3D surface camera and I'm going to come back to why. But for this test, we wanted to know the camera's field of view to have an understanding where the patient need to be put in the couch for the RTT's work. And we found that it had this period of 26-centimeter radius around the isocenter. And by looking at an illustration about the field of view and the couch position, we can say that the couch has an accepted level for RTT's work, which is good to know.
So back to the prepositioning, as I mentioned before, just the 3D camera. And the reason is that this step, the only information we have is the 3D information from the CT scan. And the step is that in the white-lined contour, it's the CTs...the body created from the CT scan and the red surface is the 3D cameras from the x-ray. And the aim is to match these two against each other. So once you place the phantom or the patient on the couch, you get the translational deviations that you can send automatically to the linac. You get also the rotations that you have manually to correct for. And once these are done, you can see that your...once these limits are...we're not out of limitation anymore so the color turned into green and the surface turned into white. And now, you can confirm your prepositioning.
This moves us to the next step, which is the first x-ray image. And once this is performed and matched in, a surface reference with both thermal and 3D information will be generated. And since at this step we want something with a varying temperature without getting our self into glowing so we used Brainlab's thermal phantom. This phantom consists of five areas where you can define your temperature and vary it.
And with that, we move to the last step in the workflow, which is the live tracking. And it's performed with a surface camera on the x-ray since they are integrated in the same system and it's used to monitor the treatment. So, how it looked like is that to the left, you have the surface information. To the right, you have the x-ray. Below, you have the tracking where the surface tracking is on all the time. And for static field, it's monitored unit trigger, which mean you can decide how often you want to take x-rays. On Arc treatment, the windows are the same. And you see the live tracking, but the x-ray, Gantry angle triggered, which mean, you can decide either if you want to take single image, dual image, or both of that alternatives, depending when the gantry is blocking one of the generators.
So moving to the third feature is that the field of view of the x-ray. And as you maybe know, the current ExacTrac has a 10-by-10-centimeter field of view which limits anatomic information. You can see or maybe limit what you use the system for. With ExacTrac Dynamic, the field of view enlarged to about 18-by-18-centimeter around the isocenter. And for us, this was of interest especially for cranial patients where the target is in the lower part of the brain. And you can see, in the lower image with the current ExacTrac, a combination between this size of the field of view and the patient rotation could be challenging for the automated match with ExacTrac. So that's why we had to erase the area around the target to be able to do the automated matc. With ExacTrac Dynamic where you can see it above, we're hoping that this larger field of view will make it easier for the system to handle the rotations, and maybe that we don't need to erase this much of an area for a quicker step. And here's also a field of view if it was a breast treatment with the isocenter in the [inaudible 00:06:39.743] part of the breast where we can see it in the white with both images.
Another thing we looked at is the accuracy between the cone-beam CT and the ExacTrac dynamics x-ray to have an understanding how good the automated match is. So we did an automated match with ExacTrac and noted the residual deviations for the cone-beam CT. And what we got is that for translationial deviations, there was less than 0.4 millimeter. And for the rotations, there was less than 0.3 degrees. This means that the automated match is accurate.
Another thing we studied was the accuracy between the ExacTrac surface camera and the x-ray and we was interesting to do if the surface camera will recognize that we moved the phantom during the treatment. We wanted to know the correlation between the surface camera and x-ray deviations when we moved the phantom. And for that, we started the treatment, went inside the room, and moved the phantom. And what we got is that since we are out of the lens for the surface camera, the treatment stopped. We got the red error and we got the values for the deviation for the camera and we got two options, either to acquire an x-ray or ignore the surface tracking. Once we acquired an x-ray, we got this deviation that the system tells us we need to match for. And by comparing the x-ray deviations with the surface camera, we can see that, for both the translational and the rotations, they are the same, which means that the x-ray and the surface camera has a good agreement.
And last thing we studied was the total treatment time for pure cone-beam CT. We just positioned the patient, take a cone-beam CT before the treatment, then treat, compared to the ExacTrac Dynamic workflow where you preposition with the 3D camera, you take x-ray before the treatment, and with the treatment, you do an evaluation x-ray for every couch kick, then you have the live tracking. And we repeated that. We started our time when the phantom was in the couch, a head phantom, before the mask was applied, and stopped as soon as the last monitor unit was delivered. And we repeated that for six times. And the mean time for this shows that ExacTrac Dynamic doesn't extend the total treatment time.
So to sum up, we can say that ExacTrac Dynamic offers a larger x-ray field of view which opens for more anatomical information and may be open for treating different treatment areas. And now, with both the surface camera and the x-ray on the same system, it was easier to work with the system and faster. And now, with the combination of two image-guided tools in the same system, I think it will open for improving the treatment and may be open for using the system for other treatment areas such as breast cancer where it can benefit from these combinations.
Another features that has to do with the live tracking is that you're now able to monitor the patient. You're able to learn more and maybe collect intrafraction surface motion data. You're able to study the correlation between the surface deviations and the x-ray deviations for a better understanding of how often you need to take x-rays during the treatment. And the automation part was, of course, easiest to work with and fully integrated with the linac. This means that if you are out of tolerance, you are sure that the treatment will stop until you correct for these deviations.
And with that, I would like to thank Novalis Circle to invite me for this talk. I would like to thank Brainlab for this collaboration. And with a thermal camera, I would like to thank my team, Kristian, Sandra, Nikolaj, Jans Peter, and Mirjana. Thank you.
So, for today's talk, I will be focusing on the new features of the ExacTrac Dynamic, the accuracy between the Cone Beam CT and the ExacTrac Dynamic x-ray, the accuracy between the ExacTrac Dynamic and the surface camera and the x-ray, and the total treatment time for pure Cone Beam CT and pure ExacTrac Dynamic workflow. And of course, all these tests have been performed with static phantom since this product is not CE-labeled yet. And once the system is accepted for clinical use, we will be doing real patient testing.
So, starting with the system, as you maybe know, the system consists of two image-guided tools. The surface camera, which you can see here, and the x-ray, and here are the flat panels for the x-ray. Now, starting with the features, the three main features I'm gonna focus on is the surface camera's filter view, the live tracking with the surface camera and the x-ray, and the size of the x-ray's field of view. Starting with the surface camera, it consists of two cameras, the 3D camera and the thermal camera. And together, they aim to give a 4D surface information.
And for the first step in the workflow, which is the prepositioning, we're just using the 3D surface camera and I'm going to come back to why. But for this test, we wanted to know the camera's field of view to have an understanding where the patient need to be put in the couch for the RTT's work. And we found that it had this period of 26-centimeter radius around the isocenter. And by looking at an illustration about the field of view and the couch position, we can say that the couch has an accepted level for RTT's work, which is good to know.
So back to the prepositioning, as I mentioned before, just the 3D camera. And the reason is that this step, the only information we have is the 3D information from the CT scan. And the step is that in the white-lined contour, it's the CTs...the body created from the CT scan and the red surface is the 3D cameras from the x-ray. And the aim is to match these two against each other. So once you place the phantom or the patient on the couch, you get the translational deviations that you can send automatically to the linac. You get also the rotations that you have manually to correct for. And once these are done, you can see that your...once these limits are...we're not out of limitation anymore so the color turned into green and the surface turned into white. And now, you can confirm your prepositioning.
This moves us to the next step, which is the first x-ray image. And once this is performed and matched in, a surface reference with both thermal and 3D information will be generated. And since at this step we want something with a varying temperature without getting our self into glowing so we used Brainlab's thermal phantom. This phantom consists of five areas where you can define your temperature and vary it.
And with that, we move to the last step in the workflow, which is the live tracking. And it's performed with a surface camera on the x-ray since they are integrated in the same system and it's used to monitor the treatment. So, how it looked like is that to the left, you have the surface information. To the right, you have the x-ray. Below, you have the tracking where the surface tracking is on all the time. And for static field, it's monitored unit trigger, which mean you can decide how often you want to take x-rays. On Arc treatment, the windows are the same. And you see the live tracking, but the x-ray, Gantry angle triggered, which mean, you can decide either if you want to take single image, dual image, or both of that alternatives, depending when the gantry is blocking one of the generators.
So moving to the third feature is that the field of view of the x-ray. And as you maybe know, the current ExacTrac has a 10-by-10-centimeter field of view which limits anatomic information. You can see or maybe limit what you use the system for. With ExacTrac Dynamic, the field of view enlarged to about 18-by-18-centimeter around the isocenter. And for us, this was of interest especially for cranial patients where the target is in the lower part of the brain. And you can see, in the lower image with the current ExacTrac, a combination between this size of the field of view and the patient rotation could be challenging for the automated match with ExacTrac. So that's why we had to erase the area around the target to be able to do the automated matc. With ExacTrac Dynamic where you can see it above, we're hoping that this larger field of view will make it easier for the system to handle the rotations, and maybe that we don't need to erase this much of an area for a quicker step. And here's also a field of view if it was a breast treatment with the isocenter in the [inaudible 00:06:39.743] part of the breast where we can see it in the white with both images.
Another thing we looked at is the accuracy between the cone-beam CT and the ExacTrac dynamics x-ray to have an understanding how good the automated match is. So we did an automated match with ExacTrac and noted the residual deviations for the cone-beam CT. And what we got is that for translationial deviations, there was less than 0.4 millimeter. And for the rotations, there was less than 0.3 degrees. This means that the automated match is accurate.
Another thing we studied was the accuracy between the ExacTrac surface camera and the x-ray and we was interesting to do if the surface camera will recognize that we moved the phantom during the treatment. We wanted to know the correlation between the surface camera and x-ray deviations when we moved the phantom. And for that, we started the treatment, went inside the room, and moved the phantom. And what we got is that since we are out of the lens for the surface camera, the treatment stopped. We got the red error and we got the values for the deviation for the camera and we got two options, either to acquire an x-ray or ignore the surface tracking. Once we acquired an x-ray, we got this deviation that the system tells us we need to match for. And by comparing the x-ray deviations with the surface camera, we can see that, for both the translational and the rotations, they are the same, which means that the x-ray and the surface camera has a good agreement.
And last thing we studied was the total treatment time for pure cone-beam CT. We just positioned the patient, take a cone-beam CT before the treatment, then treat, compared to the ExacTrac Dynamic workflow where you preposition with the 3D camera, you take x-ray before the treatment, and with the treatment, you do an evaluation x-ray for every couch kick, then you have the live tracking. And we repeated that. We started our time when the phantom was in the couch, a head phantom, before the mask was applied, and stopped as soon as the last monitor unit was delivered. And we repeated that for six times. And the mean time for this shows that ExacTrac Dynamic doesn't extend the total treatment time.
So to sum up, we can say that ExacTrac Dynamic offers a larger x-ray field of view which opens for more anatomical information and may be open for treating different treatment areas. And now, with both the surface camera and the x-ray on the same system, it was easier to work with the system and faster. And now, with the combination of two image-guided tools in the same system, I think it will open for improving the treatment and may be open for using the system for other treatment areas such as breast cancer where it can benefit from these combinations.
Another features that has to do with the live tracking is that you're now able to monitor the patient. You're able to learn more and maybe collect intrafraction surface motion data. You're able to study the correlation between the surface deviations and the x-ray deviations for a better understanding of how often you need to take x-rays during the treatment. And the automation part was, of course, easiest to work with and fully integrated with the linac. This means that if you are out of tolerance, you are sure that the treatment will stop until you correct for these deviations.
And with that, I would like to thank Novalis Circle to invite me for this talk. I would like to thank Brainlab for this collaboration. And with a thermal camera, I would like to thank my team, Kristian, Sandra, Nikolaj, Jans Peter, and Mirjana. Thank you.