|Ahead of print publication
Implementation of a visual feedback system for motion management during radiation therapy
Jamema Swamidas1, John Rose2, Supriya Chopra1, Siji N Paul1, Kishore Joshi1, Subhajit Panda1, Reena Ph1, Jai Prakash Agarwal3
1 Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Mumbai, Maharashtra, India
2 Department of Computer Engineering, Xavier Institute of Engineering, St. Xavier's Technical Institute, Mumbai, Maharashtra, India
3 Department of Radiation Oncology, Tata Memorial Centre, Mumbai, Maharashtra, India
Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Navi Mumbai - 410 210, Maharashtra
Source of Support: None, Conflict of Interest: None
Purpose: To describe the details of an in-house video goggles feedback system assembled from several commercially available components. The objective of this paper is to share our experience with this system, provide details on the equipment needed, system assembly, patient set up and user settings on some components.
Materials and Methods: The system consisted of goggles (FPView3DHD, ITV, USA), RJ45(Registered Jack) to Digital Visual Interface (DVI) converter (Tripplite), DVI to HDMI converters, Local Area Network(LAN) cable, HDMI and power extender cables. The video coaching system was implemented both in CT simulator (GE Discovery)) and in treatment delivery machine True Beam v2.1 Varian Medical Systems (VMS, Palo Alto), which was integrated with respiratory motion management (RPM V 1.7.5) system.
Results: The video feedback system is in clinical use since Aug 2017, so far, we have treated 13 patients, with approximately 150 fractions. The performance of the device was found to be satisfactory. All the patients were coached for DIBH and the usage of the goggles, which includes wearing the goggles, display details of the monitor, and the threshold levels of the breathing wave cycle. The patients understand the instructions very well and hence regulate the breathing cycle, which improves the treatment accuracy and efficiency.
Conclusion: Video feedback system for motion management, for patients undergoing radiotherapy was implemented successfully both in CT simulator and in linear accelerator.
Keywords: Deep inspiration breadth hold (DIBH), motion management, visual feedback system
|How to cite this URL:|
Swamidas J, Rose J, Chopra S, Paul SN, Joshi K, Panda S, Ph R, Agarwal JP. Implementation of a visual feedback system for motion management during radiation therapy. J Can Res Ther [Epub ahead of print] [cited 2021 Jan 18]. Available from: https://www.cancerjournal.net/preprintarticle.asp?id=270101
| > Introduction|| |
Respiratory gating and breath-hold techniques are commonly used in radiation therapy (RT) to reduce the internal motion of the organs and hence margins needed for Planning target volume (PTV) or to move out critical structures outside the radiation beam. This, thereby, allows favorable sparing of adjacent surrounding normal tissues and to limit the tumor motion caused by the patient's breathing. In our hospital, Deep Inspiratory Breath-Hold (DIBH)-based conventional/stereotactic radiation has been the preferred form of treatment execution for breast cancer, large inoperable hepatocellular carcinoma and mediastinal lymphomas, and renal cell carcinoma. DIBH for left-sided breast cancer has been reported to reduce the cardiac doses. Similarly, cardiac and lung sparing can also be achieved while treating mediastinal lymphomas with DIBH. While treating gastrointestinal or renal tumors with stereotactic radiation, DIBH minimizes organ motion (e.g heart, liver, kidney) and facilitates delivery of high dose per fraction while minimizing dose to adjacent critical structures (such as normal liver and duodenum).
The Department of Medical Physics/Radiation Oncology at the Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, initiated the DIBH program for the aforementioned sites in 2016. Patients were trained to hold their breath in deep inspiration and were advised to hold the breath for 20–25 s. In the first phase of DIBH execution, at our institution, we did not have access to visual respiratory trace biofeedback; hence, patient immobilization was used as a feedback system. However, it was found that while patients were able to maintain breath-hold, the amplitude of breath-hold may not be consistent, thereby leading to increase in DIBH acceptable threshold window (7–9 mm), which could translate to larger PTV margin [Figure 1]. Therefore, we decided to initiate a visual biofeedback system to allow real-time visual feedback that will improve the consistency of amplitude of breath-hold, which would translate not only in improved accuracy of respiratory immobilization but also to reduce the need of repeated instructions by technicians and thereby improve machine throughput.
|Figure 1: Patient waveform in deep inspiratory breath-hold showing a 5-mm margin for the waveform at the treatment planning system|
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It is to be noted that while audio coaching system can be purchased or are inbuilt, a visual respiratory biofeedback system is not currently supplied or supported by any vendors or suppliers of linear accelerators or respiratory motion management (RPM) systems. To the best of our knowledge, this is also the first such unit being implemented for clinical use in India.
In this document, we will describe the details of an in-house video goggles feedback system assembled from several commercially available components. This video goggles feedback system is currently implemented on both simulation computed tomography (CT) scanner and treatment delivery machine. The objective of this paper is to share our experiences with this system, provide details on the equipment needed, system assembly, patient setup, and provide the suggested user settings on some components.
| > Materials and Methods|| |
The video system was purchased from ITV goggles http://itvgoggles.com/medicalhealth-professionals/. This company is based in the USA since there are no distributors in India, these can either be purchased online directly from the website or from the stores in the USA. The model used for this project was FPView3DHD, which was purchased online in the USA, for which the technical details are shown in [Table 1]. The input of the goggles is in the high-definition multimedia interface (HDMI) format. Apart from the goggles, various other accessories were also purchased such as registered jack (RJ)-45 to digital visual interface (DVI) converter from Tripplite (https://www.tripplite.com/displayport-to-dvi-over-cat5-cat6-extender-kit-pigtail-style-transmitter-receiver-for-video-and-audio-1080p-60-hz~B1501A1DVI), DVI to HDMI converters, local area network (LAN) cable of length 20 m, HDMI, and power extender cables. The video coaching system was implemented both in CT simulator (GE) and in treatment delivery machine True Beam v2.1 Varian Medical Systems (VMS, Palo Alto), which was integrated with RPM (v1.7.5) system.
Computed tomography simulator
The video feedback system implemented in CT simulator (GE Discovery) as depicted in [Figure 2]a. The RPM (v1.7.5) computer system is equipped with the METROX graphics card with the display output provided with the DVI splitter. The settings in the graphics card were changed such that Ports 1 and 3 are enabled with a parallel output, where display from Port 1 is used for the RPM monitor, whereas Port 3 is reserved for the goggles, a DVI to video graphics array converter is used at the splitter followed by a HDMI converter to convert the signal which was fed to the goggles through a HDMI extender cable in the CT simulator room which is approximately 20 m long, which is permanently routed in the CT room from the control console to the scanner [Figure 2]b.
|Figure 2: (a) The schematic diagram of the video feedback system implemented in computed tomography simulator (b) video graphics array to high-definition multimedia interface converter at computed tomography simulator console|
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The linear accelerator (True Beam, VMS, PaloAlto) has two display monitors, one for machine/treatment parameters and the other for the on-board imaging (OBI). The respiratory waveform from the patient which is displayed on the OBI monitor has to be fed to the goggles as a feedback to the patient. The whole connection is depicted in block diagram [Figure 3]a. The OBI computer display output was equipped with a splitter in RJ-45 format [Figure 3]b. While one port was being used in the machine console, the other free RJ-45 port with a parallel display was used to feed to the goggles. A 20-m LAN cable was routed from the console to the treatment room, one end of which is connected with the OBI computer and the other end in the treatment room is fed to the Tripplite switch that converts RJ-45 to DVI, subsequently to a DVI to HDMI converter and to the goggles [Figure 3]c.
|Figure 3: (a) The schematic diagram of the video feedback system implemented in linear accelerator (b) on-board imaging system splitter digital visual interface to registered jack-45 at the linear accelerator machine console (c) digital visual interface to Cat5/Registered jack-45 to high-definition multimedia interface converter used at the linear accelerator machine room|
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| > Results|| |
The video feedback system is in clinical use since August 2017, so far, we have treated 13 patients, with approximately 150 fractions. The performance of the device was found to be satisfactory. All the patients were coached for DIBH and the usage of the goggles, which include wearing the goggles, display details of the monitor, and the threshold levels of the breathing wave cycle [Figure 4]a and [Figure 4]b. The patients understand the instructions very well and hence regulate the breathing cycle, which improves the treatment accuracy and efficiency [Figure 5]. The use of goggles reduced the time spent on the machine as compared to the time spent when it was not used. However, since we did not record the time before, we could not quantify the reduction in time. One treatment delivery takes about 15–20 min depending on the regularity of the breathing cycle and other plan parameters such as dose and dose rate. We need to treat large number of patients to find the life span of the goggles and other accessories. After gaining experience, we are planning to implement in other hospitals of TMC, if required.
|Figure 4: (a) Patient in computed tomography simulator with the video feedback system (b) Patient in linear accelerator during treatment with the video feedback system|
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|Figure 5: A representative waveform of the patient at the linear accelerator control console, when treated with goggles. Here, the patient is able to follow his/her breathing pattern using the feedback system efficiently|
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| > Discussion|| |
Several studies have demonstrated that an external feedback coaching system can help patients maintain and reproduce breathing patterns during treatment, thus improving the delivery accuracy and efficiency., Common coaching systems typically involve the use of an audio system, to regulate patient's respiratory pattern, which was considered as quite ineffective. In our experience, the patients were not able to follow the instructions as they were not visualizing the threshold levels of the breathing pattern. However, in the literature, it was reported that Haasbeek et al. observed an average increase on end-inspiration lung volumes of 10.2% when comparing the simulation CTs created with and without an external audio coaching system, thus improving dosimetric sparing of normal tissue. Later on, there are various other reports where the use of video feedback system is highly recommended, which is in agreement with the findings of the current study., Goossens et al. demonstrated that breathing patterns and tumor motion were controlled better and reproduced more easily with the aid of an audio and in-house video goggles feedback system. The improvement in delivery efficiency was also quantified by Linthout et al., who showed that the average treatment delivery time of a gated stereotactic body RT treatment for a free-breathing case could be reduced from 1.7 to 1.4 min per 100 MU (standard deviation [SD] = 0.6 min/100 MU) with the aid of a visual feedback system, and further reduced to 0.9 min per 100 MU (SD = 0.2 min/100 MU) with an addition of an audio coaching system. Besides the above-mentioned investigations, other studies have also demonstrated the benefits of the video feedback system for motion management. The visual feedback systems from all these studies are built in-house, however, no details on how these systems are assembled is given. However, Nguyen et al. have recently published a similar system built in-house, which also used an amplifier and a scalar.
There were two important observations during the process of implementation of the video feedback system: (a) at CT simulator and (b) scaling the display in the monitor at the linear accelerator, discussed in the following paragraph.
During the testing phase at CT simulator, we had observed a pattern which may be of use for other users who would like to implement a similar system in their department. When the RPM computer was switched on, the goggles must not be connected with the RPM computer, as the METROX graphics card considers the RPM display as the primary and goggles as the secondary. Hence, the display in the goggles will not work when it is connected with the RPM computer, while it is still booting. However, the display in the goggles can be seen only when it is connected after the RPM system is fully booted with the primary display in the RPM monitor is ON.
Second, the entire OBI display is being fed to the goggles currently. The display of the OBI monitor consists of cone-beam CT (CBCT) images of the patient, the setup error parameters, and the respiratory wave form. The feedback system must consist only of the respiratory waveform; however, currently, we are feeding the entire display of the OBI monitor which might affect the patient, although the patients were able to follow the video feedback system to a great satisfaction. Some patients get distracted with extra information available to them also they get anxious looking at the CBCT images. Hence, we are working to scale the display that consists of only the respiratory waveform in the future.
It is also important to note that the goggles and the other electronics may be sensitive to the radiation beam; hence, currently, the system is kept outside the treatment room and it is taken into the treatment room, as when the patient is taken for the treatment. In the entire video feedback system assembly, there are two high-value items which include a goggle and the tripplite switch (RJ-45 to DVI converter). It is recommended to read the user manual of the goggles carefully before using it, especially related to charging time, charging voltage, and other parameters. Inappropriate use may result in the damage of these high-value items. Other logistics issues may need attention is the international warranty, especially when local distributor is not available in India.
| > Conclusion|| |
Video feedback system for motion management and for patients undergoing radiotherapy was implemented successfully both in the CT simulator and in linear accelerator.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
The authors would like to thank the Department of Atomic Energy-Clinical Trials Centre (DAE-CTC) Government of India, Mr Kiran Gaikwad, Varian Medical Systems – India, Mr Avi Gabbay of ITV Goggles-USA, and Prabhahar Joseph-USA.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| > References|| |
Kügele M, Edvardsson A, Berg L, Alkner S, Andersson Ljus C, Ceberg S, et al.
Dosimetric effects of intrafractional isocenter variation during deep inspiration breath-hold for breast cancer patients using surface-guided radiotherapy. J Appl Clin Med Phys 2018;19:25-38.
Aristophanous M, Chi PM, Kao J, Williamson R, Tung S, Andraos T, et al.
Deep-inspiration breath-hold intensity modulated radiation therapy to the mediastinum for lymphoma patients: Setup uncertainties and margins. Int J Radiat Oncol Biol Phys 2018;100:254-62.
Darapu A, Balakrishnan R, Sebastian P, Hussain MR, Ravindran P, John S, et al.
Is the deep inspiration breath-hold technique superior to the free breathing technique in cardiac and lung sparing while treating both left-sided post-mastectomy chest wall and supraclavicular regions? Case Rep Oncol 2017;10:37-51.
Goossens S, Senny F, Lee JA, Janssens G, Geets X. Assessment of tumor motion reproducibility with audio-visual coaching through successive 4D CT sessions. J Appl Clin Med Phys 2014;15:4332.
Linthout N, Bral S, Van de Vondel I, Verellen D, Tournel K, Gevaert T, et al.
Treatment delivery time optimization of respiratory gated radiation therapy by application of audio-visual feedback. Radiother Oncol 2009;91:330-5.
Haasbeek CJ, Spoelstra FO, Lagerwaard FJ, van Sörnsen de Koste JR, Cuijpers JP, Slotman BJ, et al.
Impact of audio-coaching on the position of lung tumors. Int J Radiat Oncol Biol Phys 2008;71:1118-23.
Cossmann PH. Video-coaching as biofeedback tool to improve gated treatments: Possibilities and limitations. Z Med Phys 2012;22:224-30.
Nguyen VN, Ellerbusch DC, Cetnar AJ, James JA, Wang B. Implementation of an in-house visual feedback system for motion management during radiation therapy. J Appl Clin Med Phys 2016;17:421-7.
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