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Original Article

Progress in Medical Physics 2022; 33(4): 136-141

Published online December 31, 2022 https://doi.org/10.14316/pmp.2022.33.4.136

Copyright © Korean Society of Medical Physics.

Development of a Breath Control Training System for Breath-Hold Techniques and Respiratory-Gated Radiation Therapy

Hyung Jin Choun1 , Jung-in Kim2,3,4 , Jong Min Park2,3,4 , Jaeman Son2,3,4

1Interdisciplinary Program in Bioengineering, Seoul National University, 2Department of Radiation Oncology, Seoul National University Hospital, 3Biomedical Research Institute, Seoul National University Hospital, 4Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Korea

Correspondence to:Jaeman Son
(jaeman0410@snuh.org)
Tel: 82-2-2072-4160
Fax: 82-2-3410-2619

Received: November 18, 2022; Revised: December 21, 2022; Accepted: December 23, 2022

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Purpose: This study aimed to develop a breath control training system for breath-hold technique and respiratory-gated radiation therapy wherein the patients can learn breath-hold techniques in their convenient environment.
Methods: The breath control training system comprises a sensor device and software. The sensor device uses a loadcell sensor and an adjustable strap around the chest to acquire respiratory signals. The device connects via Bluetooth to a computer where the software is installed. The software visualizes the respiratory signal in near real-time with a graph. The developed system can signal patients through visual (software), auditory (buzzer), and tactile (vibrator) stimulation when breath-holding starts. A motion phantom was used to test the basic functions of the developed breath control training system. The relative standard deviation of the maxima of the emulated free breathing data was calculated. Moreover, a relative standard deviation of a breath-holding region was calculated for the simulated breath-holding data.
Results: The average force of the maxima was 487.71 N, and the relative standard deviation was 4.8%, while the average force of the breath hold region was 398.5 N, and the relative standard deviation was 1.8%. The data acquired through the sensor was consistent with the motion created by the motion phantom.
Conclusions: We have developed a breath control training system comprising a sensor device and software that allow patients to learn breath-hold techniques in their convenient environment.

KeywordsRespiratory-gated radiotherapy, Breath-hold, Breath control training system, Deep inspiration breath hold

Respiratory motion is a concern when treating radiation in tumors of the thorax and abdomen [1]. If the consideration was inadequate, the planned dose might not be adequately delivered to the target area due to the motion, potentially decreasing the local control [2]. Although a dosimetric margin can be employed to compensate for the effects of the respiratory motion, this may lead to a higher dose to healthy normal tissues neighboring the target areas [3]. Therefore, respiratory gating and breath-hold techniques are used to mitigate the effect of organ motion during radiotherapy [2]. Respiratory gating counters the effects of respiratory-induced organ motion by allowing the delivery of radiation only within a particular portion of the patient’s breathing cycle [4]. Deep inspiration breath-hold (DIBH) is one of the breath-hold techniques in which the patient reproduces a state of maximum breath-hold during radiation therapy [5]. DIBH reduces respiratory tumor motion and changes internal anatomy to spare critical normal tissues from the irradiation [5]. However, these techniques are most effective when patients can reproduce the stable breathing pattern or the state of the breath-hold [6,7].

Several studies suggest that audio-visual guidance can improve respiratory reproducibility and treatment efficiency [8-10]. A previous study developed a visual guidance patient-controlled respiratory gating system, which allows patients to monitor their anatomical movements visually and voluntarily control their respiration [11]. Although these methods effectively enhanced the efficiency of radiation therapy with respiratory gating or breath-hold techniques, patient collaboration and compliance were needed. For optimal patient compliance, training sessions for patients are needed [8-13]. However, providing patient training sessions could be challenging in clinics because of the insubstantial resources, including time, space, and labor.

To facilitate the training on breath control techniques in patients before treatment, a breath control training system that comprises a sensor device and software was developed. The sensor device includes a loadcell sensor and an adjustable strap around the chest to acquire respiratory signals. The device connects via Bluetooth to a computer where the software is installed. The software visualizes the respiratory signal in near real-time with a graph. The developed system can signal patients through visual (software), auditory (buzzer), and tactile (vibrator) stimulation when breath-holding starts. This study demonstrates an overview of the developed system and the results of basic function tests using a motion phantom.

1. Description of the breath control training system

Components of the breath control training system are shown in Fig. 1. An adjustable strap was made with a webbing belt, spandex, and Velcro to prevent the device from falling. The printed circuit board (PCB) consists of 32-bit MCU with Bluetooth, a C-type USB charging port, a buzzer, a vibration motor, a loadcell sensor, a battery, and other necessary parts. The force was applied to the loadcell sensor by the webbing belt (Fig. 1c). The main window of the in-house developed training software is shown in Fig 1d. The python and PyQt-based prototype software read the sensor data through Bluetooth. The software plots the near real-time sensor data on the graph widget. If the measured force is within determined thresholds, the image changes from a “Beam Off” image to a “Beam On” image where the target volume is located within the gating window.

Figure 1.The breathing control training system. (a) The sensor device strapped on a body phantom. (b) The main components of the device including printed circuit board (PCB). (c) The arrangement of the battery, the loadcell sensor, and the webbing belt. (d) The software.

Moreover, the buzzer and vibrator are activated when the measured force reaches the thresholds. The vibrator and the buzzer indicate to patients whenever the measured force enters or exits the threshold region. The vibrator and the buzzer turn on for about 0.5 seconds each time they are activated. If the user performs a breath-holding exercise, the program finds the maximum breath-hold time and displays it in the lower right corner.

The breath control training software requires patients to perform preliminary measurements before the training. The software uses initial measurements to detect the breath-holding activity and measure the length of the breath-hold time. Patients are required to measure their free breathing pattern to determine the baseline mean value of the measurement. Then, the program requires patients to perform the first breath-holding measurement. The program uses the intersection of the baseline and data to determine the breath-holding region of the plot. The longest breath-holding region is then chosen to find the upper and lower thresholds using the region between 20% and 80% of the selected phase. The breath control training procedure is depicted in Fig. 2. The procedure consists of four main steps: device connection, free breathing measurement, the first breath-holding measurement, and repetitive breath control training. The button for each necessary step is activated only when the preliminary measures are complete. The thresholds are determined as 3 standard deviations from the mean value of the chosen phase in both positive and negative directions. The schematic of the threshold determination process is shown in Fig. 3.

Figure 2.The procedure of the breath control training: device connection (upper left), free breathing measurement for baseline determination (upper right), the first breath-holding measurement for threshold determination (lower left), and repetitive breath control training session (lower right).

Figure 3.The schematic diagram showing the threshold determination process using (a) free breathing measurement and (b) breath-hold measurement.

2. Basic function tests

Basic functions of the developed breath control training system were tested with the Independently Controlled Surrogate Motion and the CIRS Motion Control Software (Model 008A Dynamic Thorax Phantom; CIRS Inc., Norfolk, VA, USA). The device was tightly strapped to the surrogate motion with the webbing belt (Fig. 4). Then, the free-breathing and breath-holding were emulated with the motion phantom. The data were collected with the developed system for 60 seconds each. CIRS Motion Control Software created a sinusoidal motion with 10 mm amplitude to mimic free breathing. To simulate the breath-holding, a single motion to 5 mm and waited for a few seconds before moving on to 1 mm, in which 5 mm represents the chest position at deep inspiration, and 1 mm represents the chest position at the release of the breath. The relative standard deviation of the maxima of the emulated free breathing data was calculated. The relative standard deviation of a breath-hold region was calculated for the simulated breath-holding data.

Figure 4.Basic function test setup with the device tightly strapped to the Surrogate Motion of the Model 008A Dynamic Thorax Phantom.

The emulated data is plotted and shown in Fig. 5. The average force of the maxima was 487.71 N and the relative standard deviation was 4.8%. The average force of the breath hold region was 398.5 N, and the relative standard deviation was 1.8%. The data acquired through the sensor was consistent with the motion created by the motion phantom. The values gradually decreased when emulating free breathing because frequent motion loosened up the strap and decreased the force applied to the load cell sensor. The data was visualized through the software in near real-time; no latency was detectable with the naked eye.

Figure 5.Force-time graph of the data acquired with the breath control training system. (a) The emulated free breathing motion data. (b) The emulated breath-holding motion data.

Anxiety or mental stress can affect respiratory patterns [14]. A study by Kim et al. [15] suggests that preparatory coaching and home practice for DIBH can improve DIBH. Although the initial coaching might be challenging in clinics, giving patients the option of home practice after a minimum on-site training could be an option. Patients can practice in their convenient environment where anxiety and stress can be reduced and improve focus and coordination, thereby improving DIBH performance.

Currently, the developed device only has a single function for training breath-hold techniques, but a training option for respiratory gating technique will be added in the future; thereby, multiple respiratory motion control techniques can be trained with the developed system. Moreover, a study on the effects of home training with the developed training in radiation therapy will be performed. Additionally, we are developing a mobile app based on the prototype developed in this study.

We have developed a breath control training system that comprises a sensor device and software. This system can help patients to train and practice breath-hold techniques in their comfortable environment. The data acquired through the sensor was consistent with the motion created by the motion phantom showing that the system can measure respiratory motion accurately for training purposes.

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (Grant No. NRF-2021R1I1A1A01059845).

The data that support the findings of this study are available on request from the corresponding author.

Conceptualization: Hyung Jin Choun and Jung-in Kim. Data curation: Hyung Jin Choun and Jaeman Son. Formal analysis: Jaeman Son and Jung-in Kim. Funding acquisition: Jung-in Kim and Jaeman Son. Investigation: Jaeman Son and Jong Min Park. Methodology: Hyung Jin Choun and Jung-in Kim. Project administration: Jaeman Son and Jung-in Kim. Resources: Jung-in Kim and Jaeman Son. Software: Hyung Jin Choun and Jung-in Kim. Supervision: Jung-in Kim and Jong Min Park. Validation: Hyung Jin Choun and Jong Min Park. Visualization: Hyung Jin Choun and Jaeman Son. Writing – original draft: Hyung Jin Choun. Writing – review & editing: Jaeman Son and Jung-in Kim.

  1. Keall PJ, Mageras GS, Balter JM, Emery RS, Forster KM, Jiang SB, et al. The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys. 2006;33:3874-3900.
    Pubmed CrossRef
  2. Redmond KJ, Song DY, Fox JL, Zhou J, Rosenzweig CN, Ford E. Respiratory motion changes of lung tumors over the course of radiation therapy based on respiration-correlated four-dimensional computed tomography scans. Int J Radiat Oncol Biol Phys. 2009;75:1605-1612.
    Pubmed CrossRef
  3. George R, Keall PJ, Kini VR, Vedam SS, Siebers JV, Wu Q, et al. Quantifying the effect of intrafraction motion during breast IMRT planning and dose delivery. Med Phys. 2003;30:552-562.
    Pubmed CrossRef
  4. Jiang SB. Technical aspects of image-guided respiration-gated radiation therapy. Med Dosim. 2006;31:141-151.
    Pubmed CrossRef
  5. Sixel KE, Aznar MC, Ung YC. Deep inspiration breath hold to reduce irradiated heart volume in breast cancer patients. Int J Radiat Oncol Biol Phys. 2001;49:199-204.
    Pubmed CrossRef
  6. Kini VR, Vedam SS, Keall PJ, Patil S, Chen C, Mohan R. Patient training in respiratory-gated radiotherapy. Med Dosim. 2003;28:7-11.
    Pubmed CrossRef
  7. Boda-Heggemann J, Knopf AC, Simeonova-Chergou A, Wertz H, Stieler F, Jahnke A, et al. Deep inspiration breath hold-based radiation therapy: a clinical review. Int J Radiat Oncol Biol Phys. 2016;94:478-492.
    Pubmed CrossRef
  8. George R, Chung TD, Vedam SS, Ramakrishnan V, Mohan R, Weiss E, et al. Audio-visual biofeedback for respiratory-gated radiotherapy: impact of audio instruction and audio-visual biofeedback on respiratory-gated radiotherapy. Int J Radiat Oncol Biol Phys. 2006;65:924-933.
    Pubmed CrossRef
  9. He P, Li Q, Zhao T, Liu X, Dai Z, Ma Y. Effectiveness of respiratory-gated radiotherapy with audio-visual biofeedback for synchrotron-based scanned heavy-ion beam delivery. Phys Med Biol. 2016;61:8541-8552.
    Pubmed CrossRef
  10. 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;91:330-335.
    Pubmed CrossRef
  11. Kim JI, Lee H, Wu HG, Chie EK, Kang HC, Park JM. Development of patient-controlled respiratory gating system based on visual guidance for magnetic-resonance image-guided radiation therapy. Med Phys. 2017;44:4838-4846.
    Pubmed CrossRef
  12. Choun HJ, Kim JI, Choi CH, Jung S, Jin H, Wu HG, et al. Performance evaluation of a visual guidance patient-controlled respiratory gating system for respiratory-gated magnetic-resonance image-guided radiation therapy. Phys Eng Sci Med. 2022;45:809-816.
    Pubmed CrossRef
  13. Berson AM, Emery R, Rodriguez L, Richards GM, Ng T, Sanghavi S, et al. Clinical experience using respiratory gated radiation therapy: comparison of free-breathing and breath-hold techniques. Int J Radiat Oncol Biol Phys. 2004;60:419-426.
    Pubmed CrossRef
  14. Masaoka Y, Homma I. Anxiety and respiratory patterns: their relationship during mental stress and physical load. Int J Psychophysiol. 1997;27:153-159.
    Pubmed CrossRef
  15. Kim A, Kalet AM, Cao N, Hippe DS, Fang LC, Young L, et al. Effects of preparatory coaching and home practice for deep inspiration breath hold on cardiac dose for left breast radiation therapy. Clin Oncol (R Coll Radiol). 2018;30:571-577.
    Pubmed CrossRef

Article

Original Article

Progress in Medical Physics 2022; 33(4): 136-141

Published online December 31, 2022 https://doi.org/10.14316/pmp.2022.33.4.136

Copyright © Korean Society of Medical Physics.

Development of a Breath Control Training System for Breath-Hold Techniques and Respiratory-Gated Radiation Therapy

Hyung Jin Choun1 , Jung-in Kim2,3,4 , Jong Min Park2,3,4 , Jaeman Son2,3,4

1Interdisciplinary Program in Bioengineering, Seoul National University, 2Department of Radiation Oncology, Seoul National University Hospital, 3Biomedical Research Institute, Seoul National University Hospital, 4Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Korea

Correspondence to:Jaeman Son
(jaeman0410@snuh.org)
Tel: 82-2-2072-4160
Fax: 82-2-3410-2619

Received: November 18, 2022; Revised: December 21, 2022; Accepted: December 23, 2022

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose: This study aimed to develop a breath control training system for breath-hold technique and respiratory-gated radiation therapy wherein the patients can learn breath-hold techniques in their convenient environment.
Methods: The breath control training system comprises a sensor device and software. The sensor device uses a loadcell sensor and an adjustable strap around the chest to acquire respiratory signals. The device connects via Bluetooth to a computer where the software is installed. The software visualizes the respiratory signal in near real-time with a graph. The developed system can signal patients through visual (software), auditory (buzzer), and tactile (vibrator) stimulation when breath-holding starts. A motion phantom was used to test the basic functions of the developed breath control training system. The relative standard deviation of the maxima of the emulated free breathing data was calculated. Moreover, a relative standard deviation of a breath-holding region was calculated for the simulated breath-holding data.
Results: The average force of the maxima was 487.71 N, and the relative standard deviation was 4.8%, while the average force of the breath hold region was 398.5 N, and the relative standard deviation was 1.8%. The data acquired through the sensor was consistent with the motion created by the motion phantom.
Conclusions: We have developed a breath control training system comprising a sensor device and software that allow patients to learn breath-hold techniques in their convenient environment.

Keywords: Respiratory-gated radiotherapy, Breath-hold, Breath control training system, Deep inspiration breath hold

Introduction

Respiratory motion is a concern when treating radiation in tumors of the thorax and abdomen [1]. If the consideration was inadequate, the planned dose might not be adequately delivered to the target area due to the motion, potentially decreasing the local control [2]. Although a dosimetric margin can be employed to compensate for the effects of the respiratory motion, this may lead to a higher dose to healthy normal tissues neighboring the target areas [3]. Therefore, respiratory gating and breath-hold techniques are used to mitigate the effect of organ motion during radiotherapy [2]. Respiratory gating counters the effects of respiratory-induced organ motion by allowing the delivery of radiation only within a particular portion of the patient’s breathing cycle [4]. Deep inspiration breath-hold (DIBH) is one of the breath-hold techniques in which the patient reproduces a state of maximum breath-hold during radiation therapy [5]. DIBH reduces respiratory tumor motion and changes internal anatomy to spare critical normal tissues from the irradiation [5]. However, these techniques are most effective when patients can reproduce the stable breathing pattern or the state of the breath-hold [6,7].

Several studies suggest that audio-visual guidance can improve respiratory reproducibility and treatment efficiency [8-10]. A previous study developed a visual guidance patient-controlled respiratory gating system, which allows patients to monitor their anatomical movements visually and voluntarily control their respiration [11]. Although these methods effectively enhanced the efficiency of radiation therapy with respiratory gating or breath-hold techniques, patient collaboration and compliance were needed. For optimal patient compliance, training sessions for patients are needed [8-13]. However, providing patient training sessions could be challenging in clinics because of the insubstantial resources, including time, space, and labor.

To facilitate the training on breath control techniques in patients before treatment, a breath control training system that comprises a sensor device and software was developed. The sensor device includes a loadcell sensor and an adjustable strap around the chest to acquire respiratory signals. The device connects via Bluetooth to a computer where the software is installed. The software visualizes the respiratory signal in near real-time with a graph. The developed system can signal patients through visual (software), auditory (buzzer), and tactile (vibrator) stimulation when breath-holding starts. This study demonstrates an overview of the developed system and the results of basic function tests using a motion phantom.

Materials and Methods

1. Description of the breath control training system

Components of the breath control training system are shown in Fig. 1. An adjustable strap was made with a webbing belt, spandex, and Velcro to prevent the device from falling. The printed circuit board (PCB) consists of 32-bit MCU with Bluetooth, a C-type USB charging port, a buzzer, a vibration motor, a loadcell sensor, a battery, and other necessary parts. The force was applied to the loadcell sensor by the webbing belt (Fig. 1c). The main window of the in-house developed training software is shown in Fig 1d. The python and PyQt-based prototype software read the sensor data through Bluetooth. The software plots the near real-time sensor data on the graph widget. If the measured force is within determined thresholds, the image changes from a “Beam Off” image to a “Beam On” image where the target volume is located within the gating window.

Figure 1. The breathing control training system. (a) The sensor device strapped on a body phantom. (b) The main components of the device including printed circuit board (PCB). (c) The arrangement of the battery, the loadcell sensor, and the webbing belt. (d) The software.

Moreover, the buzzer and vibrator are activated when the measured force reaches the thresholds. The vibrator and the buzzer indicate to patients whenever the measured force enters or exits the threshold region. The vibrator and the buzzer turn on for about 0.5 seconds each time they are activated. If the user performs a breath-holding exercise, the program finds the maximum breath-hold time and displays it in the lower right corner.

The breath control training software requires patients to perform preliminary measurements before the training. The software uses initial measurements to detect the breath-holding activity and measure the length of the breath-hold time. Patients are required to measure their free breathing pattern to determine the baseline mean value of the measurement. Then, the program requires patients to perform the first breath-holding measurement. The program uses the intersection of the baseline and data to determine the breath-holding region of the plot. The longest breath-holding region is then chosen to find the upper and lower thresholds using the region between 20% and 80% of the selected phase. The breath control training procedure is depicted in Fig. 2. The procedure consists of four main steps: device connection, free breathing measurement, the first breath-holding measurement, and repetitive breath control training. The button for each necessary step is activated only when the preliminary measures are complete. The thresholds are determined as 3 standard deviations from the mean value of the chosen phase in both positive and negative directions. The schematic of the threshold determination process is shown in Fig. 3.

Figure 2. The procedure of the breath control training: device connection (upper left), free breathing measurement for baseline determination (upper right), the first breath-holding measurement for threshold determination (lower left), and repetitive breath control training session (lower right).

Figure 3. The schematic diagram showing the threshold determination process using (a) free breathing measurement and (b) breath-hold measurement.

2. Basic function tests

Basic functions of the developed breath control training system were tested with the Independently Controlled Surrogate Motion and the CIRS Motion Control Software (Model 008A Dynamic Thorax Phantom; CIRS Inc., Norfolk, VA, USA). The device was tightly strapped to the surrogate motion with the webbing belt (Fig. 4). Then, the free-breathing and breath-holding were emulated with the motion phantom. The data were collected with the developed system for 60 seconds each. CIRS Motion Control Software created a sinusoidal motion with 10 mm amplitude to mimic free breathing. To simulate the breath-holding, a single motion to 5 mm and waited for a few seconds before moving on to 1 mm, in which 5 mm represents the chest position at deep inspiration, and 1 mm represents the chest position at the release of the breath. The relative standard deviation of the maxima of the emulated free breathing data was calculated. The relative standard deviation of a breath-hold region was calculated for the simulated breath-holding data.

Figure 4. Basic function test setup with the device tightly strapped to the Surrogate Motion of the Model 008A Dynamic Thorax Phantom.

Results

The emulated data is plotted and shown in Fig. 5. The average force of the maxima was 487.71 N and the relative standard deviation was 4.8%. The average force of the breath hold region was 398.5 N, and the relative standard deviation was 1.8%. The data acquired through the sensor was consistent with the motion created by the motion phantom. The values gradually decreased when emulating free breathing because frequent motion loosened up the strap and decreased the force applied to the load cell sensor. The data was visualized through the software in near real-time; no latency was detectable with the naked eye.

Figure 5. Force-time graph of the data acquired with the breath control training system. (a) The emulated free breathing motion data. (b) The emulated breath-holding motion data.

Discussion

Anxiety or mental stress can affect respiratory patterns [14]. A study by Kim et al. [15] suggests that preparatory coaching and home practice for DIBH can improve DIBH. Although the initial coaching might be challenging in clinics, giving patients the option of home practice after a minimum on-site training could be an option. Patients can practice in their convenient environment where anxiety and stress can be reduced and improve focus and coordination, thereby improving DIBH performance.

Currently, the developed device only has a single function for training breath-hold techniques, but a training option for respiratory gating technique will be added in the future; thereby, multiple respiratory motion control techniques can be trained with the developed system. Moreover, a study on the effects of home training with the developed training in radiation therapy will be performed. Additionally, we are developing a mobile app based on the prototype developed in this study.

Conclusions

We have developed a breath control training system that comprises a sensor device and software. This system can help patients to train and practice breath-hold techniques in their comfortable environment. The data acquired through the sensor was consistent with the motion created by the motion phantom showing that the system can measure respiratory motion accurately for training purposes.

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (Grant No. NRF-2021R1I1A1A01059845).

Conflicts of Interest

The authors have nothing to disclose.

Availability of Data and Materials

The data that support the findings of this study are available on request from the corresponding author.

Author Contributions

Conceptualization: Hyung Jin Choun and Jung-in Kim. Data curation: Hyung Jin Choun and Jaeman Son. Formal analysis: Jaeman Son and Jung-in Kim. Funding acquisition: Jung-in Kim and Jaeman Son. Investigation: Jaeman Son and Jong Min Park. Methodology: Hyung Jin Choun and Jung-in Kim. Project administration: Jaeman Son and Jung-in Kim. Resources: Jung-in Kim and Jaeman Son. Software: Hyung Jin Choun and Jung-in Kim. Supervision: Jung-in Kim and Jong Min Park. Validation: Hyung Jin Choun and Jong Min Park. Visualization: Hyung Jin Choun and Jaeman Son. Writing – original draft: Hyung Jin Choun. Writing – review & editing: Jaeman Son and Jung-in Kim.

Fig 1.

Figure 1.The breathing control training system. (a) The sensor device strapped on a body phantom. (b) The main components of the device including printed circuit board (PCB). (c) The arrangement of the battery, the loadcell sensor, and the webbing belt. (d) The software.
Progress in Medical Physics 2022; 33: 136-141https://doi.org/10.14316/pmp.2022.33.4.136

Fig 2.

Figure 2.The procedure of the breath control training: device connection (upper left), free breathing measurement for baseline determination (upper right), the first breath-holding measurement for threshold determination (lower left), and repetitive breath control training session (lower right).
Progress in Medical Physics 2022; 33: 136-141https://doi.org/10.14316/pmp.2022.33.4.136

Fig 3.

Figure 3.The schematic diagram showing the threshold determination process using (a) free breathing measurement and (b) breath-hold measurement.
Progress in Medical Physics 2022; 33: 136-141https://doi.org/10.14316/pmp.2022.33.4.136

Fig 4.

Figure 4.Basic function test setup with the device tightly strapped to the Surrogate Motion of the Model 008A Dynamic Thorax Phantom.
Progress in Medical Physics 2022; 33: 136-141https://doi.org/10.14316/pmp.2022.33.4.136

Fig 5.

Figure 5.Force-time graph of the data acquired with the breath control training system. (a) The emulated free breathing motion data. (b) The emulated breath-holding motion data.
Progress in Medical Physics 2022; 33: 136-141https://doi.org/10.14316/pmp.2022.33.4.136

References

  1. Keall PJ, Mageras GS, Balter JM, Emery RS, Forster KM, Jiang SB, et al. The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys. 2006;33:3874-3900.
    Pubmed CrossRef
  2. Redmond KJ, Song DY, Fox JL, Zhou J, Rosenzweig CN, Ford E. Respiratory motion changes of lung tumors over the course of radiation therapy based on respiration-correlated four-dimensional computed tomography scans. Int J Radiat Oncol Biol Phys. 2009;75:1605-1612.
    Pubmed CrossRef
  3. George R, Keall PJ, Kini VR, Vedam SS, Siebers JV, Wu Q, et al. Quantifying the effect of intrafraction motion during breast IMRT planning and dose delivery. Med Phys. 2003;30:552-562.
    Pubmed CrossRef
  4. Jiang SB. Technical aspects of image-guided respiration-gated radiation therapy. Med Dosim. 2006;31:141-151.
    Pubmed CrossRef
  5. Sixel KE, Aznar MC, Ung YC. Deep inspiration breath hold to reduce irradiated heart volume in breast cancer patients. Int J Radiat Oncol Biol Phys. 2001;49:199-204.
    Pubmed CrossRef
  6. Kini VR, Vedam SS, Keall PJ, Patil S, Chen C, Mohan R. Patient training in respiratory-gated radiotherapy. Med Dosim. 2003;28:7-11.
    Pubmed CrossRef
  7. Boda-Heggemann J, Knopf AC, Simeonova-Chergou A, Wertz H, Stieler F, Jahnke A, et al. Deep inspiration breath hold-based radiation therapy: a clinical review. Int J Radiat Oncol Biol Phys. 2016;94:478-492.
    Pubmed CrossRef
  8. George R, Chung TD, Vedam SS, Ramakrishnan V, Mohan R, Weiss E, et al. Audio-visual biofeedback for respiratory-gated radiotherapy: impact of audio instruction and audio-visual biofeedback on respiratory-gated radiotherapy. Int J Radiat Oncol Biol Phys. 2006;65:924-933.
    Pubmed CrossRef
  9. He P, Li Q, Zhao T, Liu X, Dai Z, Ma Y. Effectiveness of respiratory-gated radiotherapy with audio-visual biofeedback for synchrotron-based scanned heavy-ion beam delivery. Phys Med Biol. 2016;61:8541-8552.
    Pubmed CrossRef
  10. 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;91:330-335.
    Pubmed CrossRef
  11. Kim JI, Lee H, Wu HG, Chie EK, Kang HC, Park JM. Development of patient-controlled respiratory gating system based on visual guidance for magnetic-resonance image-guided radiation therapy. Med Phys. 2017;44:4838-4846.
    Pubmed CrossRef
  12. Choun HJ, Kim JI, Choi CH, Jung S, Jin H, Wu HG, et al. Performance evaluation of a visual guidance patient-controlled respiratory gating system for respiratory-gated magnetic-resonance image-guided radiation therapy. Phys Eng Sci Med. 2022;45:809-816.
    Pubmed CrossRef
  13. Berson AM, Emery R, Rodriguez L, Richards GM, Ng T, Sanghavi S, et al. Clinical experience using respiratory gated radiation therapy: comparison of free-breathing and breath-hold techniques. Int J Radiat Oncol Biol Phys. 2004;60:419-426.
    Pubmed CrossRef
  14. Masaoka Y, Homma I. Anxiety and respiratory patterns: their relationship during mental stress and physical load. Int J Psychophysiol. 1997;27:153-159.
    Pubmed CrossRef
  15. Kim A, Kalet AM, Cao N, Hippe DS, Fang LC, Young L, et al. Effects of preparatory coaching and home practice for deep inspiration breath hold on cardiac dose for left breast radiation therapy. Clin Oncol (R Coll Radiol). 2018;30:571-577.
    Pubmed CrossRef
Korean Society of Medical Physics

Vol.34 No.4
December 2023

pISSN 2508-4445
eISSN 2508-4453
Formerly ISSN 1226-5829

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