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Smart Radar Sensor for Accurate Tumor Tracking in Motion Adaptive Cancer Radiotherapy

By Changzhi Li

Radiation therapy is one of the major modalities for treating cancer patients. An increased radiation dose to the tumor will lead to improved local control and survival rates. However, because the tumors can move significantly with respiration in many anatomic sites, it is difficult to deliver sufficient radiation dose without damaging the surrounding healthy tissue. The solution to this problem is respiratory gating or tumor tracking, which require locating tumors in real-time. The state of the art technology to locate tumor is either invasive to the patients or does not have sufficient accuracy. To address these issues, we have developed a smart DC-coupled radar sensor to non-invasively track the tumor location and thus control the radiation beam. Experiments in clinical environment have demonstrated the feasibility and advantages of the smart radar sensor.

Radiation therapy is one of the major modalities for treating cancer patients. An increased radiation dose to the tumor will lead to improved local control and survival rates. However, in many anatomic sites (e.g., lung and liver), the tumors can move significantly (~2-3 cm) with respiration. The respiratory tumor motion has been a major challenge in radiotherapy to deliver sufficient radiation dose without causing secondary cancer or severe radiation damage to the surrounding healthy tissue [1].

By dynamically targeting tumors with the radiation beam, motion-adaptive radiotherapy explicitly accounts for and tackles the issue of tumor motion during radiation dose delivery. Two promising approaches are respiratory-gating and tumor tracking. Respiratory gating limits radiation exposure to a portion of the breathing cycle when the tumor is in a predefined gating window. Tumor tracking, on the other hand, allows continuous radiation dose delivery by dynamically adjusting the radiation beam so that it follows the real-time tumor movement. For either technique to be effective, the tumor location must be known, with high precision and in real time.

Conventional methods for respiration measurement are either invasive to the patient or do not have sufficient accuracy. For instance, measurement based on fiducial markers requires an invasive implantation procedure and involves serious risks to the patient, such as pneumothorax for lung cancer patients. On the other hand, measurement of external respiration surrogates using infrared reflective marker, spirometer, or pressure belt etc., generally lacks sufficient accuracy to infer the internal tumor position, because they only provide a point measurement or a numerical index of respiration. In addition, these devices have to be in close contact with the patient in order to function. This often brings discomfort to the patient and can lead to additional patient motion during dose delivery. Therefore, accurate respiration measurement that does not require invasive procedures or patient contact is urgently needed.

In recent years, continuous-wave (CW) radar sensor has attracted wide interests from the life science society because it provides a non-contact and non-invasive approach for respiration measurement [2]. If radar is used for tumor tracking to replace the marker-based technologies, it directly measures the body physiological motion, which has better correlation with the lung tumor motion. Moreover, radar detection is less sensitive to clothing and chest hair due to microwave penetration, making it better than the existing contact devices that are sensitive to the surrounding environment.

The motion-adaptive radiotherapy system based on radar respiration sensing is shown in Fig. 1. The radiotherapy process includes two steps: treatment preparation, which consists of patient simulation and treatment planning, and treatment execution, which delivers radiation dose to the patient. At the patient simulation stage, the patient and tumor geometrical information is collected through computed tomography (CT) scan and then a 3D patient model is built for the target tumor and organs at risk. Treatment planning is a virtual process that designs the patient treatment using the patient model built at the simulation stage. During the radiotherapy execution stage, a medical linear accelerator (LINAC) would work with two radar sensors that dynamically monitor the chest wall and the abdomen to provide the real-time motion information. The LINAC could also be integrated with a radar sensor having beam-scanning capability, as shown in inset (b) of Fig. 1, which makes it possible to use one radar sensor to simultaneously measure the breathing motions at multiple body locations. In Step III, the advanced tumor tracking algorithm combines the chest wall and abdomen motion information together with the pre-collected patient model to extract the tumor locations in real-time. Then a controller utilizes the extracted tumor location information to control the LINAC to either perform gated radiotherapy or steer the radiation beam to track the tumor. Inset (c) shows the designed 2.4 GHz miniature radar sensor.

Figure 1: Radar-based motion-adaptive radiotherapy.
Figure 1: Radar-based motion-adaptive radiotherapy. Insets: (a) multiple radars, (b) beam-scanning radar, and (c) designed 2.4 GHz miniature radar sensor seating with a quarter.

Click to enlarge

In order to eliminate the undesired DC offset due to reflections from stationary objects surrounding the body, traditional CW biomedical radar uses AC coupling between the radio frequency (RF) front-end and the baseband circuit. While AC coupling solves the problem of limited dynamic range due to random DC offset, it is undesirable for tumor tracking because AC coupling leads to significant signal distortion when the target motion has a very low frequency or a DC component. Respiration is such a motion with low frequency and even DC component, e.g., the subject tends to rest for a while at the end of expiration. To deal with this problem, a smart CW radar sensor was designed with a DC-coupled adaptive tuning architecture that includes RF coarse-tuning and baseband fine-tuning. Based on an RF attenuator and a phase shifter, the RF coarse tuning adds a portion of the transmitted signal to the receiver signal to cancel most of the clutter reflection. To further calibrate the remaining undesired DC offset, the baseband fine-tuning adaptively adjusts the amplifier bias to the desired level that allows both high gain amplification and maximum dynamic range at the baseband stage.

The smart radar sensor was fabricated and tested in lab environment. To demonstrate the accuracy, the sensor measured an actuator that was programmed to move sinusoidally but with a stationary moment between two adjacent cycles. The measurement result is shown in Fig. 2. It is seen that the smart DC-coupled radar successfully preserves the stationary information by precisely matching with the programmed actuator motion. As a comparison, a conventional AC-coupled radar was also used to measure the same movement. However, the AC radar measurement started to deviate from the ground truth when the stationary moment begins. Recent studies in [3] analyzed the details of the distortion problem of AC-coupled radar, and confirmed that the smart DC-coupled radar sensor is able to more precisely measure the complex respiration motion pattern.

Figure 2: Programmed actuator movement compared with the movements measured by smart radar sensor and conventional AC-coupled radar.
Figure 2: Programmed actuator movement compared with the movements measured by smart radar sensor and conventional AC-coupled radar.

Click to enlarge

Moreover, the smart radar sensor was tested with a LINAC to validate its clinical use. The radar sensor and a Real-time Position Management (RPM) system were used to measure the same motion phantom on the treatment platform, with the radiation beam turned on. The measurement demonstrated a sub-millimeter accuracy when measuring the phantom motion. Then, the smart radar was set up to measure a human subject who laid on the treatment platform. With the radar-measured accurate respiration pattern, the subject was coached to dynamically adjust his breathing, so as to generate reproducible respiration signals, from which, gating signals could be easily obtained. More measurement results can be found in [4]. It is shown that the accurate measurement of the smart radar sensor allows for both respiratory gating and tumor tracking.

In summary, the smart radar sensor with adaptive DC tuning architecture can precisely measure respiratory movements with stationary moment. The radar sensor was successfully designed and tested. Experiments in clinical environment have shown that respiration measurement with radar sensor while the radiation beam is on is feasible and the measurement has a sub-mm accuracy when measuring a phantom with complex motion pattern. The proposed radar sensor provides accurate, non-invasive, and non-contact respiration measurement and therefore has a great potential for respiratory gating and tumor tracking in motion-adaptive radiotherapy.

For Further Reading

1. S. B. Jiang, “Technical aspects of image-guided respiration gated radiation therapy,” Medical Dosimetry, Vol. 31 (2), pp.141-151, 2006.

2. C. Li, J. Cummings, J. Lam, E. Graves, W. Wu, “Radar Remote Monitoring of Vital Signs – From Science Fiction to Reality”, IEEE Microwave Magazine, vol. 10, no. 1, pp. 47-56, February 2009.

3. C. Gu, C. Li, “Distortion Analysis of Continuous-Wave Radar Sensor for Complete Respiration Pattern Monitoring,” Proceedings of IEEE Radio and Wireless Symposium, Austin, January 20-23, 2013.

4. C. Gu, R. Li, H. Zhang, A. Y. C. Fung, C. Torres, S. B Jiang, C. Li, “Accurate Respiration Measurement Using DC-Coupled Continuous-Wave Radar Sensor for Motion-Adaptive Cancer Radiotherapy,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 11, pp. 3117-3123, Nov. 2012.

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February 2013 Contributors

Nitish V. ThakorNitish V. Thakor is a Professor of Biomedical Engineering at Johns Hopkins University, Baltimore, USA, as well as the Director of the newly formed institute for neurotechnology, SiNAPSE, at the National University of Singapore. Read more

J. C. ChiaoJ. C. Chiao is a Greene endowed professor and Garrett endowed professor of Electrical Engineering at University of Texas - Arlington... Read more

Xu MengXu Meng (S'08) received a B.E. degree in electronics and telecomm. in 2006 and a M.S. degree in biomedical engineering in 2008 from the Beijing Institute of Technology... Read more

D. Kacy CullenD. Kacy Cullen has B.S. and M.S. degrees in mechanical engineering, in 2002, and a Ph.D. degree in biomedical engineering from the Georgia Institute of Technology in Atlanta, GA... Read more

Mohammad-Reza TofighiMohammad-Reza Tofighi received his B.S.E.E. degree from Sharif University of Technology, Tehran, Iran in 1989, and his M.S.E.E. from Iran University of Science and Technology, Tehran, Iran in 1993. Read more

Arye RosenArye Rosen received a Masters degree in engineering from Johns Hopkins University, a M.Sc. degree in physiology from Jefferson Medical College, and a Ph.D. degree in electrical engineering from Drexel University... Read more

Walker TurnerWalker Turner received B.S. and M.S. degrees in Electrical and Computer Engineering from the University of Florida in 2009 and 2012, respectively. Read more

Dr. Rizwan BashirullahDr. Rizwan Bashirullah received a B.S. in Electrical Engineering from the University of Central Florida and M.S. and Ph.D. degrees in Electrical Engineering from North Carolina State University. Read more

Changzhi LiChangzhi Li received a Ph.D. degree in electrical engineering from the University of Florida in 2009. Read more

Ehsan YavariEhsan Yavari received a B.S.E.E. degree from the Ferdowsi University of Mashhad, Mashhad, Iran, and a M.Sc. degree in electronics from Tarbiat Modares University, Tehran, Iran. Read more

Victor M. LubeckeVictor M. Lubecke received M.S. and Ph.D. degrees in Electrical Engineering from the California Institute of Technology, and a B.S.E.E. degree from the California State Polytechnic Institute, Pomona. Read more

Olga Boric-LubeckeOlga Boric-Lubecke received a M.S. degree from the California Institute of Technology, Pasadena, and a Ph.D. from the University of California at Los Angeles, all in electrical engineering. Read more