X-Ray Imaging and Computed Tomography

Radar-Based Respiratory Motion Monitoring

Many tomographic imaging modalities as well as most radiation treatment modalities are medical applications that suffer from organ motion as it typically occurs due to breathing or due to cardiac activity. For example the acquisition of flat detector cone-beam CT (CBCT) data, the acquisition of single-photon emission computed tomography (SPECT) or positron emission tomography (PET) data, or the acquisition of magnetic resonance (MR) images takes longer than the typical period of motion occurring in a patient. Similarly, the time required to treat a tumor with x-rays, with gamma rays, or with heavy particles is long compared to the motion periods in human beings (in the order of five seconds for a respiratory cycle, roughly one second for a cardiac cycle). To avoid blurring due to motion, or to avoid treating healthy tissue, it is of high importance to derive a synchronization signal from the patient. With such a signal, data acquisition, image reconstruction or treatment can be gated to avoid disadvantages from patient motion. Even clinical CT with its very fast scans, where scan times of one second or less are daily routine, can benefit from such an external synchronization signal, e.g. to trigger the scan start to guarantee the best possible image quality and therefore the least possible patient dose.

To measure respiratory motion many methods are in use, today. Most of them measure the respiration motion outside of the body by evaluating thorax or abdomen displacement caused by breathing. Different sensor techniques are used, like a belt with pressure sensor, laser distance-measuring units or a 3D video camera that observes an optical reflector placed on the patient [1]-[6]. Disadvantages of these techniques are the need for patient preparation and the fact, that a slight time delay between internal organ motion and displacement measured outside of the body occurs which can differ from day to day. Higher accuracy in measuring internal organ motion can be achieved by using implanted fiducials. But due to risk for complications, like pneumothorax for example, such invasive techniques are not used in many hospitals.

Fig. 1. Simplified block diagram for a two channel Doppler radar.

An interesting alternative to assess the person’s respiratory motion is a continuous wave Doppler radar. Figure 1 shows a simplified block diagram of the used Doppler radar: The operating frequency is generated in the local oscillator (LO). After amplifying with a power amplifier (PA), the signal is radiated via the transmit (TX) antenna. After receiving (RX) the reflected radio waves by the second antenna, the signal is mixed (MX1, MX2) with the transmitted signal. The result of the mixer is the phase difference between the transmitted and received signal. For all measurements, we use patch antennas as shown in image 2. The antennas are located close to the body so the radar waves propagate into the body and are reflected on boundaries between body tissues, for example between muscle and adipose tissue or on the outline of organs. Respiration results in organ motion which causes a phase shift of the reflected radar signal. By comparing the transmitted radar signals with the reflected waves the organ motion can be calculated.

Fig. 2. Patch antenna as it is used for our measurements. The external dimensions of the antenna are approximately 10 cm in height, 8 cm in width and 3 mm in thickness.

Measurements show a very good correlation regarding the prediction of respiratory motion phases between the result of the radar system and the respiratory motion recorded using a state of the art respiratory gating system. An example dataset is shown in figure 3. Additional test person measurements indicate that the radar system has the potential to accurately detect and quantify respiratory motion. Due to the fact that the radar waves measure motion inside the body we suppose that organ motion, as caused by respiration, for example, can be better predicted by using our approach than by using devices that assess respiratory motion outside of the body.

Ongoing research is done in suppressing disturbances, for example caused by patient motion. Further research is also required to validate our method and to compare it against alternatives. Potentially, future medical imaging systems may be equipped with radar-based motion monitoring. For example diagnostic CT systems may significantly benefit from a contact-less monitor that may be useful to trigger the scan start or to modulate scan parameters, such as the tube current, in order to improve image quality and to reduce patient dose.

Fig. 3. Example dataset which shows the respiratory motion measured with the radar system (blue, upper curve) and the respiratory signal from a state-of-the-art respiratory gating system (green, bottom curve). The respiratory gating system uses a load cell arranged with a belt on the patient to measure respiratory motion.

[1] R. L. Ehman, M. T. McNamara, M. Pallack, H. Hricak, and C. B. Higgins, "Magnetic resonance imaging with respiratory gating: techniques and advantages." American Journal of Roentgenology, vol. 143, no. 6, pp. 1175–1182, 1984.

[2] H. D. Kubo, P. M. Len, S. ichi Minohara, and H. Mostafavi, "Breathingsynchronized radiotherapy program at the University of California Davis Cancer Center" Med. Phys., vol. 27, no. 2, pp. 346–353, 2000.

[3] S. Minohara, T. Kanai, M. Endo, K. Noda, and M. Kanazawa, "Respiratory gated irradiation system for heavy-ion radiotherapy" International Journal of Radiation Oncology Biology Physics, vol. 47, no. 4, pp. 1097 – 1103, 2000.

[4] R. Wagman, E. Yorke, E. Ford, P. Giraud, G. Mageras, B. Minsky, and K. Rosenzweig, "Respiratory gating for liver tumors: use in dose escalation" International Journal of Radiation Oncology Biology Physics, vol. 55, no. 3, pp. 659 – 668, 2003.

[5] W. D. D’Souza, Y. Kwok, C. Deyoung, N. Zacharapoulos, M. Pepelea, P. Klahr, and C. X. Yu, "Gated CT imaging using a free-breathing respiration signal from flow-volume spirometry" Med. Phys., vol. 32, no. 12, pp. 3641–3649, 2005.

[6] D. Ionascu, S. B. Jiang, S. Nishioka, H. Shirato, and R. I. Berbeco, "Internal-external correlation investigations of respiratory induced motion of lung tumors" Med. Phys., vol. 34, no. 10, pp. 3893–903, 2007.

to top
powered by webEdition CMS