Prediction of Target Motion Using Neural Network for 4-dimensional Radiation Therapy / 의학물리

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

Prediction of Target Motion Using Neural Network for 4-dimensional Radiation Therapy / 의학물리

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

Analysis of the Imaging Dose for IGRT/Gated Treatments / 대한방사선종양학회지

PURPOSE: The introduction of image guided radiation therapy/four-dimensional radiation therapy (IGRT/4DRT) potentially increases the accumulated dose to patients from imaging and verification processes as compared to conventional practice. It is therefore essential to investigate the level of the imaging dose to patients when IGRT/4DRT devices are installed. The imaging dose level was monitored and was compared with the use of pre-IGRT practice. MATERIALS AND METHODS: A four-dimensional CT (4DCT) unit (GE, Ultra Light Speed 16), a simulator (Varian Acuity) and Varian IX unit with an on-board imager (OBI) and cone beam CT (CBCT) were installed. The surface doses to a RANDO phantom (The Phantom Laboratory, Salem, NY USA) were measured with the newly installed devices and with pre-existing devices including a single slice CT scanner (GE, Light Speed), a simulator (Varian Ximatron) and L-gram linear accelerator (Varian, 2100C Linac). The surface doses were measured using thermo luminescent dosimeters (TLDs) at eight sites-the brain, eye, thyroid, chest, abdomen, ovary, prostate and pelvis. RESULTS: Compared to imaging with the use of single slice non-gated CT, the use of 4DCT imaging increased the dose to the chest and abdomen approximately ten-fold (1.74+/-0.34 cGy versus 23.23+/-3.67 cGy ). Imaging doses with the use of the Acuity simulator were smaller than doses with the use of the Ximatron simulator, which were 0.91+/-0.89 cGy versus 6.77+/-3.56 cGy, respectively. The dose with the use of the electronic portal imaging device (EPID; Varian IX unit) was approximately 50% of the dose with the use of the L-gram linear accelerator (1.83+/-0.36 cGy versus 3.80+/-1.67 cGy). The dose from the OBI for fluoroscopy and low-dose mode CBCT were 0.97+/-0.34 cGy and 2.3+/-0.67 cGy, respectively. CONCLUSION: The use of 4DCT is the major source of an increase of the radiation (imaging) dose to patients. OBI and CBCT doses were small, but the accumulated dose associated with everyday verification need to be considered

Efficacy of a Respiratory Training System on the Regularity of Breathing / 대한방사선종양학회지

PURPOSE: In order to enhance the efficiency of respiratory gated 4-dimensional radiation therapy for more regular and stable respiratory period and amplitude, a respiration training system was designed, and its efficacy was evaluated. MATERIALS AND METHODS: The experiment was designed to measure the difference in respiration regularity following the use of a training system. A total of 11 subjects (9 volunteers and 2 patients) were included in the experiments. Three different breathing signals, including free breathing (free-breathing), guided breathing that followed training software (guided-breathing), and free breathing after the guided-breathing (post guided-breathing), were consecutively recorded in each subject. The peak-to-peak (PTP) period of the breathing signal, standard deviation (SD), peak-amplitude and its SD, area of the one cycle of the breathing wave form, and its root mean square (RMS) were measured and computed. RESULTS: The temporal regularity was significantly improved in guided-breathing since the SD of breathing period reduced (free-breathing 0.568 vs guided-breathing 0.344, p=0.0013). The SD of the breathing period representing the post guided-breathing was also reduced, but the difference was not statistically significant (free-breathing 0.568 vs. guided-breathing 0.512, p=ns). Also the SD of measured amplitude was reduced in guided-breathing (free-breathing 1.317 vs. guided-breathing 1.068, p=0.187), although not significant. This indicated that the tidal volume for each breath was kept more even in guided-breathing compared to free-breathing. There was no change in breathing pattern between free-breathing and guided-breathing. The average area of breathing wave form and its RMS in postguided-breathing, however, was reduced by 7% and 5.9%, respectively. CONCLUSION: The guided-breathing was more stable and regular than the other forms of breathing data. Therefore, the developed respiratory training system was effective in improving the temporal regularity and maintaining a more even tidal volume.