Real-time respiration monitoring using the radiotherapy treatment beam and four-dimensional computed tomography (4DCT)—a conceptual study

Abstract

Real-time knowledge of intra-fraction motion, such as respiration, is essential for four-dimensional (4D) radiotherapy. Surrogate-based and internal-fiducial-based methods may suffer from one or many drawbacks such as false correlation, being invasive, delivering extra patient radiation, and requiring complicated hardware and software development and implementation. In this paper we develop a simple non-surrogate, non-invasive method to monitor respiratory motion during radiotherapy treatments in real time. This method directly utilizes the treatment beam and thus imposes no additional radiation to the patient. The method requires a pre-treatment 4DCT and a real-time detector system. The method combines off-line processes with on-line processes. The off-line processes include 4DCT imaging and pre-calculating detector signals at each phase of the 4DCT based on the planned fluence map and the detector response function. The on-line processes include measuring detector signal from the treatment beam, and correlating the measured detector signal with the pre-calculated signals. The respiration phase is determined as the position of peak correlation. We tested our method with extensive simulations based on a TomoTherapy machine and a 4DCT of a lung cancer patient. Three types of simulations were implemented to mimic the clinical situations. Each type of simulation used three different TomoTherapy delivery sinograms, each with 800 to 1000 projections, as input fluences. Three arbitrary breathing patterns were simulated and two dose levels, 2 Gy/fraction and 2 cGy/fraction, were used for simulations to study the robustness of this method against detector quantum noise. The algorithm was used to determine the breathing phases and this result was compared with the simulated breathing patterns. For the 2 Gy/fraction simulations, the respiration phases were accurately determined within one phase error in real time for most projections of the treatment, except for a few projections at the start and end of the treatment in which beam intensities were extremely low. At 2 cGy/fraction dose level, the method can still determine the respiration phase very well with less than 10% of projections having more than two phases (approximately 1 s) error. This technique can also be applied in other delivery systems such as orthogonal x-ray systems, although in those cases it would entail the delivery of additional non-treatment radiation.

Real-time motion-adaptive delivery (MAD) using binary MLC: II. Rotational beam (tomotherapy) delivery

“…We refer the readers to the review paper by Webb (2006a) about the pros and cons of all kinds of motion detection techniques. As discussed in the previous paper, we would suggest that an internal-fiducial-based method (Shirato et al 2000, Chen et al 2001, Seppenwoolde et al 2002, Rietzel et al 2004, Willoughby et al 2006, Kupelian et al 2007, Shchory et al 2008 or the direct treatment beam based method (Lu et al 2006) be used as the real-time motion detection method because the MAD technique presented in this paper relies on the direct position information of the tumor in real time.…”

Prior Image Constrained Compressed Sensing (PICCS) and Applications in X-ray Computed Tomography

“…Thus, it is important to develop a method to monitor the potential changes to the tumor motion trajectories to verify the treatment plan before the actual treatment is delivered. This motivated investigators to extend the 4DCT concept and methodology to the 4D cone-beam CT (4DCBCT) case [10,11,64,69,[72][73][74][75], in which the data is acquired directly before treatment. However, there are two fundamental challenges in 4DCBCT that hinder the improvement of its image quality, and thus its applications: After gating, only 60-80 cone-beam projections are available to reconstruct each respiratory phase.…”

A GPU-based framework for modeling real-time 3D lung tumor conformal dosimetry with subject-specific lung tumor motion

“…A detailed discussion on the usage of 4DCT for predicting the tumor motion is given in the report of the AAPM Task-Group 76 (TG 76) (Keall et al 2006, Wong 1999. Planning evaluations on all phases of the 4DCT as well as planning on averaged or on maximum intensity images of the 4DCT dataset have been conducted in order to develop a treatment plan that most accurately accounts for the tumor motion (Low 2003, Rosu and Balter 2007, Admiraal and Schuring 2008, Low et al 2005, Lu et al 2006, Chetty et al 2003. Additionally, probabilistic models predict the motion of the tumor during the treatment (Jiang and Barnett 2007, Bortfeld et al 2002, Putra et al 2008.…”