No abstract

The signal from a spirometer is directly correlated with respiratory motion and is ideal for target respiratory motion tracking. However, its susceptibility to signal drift deters its application in radiotherapy. In this work, a few approaches are investigated to control spirometer signal drift for a Bernoulli-type spirometer. A method is presented for rapid daily calibration of the spirometer to obtain a flow sensitivity function. Daily calibration assures accurate airflow measurement and also reduces signal drift. Dynamic baseline adjustment further controls the signal drift. The accuracy of these techniques was studied and it was found that the spirometer is able to provide a long-term drift-free breathing signal. The tracking error is comprised of two components: calibration error and stochastic signal baseline variation error. The calibration error is very small ͑1% of 3 l͒ and therefore negligible. The stochastic baseline variation error can be as large as 20% of the normal breathing amplitude. In view of these uncertainties, the applications of spirometers in treatment techniques that rely on breathing monitoring are discussed. Spirometer-based monitoring is noted most suitable for deep inspiration breath-hold but less important for free breathing gating techniques.

Similar to conventional conformal radiotherapy, during lung tomotherapy, a motion margin has to be set for respiratory motion. Consequently, large volume of normal tissue is irradiated by intensive radiation. To solve this problem, we have developed a new motion mitigation method by incorporating target motion into treatment optimization. In this method, the delivery-breathing correlation is determined prior to treatment plan optimization. Beamlets are calculated by using the CT images at the corresponding breathing phases from a dynamic ͑four-dimensional͒ image sequence. With the displacement vector fields at different breathing phases, a set of deformed beamlets is obtained by mapping the dose to the primary phase. Optimization incorporating motion is then performed by using the deformed beamlets obtained by dose mapping. During treatment delivery, the same breathing-delivery correlation can be reproduced by instructing the patient to breathe following a visually displayed guiding cycle. This method was tested using a computer-simulated deformable phantom and a real lung case. Results show that treatment optimization incorporating motion achieved similar high dose conformality on a mobile target compared with static delivery. The residual motion effects due to imperfect breathing tracking were also analyzed.

Deformable image registration is an important tool for image-guided radiotherapy. Physics-modelbased deformable image registration using finite element analysis is one of the methods currently being investigated. The calculation accuracy of finite element analysis is dependent on given boundary conditions, which are usually based on the surface matching of the organ in two images. Such a surface matching, however, is hard to obtain from medical images. In this study, we developed a new boundary condition to circumvent the traditional difficulties. Finite element contact-impact analysis was employed to simulate the interaction between the organ of interest and the surrounding body. The displacement loading is not necessarily specified. The algorithm automatically deforms the organ model into the minimum internal energy state. The analysis was performed on CT images of the lung at two different breathing phases ͑exhalation and full inhalation͒. The result gave the displacement vector map inside the lung. Validation of the result showed satisfactory agreement in most parts of the lung. This approach is simple, operator independent and may provide improved accuracy of the prediction of organ deformation. © 2004 American Association of Physicists in Medicine.

The purpose of this study is to describe the comprehensive commissioning process and initial clinical experience of the Mevion S250 proton therapy system, a gantry‐mounted, single‐room proton therapy platform clinically implemented in the S. Lee Kling Proton Therapy Center at Barnes‐Jewish Hospital in St. Louis, MO, USA. The Mevion S250 system integrates a compact synchrocyclotron with a C‐inner gantry, an image guidance system and a 6D robotic couch into a beam delivery platform. We present our commissioning process and initial clinical experience, including i) CT calibration; ii) beam data acquisition and machine characteristics; iii) dosimetric commissioning of the treatment planning system; iv) validation through the Imaging and Radiation Oncology Core credentialing process, including irradiations on the spine, prostate, brain, and lung phantoms; v) evaluation of localization accuracy of the image guidance system; and vi) initial clinical experience. Clinically, the system operates well and has provided an excellent platform for the treatment of diseases with protons.PACS number(s): 87.55.ne, 87.56.bd

Machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning-based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time-consuming patient-specific OF measurements.

Stereotactic body radiotherapy (SBRT) can be used to treat small lesions in the chest. A vacuum-based immobilization system is used in our clinic for SBRT, and a motion envelope is used in treatment planning. The purpose of this study is to automatically derive motion envelopes using deformable image registration of 4D-CT images, and to assess the effect of abdominal pressure on the motion envelopes. 4D-CT scans at ten phases were acquired prior to treatment for both free and restricted breathing using a vacuum-based immobilization system that includes an abdominal pressure pillow. To study the stability of the motion envelope over the course of treatment, a mid-treatment 4D-CT scan was obtained after delivery of the third fraction for two patients. The planning target volume excluding breathing motion (PTV(ex)) was defined on the image set at full exhalation phase and transformed into all other phases using displacement maps from deformable image registration. The motion envelope was obtained as the union of PTV(ex) masks of all phases. The ratios of the motion envelope to PTV(ex) volume ranged from 1.3 to 2.5. When pressure was applied, the ratios were reduced by as much as 29% compared to free breathing for some patients, but increased by up to 9% for others. The abdominal pressure pillow has more motion restriction effects on the anterior/inferior region of the lung. For one of the two patients for whom the 4D-CT scan was repeated at mid-treatment, the motion envelope was reproducible. However, for the other patient the tumor location and lung motion pattern significantly changed due to changes in the anatomy surrounding the tumor during the course of treatment, indicating that an image-guided approach to SBRT may increase the efficacy of this treatment.

Breathing-synchronized delivery is a feasible 4D tomotherapy treatment technique with improved motion control and patient tolerance.