Conference Paper

A Novel Image Based Verification Method for Respiratory Motion Management in Radiation Therapy

Imaging and Visualization Dept., Siemens Corporate Research, 755 College Rd East, Princeton NJ 08540 USA.
DOI: 10.1109/ICCV.2007.4409134 Conference: Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
Source: IEEE Xplore

ABSTRACT Precise localization of moving targets is essential to increase local control of the cancer via dose escalation while reducing the severity of normal tissue complication. Localization of targets in real time with radio-opaque marker is less favorable considering the excess radiation dose to the patient and potential complications of implantation. Various external surrogates could provide indications of the targets' positions during the breathing process. However, there is a great deal of uncertainty in the correlation between external surrogates and internal target positions/trajectory during respiratory cycles. In order to address this problem, we have developed an algorithm that automatically establishes correspondences between the fluoroscopic sequence frames taken from the patient on the day of treatment and the various phases of a 4DCT planning data set. Image based mapping/synchronization procedure is performed using an underlying Markov model established for the breathing process. The mapping procedure is formulated as an optimization process and is solved efficiently using a dynamic programming technique. Results on the phantom, synthetic, and real patient data demonstrate the effectiveness of the proposed method in coping with respiratory correlation variations. The approach could primarily be used for automatic gating interval adaptation in the gated radiotherapy.

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    ABSTRACT: Radiation therapy is a method for treating patients with various types of cancerous tumors. A major challenge in radiation treatment planning is to treat tumors while avoiding irradiating healthy tissue and organs. The problem is that some tumors in the body are in areas where motion occurs (e.g., due to respiration or other normal functions). Radiation treatment plans must try estimate the position of the moving organ inside the body, since they cannot see inside the body. Even given 2-D and 3-D X-Ray images of the patient, it can be very difficult to understand the complex motion of a tumor. This thesis presents one interactive method for analyzing 4-D X-Ray Computed Tomography (4DCT) images for patient care and research. 4-D includes 3-D volume rendering and time (the fourth dimension). Our 4DCT visualization tools have been developed using the SCIRun Problem Solving Environment. Deformable registration is one way to observe the motion of anatomy in images from one respiratory phase to another. Our system provides users with the capability to visualize these trajectories while simultaneously viewing rendered anatomical volumes, which can greatly improve the accuracy of deformable registration as a means of analysis.
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    ABSTRACT: This paper presents a new interactive method for analyzing 4D Computed Tomography (4DCT) datasets for patient care and research. Deformable registration algorithms are commonly used to observe the trajectories of individual voxels from one respiratory phase to another. Our system provides users with the capability to visualize these trajectories while simultaneously rendering anatomical volumes, which can greatly improve the accuracy of deformable registration.
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    ABSTRACT: To ensure precise tumor irradiation in radiotherapy a stable breathing pattern is mandatory as tumors are moving due to respiratory motion. Consequentially, irregularities of respiratory patterns have to be detected immediately. The causal motion of tissue also difiers due to difierent physiological types of respiration, e.g., chest- or abdominal breathing. Currently used devices to measure respiratory motion do not incorporate complete surface deformations. Instead only small regions of interest are considered. Thereby, valuable information to detect difierent breathing patterns and types are lost. In this paper we present a system that uses a novel camera sensor called Time-of-Flight (ToF) for auto- matic classiflcation and veriflcation of breathing patterns. The proposed algorithm calculates multiple volume signals of difierent anatomical re- gions of the upper part of the patient's body. Therefore disjoint regions of interest are deflned for both, the patient's abdomen and thorax. Us- ing the calculated volume signals the type of respiration is determined in real-time by computing an energy coe-cient. Changing breathing patterns can be visualized using a 2-D histogram, which is also used to classify and detect abnormal breathing phases. We evaluated the pro- posed method on flve persons and obtained a reliable difierentation of chest- and abdominal breathing in all test cases. Furthermore, we could show that the introduced 2-D histogram enables an accurate determina- tion of changing breathing patterns.
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