Surface-Based Respiratory Motion Classification and Verification

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 differs due to different 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 different 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 automatic classification and verification of breathing patterns. The proposed algorithm calculates multiple volume signals of different anatomical regions of the upper part of the patient’s body. Therefore disjoint regions of interest are defined for both, the patient’s abdomen and thorax. Using the calculated volume signals the type of respiration is determined in real-time by computing an energy coefficient. 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 proposed method on five persons and obtained a reliable differentation of chest- and abdominal breathing in all test cases. Furthermore, we could show that the introduced 2-D histogram enables an accurate determination of changing breathing patterns.