A patient-specific respiratory model of anatomical motion for radiation treatment planning

Abstract—Objective: Modeling of respiratory motion is important for a more accurate understanding and accounting of its effect on dose to cancers in the thorax and abdomen by radiotherapy. We have developed a model of respiration- induced organ motion in the thorax, without the commonly adopted assumption of repeatable breath cycles.
Methods and Results: The model describes the motion of a volume of interest within the patient, based on a reference 3- dimensional image (at end-expiration), and the diaphragm positions at different time points. The input data are respiration-correlated CT (RCCT) images of patients treated for nonsmall cell lung cancer, consisting of 3D images, including the diaphragm positions, at 10 phases of the respiratory cycle. A deformable image registration algorithm calculates the deformation field that maps each 3D image to the reference 3D image. A principle component analysis (PCA) is performed to parameterize the 3D deformation field in terms of the diaphragm motion. We show that the first two principal components are adequate to accurately and completely describe the organ motion in the data of 4 patients. Artifacts in the RCCT images that commonly occur at the mid-respiration states are reduced in the model- generated images. Further validation of the model is demonstrated in the successful application of the parameterized 3D deformation field to RCCT data of the same patient but acquired several days later.
Conclusions: We have developed a method for predicting respiration-induced organ motion in patients that has potential for improving the accuracy of dose calculation in radiotherapy. Possible limitations of the model are cases where the correlation between lung tumor and diaphragm position is less reliable such as superiorly situated tumors, and interfraction changes in tumor-diaphragm correlation. The limited number of clinical cases examined suggests but does not confirm the model’s applicability to a wide range of patients.

Manuscript received March 9, 2006. This work is supported by XXX.

A Patient-Specific Respiratory Model of Anatomical Motion for Radiation treatment Planning
Qinghui Zhang, Ph.D. 1, Alex Pevsner, Ph.D1, Agung Hertanto, Ph.D1, Yu-Chi Hu, MS.1, Kenneth E. Rosenzweig, M.D.2, C. Clifton Ling, Ph.D. 1, Gig S Mageras, Ph.D. 1

Departments of 1Medical Physics and 2Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY

Med. Phys. 34, 4772-4781 (2007) 2

Key words: Respiration, tumor motion, computed tomography, radiation treatment planning, lung cancer

I. INTRODUCTION espiration-induced anatomic motion can limit the accuracy of dose calculation and delivery in radiotherapy of cancers in the thorax and abdomen. To improve high-precision conformal radiotherapy and intensity-modulated radiotherapy (IMRT) of these disease sites one needs to more precisely understand respiration-induced anatomical motion and account for its effect. To effect such improvements, two types of studies have been conducted: 1) to measure the positions of the tumor and organs-at-risk (OARs) at multiple phases in the respiratory cycle using respiration-correlated methods [1- 8], and 2) to estimate the effect of respiration-induced motion on the delivered dose to the tumor and OARs [9-19].
In evaluating item 2) above, most groups assume that patients breathe regularly, in spite of evidence to the contrary [20-22]. Some algorithms characterize organ motion by a periodic function as measured by certain surrogates of the breathing cycle (e.g. the Varian RPM system); however, many patients have irregular breathing such that a periodic function is inadequate to describe the organ motion [20-22]. Variability in respiration during treatment can be measured with fluoroscopy or respiration monitors, but they do not provide adequate information on the internal 3D motion. As to the possible use of a respiratory correlated computed tomography (RCCT) image set to calibrate respiration monitors, interfractional variations in internal anatomical positions would introduce uncertainties [23-27].
Recognizing the potential benefit of models of organ motion that do not assume reproducible respiration patterns, Low et al described a breathing motion model parameterized by tidal volume and airflow measured with spirometry, allowing characterization of hysteresis and irregular breathing patterns [28]. Their method uses manual segmentation of image features, thus can provide motion trajectories for only a limited number of anatomical points. Zeng et al assumed synthetic periodic respiratory motion functions and derived a 2D motion model in the thorax using projection images from cone-beam CT scans [29]. More recently, the method has been

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