Integration of multimodality imaging data for radiotherapy treatment planning.
This paper describes computational techniques to permit the quantitative integration of magnetic resonance (MR), positron emission tomography (PET), and x-ray computed tomography (CT) imaging data sets. These methods are used to incorporate unique diagnostic information provided by PET and MR imaging into CT-based treatment planning for radiotherapy of intracranial tumors and vascular malformations. Integration of information from the different imaging modalities is treated as a two-step process. The first step is to determine the set of geometric parameters relating the coordinates of two imaging data sets. No universal method for determining these parameters is appropriate because of the diversity of contemporary imaging methods and data formats. Most situations can be handled by one of the four different techniques described. These four methods make use of specific geometric objects contained in the two data sets to determine the parameters. These objects are: (a) anatomical and/or fiducial points, (b) attached line markers, (c) anatomical surfaces, and (d) outlines of anatomical structures. The second step involves using the derived transformation to transfer outlines of treatment volumes and/or anatomical structures drawn on the images of one imaging study to the images of another study, usually the treatment planning CT. Solid modelling and image processing techniques have been adapted and developed further to accomplish this task. Clinical examples and phantom studies are presented which verify the different aspects of these techniques and demonstrate the accuracy with which they can be applied. Clinical use of these techniques for treatment planning has resulted in improvements in localization of treatment volumes and critical structures in the brain. These improvements have allowed greater sparing of normal tissues and more precise delivery of energy to the desired irradiation volume. It is believed that these improvements will have a positive impact on the outcome of radiation therapy.
Kessler
M L
ML
Graduate Group in Biophysics and Medical Physics, University of California, Berkeley 94720.
Pitluck
S
S
Petti
P
P
Castro
J R
JR
D000465:Algorithms; D001254:Astrocytoma / Q000175:diagnosis / Q000532:radiotherapy; D002817:Chordoma / Q000175:diagnosis / Q000532:radiotherapy; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D014055:Tomography, Emission-Computed; D014057:Tomography, X-Ray Computed