Abstract
We are implementing the use of magnetic resonance (MR) images for head and neck radiotherapy planning, which involves their registration with computed tomography (CT). The quality assurance (QA) of the registration process was an initial step of this program.A phantom was built, and appropriate materials were identified to produce clinically relevant MR T1 and T2 contrast for its constituent “anatomy.” We performed a characterization of the distortion detectable within our phantom. Finally, we assessed the accuracy of image registration by contouring structures in the registered/fused data sets using the treatment planning system. Each structure was contoured using each modality, in turn, blind of the other. The position, area, and perimeter of each structure were assessed as a measure of accuracy of the entire image registration process.Distortion effects in the MR image were shown to be minimized by choosing a suitable (≥±30 kHz) receiver bandwidth. Remaining distortion was deemed clinically acceptable within ±15 cm of the magnetic field isocenter. A coefficient of agreement (A) analysis gave values to be within 9% of unity, where A=RaRp and Ra/p is the ratio of the area/perimeter of a particular structure on CT to that on MR. The center of each structure of interest agreed to within 1.8 mm.A QA process has been developed to assess the accuracy of using multimodality image registration in the planning of radiotherapy for the head and neck; we believe its introduction is feasible and safe.PACS numbers: 87.53.Xd, 87.57.Gg, 87.59.Fm; 87.61.‐c, 87.66.Xa
Highlights
Modern three-dimensional radiotherapy treatment planning of cancer demands we use volumetric image data sets to design the conformal therapy of tumors, and conformal avoidance of the proximal, dose limiting, organs at risk.[1]. Most commonly, X ray computed tomography (CT) is used to provide this three-dimensional model of the patient; the Hounsfield/CT number information it provides can be calibrated to give an electron density map of the patient, which is used to make corrections in dose calculations
The first point can be addressed by choosing a convolution model calculation.[3]. Fast Fourier Transform (FFT)-based algorithms can be used for most anatomical sites[4] and “full convolution” superposition[5] based algorithms when density corrections play a vital role, for example, in the lung
Using the materials described in this paper we have constructed a quality assurance phantom to evaluate MR distortions and registration accuracy
Summary
Modern three-dimensional radiotherapy treatment planning of cancer demands we use volumetric image data sets to design the conformal therapy of tumors, and conformal avoidance of the proximal, dose limiting, organs at risk.[1]. The first point can be addressed by choosing a convolution model calculation.[3] Fast Fourier Transform (FFT)-based algorithms can be used for most anatomical sites[4] and “full convolution” superposition[5] based algorithms when density corrections play a vital role, for example, in the lung. This point reinforces the need for X ray CT descriptions of the patient, which give electron density information. We are implementing multimodality planning for anatomical sites where enhanced tumor/organ delineation and more sophisticated dose calculations will provide benefit
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