Interstitial photodynamic therapy has shown promising results in the treatment of locally advanced head and neck cancer. In this therapy, systemic administration of a light-sensitive drug is followed by insertion of multiple laser fibers to illuminate the tumor and its margins. Image-based pretreatment planning is employed in order to deliver a sufficient light dose to the complex locally advanced head-and-neck cancer anatomy, in order to meet clinical requirements. Unfortunately, the tumor may deform between pretreatment imaging for the purpose of planning and intraoperative imaging when the plan is executed. Tumor deformation may result from the mechanical forces applied by the light fibers and variation of the patient’s posture. Pretreatment planning is frequently done with the assistance of computed tomography or magnetic resonance imaging in an outpatient suite, while treatment monitoring and control typically uses ultrasound imaging due to considerations of costs and availability in the operation room. This article presents a computational method designed to bridge the gap between the 2 imaging events by taking a tumor geometry, reconstructed during preplanning, and by following the displacement of fiducial markers, which are initially placed during the preplanning procedure. The deformed tumor shape is predicted by solving an inverse problem, seeking for the forces that would have resulted in the corresponding fiducial marker displacements. The computational method is studied on spheres of variable sizes and demonstrated on computed tomography reconstructed locally advanced head and neck cancer model. Results of this study demonstrate an average error of less than 1 mm in predicting the deformed tumor shape, where 1 mm is typically the order of uncertainty in distance measurements using magnetic resonance imaging or computed tomography imaging and high-quality ultrasound imaging. This study further demonstrates that the deformed shape can be calculated in a few seconds, making the proposed method clinically relevant.
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