Assessment, validation, and optimization of elastic volume-to-volume and volume-to-slice image registration algorithms for the visualization of inverse treatment planning data in MRI interventional sequences for the percutaneous 192Ir-HDR-brachytherapy of

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Magnetic resonance imaging (MRI) guided percutaneous high-dose-rate-brachytherapy with 192Iridium (192Ir-HDR-BT) facilitates the precise destruction of irresectable liver tumors. One or more flexible, biocompatible catheters with closed tips (so-called applicators) are percutaneously implanted within the tumors in the course of a 192Ir-HDR-BT session. Due to the steep dose gradient around the 192Ir-source the optimal geometric configuration of one or several applicators is crucial for the application of the prescribed cytotoxic dose within the tumor and sparing of adjacently localized structures at risk. Inverse treatment planning facilitates the definition of the optimal geometric applicator configuration before the implantation. It enables the radiotherapist to define virtual applicators, contour target lesions and structures at risk in high quality MRI datasets such as Gd-EOB-DTPA enhanced T1 weighted, fat saturated 3D-gradientecho sequences (T1w fs 3D-GRE) acquired the day before the 192Ir-HDR-BT treatment. The transfer of this structural information into the workflow of MRI-guided 192Ir-HDR-BT would increase the safety and efficiency of this method. This could be accomplished by visualization of the information in the MRI planning data acquired immediately before applicator implantation and in the MRI interventional sequence acquired for guidance during tumor targeting. The fundamental prerequisite for the visualization is the implementation of accurate and robust volume-to-volume and volume-to-slice image registration algorithms 1. to transform the structural information of the inverse treatment planning (especially the optimized applicator positions) into the MRI planning data (Gd-EOB-DTPA enhanced T1w fs 3D-GRE) and 2. to transfer the information from the planning data to the MRI interventional sequence (Gd-EOB-DTPA enhanced T1w fs single-slice [2D]-GRE). The image registration algorithms must compensate the elastic deformation of the liver, target lesions and structures at risk. They must also yield robust results in spite of differences in image acquisition parameters, signal- / contrast-to-noise-ratios and relative data sparsity of the MRI interventional sequence. Currently there is no robust approach for the volume-to-slice registration scenario of liver MRI datasets. The primary aim of this project thus is the assessment, validation and if necessary optimization of elastic volume-to-volume and volume-to-slice image registration algorithms of volume- and interventional single-slice MRI datasets of the liver utilizing the Insight Segmentation and Registration Toolkit (ITK). The assessment shall include the definition of the minimum requirement with regards to accuracy and robustness, applicable distance dimensions to validate the performance of different image registration algorithms, and especially the investigation, specification and implementation of an image registration strategy for the volume-to-slice scenario.