For the application of magnetic resonance imaging (MRI) in precision radiotherapy, image distortions must be reduced to a minimum to maintain geometrical accuracy. Recently, two-dimensional (2D) and three-dimensional (3D) algorithms for MRI-device-specific distortion corrections were developed by the manufacturers of MRI devices. A previously developed phantom (Karger C P et al 2003 Phys. Med. Biol. 48 211-21) was used to quantify and assess the size of geometrical image distortions before and after application of the 2D and 3D correction algorithm in the head region. Four different types of MRI devices with different gradient systems were measured. For comparison, measurements were also performed with two computed tomography (CT) devices. Mean distortions of up to 4.6+/-1.4 mm (maximum: 5.8 mm) were found prior to the correction. After the correction, the mean distortions were well below 2.0 mm in most cases. Distortions in the CT images were below or equal to 1.0 mm on average. Generally, the 3D algorithm produced comparable or better results than the 2D algorithm. The remaining distortions after the correction appear to be acceptable for fractionated radiotherapy.

No abstract

The purpose of this study was to test the accuracy of a commercially available deformable image registration tool in a clinical situation. In addition, to demonstrate a method to evaluate the resulting transformation of such a tool to a reference defined by multiple experts. For 16 patients (seven head and neck, four thoracic, five abdominal), 30‐50 anatomical landmarks were defined on recognizable spots of a planning CT and a corresponding fraction CT. A commercially available deformable image registration tool, Velocity AI, was used to align all fraction CTs with the respective planning CTs. The registration accuracy was quantified by means of the target registration error in respect to expert‐defined landmarks, considering the interobserver variation of five observers. The interobserver uncertainty of the landmark definition in our data sets is found to be 1.2±1.1thinmathspacemm. In general the deformable image registration tool decreases the extent of observable misalignments from 4‐8 mm to 1‐4 mm for nearly 50% of the landmarks (to 77% in sum). Only small differences are observed in the alignment quality of scans with different tumor location. Smallest residual deviations were achieved in scans of the head and neck region (79%,≤thinmathspace4thinmathspacemm) and the thoracic cases (79%,≤thinmathspace4thinmathspacemm), followed by the abdominal cases (59%,≤thinmathspace4thinmathspacemm). No difference is observed in the alignment quality of different tissue types (bony vs. soft tissue). The investigated commercially available deformable image registration tool is capable of reducing a mean target registration error to a level that is clinically acceptable for the evaluation of retreatment plans and replanning in case of gross tumor change during treatment. Yet, since the alignment quality needs to be improved further, the individual result of the deformable image registration tool has still to be judged by the physician prior to application.PACS numbers: 87.57.nj, 87.57.N‐, 87.55.‐x

No abstract

Image registration has many medical applications in diagnosis, therapy planning and therapy. Especially for time-adaptive radiotherapy, an efficient and accurate elastic registration of images acquired for treatment planning, and at the time of the actual treatment, is highly desirable. Therefore, we developed a fully automatic and fast block matching algorithm which identifies a set of anatomical landmarks in a 3D CT dataset and relocates them in another CT dataset by maximization of local correlation coefficients in the frequency domain. To transform the complete dataset, a smooth interpolation between the landmarks is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows separate processing of image discontinuities like temporally changing air cavities in the intestinal track or rectum. The result is a fully transformed 3D planning dataset (planning CT as well as delineations of tumour and organs at risk) to a verification CT, allowing evaluation and, if necessary, changes of the treatment plan based on the current patient anatomy without time-consuming manual re-contouring. Typically the total calculation time is less than 5 min, which allows the use of the registration tool between acquiring the verification images and delivering the dose fraction for online corrections. We present verifications of the algorithm for five different patient datasets with different tumour locations (prostate, paraspinal and head-and-neck) by comparing the results with manually selected landmarks, visual assessment and consistency testing. It turns out that the mean error of the registration is better than the voxel resolution (2 x 2 x 3 mm(3)). In conclusion, we present an algorithm for fully automatic elastic image registration that is precise and fast enough for online corrections in an adaptive fractionated radiation treatment course.

A new method is described that allows precise modelling of organs at risk and target volume for radiation therapy of intra-ocular tumours. The aim is to optimize the dose distribution and thus to reduce normal tissue complication probability. A geometrical 3D model based on elliptic shapes was developed that can be used for multimodal model-based segmentation of 3D patient data. The tumour volume cannot be clearly identified in CT and MR data, whereas the tumour outline can be discriminated very precisely in fundus photographs. Therefore, a multimodal 2D fundus diagram was developed, which allows us to correlate and display simultaneously information extracted from the eye model, 3D data and the fundus photograph. Thus, the connection of fundus diagram and 3D data is well-defined and the 3D volume can be calculated directly from the tumour outline drawn onto the fundus photograph and the tumour height measured by ultrasound. The method allows the calculation of a precise 3D eye model of the patient, including the different structures of the eye as well as the tumour volume. The method was developed as part of the new 3D treatment planning system OCTOPUS for proton therapy of ocular tumours within a national research project together with the Hahn-Meitner-Institut Berlin.

No abstract

The cone beam CT attached to a LINAC allows the acquisition of a CT scan of the patient in treatment position directly before treatment. Its image quality is sufficient for determining target point correction vectors. With the presented workflow, a target point correction within a clinically reasonable time frame is possible. This increases the treatment precision, and potentially the complex patient fixation techniques will become dispensable.