Applicability and accuracy of the rapidly developing tools and workflows for image-guided radiotherapy need to be validated under realistic treatment-like conditions. We present the construction of the ADAM-pelvis phantom, an anthropomorphic, deformable and multimodal (CT and MRI) phantom of the male pelvis. The phantom covers patient-like uncertainties in image-guided radiotherapy workflows including imaging artifacts for the special case of the human anatomy as well as organ motion.Principles and methods were further improved from previous work. The phantom includes surrogates for muscle tissue, adipose, inner and outer bone, as well as deformable silicone organs. Anthropomorphic shapes are realized with 3D-printing techniques for the bone and the construction of the hollow silicone organ shells. Organs are constructed from patient image segmentation and further guided by reported deformation models. Imaging markers and pockets for dosimeters are included in the organ shells.The improved phantom surrogates match imaging characteristics in MRI (T1 and T2 relaxation time) and CT (Hounsfield units) of human tissues. The surrogates are suited for long term use (several months) of the phantom. Previously reported artifacts of the muscle surrogate were avoided by improved composition of the used agarose gel. Interfractional organ motion is successfully realized for the water filled bladder and the air filled rectum and showed to be reproducible with deviation below 1 mm. Volume variations of both induce displacement, rotation and deformation of the prostate.We present solutions for the construction of an anthropomorphic phantom suitable for MRI and CT imaging including deformable organs. The developed concepts of phantom surrogates and construction techniques were successfully applied in building the ADAM-pelvis phantom and can as well be adopted for other anthropomorphic phantoms. The presented phantom allows for the systematic and controlled investigation of image-guided radiotherapy workflows in presence of organ motion. NOTEOriginal content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

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

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BackgroundTo evaluate the impact of image-guided radiation therapy (IGRT) versus non-image-guided radiation therapy (non-IGRT) on the dose to the clinical target volume (CTV) and the cervical spinal cord during fractionated intensity-modulated radiation therapy (IMRT) for head-and-neck cancer (HNC) patients.Material and MethodsFor detailed investigation, 4 exemplary patients with daily control-CT scans (total 118 CT scans) were analyzed. For the IGRT approach a target point correction (TPC) derived from a rigid registration focused to the high-dose region was used. In the non-IGRT setting, instead of a TPC, an additional cohort-based safety margin was applied. The dose distributions of the CTV and spinal cord were calculated on each control-CT and the resulting dose volume histograms (DVHs) were compared with the planned ones fraction by fraction. The D50 and D98 values for the CTV and the D5 values of the spinal cord were additionally reported.ResultsIn general, the D50 and D98 histograms show no remarkable difference between both strategies. Yet, our detailed analysis also reveals differences in individual dose coverage worth inspection. Using IGRT, the D5 histograms show that the spinal cord less frequently receives a higher dose than planned compared to the non-IGRT setting. This effect is even more pronounced when looking at the curve progressions of the respective DVHs.ConclusionsBoth approaches are equally effective in maintaining CTV coverage. However, IGRT is beneficial in spinal cord sparing. The use of an additional margin in the non-IGRT approach frequently results in a higher dose to the spinal cord than originally planned. This implies that a margin reduction combined with an IGRT correction helps to maintain spinal cord dose sparing best as possible. Yet, a detailed analysis of the dosimetric consequences dependent on the used strategy is required, to detect single fractions with unacceptable dosimetric deviations.

BackgroundTo analyse the frequency of re-planning and its variability dependent on the IGRT correction strategy and on the modification of the dosimetric criteria for re-planning for the spinal cord in head and neck IG-IMRT.MethodsDaily kV-control-CTs of six head and neck patients (=175 CTs) were analysed. All volumes of interest were re-contoured using deformable image registration. Three IGRT correction strategies were simulated and the resulting dose distributions were computed for all fractions. Different sets of criteria with varying dose thresholds for re-planning were investigated. All sets of criteria ensure equivalent target coverage of both CTVs, but vary in the tolerance threshold of the spinal cord.ResultsThe variations of the D95 and D2 in respect to the planned values ranged from -7% to +3% for both CTVs, and -2% to +6% for the spinal cord. Despite different correction vectors of the three IGRT strategies, the dosimetric differences were small. The number of fractions not requiring re-planning varied between 0% and 11% dependent on the applied IGRT correction strategy. In contrast, this number ranged between 32% and 70% dependent on the dosimetric thresholds, even though these thresholds were only gently modified.ConclusionsThe more precise the planned dose needs to be maintained over the treatment course, the more frequently re-planning is required. The influence of different IGRT correction strategies, even though geometrically notable, was found to be of only limited relevance for the re-planning frequency. In contrast, the definition and modification of thresholds for re-planning have a major impact on the re-planning frequency.

The use of deformable image registration methods in the context of adaptive radiotherapy leads to uncertainties in the simulation of the administered dose distributions during the treatment course. Evaluation of these methods is a prerequisite to decide if a plan adaptation will improve the individual treatment. Current approaches using manual references limit the validity of evaluation, especially for low-contrast regions. In particular, for the head and neck region, the highly flexible anatomy and low soft tissue contrast in control images pose a challenge to image registration and its evaluation. Biomechanical models promise to overcome this issue by providing anthropomorphic motion modelling of the patient. We introduce a novel biomechanical motion model for the generation and sampling of different postures of the head and neck anatomy. Motion propagation behaviour of the individual bones is defined by an underlying kinematic model. This model interconnects the bones by joints and thus is capable of providing a wide range of motion. Triggered by the motion of the individual bones, soft tissue deformation is described by an extended heterogeneous tissue model based on the chainmail approach. This extension, for the first time, allows the propagation of decaying rotations within soft tissue without the necessity for explicit tissue segmentation. Overall motion simulation and sampling of deformed CT scans including a basic noise model is achieved within 30 s. The proposed biomechanical motion model for the head and neck site generates displacement vector fields on a voxel basis, approximating arbitrary anthropomorphic postures of the patient. It was developed with the intention of providing input data for the evaluation of deformable image registration.

PurposeIntensity modulated radiation therapy (IMRT) of head and neck tumors allows a precise conformation of the high-dose region to clinical target volumes (CTVs) while respecting dose limits to organs a risk (OARs). Accurate patient setup reduces translational and rotational deviations between therapy planning and therapy delivery days. However, uncertainties in the shape of the CTV and OARs due to e.g. small pose variations in the highly deformable anatomy of the head and neck region can still compromise the dose conformation. Routinely applied safety margins around the CTV cause higher dose deposition in adjacent healthy tissue and should be kept as small as possible.Materials and MethodsIn this work we evaluate and compare three approaches for margin generation 1) a clinically used approach with a constant isotropic 3 mm margin, 2) a previously proposed approach adopting a spatial model of the patient and 3) a newly developed approach adopting a biomechanical model of the patient. All approaches are retrospectively evaluated using a large patient cohort of over 500 fraction control CT images with heterogeneous pose changes. Automatic methods for finding landmark positions in the control CT images are combined with a patient specific biomechanical finite element model to evaluate the CTV deformation.ResultsThe applied methods for deformation modeling show that the pose changes cause deformations in the target region with a mean motion magnitude of 1.80 mm. We found that the CTV size can be reduced by both variable margin approaches by 15.6% and 13.3% respectively, while maintaining the CTV coverage. With approach 3 an increase of target coverage was obtained.ConclusionVariable margins increase target coverage, reduce risk to OARs and improve healthy tissue sparing at the same time.

Background. To present a new method that determines an optimised IGRT couch correction vector from a displacement vector fi eld (DVF). The DVF is computed by a deformable image registration (DIR) method. The proposed method can improve the quality of volume-of-interest (VOI) alignment in image guided radiation therapy (IGRT), and can serve as a decision-making aid for re-planning. Material and methods. The proposed method was demonstrated using the CT data sets of 11 head-and-neck cancer patients with daily kilovoltage control-CTs. A DVF was computed for each control-CT using a DIR method. The DVF was used for voxel tracking and re-contouring of the VOIs in the control-CTs. Then a rigid body transformation, which could be used as couch correction vector, was optimised. The aim of the optimisation process was to fi nd a vector and rotations that map the deformed VOIs into a specifi ed territory. This territory was defi ned by a margin extension of the VOIs at the time of the planning process. Within this extension, VOI motion and deformation was tolerated. The objective function in the optimisation process was the sum of all volume fractions outside the defi ned territories. Results. The proposed method was able to fi nd a correction vector, which resulted in a coverage of the target volumes of at least 98% in 52.3% of all fractions. In contrast, a standard IGRT correction using a rigid registration method only fulfi lled this criterion in 22.6% of all fractions. The optimisation process took an average of 1.5 minutes per fraction. Conclusion. The knowledge of the deformation of the anatomy allows the determination of an optimised rigid correction vector using our method. The method ensures controlled mapping of the VOIs despite small deformations. If no optimised vector can be determined, re-planning should be considered. Thus, our method can also serve as a decision-making aid for re-planning.