Deformable image registration (DIR) is widely used in radiotherapy for dose propagation and accumulation, but uncertainty in the underlying deformation can substantially affect clinically relevant dose estimates. We present a practical probabilistic framework for propagating DIR uncertainty to voxel-wise dose statistics and dose-volume histograms (DVHs). The method models the mapped correspondence at each voxel as a random variable governed by a transparent local certainty map that can be defined by simple safety margins, structure-boundary mismatch, or structure-wise conservative uncertainty values. This yields interpretable quantities such as dose probabilities, expected dose, confidence bounds, and induced DVH envelopes. The framework is designed to remain lightweight and interpretable: it avoids complex biomechanical or ensemble-based uncertainty models and instead emphasizes simple parameterization, computational feasibility, and transparent dose metrics. We further introduce a structure-guided in/out strategy as an optional refinement that restricts mapping probabilities to anatomically plausible target regions. The approach is demonstrated on a prostate radiotherapy case study and used to compare different certainty-map strategies and probability kernels. The experiments show that the certainty-map design has a stronger effect on resulting dose and DVH uncertainty bounds than the specific kernel choice, while the additional benefit of the in/out strategy is case-dependent and modest in the present example. Overall, the proposed framework provides a transparent way to incorporate DIR uncertainty into radiotherapy dose assessment and to study how modelling choices affect propagated dose metrics.

BACKGROUND:Deformable dose accumulation (DDA) uncertainty models can inform treatment decisions by communicating the dosimetric impact of deformable image registration (DIR) errors over multiple fractions. Currently there is limited guidance on how end-users can validate such models in the clinic. PURPOSE:We propose an end-user validation sequence for DDA uncertainty modelling tools, akin to an acceptance test, using existing patient data and a clinical treatment planning system (TPS) as the evaluation platform. METHODS:The proposed test sequence begins with a single "fixed" image (planning CT with associated contours and treatment plan) and a "moving" image (e.g., fractional synthetic CT with calculated dose) and uses the TPS to simulate multiple fractional DIRs as input to a DDA uncertainty model. Outputs of the uncertainty tool-including volumetric images of DIR spatial uncertainties, and associated uncertainties on propagated dose-are imported back to the TPS and a series of visual and semi-quantitative (point-based and DVH-based) cross-checks are carried out. Emphasis is placed on the use of standard dose and distance measurement tools, in conjunction with vendor-provided equations, to evaluate correctness of the uncertainty tool outputs at contour boundaries and in bulk tissue, considering variable DIR quality for both targets and organs at risk. RESULTS:The test sequence is demonstrated for a non-clinical uncertainty tool using a clinical bladder case. Agreement within 2 voxels (for spatial uncertainties) and up to 5% of the prescribed dose (for dose uncertainties) is shown to be achievable for regions of plausible deformation and stable dose gradient (e.g., < 5%/voxel). CONCLUSIONS:As the use of DDA in adaptive treatment becomes more commonplace, use of DDA uncertainty tools will become increasingly important to inform adaptive treatment decisions. This work represents an important effort to formalize an end-user validation process using standardly available clinical tools.

Recent studies indicate that malignant breast lesions can be predicted from structural changes in prior exams of preventive breast MRI examinations. Due to non-rigid deformation between studies, spatial correspondences between structures in two consecutive studies are lost. Thus, deformable image registration can contribute to predicting individual cancer risks. This study evaluates a registration approach based on a novel breast mask segmentation and non-linear image registration based on data from 5 different sites. The landmark error (mean +/- standard deviation [1st quartile, 3rd quartile]), annotated by three radiologists, is 2.9 +/- 2.8 [1.3, 3.2] mm when leaving out two outlier cases from the evaluation for which the registration failed completely. We assess the inter-observer variabilities of keypoint errors and find an error of 3.6 +/- 4.7 [1.6, 4.0] mm, 4.4 +/- 4.9 [1.8, 4.8] mm, and 3.8 +/- 4.0 [1.7, 4.1] mm when comparing each radiologist to the mean keypoints of the other two radiologists. Our study shows that the current state of the art in registration is well suited to recover spatial correspondences of structures in cancerous and non-cancerous cases, despite the high level of difficulty of this task.

Deformable image registration (DIR) is an important tool in radio therapy where it is used in order to align a baseline CT and daily low-dose cone beam CT (CBCT) scans. DIR allows the propagation of irradiation plans, Hounsfield units and contours of anatomical structures, respectively, which enables tracking of applied doses over time and generation of daily synthetic CT images. Furthermore, DIR allows to overcome segmentation of structures in CBCT images at each fraction.

Accurate registration of CT and CBCT images is key for adaptive radiotherapy. A particular challenge is the alignment of flexible organs, such as bladder or rectum, that often yield extreme deformations. In this work we analyze the impact of so-called structure guidance for learning based registration when additional segmentation information is provided to a neural network. We present a novel weakly supervised deep learning based method for multi-modal 3D deformable CT-CBCT registration with structure guidance constraints. Our method is not supervised by ground-truth deformations and we use the energy functional of a variational registration approach as loss for training. Incorporating structure guidance constraints in our learning based approach results in an average Dice score of 0.91± 0.08 compared to a score of 0.76± 0.15 for the same method without constraints. An iterative registration approach with structure guidance results in a comparable average Dice score of 0.91± 0.09 . However, learning based registration requires only a single pass through the network, yielding computation of a deformation fields in less than 0.1 s which is more than 100 times faster than the runtime of iterative registration.

We present a highly parallel method for accurate and efficient variational deformable 3D image registration on a consumer-grade graphics processing unit (GPU). We build on recent matrix-free variational approaches and specialize the concepts to the massively-parallel manycore architecture provided by the GPU. Compared to a parallel and optimized CPU implementation, this allows us to achieve an average speedup of 32:53 on 986 real-world CT thorax-abdomen follow-up scans. At a resolution of approximately 2563 voxels, the average runtime is 1:99 seconds for the full registration. On the publicly available DIR-lab benchmark, our method ranks third with respect to average landmark error at an average runtime of 0:32 seconds.

This review provides an overview to current endovascular techniques which may reduce the amount of contrast agents and radiation exposure to patient and staff. One integral part for the success of endo vas cular procedures is innovative and improved vascular imaging. A major challenge during these interventions is visualizing the position and orientation of the catheter being inserted. This is typically achieved by intermittent X-ray imaging and contrast agent application. While endovascular techniques are improving, imaging during the procedure is still dependent on contrast agents and X-Rays with their known disadvantages. Looking at current developments towards radiation-free localization of endo vascular tools, the visualization and proper integration of this spatial information will become a key technology. After an extensive literature review of pathophysiology and clinical side effects of contrast agents and radiation exposure, we describe established procedures added with current experimental work to reduce these well known side effects in endovascular procedures. Based on the ALARA-principles modern angiography systems show optimized technical settings to deliver the best image quality at low radiation levels, such as optimal collimation, flat panel detector technology, pulse mode, auto exposure settings, low-dose modes and anti-scatter grids. Carbon dioxide (CO2) angiography, contrast-enhanced ultrasound (CEUS) imaging, appropriate C-arm angulation for optimal visualization have become standard procedures in large vascular centers. Optical fiber technologies combined with navigation techniques, augmented reality and holographic visualization techniques are under current experimental development. These techniques seem to have the potential as a disruptive technology in future endovascular therapy. Advanced image application and upcoming techniques focus contrast agent and radiation exposure and even some show a disruptive character. The navigated visualization of vessels and spatial position and endovascular tools during interventional procedures will probably become a key technology in future.

BACKGROUND:Deformable image registration (DIR) is a key component in many radiotherapy applications. However, often resulting deformations are not satisfying, since varying deformation properties of different anatomical regions are not considered. To improve the plausibility of DIR in adaptive radiotherapy in the male pelvic area, this work integrates a local rigidity deformation model into a DIR algorithm.METHODS:A DIR framework is extended by constraints, enforcing locally rigid deformation behavior for arbitrary delineated structures. The approach restricts those structures to rigid deformations, while surrounding tissue is still allowed to deform elastically. The algorithm is tested on ten CT/CBCT male pelvis datasets with active rigidity constraints on bones and prostate and compared to the Varian SmartAdapt deformable registration (VSA) on delineations of bladder, prostate and bones.RESULTS:The approach with no rigid structures (REG0) obtains an average dice similarity coefficient (DSC) of 0.87 ± 0.06 and a Hausdorff-Distance (HD) of 8.74 ± 5.95 mm. The new approach with rigid bones (REG1) yields a DSC of 0.87 ± 0.07, HD 8.91 ± 5.89 mm. Rigid deformation of bones and prostate (REG2) obtains 0.87 ± 0.06, HD 8.73 ± 6.01 mm, while VSA yields a DSC of 0.86 ± 0.07, HD 10.22 ± 6.62 mm. No deformation grid foldings are observed for REG0 and REG1 in 7 of 10 cases; for REG2 in 8 of 10 cases, with no grid foldings in prostate, an average of 0.08 % in bladder (REG2: no foldings) and 0.01 % inside the body contour. VSA exhibits grid foldings in each case, with an average percentage of 1.81 % for prostate, 1.74 % for bladder and 0.12 % for the body contour. While REG1 and REG2 keep bones rigid, elastic bone deformations are observed with REG0 and VSA. An average runtime of 26.2 s was achieved with REG1; 31.1 s with REG2, compared to 10.5 s with REG0 and 10.7 s with VMS.CONCLUSIONS:With accuracy in the range of VSA, the new approach with constraints delivers physically more plausible deformations in the pelvic area with guaranteed rigidity of arbitrary structures. Although the algorithm uses an advanced deformation model, clinically feasible runtimes are achieved.

3rd ESTRO Forum 2015 S197 shallow depths with a Markus parallel plate ionization chamber (PTW, type 23343).Second, the rotation of the Xray tube was implemented in the MC code to mimic the volumetric (3D) acquisition mode, whose dosimetric validation is currently undergoing.Results: Dose measurements and MC simulation are in agreement: a passing rate greater than 95% and 82 % was observed for depth dose curves and dose profiles respectively, for local gamma-index criteria of 2%/2 mm (cf.Fig. 1.).Some discrepancies were noticed, in the first millimeters of the depth dose curves, resulting of difficulties to perform accurate measurements at the surface, even with a plate chamber.Finally, the HVL values obtained by simulation were in close agreement with measured values (cf.Tab.1.).Tab. 1. Measured and calculated HVL values at 120 kV with and without bowtie filter. Conclusions:The XVI MC model developed with PENELOPE was successfully validated in 2D mode against an extensive set of measurements, and preliminary results for the 3D acquisition mode are very encouraging.Future work includes a comprehensive validation of the simulation tool by comparing simulation results with dosimetric measurements in anthropomorphic phantoms.

We introduce a new highly parallel and memory efficient deformable image registration algorithm to handle challenging clinical applications. The algorithm is based on the normalized gradient fields (NGF) distance measure and Gauss-Newton numerical optimization. By carefully analyzing the mathematical structure of the problem, a matrix-free Hessian-vector multiplication for NGF is derived, giving a highly integrated formulation. Embedding the new scheme in a full, non-linear image registration algorithm enables fast calculations on high resolutions with dramatically reduced memory consumption. The new approach provides linear scalability compared with a traditional sparse-matrix-based scheme. The algorithm is evaluated on a challenging problem from radiotherapy, where pelvis cone-beam CT and planning CT images are registered.Speedups up to a factor of 149.3 for a single Hessian-vector multiplication and of 20.3 for a complete non-linear registration are achieved.

INTRODUCTION Over the last decade endovascular stenting of aortic aneurysm (EVAR) has been developed from single centre experiences to a standard procedure. With increasing clinical expertise and medical technology advances treatment of even complex aneurysms are feasible by endovascular methods. One integral part for the success of this minimally invasive procedure is innovative and improved vascular imaging to generate exact measurements and correct placement of stent prosthesis. One of the greatest difficulty in learning and performing this endovascular therapy is the fact that the three-dimensional vascular tree has to be overlaid with the two-dimensional angiographic scene by the vascular surgeon. MATERIAL AND METHODS We report the development of real-time navigation software, which allows a three-dimensional endoluminal view of the vascular system during an EVAR procedure in patients with infrarenal aortic aneurysm. We used the preoperative planning CT angiography for three-dimensional reconstruction of aortic anatomy by volume-rendered segmentation. At the beginning of the intervention the relevant landmarks are matched in real-time with the two-dimensional angiographic scene. During the intervention the software continously registers the position of the guide-wire or the stent. An additional 3D-screen shows the generated endoluminal view during the whole intervention in real-time. RESULTS We examined the combination of hardware and software components including complex image registration and fibre optic sensor technology (fibre-bragg navigation) with integration in stent graft introducer sheaths using patient-specific vascular phantoms in an experimental setting. From a technical point of view the feasibility of fibre-Bragg navigation has been proven in our experimental setting with patient-based vascular models. Three-dimensional preoperative planning including registration and simulation of virtual angioscopy in real time are realised. CONCLUSION The aim of the Nav-CARS-EVAR concept is reduction of contrast medium and radiation dose by a three-dimensional navigation during the EVAR procedure. To implement fibre-Bragg navigation further experimental studies are necessary to verify accuracy before clinical application.

This article presents a brief review on nonlinear registration techniques based on a variational formulation. The main advantages of this setting are its great modeling potential and its modular setting. This enables easy changes and adaptations for fine-tuning for particular applications. Furthermore, this setting also enables the integration of additional information. This can be formulated in terms of either soft or hard constraints, such as privileged handling of anatomical landmarks, local rigidity of structures, and emphasis on volume preservation or limitations of volume changes. The latter is of particular interest for one-to-one displacement fields (also known as diffeomorphic registration). The setting is supported by a rigorous mathematical theory and can be translated into fast and stable implementations. Although the focus of this article is not on the practical realization, important concepts such as multiscale and multilevel representations and reduction of ambiguities of results based on proper regularization are also briefly discussed.

Introduction: Over the last decade, interventional endovascular stenting of aortic aneurysm has been developed from single center experiences to a standard procedure in many countries. One integral part for the success of this minimally-invasive procedure is innovative and improved vascular imaging. One of the most difficulty in learning and performing this interventional therapy is the fact, that the three-dimensional vascular tree has to be overlain with the two-dimensional angiographic scene by the vascular surgeon. Methods: We report the development of real-time navigation software, which allows a three-dimensional endoluminal view of the vascular system during an EVAR-procedure in patients with infrarenal aortic aneurysm. Even in patients with complex anatomy, the surgeon or the interventionalist achieves an accurate option of spatial perception concerning the current vascular anatomy, e.g. the position of the guide-wire. We analyzed patients with an infra-renal aortic aneurysm in which the planning CT-Scans were volume-rendered. At the beginning of the intervention the relevant landmarks were matched in real-time with the two-dimensional angiographic scene. During intervention the software continuously registers the position of the guide-wire or the stent. An additional 3d-screen shows the generated endoluminal view during the whole intervention in real-time. Results: Our preliminary results of navigated endoluminal virtual angioscopy are promising. The “Virtual angioscope” may improve intraoperative visualization, placement of guide-wires and stents. It may reduce the amount of contrast agents and exposure to x-Rays. The prototype also offers the possibility of intervention planning and simulation which may lead to a reduced learning curve and therefore patient safety. Conclusion: Not only for pre-interventional simulation, even in training of inexperienced surgeons or interventionalists, our 3D navigation system may offer better visualization in complex endoluminal aortic procedures.

We present a super fast variational algorithm for the challenging problem of multimodal image registration. It is capable of registering full-body CT and PET images in about a second on a standard CPU with virtually no memory requirements. The algorithm is founded on a Gauss-Newton optimization scheme with specifically tailored, mathematically optimized computations for objective function and derivatives. It is fully parallelized and perfectly scalable, thus directly suitable for usage in many-core environments. The accuracy of our method was tested on 21 PET-CT scan pairs from clinical routine. The method was able to correct random distortions in the range from -10 cm to 10 cm translation and from -15° to 15° degree rotation to subvoxel accuracy. In addition, it exhibits excellent robustness to noise.

In navigated liver surgery it is an important task to align intra-operative data to pre-operative planning data. This work describes a method to register pre-operative 3D-CT-data to tracked intra-operative 2D US-slices. Instead of reconstructing a 3D-volume out of the two-dimensional US-slice sequence we directly apply the registration scheme to the 2D-slices. The advantage of this approach is manyfold. We circumvent the time consuming compounding process, we use only known information, and the complexity of the scheme reduces drastically. As the liver is a non-rigid organ, we apply non-linear techniques to take care of deformations occurring during the intervention. During the surgery, computing time is a crucial issue. As the complexity of the scheme is proportional to the number of acquired slices, we devise a scheme which starts out by selecting a few "key-slices" to be used in the non-linear registration scheme. This step is followed by multi-level/multi-scale strategies and fast optimization techniques. In this abstract we briefly describe the new method and show first convincing results.

In der plastischen Chirurgie ist es zur Bestimmung der Therapie notwendig, den Schweregrad einer Verbrennung genau einzusch ätzen. Hierzu ist ein Verfahren entwickelt worden, das auf der Verarbeitung visueller Aufnahmen der Wunde in unterschiedlichen Farbspektren beruht. Diese müssen zur Weiterverarbeitung unbedingt deckungsgleich sein, so dass ein Registrierungsproblem gegeben ist. Zur Lösung dieses Problems präsentieren wir einen parametrischen Registrierungsansatz, der auf einer speziellen Vorverarbeitung und der Modifikation eines bekannten Distanzmaßes beruht. Die einzelnen Schritte und Bausteine werden erläutert. Das Verfahren ist an 50 Datensätzen aus der klinischen Praxis getestet worden, wobei es sich als besonders geeignet erwiesen hat.