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Purpose: Radio-frequency ablations play an important role in the therapy of malignant liver lesions. The navigation of a needle to the lesion poses a challenge for both the trainees and intervening physicians. Methods: This publication presents a new GPU-based, accurate method for the simulation of radio-frequency ablations for lesions at the needle tip in general and for an existing visuo-haptic 4D VR simulator. The method is implemented real-time capable with Nvidia CUDA. Results: It performs better than a literature method concerning the theoretical characteristic of monotonic convergence of the bioheat PDE and a in vitro gold standard with significant improvements (p < 0.05) in terms of Pearson correlations. It shows no failure modes or theoretically inconsistent individual simulation results after the initial phase of 10 seconds. On the Nvidia 1080 Ti GPU it achieves a very high frame rendering performance of >480 Hz. Conclusion: Our method provides a more robust and safer real-time ablation planning and intraoperative guidance technique, especially avoiding the over-estimation of the ablated tissue death zone, which is risky for the patient in terms of tumor recurrence. Future in vitro measurements and optimization shall further improve the conservative estimate., 18 pages, 14 figures, 1 table, 2 algorithms, 2 movies

We compare axial 2D U-Nets and their 3D counterparts for pixel/voxel-based segmentation of five abdominal organs in CT scans. For each organ, two competing CNNs are trained. They are evaluated by performing five-fold cross-validation on 80 3D images. In a two-step concept, the relevant area containing the organ is first extracted by detected bounding boxes and then passed as input to the organ-specific U-Net. Furthermore, a random regression forest approach for the automatic detection of bounding boxes is summarized from our previous work. The results show that the 2D U-Net is mostly on par with the 3D U-Net or even outperforms it. Especially for the kidneys, it is significantly better suited in this study.

A two-step concept for 3D segmentation on 5 abdominal organs inside volumetric CT images is presented. Firsteach relevant organ’s volume of interest is extracted as bounding box. The extracted volume acts as input for asecond stage, wherein two compared U-Nets with different architectural dimensions re-construct an organ segmen-tation as label mask. In this work, we focus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial resultsindicate Dice improvements of about 6% at maximum. In this study to our surprise, liver and kidneys for instancewere tackled significantly better using the faster and GPU-memory saving 2D U-Nets. For other abdominal keyorgans, there were no significant differences, but we observe highly significant advantages for the 2D U-Net interms of GPU computational efforts for all organs under study.

The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49 mm; 51 of the outliers deviated more than two catheter widths (3.4 mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed–Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3 σ maximum errors were 2.44 mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.

The ability to generate 3D patient models in a fast and reliable way, is of great importance, e.g. for the simulation of liver punctures in virtual reality simulations. The aim is to automatically detect and segment abdominal structures in CT scans. In particular in the selected organ group, the pancreas poses a challenge. We use a combination of random regression forests and 2D U-Nets to detect bounding boxes and generate segmentation masks for five abdominal organs (liver, kidneys, spleen, pancreas). Training and testing is carried out on 50 CT scans from various public sources. The results show Dice coefficients of up to 0.71. The proposed method can theoretically be used for any anatomical structure, as long as sufficient training data is available.

Volumenwachstumssegmentierungstechniken leiden oftmals unter dem ¨ Uberlauf in falsch positive Organe oder Strukturen. Segmentierungs- barrieren können hier abhelfen, indem wie bei einem Staudamm eine kün- stliche Grenze gezogen wird. Aktuell werden diese Markierungen häufig noch als manuelle Scribbles vom Benutzer i.d.R. mühsam schichtweise erstellt. Hier wird ein neuer vollautomatischer Ansatz zum Finden von 3D-Barrieren mit maschinellen Lernmethoden vorgestellt. Die Barrieren- fehler unmittelbar angrenzender Organe liegen zwischen 4,9±1,3 und 10,3±3,6 mm.

Easily adaptable system concept for other fields of intervention.Whole body part segmentation, i.e. upper abdomen, of CT image data for liver punctures.Efficiently processable segmentation system pipeline.Thorough evaluation of segmentation results with 10 test patients. Background and ObjectiveThis work presents a new time-saving virtual patient modeling system by way of example for an existing visuo-haptic training and planning virtual reality (VR) system for percutaneous transhepatic cholangio-drainage (PTCD). MethodsOur modeling process is based on a generic patient atlas to start with. It is defined by organ-specific optimized models, method modules and parameters, i.e. mainly individual segmentation masks, transfer functions to fill the gaps between the masks and intensity image data. In this contribution, we show how generic patient atlases can be generalized to new patient data. The methodology consists of patient-specific, locally-adaptive transfer functions and dedicated modeling methods such as multi-atlas segmentation, vessel filtering and spline-modeling. ResultsOur full image volume segmentation algorithm yields median DICE coefficients of 0.98, 0.93, 0.82, 0.74, 0.51 and 0.48 regarding soft-tissue, liver, bone, skin, blood and bile vessels for ten test patients and three selected reference patients. Compared to standard slice-wise manual contouring time saving is remarkable. ConclusionsOur segmentation process shows out efficiency and robustness for upper abdominal puncture simulation systems. This marks a significant step toward establishing patient-specific training and hands-on planning systems in a clinical environment.

Radio-Frequenz-Ablationen spielen eine wichtige Rolle in der Therapie von malignen Leberherden. Die Navigation einer Nadel zur Lasion stellt eine Herausforderung fur den auszubildenden und auch fur den intervenierenden Arzt dar. Daher ist es wunschenswert, Trainings- und Planungssysteme basierend auf medizinischen Bilddaten und Methoden der visuo-haptischen Virtual-Reality-Simulation anzubieten. In diesem Papier wird eine Methode zur Simulation von Ablationen an der Nadelspitze fur einen bestehenden VR-Simulator nach erfolgreicher Nadelnavigation zum Lasionsherd vorgestellt. Ein verbessertes Modell wurde echtzeitfahig (CUDA) umgesetzt, evaluiert und erreicht hochperformant robustere und sicherere Planungsergebnisse als die Literatur.

This article presents methods for direct visuo-haptic 4D volume rendering of virtual patient models under respiratory motion. Breathing models are computed based on patient-specific 4D CT image data sequences. Virtual patient models are visualized in real-time by ray casting based rendering of a reference CT image warped by a time-variant displacement field, which is computed using the motion models at run-time. Furthermore, haptic interaction with the animated virtual patient models is provided by using the displacements computed at high rendering rates to translate the position of the haptic device into the space of the reference CT image. This concept is applied to virtual palpation and the haptic simulation of insertion of a virtual bendable needle. To this aim, different motion models that are applicable in real-time are presented and the methods are integrated into a needle puncture training simulation framework, which can be used for simulated biopsy or vessel puncture in the liver. To confirm real-time applicability, a performance analysis of the resulting framework is given. It is shown that the presented methods achieve mean update rates around 2,000 Hz for haptic simulation and interactive frame rates for volume rendering and thus are well suited for visuo-haptic rendering of virtual patients under respiratory motion.

Atembewegte Avatare konnen in einem kurzlich vorgestellten visuo-haptischen Virtual Reality (VR) 4D-Simulatorkonzept modelliert [1] und GPU-basiert dargestellt [2] werden. Nadelinterventionsimulationen im hepatischen Bereich mit atmenden virtuellen Patientenkorpern sind aktuell ohne die patientenspezifische, dosisrelevante 4D-Datenerfassung nicht durchfuhrbar. Hierbei kann ein populationsbasierter Ansatz zur Modellierung eines gemittelten, ubertragbaren (4DAtembewegungsatlas) abhelfen und die Risiken einer dosisrelevanten und teuren Erfassung eines 4D-Datensatzes mindern [3].

Virtual reality (VR) training simulators of liver needle insertion in the hepatic area of breathing virtual patients currently need 4D data acquisitions as a prerequisite. Here, first a population-based breathing virtual patient 4D atlas can be built and second the requirement of a dose-relevant or expensive acquisition of a 4D data set for a new static 3D patient can be mitigated by warping the mean atlas motion. The breakthrough contribution of this work is the construction and reuse of population-based learned 4D motion models., 7 pages, 4 figures, 1 movie, Proc. SPIE Medical Imaging: Image Processing 2018

External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0 +/- 3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60 +/- 0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.

Development of new needle insertion force feedback algorithms requires comparison with a gold standard method. A new evaluation framework was formulated and tested on needle punctures for percutaneous transhepatic catheter drainage (PTCD). Needle insertion is an established procedure for minimally invasive interventions in the liver. Up-to-date, needle insertions are precisely planned using 2D axial CT slices from 3D data sets. To provide a 3D virtual reality and haptic training and planning environment, the full segmentation of patient data is often a mandatory step. To lessen the time required for manual segmentation, we propose direct haptic volume-rendering based on CT gray values and partially segmented patient data. The core contribution is a new force output evaluation method driven by a ray-casting technique that defines paths from the skin to target structures, i.e., the right hepatic duct near the juncture with the common hepatic duct. A ray-casting method computes insertion trajectories from the skin to the duct considering no-go structures and plausibility criteria. A rating system scores each trajectory. Finally, the best insertion trajectories are selected that reach the target. Along the selected paths, force output comparison between a reference system and the new haptic force output algorithm is carried out, quantified and visualized. The evaluation framework is presented along with an exemplary study of the liver using the atlas data set from a reference patient. In a comparison of our reference method to a newer algorithm, force outputs are found to be similar in 99 % of the paths. The proposed evaluation framework allows reliable detection of problematic PTCD trajectories and provides valuable hints to improve force feedback algorithm development.

Aktuelle VR-Trainingssimulatoren von Punktionen gehen oft von statischen 3D-Patientendaten aus oder verwenden eine unrealistisch- periodische Animation der Atembewegung. Existierende Methoden zur Modellierung der Atembewegung schatzen personalisierte Atemmodelle, die auch in VR-Trainingssimulatoren verwendet werden konnen. Fur jeden neuen Patienten ist jedoch eine stark belastende bzw. teure 4DDatenakquisition als Vorraussetzung der Modellbildung notwendig. Die hier entwickelte Methodik erlaubt, eine plausible ubertragung existierender Atembewegungsmodelle eines Referenzpatienten auf einen neuen statischen Patienten. Dieser kosten- und dosissparende Ansatz wird hier als Proof-of-Concept fur das VR-Training im Leberbereich atmender virtueller Patienten gezeigt.

One draw-back of most existing VR ultrasound training simulators is the use of static 3D patient models neglecting physiological changes induced e.g. by respiration or heart motion. In this paper to the aim of more realistic Ultrasound simulation, breathing motion extracted from 4D CT image data is integrated into our visuo-haptic simulation framework. The simulated ultrasound images are used for the training of US-guided needle insertion procedures in liver surgery. The methodology developed enables US simulation, 3D visualization and haptic steering of the ultrasound probe and the needle in real-time in breathing virtual bodies.

For patient-specific voxel-based visuo-haptic rendering of CT scans of the liver area, the fully automatic segmentation of large volume structures such as skin, soft tissue, lungs and intestine (risk structures) is important. Using a machine learning based approach, several existing segmentations from 10 segmented gold-standard patients are learned by random decision forests individually and collectively. The core of this paper is feature selection and the application of the learned classifiers to a new patient data set. In a leave-some-out cross-validation, the obtained full volume segmentations are compared to the gold-standard segmentations of the untrained patients. The proposed classifiers use a multi-dimensional feature space to estimate the hidden truth, instead of relying on clinical standard threshold and connectivity based methods. The result of our efficient whole-body section classification are multi-label maps with the considered tissues. For visuo-haptic simulation, other small volume structures would have to be segmented additionally. We also take a look into these structures (liver vessels). For an experimental leave-some-out study consisting of 10 patients, the proposed method performs much more efficiently compared to state of the art methods. In two variants of leave-some-out experiments we obtain best mean DICE ratios of 0.79, 0.97, 0.63 and 0.83 for skin, soft tissue, hard bone and risk structures. Liver structures are segmented with DICE 0.93 for the liver, 0.43 for blood vessels and 0.39 for bile vessels.

We have developed a new hierarchical 3D technique to segment the vertebral bodies in order to measure bone mineral density (BMD) with high trueness and precision in volumetric CT datasets. The hierarchical approach starts with a coarse separation of the individual vertebrae, applies a variety of techniques to segment the vertebral bodies with increasing detail and ends with the definition of an anatomic coordinate system for each vertebral body, relative to which up to 41 trabecular and cortical volumes of interest are positioned. In a pre-segmentation step constraints consisting of Boolean combinations of simple geometric shapes are determined that enclose each individual vertebral body. Bound by these constraints viscous deformable models are used to segment the main shape of the vertebral bodies. Volume growing and morphological operations then capture the fine details of the bone-soft tissue interface. In the volumes of interest bone mineral density and content are determined. In addition, in the segmented vertebral bodies geometric parameters such as volume or the length of the main axes of inertia can be measured. Intra- and inter-operator precision errors of the segmentation procedure were analyzed using existing clinical patient datasets. Results for segmented volume, BMD, and coordinate system position were below 2.0%, 0.6%, and 0.7%, respectively. Trueness was analyzed using phantom scans. The bias of the segmented volume was below 4%; for BMD it was below 1.5%. The long-term goal of this work is improved fracture prediction and patient monitoring in the field of osteoporosis. A true 3D segmentation also enables an accurate measurement of geometrical parameters that may augment the clinical value of a pure BMD analysis.

To provide real-time visualization of deformed volumetric image data for virtual surgery simulation, a resampling algorithm based on deformed tetrahedral structures has been developed. Deformations of the tetrahedral mesh are computed by a soft-tissue simulation. The major advantage of this approach is the possibility to use the resampled image data in different rendering methods such as ray casting, simulated ultrasound or simulated X-ray imaging. To achieve real-time capability, the algorithm was parallelized on the GPU using Nvidia Cuda. Performance measurements have been done on different mesh resolutions. For a subset of 1688 tetrahedrons, short resamplings times of around 2.3 ms are measured on an Nvidia GTX 680, endorsing the algorithm for real-time application.

Virtual-Reality-Simulatoren bieten Medizinern eine virtuelle Trainingsumgebung, in der Eingriffe kostengunstig trainiert und geplant werden konnen, ohne hierbei reale Patienten zu gefahrden. Eine Einschrankung der meisten VR-Trainingssimulatoren ist, dass sie von einem statischen Patienten ausgehen, dessen Anatomie im Bereich des simulierten Eingriffs wahrend der Simulation keiner durch die Atmung verursachten Bewegung unterliegt. In diesem Beitrag wird gezeigt, wie Methoden zur Modellierung und Schatzung der Atembewegung aus dem Bereich der Strahlentherapie bewegter Tumoren genutzt werden konnen, um eine realistische Simulation komplexer, variabler Atembewegungen in VR-Trainingssimulatoren zu erreichen. Die entwickelte Methodik erlaubt eine Visualisierung der Atembewegung in Echtzeit und ermoglicht eine haptische Interaktion mit dem atmenden virtuellen Korper. Dies wird exemplarisch fur das Szenario der Leberpunktion gezeigt.

This study presents a new visuo-haptic virtual reality (VR) training and planning system for percutaneous transhepatic cholangio-drainage (PTCD) based on partially segmented virtual patient models. We only use partially segmented image data instead of a full segmentation and circumvent the necessity of surface or volume mesh models. Haptic interaction with the virtual patient during virtual palpation, ultrasound probing and needle insertion is provided. Furthermore, the VR simulator includes X-ray and ultrasound simulation for image-guided training. The visualization techniques are GPU-accelerated by implementation in Cuda and include real-time volume deformations computed on the grid of the image data. Computation on the image grid enables straightforward integration of the deformed image data into the visualization components. To provide shorter rendering times, the performance of the volume deformation algorithm is improved by a multigrid approach. To evaluate the VR training system, a user evaluation has been performed and deformation algorithms are analyzed in terms of convergence speed with respect to a fully converged solution. The user evaluation shows positive results with increased user confidence after a training session. It is shown that using partially segmented patient data and direct volume rendering is suitable for the simulation of needle insertion procedures such as PTCD.

Palpation is the first step for many medical interventions. To provide an immersive virtual training and planning environment, the palpation step has to be successfully modeled and simulated. Here, we present a multiproxy approach that calculates friction and surface resistance forces for multiple contact points on finger tips or virtual tools like ultrasound probes and displays the resulting force and torque on a 6DOF haptic device. No manual or time intensive segmentation of patient image data is needed to create a simulation based on CT data and thus our approach is usable for patient-specific simulation of palpation.

Um die Multi-Atlas-Segmentierung der Leber zu beschleunigen, wurde ein Ansatz zur Vorauswahl der ahnlichsten Bilder nach affiner Registrierung untersucht. Die Auswahl der Datensatze wurden mit den Metriken Mean Squares Mutual Information und Normalized Correlation vorgenommen. Die Qualitat der Selektion wurde nach der nicht-linearen Registrierung und anschliesender Label-Fusion mit dem Dice-Koeffizienten bewertet. Es ergab sich eine Reduktionmoglichkeit der Anzahl der aufwandigen nicht-linearen Registrierung von 50% bis 70% bei gleichzeitiger Verbesserung der Segmentierungsqualitat.

A system for the fully automatic segmentation of the liver and spleen is presented. In a multi-atlas based segmentation framework, several existing segmentations are deformed in parallel to image intensity based registrations targeting the unseen patient. A new locally adaptive label fusion method is presented as the core of this paper. In a patch comparison approach, the transformed segmentations are compared to a weak segmentation of the target organ in the unseen patient. The weak segmentation roughly estimates the hidden truth. Traditional fusion approaches just rely on the deformed expert segmentations only. The result of patch comparison is a confidence weight for a neighboring voxel-label in the atlas label images to contribute to the voxel under study. Fusion is finally carried out in a weighted averaging scheme. The new contribution is the incorporation of locally determined confidence features of the unseen patient into the fusion process. For a small experimental set-up consisting of 12 patients, the proposed method performs favorable to standard classifier label fusion methods. In leave-one-out experiments, we obtain a mean Dice ratio of 0.92 for the liver and 0.82 for the spleen.

Real-time surgical simulation relies on the fast computation of soft tissue deformations. In this paper, we present image-based algorithms for computing the deformations of a volumetric image during a needle insertion in real-time. The algorithms are based on diffusive and linear elastic finite difference methods as utilized in image registration. For an evaluation, the methods are compared to a finite element simulation of the pre-puncture phase of a needle insertion. Furthermore, the methods are improved and tested; contrary to our assumption, an improved diffusion based approach outperforms a linear elastic approach. The algorithms are used to perform a VR simulation of a needle insertion with visuo-haptic feedback.

Segmentation of patient data often is a mandatory step for surgical simulations featuring haptic rendering. To alleviate the burden of manual segmentation we chose direct haptic volume rendering based on CT gray values and partially segmented patient data. In this study, the fields of application are lumbar puncture and spinal anesthesia. We focus on a new evaluation method driven by ray-casting to define paths from the skin to the spinal canal. In a comparison of our reference system AcusVR to a newer algorithm, force outputs are found to be similar in 99 % of the tested paths.

Die Nadelpunktion ist ein etabliertes Verfahren fur die minimal-invasive Behandlung von Lasionen in der Leber. Nadelpunktionen werden prazise anhand von CT-Daten des Patienten geplant, um das Risiko einer Komplikation zu minimieren. Die vorliegende Arbeit hat die Planung von Nadelpfaden anhand von 4D-Bilddaten bei Punktionseingriffen unter Berucksichtigung der durch die Atmung hervorgerufenen Bewegungen zum Ziel. In allen Phasen des Atemzyklus berechnet ein Ray-Casting-Verfahren Verbindungspfade von der Haut zu der Lasion unter Berucksichtigung undurchdringlicher Strukturen. Ein Bewertungssystem bewertet jeden Pfad in allen Phasen. Schlieslich wird der beste Pfad mit einem Zwei-Stufen-Min-Max-Schema gewahlt. Die Planungsergebnisse werden anhand von 6 Patientendatensatzen getestet. Die einfache Planung anhand einer Phase wird mit der Planung unter Berucksichtigung der Atmung verglichen. In einer Studie konnte eine tendenzielle Verbesserung der Planung mit 4D-Bilddaten gegenuber der Planung mit 3D-Bilddaten erreicht werden.

Virtual reality surgery simulation can provide an environment for the safe training of medical interventions. In many of these interventions palpation of target organs is common to search for certain anatomical structures in a first step. We present a method for visuohaptic simulation with tissue deformation caused by palpation solely based on CT data of a patient. Generation of haptic force feedback involves a force parameter image based on distances to the patient’s skin and bone. To create a deformed version of the patient’s image data, the ChainMail method is applied; bone structures are considered to be undeformable. The simulation can be used to palpate the iliac crest and spinous processes for the preparation of a lumbar puncture or for palpation of the ribcage.

The preparation phase for surgical simulations often comprises the segmentation of patient data, which is needed for realistic visual and haptic rendering. Expert segmentation of 3D patient data sets can last from several hours to days. In this paper we introduce a direct haptic volume rendering approach for lumbar punctures. Preparation time spent for segmentation is much shorter and compared to our reference system nearly identical force output at the needle tip can be observed. The number of structures to be completely segmented by an expert is reduced from 11 to 3 tissues in abdominal data sets with 300 slices.

Virtual reality simulations can be used for training of surgery procedures such as needle insertion. Using a haptic force-feedback device a realistic virtual environment can be provided by computation of forces for specific patient data. This work presents an algorithm to calculate and visualize deformations of volumetric data representing softtissue inspired by the relaxation step of the ChainMail algorithm. It uses the coupling of haptic force-feedback computation and the deformation visualization algorithm to enhance the visual experience of our needle insertion training simulation. Real-time performance is achieved by implementing the relaxation on the GPU which outperforms a CPU-based implementation.

The segmentation of patient data often is mandatory for surgical simulations to enable realistic visual and haptic rendering. The necessary preparation time lies in the range from several hours to days. Here we augment a direct haptic volume rendering approach for lumbar punctures by edge-preserving smoothing preprocessing. Evaluation is carried out on user defined paths. Compared to our reference system force output can be improved over non-preprocessed image data.

If organs are represented by a compact and non-ambiguous mathematical function, many actually interactive diagnostics on tomographic images can be performed automated. Such a representation is constructed from the organ's surface by mapping characteristic topographic structures (landmarks) onto identical function variables. Organs scanned by tomographic images series--periacetabular region of 14 hip joints imaged by X-ray CT--may be segmented. Their surface's contour points are approximated by tensor-product-B-splines (TPBSs). In a reference TPBS surface model landmarks are denoted interactively to define a mapping `variable-pair of the TPBS vs. landmark'. The patient TPBS models are mapped onto the reference model by fitting the model function values. The fit, and so the landmark identification, is performed by a homology function, which is applied to the patient model's variable plane. For simply shaped organs, the transformation of the tomographic to the topographic representation was possible using only the values and first order derivatives of the TPBSs. The presented landmark identification method avoids unnecessary assumptions of model deformation mechanism and has low computational costs.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Current virtual reality (VR) training simulators of liver punctures often rely on static 3D patient data and use an unrealistic (sinusoidal) periodic animation of the respiratory movement. Existing methods for the animation of breathing motion support simple mathematical or patient-specific, estimated breathing models. However with personalized breathing models for each new patient, a heavily dose relevant or expensive 4D data acquisition is mandatory for keyframe-based motion modeling. Given the reference 4D data, first a model building stage using linear regression motion field modeling takes place. Then the methodology shown here allows the transfer of existing reference respiratory motion models of a 4D reference patient to a new static 3D patient. This goal is achieved by using non-linear inter-patient registration to warp one personalized 4D motion field model to new 3D patient data. This cost- and dose-saving new method is shown here visually in a qualitative proof-of-concept study., Comment: World Society for Computer Graphics - WSCG 2017 publication, 9 pages, 5 figures, 1 movie online