Andre Mastmeyer

A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine

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.

Reanalysis precision of 3D quantitative computed tomography (QCT) of the spine

PURPOSEnTo evaluate the precision of 3D QCT of the spine.nnnMETHODSnInteroperator analysis reproducibility of two different 3D QCT analysis systems (QCTPro from Mindways Software Inc and MIAF-Spine from the Institute of Medical Physics, University of Erlangen) was evaluated in 29 postmenopausal women. For each analysis system four different trained operators analyzed all scans independently. Results of the vertebrae L1 and L2 were averaged. With QCTPro BMD of the central trabecular elliptical VOI was analyzed. With MIAF-Spine integral, trabecular and cortical BMD, BMC and volume were analyzed in the total vertebral body, the elliptical cylinder and the Osteo VOIs that were further subdivided into superior, mid and inferior subVOIs, each.nnnRESULTSnPrecision errors (%CVrms) for the central trabecular VOI that is also used in the traditional single slice QCT techniques were 1.7+/-2.2% and 0.6+/-0.6% for QCTPro and MIAF-Spine, respectively. For MIAF-Spine integral BMD precision errors were lowest in the total and mid Osteo subVOIs (0.5+/-0.5%). Trabecular BMD precision errors were lowest in the mid subVOIs (0.6+/-0.6%). For trabecular BMD there were no differences among the total vertebral body, elliptical cylinder and Osteo VOIs. Cortical BMD precision errors were lowest in the mid total vertebral body subVOI (2.1+/-1.9%) and slightly higher in the mid of the Osteo subVOI. Precision errors in the superior and inferior subVOIs were typically 50% to 100% higher compared to the mid subVOIs.nnnDISCUSSIONnCompared to QCTPro MIAF-Spine uses an automated 3D segmentation and an anatomic vertebral coordinate system to position a variety of analysis VOIs. This results in better precision than the more manually assisted analysis used by QCTPro. In-vivo precision errors will be approximately 0.5% higher compared to the analysis precision errors reported here (13). The results demonstrate that with 3D QCT in-vivo precision errors of about 1%-1.5% for trabecular and 2.5% to 3% for cortical bone can be obtained in postmenopausal women.

A Virtual Reality System for PTCD Simulation Using Direct Visuo-Haptic Rendering of Partially Segmented Image Data

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.

Image-Based Palpation Simulation With Soft Tissue Deformations Using Chainmail on the GPU

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.

Ray-casting based evaluation framework for haptic force feedback during percutaneous transhepatic catheter drainage punctures

Purposexa0xa0xa0Development 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).Methodsxa0xa0xa0Needle 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.Resultsxa0xa0xa0The 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 99xa0% of the paths.Conclusionxa0xa0xa0The proposed evaluation framework allows reliable detection of problematic PTCD trajectories and provides valuable hints to improve force feedback algorithm development.

Evaluation of Direct Haptic 4D Volume Rendering of Partially Segmented Data for Liver Puncture Simulation

This work presents an evaluation study using a force feedback evaluation framework for a novel direct needle force volume rendering concept in the context of liver puncture simulation. PTC/PTCD puncture interventions targeting the bile ducts have been selected to illustrate this concept. The haptic algorithms of the simulator system are based on (1) partially segmented patient image data and (2) a non-linear spring model effective at organ borders. The primary aim is to quantitatively evaluate force errors caused by our patient modeling approach, in comparison to haptic force output obtained from using gold-standard, completely manually-segmented data. The evaluation of the force algorithms compared to a force output from fully manually segmented gold-standard patient models, yields a low mean of 0.12u2009N root mean squared force error and up to 1.6u2009N for systematic maximum absolute errors. Force errors were evaluated on 31,222 preplanned test paths from 10 patients. Only twelve percent of the emitted forces along these paths were affected by errors. This is the first study evaluating haptic algorithms with deformable virtual patients in silico. We prove haptic rendering plausibility on a very high number of test paths. Important errors are below just noticeable differences for the hand-arm system.

Direct Visuo-Haptic 4D Volume Rendering Using Respiratory Motion Models

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.

GPU-Based Visualization of Deformable Volumetric Soft-Tissue for Real-Time Simulation of Haptic Needle Insertion

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.

Patch-based label fusion using local confidence-measures and weak segmentations

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.

Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images

HighlightsSegmentation and catheter identification in MRI images for brachytherapy.ONE CLICK user interaction per catheter.Coupling of mechanical model and image features.Outlier detection and correction.93% accuracy and 0.29 mm precision error. Graphical abstract Figure. No caption available. ABSTRACT 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&sgr; 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.