Stefan Wesarg

VR-Based Simulators for Training in Minimally Invasive Surgery

Simulation-based training using VR techniques is a promising alternative to traditional training in minimally invasive surgery (MIS). Simulators let the trainee touch, feel, and manipulate virtual tissues and organs through the same surgical tool handles used in actual MIS while viewing images of tool-tissue interactions on a monitor as in real laparoscopic procedures

Dual-energy CT-based Assessment of the Trabecular Bone in Vertebrae

BACKGROUNDnOsteoporosis can cause severe fractures of bone structures. One important indicator for pathology is a lowered bone mineral density (BMD) - conventionally assessed by dual-energy X-ray absorptiometry (DXA). Dual-energy CT (DECT) - being an alternative that is increasingly used in the clinics - allows the computation of the spatial BMD distribution.nnnOBJECTIVESnUsing DECT, the trabecular bone of vertebrae is examined. Several analysis methods for revealing the bone density distribution as well as appropriate visualization methods for detecting regions of lowered BMD are needed for computer-assisted diagnosis (CAD) of osteoporosis. The hypothesis that DECT is better suited than DXA for the computation of local BMD is investigated.nnnMETHODSnBuilding on a model of the interaction of X-rays with bone tissue, novel methods for assessing the spatial structure of the trabecular bone are presented. CAD of DECT image data is facilitated by segmenting the regions of interest interactively and with an Active Shape Model, respectively. The barycentric space of fractional volumes is introduced as a novel means for analyzing bone constitution. For 29 cadaver specimens, DECT as well as DXA has been examined. BMD values derived from both modalities are compared to local force measurements. In addition, clinical data from two patients who underwent DECT scanning for a different reason is analyzed retrospectively.nnnRESULTSnA novel automated delineation method for vertebrae has been successfully applied to DECT data sets. It is shown that localized BMD measurements based on DECT show a stronger linear correlation (R² = 0.8242, linear regression) to local force measurements than density values derived from DXA (R² = 0.4815).nnnCONCLUSIONSnDECT based BMD assessment is a method to extend the usage of increasingly acquired DECT image data. The developed DECT based analysis methods in conjunction with the visualization provide more detailed information for both, the radiologist and the orthopedist, compared to standard DXA based analysis.

Facilitating coronary artery evaluation in MDCT using a 3D automatic vessel segmentation tool

The purpose of this study was to investigate a 3D coronary artery segmentation algorithm using 16-row MDCT data sets. Fifty patients underwent cardiac CT (Sensation 16, Siemens) and coronary angiography. Automatic and manual detection of coronary artery stenosis was performed. A 3D coronary artery segmentation algorithm (Fraunhofer Institute for Computer Graphics, Darmstadt) was used for automatic evaluation. All significant stenoses (>50%) in vessels >1.5xa0mm in diameter were protocoled. Each detection tool was used by one reader who was blinded to the results of the other detection method and the results of coronary angiography. Sensitivity and specificity were determined for automatic and manual detection as well as was the time for both CT-based evaluation methods. The overall sensitivity and specificity of the automatic and manual approach were 93.1 vs. 95.83% and 86.1 vs. 81.9%. The time required for automatic evaluation was significantly shorter than with the manual approach, i.e., 246.04±43.17 s for the automatic approach and 526.88±45.71xa0s for the manual approach (P<0.0001). In 94% of the coronary artery branches, automatic detection required less time than the manual approach. Automatic coronary vessel evaluation is feasible. It reduces the time required for cardiac CT evaluation with similar sensitivity and specificity as well as facilitates the evaluation of MDCT coronary angiography in a standardized fashion.

Parker weights revisited

The short-scan case in fan-beam computed tomography requires the introduction of a weighting function to handle redundant data. Parker introduced such a weighting function for a scan over pi plus the opening angle of the fan. In this article we derive a general class of weighting functions for arbitrary scan angles between pi plus fan angle and 2pi (over-scan). These weighting functions lead to mathematically exact reconstructions in the continuous case. Parker weights are a special case of a weighting function that belongs to this class. It will be shown that Parker weights are not generally the best choice in terms of noise reduction, especially when there is considerable over-scan. We derive a new weighting function that has a value of 0.5 for most of the redundant data and is smooth at the boundaries.

Accuracy of needle implantation in brachytherapy using a medical AR system: a phantom study

Brachytherapy is the treatment method of choice for patients with a tumor relapse after a radiation therapy nwith external beams or tumors in regions with sensitive surrounding organs-at-risk, e. g. prostate tumors. The nstandard needle implantation procedure in brachytherapy uses pre-operatively acquired image data displayed as nslices on a monitor beneath the operation table. Since this information allows only a rough orientation for the nsurgeon, the position of the needles has to be verified repeatedly during the intervention. nWithin the project Medarpa a transparent display being the core component of a medical Augmented nReality (AR) system has been developed. There, pre-operatively acquired image data is displayed together with nthe position of the tracked instrument allowing a navigated implantation of the brachytherapy needles. The nsurgeon is enabled to see the anatomical information as well as the virtual instrument in front of the operation narea. Thus, the Medarpa system serves as window into the patient. nThis paper deals with the results of first clinical trials of the system. Phantoms have been used for evaluating nthe achieved accuracy of the needle implantation. This has been done by comparing the output of the system n(instrument positions relative to the phantom) with the real positions of the needles measured by means of a nverification CT scan.

Evaluation of segmentation methods on head and neck CT: Auto‐segmentation challenge 2015

Purpose Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. Methods In this work, we describe and present the results of the Head and Neck Auto‐Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. Results This paper presents the quantitative results of this challenge using multiple established error metrics and a well‐defined ranking system. The strengths and weaknesses of the different auto‐segmentation approaches are analyzed and discussed. Conclusions The Head and Neck Auto‐Segmentation Challenge 2015 was a good opportunity to assess the current state‐of‐the‐art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure‐specific segmentation algorithms.

Registration of 3D U/S and CT images of the prostate

Radiotherapy is a rapidly growing cancer treatment technique. In brachytherapy — one radiotherapy treatment technique — pre- and post-planning is usually carried out using CT imaging. As CT scanners cannot easily be moved from one operation room to an other and as CT does not have real-time imaging capability, alternative imaging modalities are needed to realize the vision of image guided surgery. Ultrasound (U/S) is such an alternative imaging modality. For the comparison of U/S and CT image fusion is very useful. After volume segmentation (see ref. 1) the volumes have to be registered, and afterwards the fused volume can be displayed. In this paper we investigate the registration part. We present two different approaches for the 3D registration of CT and U/S volumes. They are compared regarding accuracy and speed of calculation. The resulting fused volumes are visualized using the “InViVo” software where the registration routines have been integrated into.

Mutual-information-based registration for ultrasound and CT datasets

In many applications for minimal invasive surgery the acquisition of intra-operative medical images is helpful if not absolutely necessary. Especially for Brachytherapy imaging is critically important to the safe delivery of the therapy. Modern computed tomography (CT) and magnetic resonance (MR) scanners allow minimal invasive procedures to be performed under direct imaging guidance. However, conventional scanners do not have real-time imaging capability and are expensive technologies requiring a special facility. Ultrasound (U/S) is a much cheaper and one of the most flexible imaging modalities. It can be moved to the application room as required and the physician sees what is happening as it occurs. Nevertheless it may be easier to interpret these 3D intra-operative U/S images if they are used in combination with less noisier preoperative data such as CT. The purpose of our current investigation is to develop a registration tool for automatically combining pre-operative CT volumes with intra-operatively acquired 3D U/S datasets. The applied alignment procedure is based on the information theoretic approach of maximizing the mutual information of two arbitrary datasets from different modalities. Since the CT datasets include a much bigger field of view we introduced a bounding box to narrow down the region of interest within the CT dataset. We conducted a phantom experiment using a CIRS Model 53 U/S Prostate Training Phantom to evaluate the feasibility and accuracy of the proposed method.

Validation of 3D ultrasound: CT registration of prostate images

All over the world 20% of men are expected to develop prostate cancer sometime in his life. In addition to surgery - being the traditional treatment for cancer - the radiation treatment is getting more popular. The most interesting radiation treatment regarding prostate cancer is Brachytherapy radiation procedure. For the safe delivery of that therapy imaging is critically important. In several cases where a CT device is available a combination of the information provided by CT and 3D Ultrasound (U/S) images offers advantages in recognizing the borders of the lesion and delineating the region of treatment. For these applications the CT and U/S scans should be registered and fused in a multi-modal dataset. Purpose of the present development is a registration tool (registration, fusion and validation) for available CT volumes with 3D U/S images of the same anatomical region, i.e. the prostate. The combination of these two imaging modalities interlinks the advantages of the high-resolution CT imaging and low cost real-time U/S imaging and offers a multi-modality imaging environment for further target and anatomy delineation. This tool has been integrated into the visualization software InViVo which has been developed over several years in Fraunhofer IGD in Darmstadt.

Automated Kidney Detection and Segmentation in 3D Ultrasound

Ultrasound provides the physical capabilities for a fast and save disease diagnosis in various medical scenarios including renal exams and patient trauma assessment. However, the experience of the ultrasound operator is the key element in performing ultrasound diagnosis. Thus, we like to introduce our automatic kidney detection and segmentation algorithm for 3D ultrasound. The approach utilizes basic kidney shape information to detect the kidney position. Following, the Level Set algorithm is applied to segment the detection result. In combination this method may help physicians and inexperienced trainees to achieve kidney detection and segmentation for diagnostic purposes.