We have developed a new integrated approach for quantitative computed tomography of the knee in order to quantify bone mineral density (BMD) and subchondral bone structure. The present framework consists of image acquisition and reconstruction, 3-D segmentation, determination of anatomic coordinate systems, and reproducible positioning of analysis volumes of interest (VOI). Novel segmentation algorithms were developed to identify growth plates of the tibia and femur and the joint space with high reproducibility. Five different VOIs with varying distance to the articular surface are defined in the epiphysis. Each VOI is further subdivided into a medial and a lateral part. In each VOI, BMD is determined. In addition, a texture analysis is performed on a high-resolution computed tomography (CT) reconstruction of the same CT scan in order to quantify subchondral bone structure. Local and global homogeneity, as well as local and global anisotropy were measured in all VOIs. Overall short-term precision of the technique was evaluated using double measurements of 20 osteoarthritic cadaveric human knees. Precision errors for volume were about 2-3% in the femur and 3-5% in the tibia. Precision errors for BMD were about 1-2% lower. Homogeneity parameters showed precision errors up to about 2% and anisotropy parameters up to about 4%.

Purpose: Magnetic resonance imaging (MRI) started to be used for evaluation of osteoarthritic knee (KOA) severity.We have developed a new method, the Irregularity Index System (IIS), to semi-automatically measure irregularity of the contour of the femoral condyle using standard sagittal proton-density-weighted MR images and demonstrated that the Irregularity Index could reflect the severity of KOA.Unique feature of this method is that irregularity of contour rely solely on pathological changes occurred in the subchondral bone of the femoral condyle.On the other hand, the Whole-Organ Magnetic Resonance Imaging Score (WORMS) is representative evaluation method of KOA semi-quantitively and was reported to be a reliable evaluating method.Big difference between the IIS and the WORMS is that WORMS deals with eight items (tissues) including cartilage, bone marrow, meniscus, synovium, etc.Although basic idea between the two methods is different, both methods had almost the same level of correlation to clinical knee score.The purpose of this study was to compare the two methods especially to examine what items of the WORMS affects the IIS score.Methods: The subjects were recruited from the patients who visited our hospital for treatment of KOA.The inclusion criteria were medial-type OA knees with ≥ grade II in Kellgren/Lawrence (K/L) grading.Twenty-five patients (25 knees) with a mean age of 69 (range, 58 to 77) years old that consented to participate in this study were enrolled.All patients underwent antero-posterior, weight-bearing x-ray at their first visit, and they were graded according to the K/L grading.They were also clinically examined and were scored with the Japanese Orthopaedic Association OA knee score (JOA score) and the Japanese Knee Osteoarthritis Measure (JKOM).All patients underwent MRI of the affected knees within 2 weeks of their first visit.MRI was performed with a 1.5-T scanner (Signa, GE medical systems) equipped with a knee surface coil.The sequence suitable for the IIS and the WORMS were determined by previous studies.Correlations between the IIS or the WORMS and the clinical scores were analyzed with Pearson's correlation coefficient.Relationship between the items of the WORMS and the IIS as well as the clinical scores were analyzed with regression analysis.Statistical significance was defined as p< 0.05.All statistical analyses were performed with Statview 5.0 (SAS Institute Inc., Cary, NC).Results: Both the IIS and the WORMS demonstrated strong negative correlations with JOA (r= -0.75, p< 0.0001; r= -0.79, p< 0.0001, respectively), and moderate positive correlations with JKOM (r= 0.40,p=0.04;r= 0.47, p= 0.008, respectively).The IIS has statistically significance with the medial femora-tibial joint (MFTJ) bone cysts (p=0.004) of the WORMS and the other items were not correlated.The JOA score has statistically significance with MFTJ-cartilage (p=0.044) of the WORMS and the JKOM has with PF-cartilage (p=0.036).The other items of the WORMS were not correlated with neither of the JOA score and the JKOM.Conclusions: Both the IIS and the WORMS had correlations with the clinical scores.Among several items of the WORMS, only bone cysts were related to the IIS.This would be consistent with our previous study that showed strong correlation between the IIS score and density of bone resorption pits (BRP) formed in the subchondral bone using specimens derived at the time of total knee arthroplasty because not all but part of the BRP would account for bone cysts on MRI examination.

Osteoarthritis changes the load distribution within joints and also changes bone density and structure. Within typical timelines of clinical studies these changes can be very small. Therefore precise definition of evaluation regions which are highly robust and show little to no interand intra-operator variance are essential for high quality quantitative analysis. To achieve this goal we have developed a system for the definition of such regions with minimal user input.

Rigid registration of bone structures in stacks of CT images can be improved by using a binary segmentation mask for the registration compared to using the segmented grey values directly. Three criteria are applied to the different reference and template datasets in order to quantify registration results. For all three criteria a statistically significant superiority of the technique using the binary segmentation masks is demonstrated. First results for a multimodal registration of a μCT with μMR dataset also show better results if binary segmentation masks are used for the registration.

This paper presents a method for computer assisted selection of optimal donor sites for autologous grafts in the craniofacial surgery planning. The method consists of two stages. The non-automatic graft design step is followed by a fully automatic procedure to find the best harvesting site in the predefined donor region. The main idea of the proposed method is based on the registration paradigm. The optimal donor site is identified by performing an optimization of the surface based similarity measure between the donor region and the designed graft template. An efficient optimization method based on the Levenberg-Marquardt algorithm has been implemented. It enables, once the preprocessing step has been performed, selection of the optimal donor site in time less than one minute.

In this paper we present in depth one of the preprocessing steps of our software system for the simulation of soft tissue behaviour and biomechanical processes. The entire system can be used by surgeons for preoperative planning and prediction of postoperative results. The software creates a pipeline to automatically construct meshed datasets for finite element modelling from presegmented patient data.

In this paper we present in depth one of the preprocessing steps of our software system for the simulation of soft tissue behaviour and biomechanical processes. The entire system can be used by surgeons for preoperative planning and prediction of postoperative results. The software creates a pipeline to automatically construct meshed datasets for finite element modelling from presegmented patient data. In further parts of the system resulting deformations of soft tissue from surgical interventions and motion of bones are computed for Visual and haptic display.

Bone graft surgery is often necessary for reconstruction of craniofacial defects after trauma, tumor, infection or congenital malformation. In this operative technique the removed or missing bone segment is filled with a bone graft. The mainstay of the craniofacial reconstruction rests with the replacement of the defected bone by autogeneous bone grafts. To achieve sufficient incorporation of the autograft into the host bone, precise planning and simulation of the surgical intervention is required. The major problem is to determine as accurately as possible the donor site where. the graft should be dissected from and to define the shape of the desired transplant. A computer-aided method for semi-automatic selection of optimal donor sites for autografts in craniofacial reconstructive surgery has been developed. The non-automatic step of graft design and constraint setting is followed by a fully automatic procedure to find the best fitting position. In extension to preceding work, a new optimization approach based on the Levenberg-Marquardt method has been implemented and embedded into our computer-based surgical planning system. This new technique enables, once the pre-processing step has been performed, selection of the optimal donor site in time less than one minute. The method has been applied during surgery planning step in more than 20 cases. The postoperative observations have shown that functional results, such as speech and chewing ability as well as restoration of bony continuity were clearly better compared to conventionally planned operations. Moreover, in most cases the duration of the surgical interventions has been distinctly reduced.

Haptic exploration of detailed models from patient specific anatomical structures places high demands on computational resources. Constant collision detection and correct calculation of forces fed back to the operator quickly exceed available processing power with increasing level of detail of the represented objects. To alleviate the strain on computational resources optimisation schemes are employed to speed up processing and guarantee glitch-free operation as well as smooth haptic feedback. Among these are the generation of a local haptic model, a look-ahead strategy to increase its range of validity, small world-shift operations and force extrapolation algorithms for smooth feedback.

This paper presents a method for computer assisted selection of optimal donor sites for autologous osseous grafts in the craniofacial surgery. At the initial graft design stage the surgeon defines in the CT data set the shape of the bone segment to be reconstructed and in the donor region CT data set a set of constraints for the optimization task. This non-automatic step is followed by a fully automatic optimization stage, which delivers a set of sub-optimal and optimal donor sites for a given template. Such approach permits the surgeon to find the best site for harvesting the graft and enables an exact anatomical reconstruction of the osseous section.

This paper presents a method for computer assisted selection of optimal donor sites for autologous osseous grafts in the craniofacial surgery. At the initial graft design stage the surgeon defines in the CT data set the shape of the bone segment to be reconstructed and in the donor region CT data set a set of constraints for the optimization task. This non-automatic step is followed by a fully automatic optimization stage, which delivers a set of sub-optimal and optimal donor sites for a given template. Such approach permits the surgeon to find the best site for harvesting the graft and enables an exact anatomical reconstruction of the osseous section.

For reconstruction of some craniofacial defects which may be caused by trauma, removal of cancer, or due to congenital absence, the procedure of bone grafting is necessary. In this paper, we propose a method to identify an optimal donor site for autologous grafts. It is done by performing an optimization of appropriate surface based and voxel based similarity measures between donor region and a defined graft template. All generated solutions can be evaluated interactively by the surgeon on the computer display using an efficient graphical interface. By this approach, the operation time can be considerably shortened.

Autologous grafts serve as the standard grafting material in the treatment of maxillofacial bone tumors, traumatic defects or congenital malformations. The pre-selection of a donor site depends primarily on the morphological fit of the available bone mass and the shape of the part that has to be transplanted. To achieve sufficient incorporation of the autograft into the host bone, precise planning and simulation of the surgical intervention based on three-dimensional CT studies is required. This paper presents a method to identify an optimal donor site by performing an optimization of appropriate similarity measures between donor region and a given transplant. At the initial stage the surgeon has to delineate the osteotomy border lines in the template CT data set and to define a set of constraints for the optimization task in the donor site CT data set. The following fully automatic optimization stage delivers a set of sub-optimal and optimal donor sites for a given template. All generated solutions can be explored interactively on the computer display using an efficient graphical interface. Reconstructive operations supported by our system were performed on 28 patients. We found that the operation time can be considerably shortened by this approach.

In this paper we introduce the extendable and cross-platform software framework JULIUS, which will become public available by the end of this year. JULIUS consists of three conceptual layers and provides diverse assistance for medical visualization, surgical planning and image-guided navigation. The system features a modular and portable design and combines both pre-operative planning and intra-operative assistance within one single environment.

In this paper we present an extendable framework for interactive biomechanical simulation and surgical planning. Three-dimensional reconstructions of patient specific data are visualized and the elastic properties of individual anatomical structures are modeled. To avoid interpenetration of the virtual objects, fast collision detection has been implemented. The kinematics are controlled by a special haptic interface which provides force feedback to the user. The ongoing work will lead to an entire system for physical-based biomechanical simulation and pre-operative visualization of surgical outcomes.

Segmenting medical structures is mandatory in any computer assisted surgery system. This major field must be addressed in order to build realistic and accurate 3D models of patient individual anatomical structures.Magnetic Resonance Imaging (MRI) is becoming part of daily routine in clinical work. Whereas scanning speed and slice numbers increase each year, segmenting such data is still a challenging problem. Moreover, the segmentation stage remains time limiting in pre-operative planning and intra-operative guidance. Indeed, interactive tools, like live wire or intensity-based thresholding, requires a pre or post-filtering to homogenize areas. Common medical filters, such as median or morphology-based, are actually non adapted for MR noise removal. Their main side effect is to remove boundaries when applied on Gaussian corrupted data. Next, numerous steps spend efforts in reconstructing lost information and current approaches are therefore non interactive.