101 resultados para patient-specific biomechanical model


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Intraoperative molding of polymethyl-methacrylate into complex three-dimensional shapes with correct thickness is often a time-consuming process and may lead to unsatisfying cosmetical results. This article describes an intraoperative technique to assemble a polymethyl-methacrylate implant as a replica of the patient's bone flap. This approach provides a fast and inexpensive alternative technique with good cosmetic outcome. The technique is feasible and can be applied in early and delayed cranioplasty procedures. In selected patients, immediate single-stage reconstruction avoids a second operation.

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BACKGROUND Intraoperatively fabricated polymethylmethacrylate (PMMA) implants based on computer-designed moulds were used to improve cosmetic results after hard tissue replacement. To assess the implant's cosmetic and functional results we performed both subjective and objective assessments. METHODS This retrospective analysis was performed using a cohort of 28 patients who received PMMA implants between February 2009 and March 2012. The cosmetic and functional results were assessed using a patient questionnaire. Furthermore an objective volumetric subtraction score (0-100) was applied and implant thickness, as well as gaps and tiers, were measured. RESULTS Patients mainly judged their cosmetic result as "good". Two of the 28 patients found their cosmetic result unfavourable. The functional result and stability was mainly judged to be good. Measurements of implant thickness showed a very high correlation with the thickness of the contralateral bone. Volumetric subtraction led to a median quality of 80 on a scale from 0 to 100. Median gaps around the margins of the implant were 1.5 mm parietally, 1.7 mm frontally and 3.5 mm fronto-orbitally, and median tiers were 1.2 mm, 0 mm and 0 mm respectively. The overall rate of surgical revisions was 10.7 % (three patients). Two patients suffered from wound healing disturbances (7.1 %). The overall complication rate was comparable to other reports in the literature. CONCLUSIONS Implantation of intraoperatively fabricated patient-specific moulded implants is a cost-effective and safe technique leading to good clinical results with a low complication rate.

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In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

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Background Acetabular fractures still are among the most challenging fractures to treat because of complex anatomy, involved surgical access to fracture sites and the relatively low incidence of these lesions. Proper evaluation and surgical planning is necessary to achieve anatomic reduction of the articular surface and stable fixation of the pelvic ring. The goal of this study was to test the feasibility of preoperative surgical planning in acetabular fractures using a new prototype planning tool based on an interactive virtual reality-style environment. Methods 7 patients (5 male and 2 female; median age 53 y (25 to 92 y)) with an acetabular fracture were prospectively included. Exclusion criterions were simple wall fractures, cases with anticipated surgical dislocation of the femoral head for joint debridement and accurate fracture reduction. According to the Letournel classification 4 cases had two column fractures, 2 cases had anterior column fractures and 1 case had a T-shaped fracture including a posterior wall fracture. The workflow included following steps: (1) Formation of a patient-specific bone model from preoperative computed tomography scans, (2) interactive virtual fracture reduction with visuo-haptic feedback, (3) virtual fracture fixation using common osteosynthesis implants and (4) measurement of implant position relative to landmarks. The surgeon manually contoured osteosynthesis plates preoperatively according to the virtually defined deformation. Screenshots including all measurements for the OR were available. The tool was validated comparing the preoperative planning and postoperative results by 3D-superimposition. Results Preoperative planning was feasible in all cases. In 6 of 7 cases superimposition of preoperative planning and postoperative follow-up CT showed a good to excellent correlation. In one case part of the procedure had to be changed due to impossibility of fracture reduction from an ilioinguinal approach. In 3 cases with osteopenic bone patient-specific prebent fixation plates were helpful in guiding fracture reduction. Additionally, anatomical landmark based measurements were helpful for intraoperative navigation. Conclusion The presented prototype planning tool for pelvic surgery was successfully integrated in a clinical workflow to improve patient-specific preoperative planning, giving visual and haptic information about the injury and allowing a patient-specific adaptation of osteosynthesis implants to the virtually reduced pelvis.

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This paper presents a new approach for reconstructing a patient-specific shape model and internal relative intensity distribution of the proximal femur from a limited number (e.g., 2) of calibrated C-arm images or X-ray radiographs. Our approach uses independent shape and appearance models that are learned from a set of training data to encode the a priori information about the proximal femur. An intensity-based non-rigid 2D-3D registration algorithm is then proposed to deformably fit the learned models to the input images. The fitting is conducted iteratively by minimizing the dissimilarity between the input images and the associated digitally reconstructed radiographs of the learned models together with regularization terms encoding the strain energy of the forward deformation and the smoothness of the inverse deformation. Comprehensive experiments conducted on images of cadaveric femurs and on clinical datasets demonstrate the efficacy of the present approach.

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A patient-specific surface model of the proximal femur plays an important role in planning and supporting various computer-assisted surgical procedures including total hip replacement, hip resurfacing, and osteotomy of the proximal femur. The common approach to derive 3D models of the proximal femur is to use imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). However, the high logistic effort, the extra radiation (CT-imaging), and the large quantity of data to be acquired and processed make them less functional. In this paper, we present an integrated approach using a multi-level point distribution model (ML-PDM) to reconstruct a patient-specific model of the proximal femur from intra-operatively available sparse data. Results of experiments performed on dry cadaveric bones using dozens of 3D points are presented, as well as experiments using a limited number of 2D X-ray images, which demonstrate promising accuracy of the present approach.

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In this chapter a low-cost surgical navigation solution for periacetabular osteotomy (PAO) surgery is described. Two commercial inertial measurement units (IMU, Xsens Technologies, The Netherlands), are attached to a patient’s pelvis and to the acetabular fragment, respectively. Registration of the patient with a pre-operatively acquired computer model is done by recording the orientation of the patient’s anterior pelvic plane (APP) using one IMU. A custom-designed device is used to record the orientation of the APP in the reference coordinate system of the IMU. After registration, the two sensors are mounted to the patient’s pelvis and acetabular fragment, respectively. Once the initial position is recorded, the orientation is measured and displayed on a computer screen. A patient-specific computer model generated from a pre-operatively acquired computed tomography (CT) scan is used to visualize the updated orientation of the acetabular fragment. Experiments with plastic bones (7 hip joints) performed in an operating room comparing a previously developed optical navigation system with our inertial-based navigation system showed no statistical difference on the measurement of acetabular component reorientation (anteversion and inclination). In six out of seven hip joints the mean absolute difference was below five degrees for both anteversion and inclination.