Computer-aided operation planning and 3-dimensional osteotomy guidance for alveolar distraction

Objectives: In alveolar distraction, in cases of severe atrophy in particular, it is often difficult to perform osteotomies in order to make a transport segment in optimal size and shape. Moreover care must be taken, not to damage the closely locating anato- mical structures such as the maxillary sinus, the inferior alveolar nerve, and the roots of the neighboring teeth. For setting ideal osteotomy lines exactly, we have developed a CT-based preoperative planning tool. Methods: 3-dimensional visual reconstruction of the jaw is created from the preoperative CT scans (1.0-mm slice thick- ness). Using the image-processing software Mimics (Materialise, Yokohama, Japan), various procedures of virtual cutting are simulated first to determine optimal osteotomy lines and to design an ideal transport segment. After the computer planning, data from the virtual solid model are transferred to a rapid prototype model, and a guiding splint is made to transfer the planned surgical simulation to the actual surgery. Results: The method was used in a case of severe atrophy of the anterior maxilla. The patient had a large maxillary sinus requir- ing a precise osteotomy in this critical area. Using the splint allowing a 3-dimensional guidance, alveolar osteotomies were easily done to achieve a transport segment in sufficient dimen- sion as planned, and any perforation of the maxillary sinus could be avoided. Finally the alveolar distraction of 10mm has suc- cessfully been performed. Conclusion: The preoperative planning method and the guiding splint described here are useful in problematic cases requiring an extremely precise osteotomy due to lack of bony space.

Computer technology applications in surgical implant dentistry: a systematic review

PURPOSE: To assess the literature on accuracy and clinical performance of computer technology applications in surgical implant dentistry. MATERIALS AND METHODS: Electronic and manual literature searches were conducted to collect information about (1) the accuracy and (2) clinical performance of computer-assisted implant systems. Meta-regression analysis was performed for summarizing the accuracy studies. Failure/complication rates were analyzed using random-effects Poisson regression models to obtain summary estimates of 12-month proportions. RESULTS: Twenty-nine different image guidance systems were included. From 2,827 articles, 13 clinical and 19 accuracy studies were included in this systematic review. The meta-analysis of the accuracy (19 clinical and preclinical studies) revealed a total mean error of 0.74 mm (maximum of 4.5 mm) at the entry point in the bone and 0.85 mm at the apex (maximum of 7.1 mm). For the 5 included clinical studies (total of 506 implants) using computer-assisted implant dentistry, the mean failure rate was 3.36% (0% to 8.45%) after an observation period of at least 12 months. In 4.6% of the treated cases, intraoperative complications were reported; these included limited interocclusal distances to perform guided implant placement, limited primary implant stability, or need for additional grafting procedures. CONCLUSION: Differing levels and quantity of evidence were available for computer-assisted implant placement, revealing high implant survival rates after only 12 months of observation in different indications and a reasonable level of accuracy. However, future long-term clinical data are necessary to identify clinical indications and to justify additional radiation doses, effort, and costs associated with computer-assisted implant surgery.

Dual-Domain Image Denoising

Image denoising methods have been implemented in both spatial and transform domains. Each domain has its advantages and shortcomings, which can be complemented by each other. State-of-the-art methods like block-matching 3D filtering (BM3D) therefore combine both domains. However, implementation of such methods is not trivial. We offer a hybrid method that is surprisingly easy to implement and yet rivals BM3D in quality.

Fully automatic X-ray image segmentation via joint estimation of image displacements.

We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.

Progressive Image Denoising

Image denoising continues to be an active research topic. Although state-of-the-art denoising methods are numerically impressive and approch theoretical limits, they suffer from visible artifacts.While they produce acceptable results for natural images, human eyes are less forgiving when viewing synthetic images. At the same time, current methods are becoming more complex, making analysis, and implementation difficult. We propose image denoising as a simple physical process, which progressively reduces noise by deterministic annealing. The results of our implementation are numerically and visually excellent. We further demonstrate that our method is particularly suited for synthetic images. Finally, we offer a new perspective on image denoising using robust estimators.

Medical image computing in diagnosis and intervention of spinal diseases

Spinal image analysis and computer assisted intervention have emerged as new and independent research areas, due to the importance of treatment of spinal diseases, increasing availability of spinal imaging, and advances in analytics and navigation tools. Among others, multiple modality spinal image analysis and spinal navigation tools have emerged as two keys in this new area. We believe that further focused research in these two areas will lead to a much more efficient and accelerated research path, avoiding detours that exist in other applications, such as in brain and heart.

A clinically applicable laser-based image-guided system for laparoscopic liver procedures

PURPOSE Laser range scanners (LRS) allow performing a surface scan without physical contact with the organ, yielding higher registration accuracy for image-guided surgery (IGS) systems. However, the use of LRS-based registration in laparoscopic liver surgery is still limited because current solutions are composed of expensive and bulky equipment which can hardly be integrated in a surgical scenario. METHODS In this work, we present a novel LRS-based IGS system for laparoscopic liver procedures. A triangulation process is formulated to compute the 3D coordinates of laser points by using the existing IGS system tracking devices. This allows the use of a compact and cost-effective LRS and therefore facilitates the integration into the laparoscopic setup. The 3D laser points are then reconstructed into a surface to register to the preoperative liver model using a multi-level registration process. RESULTS Experimental results show that the proposed system provides submillimeter scanning precision and accuracy comparable to those reported in the literature. Further quantitative analysis shows that the proposed system is able to achieve a patient-to-image registration accuracy, described as target registration error, of [Formula: see text]. CONCLUSIONS We believe that the presented approach will lead to a faster integration of LRS-based registration techniques in the surgical environment. Further studies will focus on optimizing scanning time and on the respiratory motion compensation.

Feasibility of stereotactic MRI-based image guidance for the treatment of vascular malformations: a phantom study.

PURPOSE Treatment of vascular malformations requires the placement of a needle within vessels which may be as small as 1 mm, with the current state of the art relying exclusively on two-dimensional fluoroscopy images for guidance. We hypothesize that the combination of stereotactic image guidance with existing targeting methods will result in faster and more reproducible needle placements, as well as reduced radiationexposure, when compared to standard methods based on fluoroscopy alone. METHODS The proposed navigation approach was evaluated in a phantom experiment designed to allow direct comparison with the conventional method. An anatomical phantom of the left forearm was constructed, including an independent control mechanism to indicate the attainment of the target position. Three interventionalists (one inexperienced, two of them frequently practice the conventional fluoroscopic technique) performed 45 targeting attempts utilizing the combined and 45 targeting attempts utilizing the standard approaches. RESULTS In all 45 attempts, the users were able to reach the target when utilizing the combined approach. In two cases, targeting was stopped after 15 min without reaching the target when utilizing only the C-arm. The inexperienced user was faster when utilizing the combined approach and applied significantly less radiation than when utilizing the conventional approach. Conversely, both experienced users were faster when using the conventional approach, in one case significantly so, with no significant difference in radiation dose when compared to the combined approach. CONCLUSIONS This work presents an initial evaluation of a combined navigation fluoroscopy targeting technique in a phantom study. The results suggest that, especially for inexperienced interventionalists, navigation may help to reduce the time and the radiation dose. Future work will focus on the improvement and clinical evaluation of the proposed method.

Virtobot--a multi-functional robotic system for 3D surface scanning and automatic post mortem biopsy

The Virtopsy project, a multi-disciplinary project that involves forensic science, diagnostic imaging, computer science, automation technology, telematics and biomechanics, aims to develop new techniques to improve the outcome of forensic investigations. This paper presents a new approach in the field of minimally invasive virtual autopsy for a versatile robotic system that is able to perform three-dimensional (3D) surface scans as well as post mortem image-guided soft tissue biopsies.

Anatomically-Driven Soft-Tissue Simulation Strategy for Cranio-Maxillofacial Surgery Using Facial Muscle Template Model

We propose a computationally efficient and biomechanically relevant soft-tissue simulation method for cranio-maxillofacial (CMF) surgery. A template-based facial muscle reconstruction was introduced to minimize the efforts on preparing a patient-specific model. A transversely isotropic mass-tensor model (MTM) was adopted to realize the effect of directional property of facial muscles in reasonable computation time. Additionally, sliding contact around teeth and mucosa was considered for more realistic simulation. Retrospective validation study with postoperative scan of a real patient showed that there were considerable improvements in simulation accuracy by incorporating template-based facial muscle anatomy and sliding contact.

Determination of Pelvic Orientation from Ultrasound Images Using Patch-SSMs and a Hierarchical Speed of Sound Compensation Strategy

In the field of computer assisted orthopedic surgery (CAOS) the anterior pelvic plane (APP) is a common concept to determine the pelvic orientation by digitizing distinct pelvic landmarks. As percutaneous palpation is - especially for obese patients - known to be error-prone, B-mode ultrasound (US) imaging could provide an alternative means. Several concepts of using ultrasound imaging to determine the APP landmarks have been introduced. In this paper we present a novel technique, which uses local patch statistical shape models (SSMs) and a hierarchical speed of sound compensation strategy for an accurate determination of the APP. These patches are independently matched and instantiated with respect to associated point clouds derived from the acquired ultrasound images. Potential inaccuracies due to the assumption of a constant speed of sound are compensated by an extended reconstruction scheme. We validated our method with in-vitro studies using a plastic bone covered with a soft-tissue simulation phantom and with a preliminary cadaver trial.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Minimization of Intra-Operative Shaping of Orthopaedic Fixation Plates: A Population-Based Design

In this paper we present a new population-based method for the design of bone fixation plates. Standard pre-contoured plates are designed based on the mean shape of a certain population. We propose a computational process to design implants while reducing the amount of required intra-operative shaping, thus reducing the mechanical stresses applied to the plate. A bending and torsion model was used to measure and minimize the necessary intra-operative deformation. The method was applied and validated on a population of 200 femurs that was further augmented with a statistical shape model. The obtained results showed substantial reduction in the bending and torsion needed to shape the new design into any bone in the population when compared to the standard mean-based plates.