Robust point correspondence applied to two and three-dimensional image registration

Accurate and robust correspondence calculations are very important in many medical and biological applications. Often, the correspondence calculation forms part of a rigid registration algorithm, but accurate correspondences are especially important for elastic registration algorithms and for quantifying changes over time. In this paper, a new correspondence calculation algorithm, CSM (correspondence by sensitivity to movement), is described. A robust corresponding point is calculated by determining the sensitivity of a correspondence to movement of the point being matched. If the correspondence is reliable, a perturbation in the position of this point should not result in a large movement of the correspondence. A measure of reliability is also calculated. This correspondence calculation method is independent of the registration transformation and has been incorporated into both a 2D elastic registration algorithm for warping serial sections and a 3D rigid registration algorithm for registering pre and postoperative facial range scans. These applications use different methods for calculating the registration transformation and accurate rigid and elastic alignment of images has been achieved with the CSM method. It is expected that this method will be applicable to many different applications and that good results would be achieved if it were to be inserted into other methods for calculating a registration transformation from correspondence

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Panorama imaging for image-to-physical registration of narrow drill holes inside spongy bones

Image-to-physical registration based on volumetric data like computed tomography on the one side and intraoperative endoscopic images on the other side is an important method for various surgical applications. In this contribution, we present methods to generate panoramic views from endoscopic recordings for image-to-physical registration of narrow drill holes inside spongy bone. One core application is the registration of drill poses inside the mastoid during minimally invasive cochlear implantations. Besides the development of image processing software for registration, investigations are performed on a miniaturized optical system, achieving 360° radial imaging with one shot by extending a conventional, small, rigid, rod lens endoscope. A reflective cone geometry is used to deflect radially incoming light rays into the endoscope optics. Therefore, a cone mirror is mounted in front of a conventional 0° endoscope. Furthermore, panoramic images of inner drill hole surfaces in artificial bone material are created. Prior to drilling, cone beam computed tomography data is acquired from this artificial bone and simulated endoscopic views are generated from this data. A qualitative and quantitative image comparison of resulting views in terms of image-to-image registration is performed. First results show that downsizing of panoramic optics to a diameter of 3mm is possible. Conventional rigid rod lens endoscopes can be extended to produce suitable panoramic one-shot image data. Using unrolling and stitching methods, images of the inner drill hole surface similar to computed tomography image data of the same surface were created. Registration is performed on ten perturbations of the search space and results in target registration errors of (0:487 ± 0:438)mm at the entry point and (0:957 ± 0:948)mm at the exit as well as an angular error of (1:763 ± 1:536)°. The results show suitability of this image data for image-to-image registration. Analysis of the error components in different directions reveals a strong influence of the pattern structure, meaning higher diversity results into smaller errors. © 2017 SPIE

Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph

Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US examinations, such as inter-operator variations. However, it is still challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications with high acoustic-impedance bone structures under the skin. To address this challenge, a graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup by explicitly considering subcutaneous bone surface features instead of the skin surface. To this end, the sternum and cartilage branches are segmented using a template matching to assist coarse alignment of US and CT point clouds. Afterward, a directed graph is generated based on the CT template. Then, the self-organizing map using geographical distance is successively performed twice to extract the optimal graph representations for CT and US point clouds, individually. To evaluate the proposed approach, five cartilage point clouds from distinct patients are employed. The results demonstrate that the proposed graph-based registration can effectively map trajectories from CT to the current setup for displaying US views through limited intercostal space. The non-rigid registration results in terms of Hausdorff distance (Mean$\pm$SD) is 9.48$\pm$0.27 mm and the path transferring error in terms of Euclidean distance is 2.21$\pm$1.11 mm.Comment: Accepted by IROS2

Towards automation of forensic facial reconstruction

Forensic facial reconstruction is a blend of art and science thus computerizing the process leads to numerous solutions. However, complete automation remains a challenge. This research concentrates on automating the first phase of forensic facial reconstruction which is automatic landmark detection by model fitting and extraction of feature points. Detection of landmarks is a challenging task since the skull orientation in a 3D scanned data cloud is generally arbitrary and unknown. To address the issue, well defined skull and mandible models with known geometric structure, features and orientation are (1) aligned and (2) fit to the scanned data. After model fitting is complete, landmarks can be extracted, within reasonable tolerance, from the dataset. Several methods exist for automatic registration (alignment); however, most suffer ambiguity or require interaction to manage symmetric 3D objects. A new alternative 3D model to data registration technique is introduced which works successfully for both symmetric and non-symmetric objects. It takes advantage of the fact that the model and data have similar shape and known geometric features. Therefore, a similar canonical frame of reference can be developed for both model and data. Once the canonical frame of reference is defined, the model can be easily aligned to data by a euclidian transformation of its coordinate system. Once aligned, the model is scaled and deformed globally to accommodate the overall size the object and bring the model in closer proximity to the data. Lastly, the model is deformed locally to better fit the scanned data. With fitting completed, landmark locations on the model can be utilized to isolate and select corresponding landmarks in the dataset. The registration, fitting and landmark detection techniques were applied to a set of six mandible and three skull body 3D scanned datasets. Results indicate the canonical axes formulation is a good candidate for automatic registration of complex 3D objects. The alternate approach posed for deformation and surface fitting of datasets also shows promise for landmark detection when using well constructed NURBS models. Recommendations are provided for addressing the algorithms limitations and to improve its overall performance

Optimization of craniosynostosis surgery: virtual planning, intraoperative 3D photography and surgical navigation

Mención Internacional en el título de doctorCraniosynostosis is a congenital defect defined as the premature fusion of one or more cranial sutures. This fusion leads to growth restriction and deformation of the cranium, caused by compensatory expansion parallel to the fused sutures. Surgical correction is the preferred treatment in most cases to excise the fused sutures and to normalize cranial shape. Although multiple technological advancements have arisen in the surgical management of craniosynostosis, interventional planning and surgical correction are still highly dependent on the subjective assessment and artistic judgment of craniofacial surgeons. Therefore, there is a high variability in individual surgeon performance and, thus, in the surgical outcomes. The main objective of this thesis was to explore different approaches to improve the surgical management of craniosynostosis by reducing subjectivity in all stages of the process, from the preoperative virtual planning phase to the intraoperative performance. First, we developed a novel framework for automatic planning of craniosynostosis surgery that enables: calculating a patient-specific normative reference shape to target, estimating optimal bone fragments for remodeling, and computing the most appropriate configuration of fragments in order to achieve the desired target cranial shape. Our results showed that automatic plans were accurate and achieved adequate overcorrection with respect to normative morphology. Surgeons’ feedback indicated that the integration of this technology could increase the accuracy and reduce the duration of the preoperative planning phase. Second, we validated the use of hand-held 3D photography for intraoperative evaluation of the surgical outcome. The accuracy of this technology for 3D modeling and morphology quantification was evaluated using computed tomography imaging as gold-standard. Our results demonstrated that 3D photography could be used to perform accurate 3D reconstructions of the anatomy during surgical interventions and to measure morphological metrics to provide feedback to the surgical team. This technology presents a valuable alternative to computed tomography imaging and can be easily integrated into the current surgical workflow to assist during the intervention. Also, we developed an intraoperative navigation system to provide real-time guidance during craniosynostosis surgeries. This system, based on optical tracking, enables to record the positions of remodeled bone fragments and compare them with the target virtual surgical plan. Our navigation system is based on patient-specific surgical guides, which fit into the patient’s anatomy, to perform patient-to-image registration. In addition, our workflow does not rely on patient’s head immobilization or invasive attachment of dynamic reference frames. After testing our system in five craniosynostosis surgeries, our results demonstrated a high navigation accuracy and optimal surgical outcomes in all cases. Furthermore, the use of navigation did not substantially increase the operative time. Finally, we investigated the use of augmented reality technology as an alternative to navigation for surgical guidance in craniosynostosis surgery. We developed an augmented reality application to visualize the virtual surgical plan overlaid on the surgical field, indicating the predefined osteotomy locations and target bone fragment positions. Our results demonstrated that augmented reality provides sub-millimetric accuracy when guiding both osteotomy and remodeling phases during open cranial vault remodeling. Surgeons’ feedback indicated that this technology could be integrated into the current surgical workflow for the treatment of craniosynostosis. To conclude, in this thesis we evaluated multiple technological advancements to improve the surgical management of craniosynostosis. The integration of these developments into the surgical workflow of craniosynostosis will positively impact the surgical outcomes, increase the efficiency of surgical interventions, and reduce the variability between surgeons and institutions.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidente: Norberto Antonio Malpica González.- Secretario: María Arrate Muñoz Barrutia.- Vocal: Tamas Ung

Motion compensated micro-CT reconstruction for in-situ analysis of dynamic processes

This work presents a framework to exploit the synergy between Digital Volume Correlation ( DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-mu CT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360 degrees rotation

Multiphoton imaging for morphometry of the sandwich-beam structure of the human stapedial annular ligament

Background The annular ligament of the human stapes constitutes a compliant connection between the stapes footplate and the peripheral cochlear wall at the oval window. The cross section of the human annular ligament is characterized by a three-layered structure, which resembles a sandwich-shaped composite structure. As accurate and precise descriptions of the middle-ear behavior are constrained by lack of information on the complex geometry of the annular ligament, this study aims to obtain comprehensive geometrical data of the annular ligament via multiphoton imaging. Methods The region of interest containing the stapes and annular ligament was harvested from a fresh-frozen human temporal bone of a 46-years old female. Multiphoton imaging of the unstained sample was performed by detecting the second-harmonic generation of collagen and the autofluorescence of elastin, which are constituents of the annular ligament. The multiphoton scans were conducted on the middle-ear side and cochlear side of the annular ligament to obtain accurate images of the face layers on both sides. The face layers of the annular ligament were manually segmented on both multiphoton scans, and then registered to high-resolution μCT images. Results Multiphoton scans of the annular ligament revealed 1) relatively large thickness of the core layer compared to the face layers, 2) asymmetric geometry of the face layers between the middle-ear side and cochlear side, and variation of their thickness and width along the footplate boundary, 3) divergent relative alignment of the two face layers, and 4) different fiber composition of the face layers along the boundary with a collagen-reinforcement near the anterior pole on the middle-ear side. Conclusion and outlook Multiphoton microscopy is a feasible approach to obtain the detailed three-dimensional features of the human stapedial annular ligament along its full boundary. The detailed description of the sandwich-shaped structures of the annular ligament is expected to contribute to modeling of the human middle ear for precise simulation of middle-ear behavior. Further, established methodology in this study may be applicable to imaging of other middle-ear structures. Keywords Annular ligament Stapes Multiphoton microscopy Two-photon microscopy Face layer Core laye