MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem

In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.Comment: Submitted to the ISPRS congress (2016) in Prague. Oral Presentation. Published in ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 131-13

Ultrasound-Augmented Laparoscopy

Laparoscopic surgery is perhaps the most common minimally invasive procedure for many diseases in the abdomen. Since the laparoscopic camera provides only the surface view of the internal organs, in many procedures, surgeons use laparoscopic ultrasound (LUS) to visualize deep-seated surgical targets. Conventionally, the 2D LUS image is visualized in a display spatially separate from that displays the laparoscopic video. Therefore, reasoning about the geometry of hidden targets requires mentally solving the spatial alignment, and resolving the modality differences, which is cognitively very challenging. Moreover, the mental representation of hidden targets in space acquired through such cognitive medication may be error prone, and cause incorrect actions to be performed. To remedy this, advanced visualization strategies are required where the US information is visualized in the context of the laparoscopic video. To this end, efficient computational methods are required to accurately align the US image coordinate system with that centred in the camera, and to render the registered image information in the context of the camera such that surgeons perceive the geometry of hidden targets accurately. In this thesis, such a visualization pipeline is described. A novel method to register US images with a camera centric coordinate system is detailed with an experimental investigation into its accuracy bounds. An improved method to blend US information with the surface view is also presented with an experimental investigation into the accuracy of perception of the target locations in space

Registration of medical images for applications in minimally invasive procedures

Il punto di partenza di questa tesi \ue8 l'analisi dei metodi allo stato dell'arte di registrazione delle immagini mediche per verificare se sono adatti ad essere utilizzati per assistere il medico durante una procedura minimamente invasiva , ad esempio una procedura percutanea eseguita manualmente o un intervento teleoperato eseguito per mezzo di un robot . La prima conclusione \ue8 che, anche se ci sono tanti lavori dedicati allo sviluppo di algoritmi di registrazione da applicare nel contesto medico, la maggior parte di essi non sono stati progettati per essere utilizzati nello scenario della sala operatoria (OR) anche perch\ue9, rispetto ad altre applicazioni , OR richiede anche la validazione, prestazioni in tempo reale e la presenza di altri strumenti . Gli algoritmi allo stato dell'arte sono basati su un iterazione in tre fasi : ottimizzazione - trasformazione - valutazione della somiglianza delle immagini registrate. In questa tesi, studiamo la fattibilit\ue0 dell'approccio in tre fasi per applicazioni OR, mostrando i limiti che tale approccio incontra nelle applicazioni che stiamo considerando. Verr\ue0 dimostrato come un metodo semplice si potrebbe utilizzare nella OR. Abbiamo poi sviluppato una teoria che \ue8 adatta a registrare grandi insiemi di dati non strutturati estratti da immagini mediche, tenendo conto dei vincoli della OR . Vista l'impossibilit\ue0 di lavorare con dati medici di tipo DICOM, verr\ue0 impiegato un metodo per registrare dataset composti da insiemi di punti non strutturati. Gli algoritmi proposti sono progettati per trovare la corrispondenza spaziale in forma chiusa tenendo conto del tipo di dati, il vincolo del tempo e la presenza di rumore e /o piccole deformazioni. La teoria e gli algoritmi che abbiamo sviluppato sono derivati dalla teoria delle forme proposta da Kendall (Kendall's shapes) e utilizza un descrittore globale della forma per calcolare le corrispondenze e la distanza tra le strutture coinvolte . Poich\ue9 la registrazione \ue8 solo una componente nelle applicazioni mediche, l' ultima parte della tesi \ue8 dedicata ad alcune applicazioni pratiche in OR che possono beneficiare della procedura di registrazione .The registration of medical images is necessary to establish spatial correspondences across two or more images. Registration is rarely the end-goal, but instead, the results of image registration are used in other tasks. The starting point of this thesis is to analyze which methods at the state of the art of image registration are suitable to be used in assisting a physician during a minimally invasive procedure, such as a percutaneous procedure performed manually or a teleoperated intervention performed by the means of a robot. The first conclusion is that, even if much previous work has been devoted to develop registration algorithms to be applied in the medical context, most of them are not designed to be used in the operating room scenario (OR) because, compared to other applications, the OR requires also a strong validation, real-time performance and the presence of other instruments. Almost all of these algorithms are based on a three phase iteration: optimize-transform-evaluate similarity. In this thesis, we study the feasibility of this three steps approach in the OR, showing the limits that such approach encounter in the applications we are considering. We investigate how could a simple method be realizable and what are the assumptions for such a method to work. We then develop a theory that is suitable to register large sets of unstructured data extracted from medical images keeping into account the constraints of the OR. The use of the whole radiologic information is not feasible in the OR context, therefore the method we are introducing registers processed dataset extracted from the original medical images. The framework we propose is designed to find the spatial correspondence in closed form keeping into account the type of the data, the real-time constraint and the presence of noise and/or small deformations. The theory and algorithms we have developed are in the framework of the shape theory proposed by Kendall (Kendall's shapes) and uses a global descriptor of the shape to compute the correspondences and the distance between shapes. Since the registration is only a component of a medical application, the last part of the thesis is dedicated to some practical applications in the OR that can benefit from the registration procedure

Augmented Reality Ultrasound Guidance in Anesthesiology

Real-time ultrasound has become a mainstay in many image-guided interventions and increasingly popular in several percutaneous procedures in anesthesiology. One of the main constraints of ultrasound-guided needle interventions is identifying and distinguishing the needle tip from needle shaft in the image. Augmented reality (AR) environments have been employed to address challenges surrounding surgical tool visualization, navigation, and positioning in many image-guided interventions. The motivation behind this work was to explore the feasibility and utility of such visualization techniques in anesthesiology to address some of the specific limitations of ultrasound-guided needle interventions. This thesis brings together the goals, guidelines, and best development practices of functional AR ultrasound image guidance (AR-UIG) systems, examines the general structure of such systems suitable for applications in anesthesiology, and provides a series of recommendations for their development. The main components of such systems, including ultrasound calibration and system interface design, as well as applications of AR-UIG systems for quantitative skill assessment, were also examined in this thesis. The effects of ultrasound image reconstruction techniques, as well as phantom material and geometry on ultrasound calibration, were investigated. Ultrasound calibration error was reduced by 10% with synthetic transmit aperture imaging compared with B-mode ultrasound. Phantom properties were shown to have a significant effect on calibration error, which is a variable based on ultrasound beamforming techniques. This finding has the potential to alter how calibration phantoms are designed cognizant of the ultrasound imaging technique. Performance of an AR-UIG guidance system tailored to central line insertions was evaluated in novice and expert user studies. While the system outperformed ultrasound-only guidance with novice users, it did not significantly affect the performance of experienced operators. Although the extensive experience of the users with ultrasound may have affected the results, certain aspects of the AR-UIG system contributed to the lackluster outcomes, which were analyzed via a thorough critique of the design decisions. The application of an AR-UIG system in quantitative skill assessment was investigated, and the first quantitative analysis of needle tip localization error in ultrasound in a simulated central line procedure, performed by experienced operators, is presented. Most participants did not closely follow the needle tip in ultrasound, resulting in 42% unsuccessful needle placements and a 33% complication rate. Compared to successful trials, unsuccessful procedures featured a significantly greater (p=0.04) needle-tip to image-plane distance. Professional experience with ultrasound does not necessarily lead to expert level performance. Along with deliberate practice, quantitative skill assessment may reinforce clinical best practices in ultrasound-guided needle insertions. Based on the development guidelines, an AR-UIG system was developed to address the challenges in ultrasound-guided epidural injections. For improved needle positioning, this system integrated A-mode ultrasound signal obtained from a transducer housed at the tip of the needle. Improved needle navigation was achieved via enhanced visualization of the needle in an AR environment, in which B-mode and A-mode ultrasound data were incorporated. The technical feasibility of the AR-UIG system was evaluated in a preliminary user study. The results suggested that the AR-UIG system has the potential to outperform ultrasound-only guidance

On uncertainty propagation in image-guided renal navigation: Exploring uncertainty reduction techniques through simulation and in vitro phantom evaluation

Image-guided interventions (IGIs) entail the use of imaging to augment or replace direct vision during therapeutic interventions, with the overall goal is to provide effective treatment in a less invasive manner, as an alternative to traditional open surgery, while reducing patient trauma and shortening the recovery time post-procedure. IGIs rely on pre-operative images, surgical tracking and localization systems, and intra-operative images to provide correct views of the surgical scene. Pre-operative images are used to generate patient-specific anatomical models that are then registered to the patient using the surgical tracking system, and often complemented with real-time, intra-operative images. IGI systems are subject to uncertainty from several sources, including surgical instrument tracking / localization uncertainty, model-to-patient registration uncertainty, user-induced navigation uncertainty, as well as the uncertainty associated with the calibration of various surgical instruments and intra-operative imaging devices (i.e., laparoscopic camera) instrumented with surgical tracking sensors. All these uncertainties impact the overall targeting accuracy, which represents the error associated with the navigation of a surgical instrument to a specific target to be treated under image guidance provided by the IGI system. Therefore, understanding the overall uncertainty of an IGI system is paramount to the overall outcome of the intervention, as procedure success entails achieving certain accuracy tolerances specific to individual procedures. This work has focused on studying the navigation uncertainty, along with techniques to reduce uncertainty, for an IGI platform dedicated to image-guided renal interventions. We constructed life-size replica patient-specific kidney models from pre-operative images using 3D printing and tissue emulating materials and conducted experiments to characterize the uncertainty of both optical and electromagnetic surgical tracking systems, the uncertainty associated with the virtual model-to-physical phantom registration, as well as the uncertainty associated with live augmented reality (AR) views of the surgical scene achieved by enhancing the pre-procedural model and tracked surgical instrument views with live video views acquires using a camera tracked in real time. To better understand the effects of the tracked instrument calibration, registration fiducial configuration, and tracked camera calibration on the overall navigation uncertainty, we conducted Monte Carlo simulations that enabled us to identify optimal configurations that were subsequently validated experimentally using patient-specific phantoms in the laboratory. To mitigate the inherent accuracy limitations associated with the pre-procedural model-to-patient registration and their effect on the overall navigation, we also demonstrated the use of tracked video imaging to update the registration, enabling us to restore targeting accuracy to within its acceptable range. Lastly, we conducted several validation experiments using patient-specific kidney emulating phantoms using post-procedure CT imaging as reference ground truth to assess the accuracy of AR-guided navigation in the context of in vitro renal interventions. This work helped find answers to key questions about uncertainty propagation in image-guided renal interventions and led to the development of key techniques and tools to help reduce optimize the overall navigation / targeting uncertainty

New Mechatronic Systems for the Diagnosis and Treatment of Cancer

Both two dimensional (2D) and three dimensional (3D) imaging modalities are useful tools for viewing the internal anatomy. Three dimensional imaging techniques are required for accurate targeting of needles. This improves the efficiency and control over the intervention as the high temporal resolution of medical images can be used to validate the location of needle and target in real time. Relying on imaging alone, however, means the intervention is still operator dependent because of the difficulty of controlling the location of the needle within the image. The objective of this thesis is to improve the accuracy and repeatability of needle-based interventions over conventional techniques: both manual and automated techniques. This includes increasing the accuracy and repeatability of these procedures in order to minimize the invasiveness of the procedure. In this thesis, I propose that by combining the remote center of motion concept using spherical linkage components into a passive or semi-automated device, the physician will have a useful tracking and guidance system at their disposal in a package, which is less threatening than a robot to both the patient and physician. This design concept offers both the manipulative transparency of a freehand system, and tremor reduction through scaling currently offered in automated systems. In addressing each objective of this thesis, a number of novel mechanical designs incorporating an remote center of motion architecture with varying degrees of freedom have been presented. Each of these designs can be deployed in a variety of imaging modalities and clinical applications, ranging from preclinical to human interventions, with an accuracy of control in the millimeter to sub-millimeter range

Exploiting Temporal Image Information in Minimally Invasive Surgery

Minimally invasive procedures rely on medical imaging instead of the surgeons direct vision. While preoperative images can be used for surgical planning and navigation, once the surgeon arrives at the target site real-time intraoperative imaging is needed. However, acquiring and interpreting these images can be challenging and much of the rich temporal information present in these images is not visible. The goal of this thesis is to improve image guidance for minimally invasive surgery in two main areas. First, by showing how high-quality ultrasound video can be obtained by integrating an ultrasound transducer directly into delivery devices for beating heart valve surgery. Secondly, by extracting hidden temporal information through video processing methods to help the surgeon localize important anatomical structures. Prototypes of delivery tools, with integrated ultrasound imaging, were developed for both transcatheter aortic valve implantation and mitral valve repair. These tools provided an on-site view that shows the tool-tissue interactions during valve repair. Additionally, augmented reality environments were used to add more anatomical context that aids in navigation and in interpreting the on-site video. Other procedures can be improved by extracting hidden temporal information from the intraoperative video. In ultrasound guided epidural injections, dural pulsation provides a cue in finding a clear trajectory to the epidural space. By processing the video using extended Kalman filtering, subtle pulsations were automatically detected and visualized in real-time. A statistical framework for analyzing periodicity was developed based on dynamic linear modelling. In addition to detecting dural pulsation in lumbar spine ultrasound, this approach was used to image tissue perfusion in natural video and generate ventilation maps from free-breathing magnetic resonance imaging. A second statistical method, based on spectral analysis of pixel intensity values, allowed blood flow to be detected directly from high-frequency B-mode ultrasound video. Finally, pulsatile cues in endoscopic video were enhanced through Eulerian video magnification to help localize critical vasculature. This approach shows particular promise in identifying the basilar artery in endoscopic third ventriculostomy and the prostatic artery in nerve-sparing prostatectomy. A real-time implementation was developed which processed full-resolution stereoscopic video on the da Vinci Surgical System

Three dimensional study to quantify the relationship between facial hard and soft tissue movement as a result of orthognathic surgery

Introduction Prediction of soft tissue changes following orthognathic surgery has been frequently attempted in the past decades. It has gradually progressed from the classic “cut and paste” of photographs to the computer assisted 2D surgical prediction planning; and finally, comprehensive 3D surgical planning was introduced to help surgeons and patients to decide on the magnitude and direction of surgical movements as well as the type of surgery to be considered for the correction of facial dysmorphology. A wealth of experience was gained and numerous published literature is available which has augmented the knowledge of facial soft tissue behaviour and helped to improve the ability to closely simulate facial changes following orthognathic surgery. This was particularly noticed following the introduction of the three dimensional imaging into the medical research and clinical applications. Several approaches have been considered to mathematically predict soft tissue changes in three dimensions, following orthognathic surgery. The most common are the Finite element model and Mass tensor Model. These were developed into software packages which are currently used in clinical practice. In general, these methods produce an acceptable level of prediction accuracy of soft tissue changes following orthognathic surgery. Studies, however, have shown a limited prediction accuracy at specific regions of the face, in particular the areas around the lips. Aims The aim of this project is to conduct a comprehensive assessment of hard and soft tissue changes following orthognathic surgery and introduce a new method for prediction of facial soft tissue changes.   Methodology The study was carried out on the pre- and post-operative CBCT images of 100 patients who received their orthognathic surgery treatment at Glasgow dental hospital and school, Glasgow, UK. Three groups of patients were included in the analysis; patients who underwent Le Fort I maxillary advancement surgery; bilateral sagittal split mandibular advancement surgery or bimaxillary advancement surgery. A generic facial mesh was used to standardise the information obtained from individual patient’s facial image and Principal component analysis (PCA) was applied to interpolate the correlations between the skeletal surgical displacement and the resultant soft tissue changes. The identified relationship between hard tissue and soft tissue was then applied on a new set of preoperative 3D facial images and the predicted results were compared to the actual surgical changes measured from their post-operative 3D facial images. A set of validation studies was conducted. To include: • Comparison between voxel based registration and surface registration to analyse changes following orthognathic surgery. The results showed there was no statistically significant difference between the two methods. Voxel based registration, however, showed more reliability as it preserved the link between the soft tissue and skeletal structures of the face during the image registration process. Accordingly, voxel based registration was the method of choice for superimposition of the pre- and post-operative images. The result of this study was published in a refereed journal. • Direct DICOM slice landmarking; a novel technique to quantify the direction and magnitude of skeletal surgical movements. This method represents a new approach to quantify maxillary and mandibular surgical displacement in three dimensions. The technique includes measuring the distance of corresponding landmarks digitized directly on DICOM image slices in relation to three dimensional reference planes. The accuracy of the measurements was assessed against a set of “gold standard” measurements extracted from simulated model surgery. The results confirmed the accuracy of the method within 0.34mm. Therefore, the method was applied in this study. The results of this validation were published in a peer refereed journal. • The use of a generic mesh to assess soft tissue changes using stereophotogrammetry. The generic facial mesh played a major role in the soft tissue dense correspondence analysis. The conformed generic mesh represented the geometrical information of the individual’s facial mesh on which it was conformed (elastically deformed). Therefore, the accuracy of generic mesh conformation is essential to guarantee an accurate replica of the individual facial characteristics. The results showed an acceptable overall mean error of the conformation of generic mesh 1 mm. The results of this study were accepted for publication in peer refereed scientific journal. Skeletal tissue analysis was performed using the validated “Direct DICOM slices landmarking method” while soft tissue analysis was performed using Dense correspondence analysis. The analysis of soft tissue was novel and produced a comprehensive description of facial changes in response to orthognathic surgery. The results were accepted for publication in a refereed scientific Journal. The main soft tissue changes associated with Le Fort I were advancement at the midface region combined with widening of the paranasal, upper lip and nostrils. Minor changes were noticed at the tip of the nose and oral commissures. The main soft tissue changes associated with mandibular advancement surgery were advancement and downward displacement of the chin and lower lip regions, limited widening of the lower lip and slight reversion of the lower lip vermilion combined with minimal backward displacement of the upper lip were recorded. Minimal changes were observed on the oral commissures. The main soft tissue changes associated with bimaxillary advancement surgery were generalized advancement of the middle and lower thirds of the face combined with widening of the paranasal, upper lip and nostrils regions. In Le Fort I cases, the correlation between the changes of the facial soft tissue and the skeletal surgical movements was assessed using PCA. A statistical method known as ’Leave one out cross validation’ was applied on the 30 cases which had Le Fort I osteotomy surgical procedure to effectively utilize the data for the prediction algorithm. The prediction accuracy of soft tissue changes showed a mean error ranging between (0.0006mm±0.582) at the nose region to (-0.0316mm±2.1996) at the various facial regions