Accurate geometry reconstruction of vascular structures using implicit splines

3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy

3D shape instantiation for intra-operative navigation from a single 2D projection

Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeon’s view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs). For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies. For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed. For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique. The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces

Advances in navigation and intraoperative imaging for intraoperative electron radiotherapy

Mención Internacional en el título de doctorEsta tesis se enmarca dentro del campo de la radioterapia y trata específicamente sobre la radioterapia intraoperatoria (RIO) con electrones. Esta técnica combina la resección quirúrgica de un tumor y la radiación terapéutica directamente aplicada sobre el lecho tumoral post-resección o sobre el tumor no resecado. El haz de electrones de alta energía es colimado y conducido por un aplicador específico acoplado a un acelerador lineal. La planificación de la RIO con electrones es compleja debido a las modificaciones geométricas y anatómicas producidas por la retracción de estructuras y la eliminación de tejidos cancerosos durante la cirugía. Actualmente, no se dispone del escenario real en este tipo de tratamientos (por ejemplo, la posición/orientación del aplicador respecto a la anatomía del paciente o las irregularidades en la superficie irradiada), sólo de una estimación grosso modo del tratamiento real administrado al paciente. Las imágenes intraoperatorias del escenario real durante el tratamiento (concretamente imágenes de tomografía axial computarizada [TAC]) serían útiles no sólo para la planificación intraoperatoria, sino también para registrar y evaluar el tratamiento administrado al paciente. Esta información es esencial en estudios prospectivos. En esta tesis se evaluó en primer lugar la viabilidad de un sistema de seguimiento óptico de varias cámaras para obtener la posición/orientación del aplicador en los escenarios de RIO con electrones. Los resultados mostraron un error de posición del aplicador inferior a 2 mm (error medio del centro del bisel) y un error de orientación menor de 2º (error medio del eje del bisel y del eje longitudinal del aplicador). Estos valores están dentro del rango propuesto por el Grupo de Trabajo 147 (encargo del Comité de Terapia y del Subcomité para la Mejora de la Garantía de Calidad y Resultados de la Asociación Americana de Físicos en Medicina [AAPM] para estudiar en radioterapia externa la exactitud de la localización con métodos no radiográficos, como los sistemas infrarrojos). Una limitación importante de la solución propuesta es que el aplicador se superpone a la imagen preoperatoria del paciente. Una imagen intraoperatoria proporcionaría información anatómica actualizada y permitiría estimar la distribución tridimensional de la dosis. El segundo estudio específico de esta tesis evaluó la viabilidad de adquirir con un TAC simulador imágenes TAC intraoperatorias de escenarios reales de RIO con electrones. No hubo complicaciones en la fase de transporte del paciente utilizando la camilla y su acople para el transporte, o con la adquisición de imágenes TAC intraoperatorias en la sala del TAC simulador. Los estudios intraoperatorios adquiridos se utilizaron para evaluar la mejora obtenida en la estimación de la distribución de dosis en comparación con la obtenida a partir de imágenes TAC preoperatorias, identificando el factor dominante en esas estimaciones (la región de aire y las irregularidades en la superficie, no las heterogeneidades de los tejidos). Por último, el tercer estudio específico se centró en la evaluación de varias tecnologías TAC de kilovoltaje, aparte del TAC simulador, para adquirir imágenes intraoperatorias con las que estimar la distribución de la dosis en RIO con electrones. Estos dispositivos serían necesarios en el caso de disponer de aceleradores lineales portátiles en el quirófano ya que no se aprobaría mover al paciente a la sala del TAC simulador. Los resultados con un maniquí abdominal mostraron que un TAC portátil (BodyTom) e incluso un acelerador lineal con un TAC de haz de cónico (TrueBeam) serían adecuados para este propósito.This thesis is framed within the field of radiotherapy, specifically intraoperative electron radiotherapy (IOERT). This technique combines surgical resection of a tumour and therapeutic radiation directly applied to a post-resection tumour bed or to an unresected tumour. The high-energy electron beam is collimated and conducted by a specific applicator docked to a linear accelerator (LINAC). Dosimetry planning for IOERT is challenging owing to the geometrical and anatomical modifications produced by the retraction of structures and removal of cancerous tissues during the surgery. No data of the actual IOERT 3D scenario is available (for example, the applicator pose in relation to the patient’s anatomy or the irregularities in the irradiated surface) and consequently only a rough approximation of the actual IOERT treatment administered to the patient can be estimated. Intraoperative computed tomography (CT) images of the actual scenario during the treatment would be useful not only for intraoperative planning but also for registering and evaluating the treatment administered to the patient. This information is essential for prospective trials. In this thesis, the feasibility of using a multi-camera optical tracking system to obtain the applicator pose in IOERT scenarios was firstly assessed. Results showed that the accuracy of the applicator pose was below 2 mm in position (mean error of the bevel centre) and 2º in orientation (mean error of the bevel axis and the longitudinal axis), which are within the acceptable range proposed in the recommendation of Task Group 147 (commissioned by the Therapy Committee and the Quality Assurance and Outcomes Improvement Subcommittee of the American Association of Physicists in Medicine [AAPM] to study the localization accuracy with non-radiographic methods such as infrared systems in external beam radiation therapy). An important limitation of this solution is that the actual pose of applicator is superimposed on a patient’s preoperative image. An intraoperative image would provide updated anatomical information and would allow estimating the 3D dose distribution. The second specific study of this thesis evaluated the feasibility of acquiring intraoperative CT images with a CT simulator in real IOERT scenarios. There were no complications in the whole procedure related to the transport step using the subtable and its stretcher or the acquisition of intraoperative CT images in the CT simulator room. The acquired intraoperative studies were used to evaluate the improvement achieved in the dose distribution estimation when compared to that obtained from preoperative CT images, identifying the dominant factor in those estimations (air gap and the surface irregularities, not tissue heterogeneities). Finally, the last specific study focused on assessing several kilovoltage (kV) CT technologies other than CT simulators to acquire intraoperative images for estimating IOERT dose distribution. That would be necessary when a mobile electron LINAC was available in the operating room as transferring the patient to the CT simulator room could not be approved. Our results with an abdominal phantom revealed that a portable CT (BodyTom) and even a LINAC with on-board kV cone-beam CT (TrueBeam) would be suitable for this purpose.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín López Herráiz.- Secretario: María Arrate Muñoz Barrutia.- Vocal: Óscar Acosta Tamay

Atlas-Based Interpretable Age Prediction

Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting the discrepancy between chronological and biological age. To gain a comprehensive understanding of age-related changes observed in various body parts, we investigate them on a larger scale by using whole-body images. We utilise the Grad-CAM interpretability method to determine the body areas most predictive of a person's age. We expand our analysis beyond individual subjects by employing registration techniques to generate population-wide interpretability maps. Furthermore, we set state-of-the-art whole-body age prediction with a model that achieves a mean absolute error of 2.76 years. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance

Focal Spot, Winter 2004/2005

https://digitalcommons.wustl.edu/focal_spot_archives/1098/thumbnail.jp

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi

Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures

PURPOSE: Navigation of endoscopic ultrasound (EUS)-guided procedures of the upper gastrointestinal (GI) system can be technically challenging due to the small fields-of-view of ultrasound and optical devices, as well as the anatomical variability and limited number of orienting landmarks during navigation. Co-registration of an EUS device and a pre-procedure 3D image can enhance the ability to navigate. However, the fidelity of this contextual information depends on the accuracy of registration. The purpose of this study was to develop and test the feasibility of a simulation-based planning method for pre-selecting patient-specific EUS-visible anatomical landmark locations to maximise the accuracy and robustness of a feature-based multimodality registration method. METHODS: A registration approach was adopted in which landmarks are registered to anatomical structures segmented from the pre-procedure volume. The predicted target registration errors (TREs) of EUS-CT registration were estimated using simulated visible anatomical landmarks and a Monte Carlo simulation of landmark localisation error. The optimal planes were selected based on the 90th percentile of TREs, which provide a robust and more accurate EUS-CT registration initialisation. The method was evaluated by comparing the accuracy and robustness of registrations initialised using optimised planes versus non-optimised planes using manually segmented CT images and simulated ([Formula: see text]) or retrospective clinical ([Formula: see text]) EUS landmarks. RESULTS: The results show a lower 90th percentile TRE when registration is initialised using the optimised planes compared with a non-optimised initialisation approach (p value [Formula: see text]). CONCLUSIONS: The proposed simulation-based method to find optimised EUS planes and landmarks for EUS-guided procedures may have the potential to improve registration accuracy. Further work will investigate applying the technique in a clinical setting