Advanced tracking and image registration techniques for intraoperative radiation therapy

Mención Internacional en el título de doctorIntraoperative electron radiation therapy (IOERT) is a technique used to deliver radiation to the surgically opened tumor bed without irradiating healthy tissue. Treatment planning systems and mobile linear accelerators enable clinicians to optimize the procedure, minimize stress in the operating room (OR) and avoid transferring the patient to a dedicated radiation room. However, placement of the radiation collimator over the tumor bed requires a validation methodology to ensure correct delivery of the dose prescribed in the treatment planning system. In this dissertation, we address three well-known limitations of IOERT: applicator positioning over the tumor bed, docking of the mobile linear accelerator gantry with the applicator and validation of the dose delivery prescribed. This thesis demonstrates that these limitations can be overcome by positioning the applicator appropriately with respect to the patient’s anatomy. The main objective of the study was to assess technological and procedural alternatives for improvement of IOERT performance and resolution of problems of uncertainty. Image-to-world registration, multicamera optical trackers, multimodal imaging techniques and mobile linear accelerator docking are addressed in the context of IOERT. IOERT is carried out by a multidisciplinary team in a highly complex environment that has special tracking needs owing to the characteristics of its working volume (i.e., large and prone to occlusions), in addition to the requisites of accuracy. The first part of this dissertation presents the validation of a commercial multicamera optical tracker in terms of accuracy, sensitivity to miscalibration, camera occlusions and detection of tools using a feasible surgical setup. It also proposes an automatic miscalibration detection protocol that satisfies the IOERT requirements of automaticity and speed. We show that the multicamera tracker is suitable for IOERT navigation and demonstrate the feasibility of the miscalibration detection protocol in clinical setups. Image-to-world registration is one of the main issues during image-guided applications where the field of interest and/or the number of possible anatomical localizations is large, such as IOERT. In the second part of this dissertation, a registration algorithm for image-guided surgery based on lineshaped fiducials (line-based registration) is proposed and validated. Line-based registration decreases acquisition time during surgery and enables better registration accuracy than other published algorithms. In the third part of this dissertation, we integrate a commercial low-cost ultrasound transducer and a cone beam CT C-arm with an optical tracker for image-guided interventions to enable surgical navigation and explore image based registration techniques for both modalities. In the fourth part of the dissertation, a navigation system based on optical tracking for the docking of the mobile linear accelerator to the radiation applicator is assessed. This system improves safety and reduces procedure time. The system tracks the prescribed collimator location to solve the movements that the linear accelerator should perform to reach the docking position and warns the user about potentially unachievable arrangements before the actual procedure. A software application was implemented to use this system in the OR, where it was also evaluated to assess the improvement in docking speed. Finally, in the last part of the dissertation, we present and assess the installation setup for a navigation system in a dedicated IOERT OR, determine the steps necessary for the IOERT process, identify workflow limitations and evaluate the feasibility of the integration of the system in a real OR. The navigation system safeguards the sterile conditions of the OR, clears the space available for surgeons and is suitable for any similar dedicated IOERT OR.La Radioterapia Intraoperatoria por electrones (RIO) consiste en la aplicación de radiación de alta energía directamente sobre el lecho tumoral, accesible durante la cirugía, evitando radiar los tejidos sanos. Hoy en día, avances como los sistemas de planificación (TPS) y la aparición de aceleradores lineales móviles permiten optimizar el procedimiento, minimizar el estrés clínico en el entorno quirúrgico y evitar el desplazamiento del paciente durante la cirugía a otra sala para ser radiado. La aplicación de la radiación se realiza mediante un colimador del haz de radiación (aplicador) que se coloca sobre el lecho tumoral de forma manual por el oncólogo radioterápico. Sin embargo, para asegurar una correcta deposición de la dosis prescrita y planificada en el TPS, es necesaria una adecuada validación de la colocación del colimador. En esta Tesis se abordan tres limitaciones conocidas del procedimiento RIO: el correcto posicionamiento del aplicador sobre el lecho tumoral, acoplamiento del acelerador lineal con el aplicador y validación de la dosis de radiación prescrita. Esta Tesis demuestra que estas limitaciones pueden ser abordadas mediante el posicionamiento del aplicador de radiación en relación con la anatomía del paciente. El objetivo principal de este trabajo es la evaluación de alternativas tecnológicas y procedimentales para la mejora de la práctica de la RIO y resolver los problemas de incertidumbre descritos anteriormente. Concretamente se revisan en el contexto de la radioterapia intraoperatoria los siguientes temas: el registro de la imagen y el paciente, sistemas de posicionamiento multicámara, técnicas de imagen multimodal y el acoplamiento del acelerador lineal móvil. El entorno complejo y multidisciplinar de la RIO precisa de necesidades especiales para el empleo de sistemas de posicionamiento como una alta precisión y un volumen de trabajo grande y propenso a las oclusiones de los sensores de posición. La primera parte de esta Tesis presenta una exhaustiva evaluación de un sistema de posicionamiento óptico multicámara comercial. Estudiamos la precisión del sistema, su sensibilidad a errores cometidos en la calibración, robustez frente a posibles oclusiones de las cámaras y precisión en el seguimiento de herramientas en un entorno quirúrgico real. Además, proponemos un protocolo para la detección automática de errores por calibración que satisface los requisitos de automaticidad y velocidad para la RIO demostrando la viabilidad del empleo de este sistema para la navegación en RIO. Uno de los problemas principales de la cirugía guiada por imagen es el correcto registro de la imagen médica y la anatomía del paciente en el quirófano. En el caso de la RIO, donde el número de posibles localizaciones anatómicas es bastante amplio, así como el campo de trabajo es grande se hace necesario abordar este problema para una correcta navegación. Por ello, en la segunda parte de esta Tesis, proponemos y validamos un nuevo algoritmo de registro (LBR) para la cirugía guiada por imagen basado en marcadores lineales. El método propuesto reduce el tiempo de la adquisición de la posición de los marcadores durante la cirugía y supera en precisión a otros algoritmos de registro establecidos y estudiados en la literatura. En la tercera parte de esta tesis, integramos un transductor de ultrasonido comercial de bajo coste, un arco en C de rayos X con haz cónico y un sistema de posicionamiento óptico para intervenciones guiadas por imagen que permite la navegación quirúrgica y exploramos técnicas de registro de imagen para ambas modalidades. En la cuarta parte de esta tesis se evalúa un navegador basado en el sistema de posicionamiento óptico para el acoplamiento del acelerador lineal móvil con aplicador de radiación, mejorando la seguridad y reduciendo el tiempo del propio acoplamiento. El sistema es capaz de localizar el colimador en el espacio y proporcionar los movimientos que el acelerador lineal debe realizar para alcanzar la posición de acoplamiento. El sistema propuesto es capaz de advertir al usuario de aquellos casos donde la posición de acoplamiento sea inalcanzable. El sistema propuesto de ayuda para el acoplamiento se integró en una aplicación software que fue evaluada para su uso final en quirófano demostrando su viabilidad y la reducción de tiempo de acoplamiento mediante su uso. Por último, presentamos y evaluamos la instalación de un sistema de navegación en un quirófano RIO dedicado, determinamos las necesidades desde el punto de vista procedimental, identificamos las limitaciones en el flujo de trabajo y evaluamos la viabilidad de la integración del sistema en un entorno quirúrgico real. El sistema propuesto demuestra ser apto para el entorno RIO manteniendo las condiciones de esterilidad y dejando despejado el campo quirúrgico además de ser adaptable a cualquier quirófano similar.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Raúl San José Estépar.- Secretario: María Arrate Muñoz Barrutia.- Vocal: Carlos Ferrer Albiac

Improving 3D ultrasound prostate localisation in radiotherapy through increased automation of interfraction matching.

Background and purpose Daily image guidance is standard care for prostate radiotherapy. Innovations which improve the accuracy and efficiency of ultrasound guidance are needed, particularly with respect to reducing interobserver variation. This study explores automation tools for this purpose, demonstrated on the Elekta Clarity Autoscan®. The study was conducted as part of the Clarity-Pro trial (NCT02388308). Materials and methods Ultrasound scan volumes were collected from 32 patients. Prostate matches were performed using two proposed workflows and the results compared with Clarity's proprietary software. Gold standard matches derived from manually localised landmarks provided a reference. The two workflows incorporated a custom 3D image registration algorithm, which was benchmarked against a third-party application (Elastix). Results Significant reductions in match errors were reported from both workflows compared to standard protocol. Median (IQR) absolute errors in the left-right, anteroposterior and craniocaudal axes were lowest for the Manually Initiated workflow: 0.7(1.0) mm, 0.7(0.9) mm, 0.6(0.9) mm compared to 1.0(1.7) mm, 0.9(1.4) mm, 0.9(1.2) mm for Clarity. Median interobserver variation was ≪0.01 mm in all axes for both workflows compared to 2.2 mm, 1.7 mm, 1.5 mm for Clarity in left-right, anteroposterior and craniocaudal axes. Mean matching times was also reduced to 43 s from 152 s for Clarity. Inexperienced users of the proposed workflows attained better match precision than experienced users on Clarity. Conclusion Automated image registration with effective input and verification steps should increase the efficacy of interfraction ultrasound guidance compared to the current commercially available tools

Temporal Interpolation via Motion Field Prediction

Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high contrast 4D MR imaging during free breathing and provides in-vivo observations for treatment planning and guidance. Navigator slices are vital for retrospective stacking of 2D data slices in this method. However, they also prolong the acquisition sessions. Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking. In this work, we propose a convolutional neural network (CNN) based method for temporal interpolation via motion field prediction. The proposed formulation incorporates the prior knowledge that a motion field underlies changes in the image intensities over time. Previous approaches that interpolate directly in the intensity space are prone to produce blurry images or even remove structures in the images. Our method avoids such problems and faithfully preserves the information in the image. Further, an important advantage of our formulation is that it provides an unsupervised estimation of bi-directional motion fields. We show that these motion fields can be used to halve the number of registrations required during 4D reconstruction, thus substantially reducing the reconstruction time.Comment: Submitted to 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherland

Mesh-to-raster based non-rigid registration of multi-modal images

Region of interest (ROI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from CAT scanners as pixel or voxel data. Previously, we presented a 2D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a 3D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2D using ground truth provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem where the objective consists of a data term, which involves the signed distance function of the ROI from the reference image, and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The ROI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit (ITK) and Elastix

Implementation of safe human robot collaboration for ultrasound guided radiation therapy

This thesis shows that safe human-robot-interaction and Human Robot Collaboration is possible for Ultrasound (US) guided radiotherapy. Via the chosen methodology, all components (US, optical room monitoring and robot) could be linked and integrated and realized in a realistic clinical workflow. US guided radiotherapy offers a complement and alternative to existing image-guided therapy approaches. The real-time capability of US and high soft tissue contrast allow target structures to be tracked and radiation delivery to be modulated. However, Ultrasound guided radiation therapy (USgRT) is not yet clinically established but is still under development, as reliable and safe methods of image acquisition are not yet available. In particular, the loss of contact of the US probe to the patient surface poses a problem for patient movements such as breathing. For this purpose, a Breathing and motion compensation (BaMC) was developed in this work, which together with the safe control of a lightweight robot represents a new development for USgRT. The developed BaMC can be used to control the US probe with contact to the patient. The conducted experiments have confirmed that a steady contact with the patient surface and thus a continuous image acquisition can be ensured by the developed methodology. In addition, the image position in space can be accurately maintained in the submillimeter range. The BaMC seamlessly integrates into a developed clinical workflow. The graphical user interfaces developed for this purpose, as well as direct haptic control with the robot, provide an easy interaction option for the clinical user. The developed autonomous positioning of the transducer represents a good example of the feasibility of the approach. With the help of the user interface, an acoustic plane can be defined and autonomously approached via the robot in a time-efficient and precise manner. The tests carried out show that this methodology is suitable for a wide range of transducer positions. Safety in a human-robot interaction task is essential and requires individually customized concepts. In this work, adequate monitoring mechanisms could be found to ensure both patient and staff safety. In collision tests it could be shown that the implemented detection measures work and that the robot moves into a safe parking position. The forces acting on the patient could thus be pushed well below the limits required by the standard. This work has demonstrated the first important steps towards safe robot-assisted ultrasound imaging, which is not only applicable to USgRT. The developed interfaces provide the basis for further investigations in this field, especially in the area of image recognition, for example to determine the position of the target structure. With the proof of safety of the developed system, first study in human can now follow

Deformation Estimation and Assessment of Its Accuracy in Ultrasound Images

This thesis aims to address two problems; one in ultrasound elastography and one in image registration. The first problem entails estimation of tissue displacement in Ultrasound Elastography (UE). UE is an emerging technique used to estimate mechanical properties of tissue. It involves calculating the displacement field between two ultrasound Radio Frequency (RF) frames taken before and after a tissue deformation. A common way to calculate the displacement is to use correlation based approaches. However, these approaches fail in the presence of signal decorrelation. To address this issue, Dynamic Programming was used to find the optimum displacement using all the information on the RF-line. Although taking this approach improved the results, some failures persisted. In this thesis, we have formulated the DP method on a tree. Doing so allows for more information to be used for estimating the displacement and therefore reducing the error. We evaluated our method on simulation, phantom and real patient data. Our results shows that the proposed method outperforms the previous method in terms of accuracy with small added computational cost. In this work, we also address a problem in image registration. Although there is a vast literature in image registration, quality evaluation of registration is a field that has not received as much attention. This evaluation becomes even more crucial in medical imaging due to the sensitive nature of the field. We have addressed the said problem in the context of ultrasound guided radiotherapy. Image guidance has become an important part of radiotherapy wherein image registration is a critical step. Therefore, an evaluation of this registration can play an important role in the outcome of the therapy. In this work, we propose using both bootstrapping and supervised learning methods to evaluate the registration. We test our methods on 2D and 3D data acquired from phantom and patients. According to our results, both methods perform well while having advantages and disadvantages over one another. Supervised learning methods offer more accuracy and less computation time. On the other hand, for bootstrapping, no training data is required and also offers more sensitivity