Hacia el modelado 3d de tumores cerebrales mediante endoneurosonografía y redes neuronales

Las cirugías mínimamente invasivas se han vuelto populares debido a que implican menos riesgos con respecto a las intervenciones tradicionales. En neurocirugía, las tendencias recientes sugieren el uso conjunto de la endoscopia y el ultrasonido, técnica llamada endoneurosonografía (ENS), para la virtualización 3D de las estructuras del cerebro en tiempo real. La información ENS se puede utilizar para generar modelos 3D de los tumores del cerebro durante la cirugía. En este trabajo, presentamos una metodología para el modelado 3D de tumores cerebrales con ENS y redes neuronales. Específicamente, se estudió el uso de mapas auto-organizados (SOM) y de redes neuronales tipo gas (NGN). En comparación con otras técnicas, el modelado 3D usando redes neuronales ofrece ventajas debido a que la morfología del tumor se codifica directamente sobre los pesos sinápticos de la red, no requiere ningún conocimiento a priori y la representación puede ser desarrollada en dos etapas: entrenamiento fuera de línea y adaptación en línea. Se realizan pruebas experimentales con maniquíes médicos de tumores cerebrales. Al final del documento, se presentan los resultados del modelado 3D a partir de una base de datos ENS.Minimally invasive surgeries have become popular because they reduce the typical risks of traditional interventions. In neurosurgery, recent trends suggest the combined use of endoscopy and ultrasound (endoneurosonography or ENS) for 3D virtualization of brain structures in real time. The ENS information can be used to generate 3D models of brain tumors during a surgery. This paper introduces a methodology for 3D modeling of brain tumors using ENS and unsupervised neural networks. The use of self-organizing maps (SOM) and neural gas networks (NGN) is particularly studied. Compared to other techniques, 3D modeling using neural networks offers advantages, since tumor morphology is directly encoded in synaptic weights of the network, no a priori knowledge is required, and the representation can be developed in two stages: off-line training and on-line adaptation. Experimental tests were performed using virtualized phantom brain tumors. At the end of the paper, the results of 3D modeling from an ENS database are presented

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

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio

Influence of shear waves on transcranial ultrasound propagation in cortical brain regions

Transcranial ultrasound applications require accurate simulations to predict intracranial acoustic pressure fields. The current gold standard typically consists of calculating a longitudinal ultrasound wave propagation using a fluid skull model, which is based on full head CT images for retrieving the skull's geometry and elastic constants. Although this approach has extensively been validated for deep brain targets and routinely used in transcranial ultrasound ablation procedures, its accuracy in shallow cortical regions remains unexplored. In this study, we explore the shear wave effects associated with transcranial focused ultrasound propagation, both numerically and experimentally. The intracranial acoustic pressure was measured at different incidence angles at the parietal and frontal regions in an ex vivo human skull. The fluid-like skull model was then compared to the solid model comprising both longitudinal and shear waves. The results consistently show a larger error and variability for both models when considering an oblique incidence, reaching a maximum of 125% mean deviation of the focal area when employing the fluid skull model. Statistical assessments further revealed that ignoring shear waves results in an average ~40% overestimation of the intracranial acoustic pressure and inability to obtain an accurate intracranial acoustic pressure distribution. Moreover, the solid model has a more stable performance, even when small variations in the skull-transducer relative position are introduced. Our results could contribute to the refinement of the transcranial ultrasound propagation modeling methods thus help improving the safety and outcome of transcranial ultrasound therapy in the cortical brain areas

Fusion of interventional ultrasound & X-ray

In einer immer älter werdenden Bevölkerung wird die Behandlung von strukturellen Herzkrankheiten zunehmend wichtiger. Verbesserte medizinische Bildgebung und die Einführung neuer Kathetertechnologien führten dazu, dass immer mehr herkömmliche chirurgische Eingriffe am offenen Herzen durch minimal invasive Methoden abgelöst werden. Diese modernen Interventionen müssen durch verschiedenste Bildgebungsverfahren navigiert werden. Hierzu werden hauptsächlich Röntgenfluoroskopie und transösophageale Echokardiografie (TEE) eingesetzt. Röntgen bietet eine gute Visualisierung der eingeführten Katheter, was essentiell für eine gute Navigation ist. TEE hingegen bietet die Möglichkeit der Weichteilgewebedarstellung und kann damit vor allem zur Darstellung von anatomischen Strukturen, wie z.B. Herzklappen, genutzt werden. Beide Modalitäten erzeugen Bilder in Echtzeit und werden für die erfolgreiche Durchführung minimal invasiver Herzchirurgie zwingend benötigt. Üblicherweise sind beide Systeme eigenständig und nicht miteinander verbunden. Es ist anzunehmen, dass eine Bildfusion beider Welten einen großen Vorteil für die behandelnden Operateure erzeugen kann, vor allem eine verbesserte Kommunikation im Behandlungsteam. Ebenso können sich aus der Anwendung heraus neue chirurgische Worfklows ergeben. Eine direkte Fusion beider Systeme scheint nicht möglich, da die Bilddaten eine zu unterschiedliche Charakteristik aufweisen. Daher kommt in dieser Arbeit eine indirekte Registriermethode zum Einsatz. Die TEE-Sonde ist während der Intervention ständig im Fluoroskopiebild sichtbar. Dadurch wird es möglich, die Sonde im Röntgenbild zu registrieren und daraus die 3D Position abzuleiten. Der Zusammenhang zwischen Ultraschallbild und Ultraschallsonde wird durch eine Kalibrierung bestimmt. In dieser Arbeit wurde die Methode der 2D-3D Registrierung gewählt, um die TEE Sonde auf 2D Röntgenbildern zu erkennen. Es werden verschiedene Beiträge präsentiert, welche einen herkömmlichen 2D-3D Registrieralgorithmus verbessern. Nicht nur im Bereich der Ultraschall-Röntgen-Fusion, sondern auch im Hinblick auf allgemeine Registrierprobleme. Eine eingeführte Methode ist die der planaren Parameter. Diese verbessert die Robustheit und die Registriergeschwindigkeit, vor allem während der Registrierung eines Objekts aus zwei nicht-orthogonalen Richtungen. Ein weiterer Ansatz ist der Austausch der herkömmlichen Erzeugung von sogenannten digital reconstructed radiographs. Diese sind zwar ein integraler Bestandteil einer 2D-3D Registrierung aber gleichzeitig sehr zeitaufwendig zu berechnen. Es führt zu einem erheblichen Geschwindigkeitsgewinn die herkömmliche Methode durch schnelles Rendering von Dreiecksnetzen zu ersetzen. Ebenso wird gezeigt, dass eine Kombination von schnellen lernbasierten Detektionsalgorithmen und 2D-3D Registrierung die Genauigkeit und die Registrierreichweite verbessert. Zum Abschluss werden die ersten Ergebnisse eines klinischen Prototypen präsentiert, welcher die zuvor genannten Methoden verwendet.Today, in an elderly community, the treatment of structural heart disease will become more and more important. Constant improvements of medical imaging technologies and the introduction of new catheter devices caused the trend to replace conventional open heart surgery by minimal invasive interventions. These advanced interventions need to be guided by different medical imaging modalities. The two main imaging systems here are X-ray fluoroscopy and Transesophageal  Echocardiography (TEE). While X-ray provides a good visualization of inserted catheters, which is essential for catheter navigation, TEE can display soft tissues, especially anatomical structures like heart valves. Both modalities provide real-time imaging and are necessary to lead minimal invasive heart surgery to success. Usually, the two systems are detached and not connected. It is conceivable that a fusion of both worlds can create a strong benefit for the physicians. It can lead to a better communication within the clinical team and can probably enable new surgical workflows. Because of the completely different characteristics of the image data, a direct fusion seems to be impossible. Therefore, an indirect registration of Ultrasound and X-ray images is used. The TEE probe is usually visible in the X-ray image during the described minimal-invasive interventions. Thereby, it becomes possible to register the TEE probe in the fluoroscopic images and to establish its 3D position. The relationship of the Ultrasound image to the Ultrasound probe is known by calibration. To register the TEE probe on 2D X-ray images, a 2D-3D registration approach is chosen in this thesis. Several contributions are presented, which are improving the common 2D-3D registration algorithm for the task of Ultrasound and X-ray fusion, but also for general 2D-3D registration problems. One presented approach is the introduction of planar parameters that increase robustness and speed during the registration of an object on two non-orthogonal views. Another approach is to replace the conventional generation of digital reconstructedradiographs, which is an integral part of 2D-3D registration but also a performance bottleneck, with fast triangular mesh rendering. This will result in a significant performance speed-up. It is also shown that a combination of fast learning-based detection algorithms with 2D-3D registration will increase the accuracy and the capture range, instead of employing them solely to the  registration/detection of a TEE probe. Finally, a first clinical prototype is presented which employs the presented approaches and first clinical results are shown

Computational ultrasound tissue characterisation for brain tumour resection

In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases

Integrated navigation and visualisation for skull base surgery

Skull base surgery involves the management of tumours located on the underside of the brain and the base of the skull. Skull base tumours are intricately associated with several critical neurovascular structures making surgery challenging and high risk. Vestibular schwannoma (VS) is a benign nerve sheath tumour arising from one of the vestibular nerves and is the commonest pathology encountered in skull base surgery. The goal of modern VS surgery is maximal tumour removal whilst preserving neurological function and maintaining quality of life but despite advanced neurosurgical techniques, facial nerve paralysis remains a potentially devastating complication of this surgery. This thesis describes the development and integration of various advanced navigation and visualisation techniques to increase the precision and accuracy of skull base surgery. A novel Diffusion Magnetic Resonance Imaging (dMRI) acquisition and processing protocol for imaging the facial nerve in patients with VS was developed to improve delineation of facial nerve preoperatively. An automated Artificial Intelligence (AI)-based framework was developed to segment VS from MRI scans. A user-friendly navigation system capable of integrating dMRI and tractography of the facial nerve, 3D tumour segmentation and intraoperative 3D ultrasound was developed and validated using an anatomically-realistic acoustic phantom model of a head including the skull, brain and VS. The optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue were examined across a wavelength spectrum of 400 nm to 800 nm in order to inform the development of an Intraoperative Hypserpectral Imaging (iHSI) system. Finally, functional and technical requirements of an iHSI were established and a prototype system was developed and tested in a first-in-patient study

Open-source software for ultrasound-based guidance in spinal fusion surgery.

Spinal instrumentation and surgical manipulations may cause loss of navigation accuracy requiring an efficient re-alignment of the patient anatomy with pre-operative images during surgery. While intra-operative ultrasound (iUS) guidance has shown clear potential to reduce surgery time, compared with clinical computed tomography (CT) guidance, rapid registration aiming to correct for patient misalignment has not been addressed. In this article, we present an open-source platform for pedicle screw navigation using iUS imaging. The alignment method is based on rigid registration of CT to iUS vertebral images and has been designed for fast and fully automatic patient re-alignment in the operating room. Two steps are involved: first, we use the iUS probe's trajectory to achieve an initial coarse registration; then, the registration transform is refined by simultaneously optimizing gradient orientation alignment and mean of iUS intensities passing through the CT-defined posterior surface of the vertebra. We evaluated our approach on a lumbosacral section of a porcine cadaver with seven vertebral levels. We achieved a median target registration error of 1.47 mm (100% success rate, defined by a target registration error <2 mm) when applying the probe's trajectory initial alignment. The approach exhibited high robustness to partial visibility of the vertebra with success rates of 89.86% and 88.57% when missing either the left or right part of the vertebra and robustness to initial misalignments with a success rate of 83.14% for random starts within ±20° rotation and ±20 mm translation. Our graphics processing unit implementation achieves an efficient registration time under 8 s, which makes the approach suitable for clinical application