13,476 research outputs found
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
In vivo measurement of human brain elasticity using a light aspiration device
The brain deformation that occurs during neurosurgery is a serious issue
impacting the patient "safety" as well as the invasiveness of the brain
surgery. Model-driven compensation is a realistic and efficient solution to
solve this problem. However, a vital issue is the lack of reliable and easily
obtainable patient-specific mechanical characteristics of the brain which,
according to clinicians' experience, can vary considerably. We designed an
aspiration device that is able to meet the very rigorous sterilization and
handling process imposed during surgery, and especially neurosurgery. The
device, which has no electronic component, is simple, light and can be
considered as an ancillary instrument. The deformation of the aspirated tissue
is imaged via a mirror using an external camera. This paper describes the
experimental setup as well as its use during a specific neurosurgery. The
experimental data was used to calibrate a continuous model. We show that we
were able to extract an in vivo constitutive law of the brain elasticity: thus
for the first time, measurements are carried out per-operatively on the
patient, just before the resection of the brain parenchyma. This paper
discloses the results of a difficult experiment and provide for the first time
in-vivo data on human brain elasticity. The results point out the softness as
well as the highly non-linear behavior of the brain tissue.Comment: Medical Image Analysis (2009) accept\'
Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation
In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is
required for subsurface visualisation to characterise the state of the tissue.
However, scanning of large tissue surfaces in the presence of deformation is a
challenging task for the surgeon. Recently, robot-assisted local tissue
scanning has been investigated for motion stabilisation of imaging probes to
facilitate the capturing of good quality images and reduce the surgeon's
cognitive load. Nonetheless, these approaches require the tissue surface to be
static or deform with periodic motion. To eliminate these assumptions, we
propose a visual servoing framework for autonomous tissue scanning, able to
deal with free-form tissue deformation. The 3D structure of the surgical scene
is recovered and a feature-based method is proposed to estimate the motion of
the tissue in real-time. A desired scanning trajectory is manually defined on a
reference frame and continuously updated using projective geometry to follow
the tissue motion and control the movement of the robotic arm. The advantage of
the proposed method is that it does not require the learning of the tissue
motion prior to scanning and can deal with free-form deformation. We deployed
this framework on the da Vinci surgical robot using the da Vinci Research Kit
(dVRK) for Ultrasound tissue scanning. Since the framework does not rely on
information from the Ultrasound data, it can be easily extended to other
probe-based imaging modalities.Comment: 7 pages, 5 figures, ICRA 202
Using CamiTK for rapid prototyping of interactive Computer Assisted Medical Intervention applications
Computer Assisted Medical Intervention (CAMI hereafter) is a complex
multi-disciplinary field. CAMI research requires the collaboration of experts
in several fields as diverse as medicine, computer science, mathematics,
instrumentation, signal processing, mechanics, modeling, automatics, optics,
etc
Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots
Reliable and real-time 3D reconstruction and localization functionality is a
crucial prerequisite for the navigation of actively controlled capsule
endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic
technology for use in the gastrointestinal (GI) tract. In this study, we
propose a fully dense, non-rigidly deformable, strictly real-time,
intraoperative map fusion approach for actively controlled endoscopic capsule
robot applications which combines magnetic and vision-based localization, with
non-rigid deformations based frame-to-model map fusion. The performance of the
proposed method is demonstrated using four different ex-vivo porcine stomach
models. Across different trajectories of varying speed and complexity, and four
different endoscopic cameras, the root mean square surface reconstruction
errors 1.58 to 2.17 cm.Comment: submitted to IROS 201
Respiratory organ motion in interventional MRI : tracking, guiding and modeling
Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy.
In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities.
Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well.
Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence
Hacia el modelado 3d de tumores cerebrales mediante endoneurosonografĂa y redes neuronales
Las cirugĂas mĂnimamente invasivas se han vuelto populares debido a que implican menos riesgos con respecto a las intervenciones tradicionales. En neurocirugĂa, las tendencias recientes sugieren el uso conjunto de la endoscopia y el ultrasonido, tĂ©cnica llamada endoneurosonografĂa (ENS), para la virtualizaciĂłn 3D de las estructuras del cerebro en tiempo real. La informaciĂłn ENS se puede utilizar para generar modelos 3D de los tumores del cerebro durante la cirugĂa. En este trabajo, presentamos una metodologĂa para el modelado 3D de tumores cerebrales con ENS y redes neuronales. EspecĂficamente, se estudiĂł el uso de mapas auto-organizados (SOM) y de redes neuronales tipo gas (NGN). En comparaciĂłn con otras tĂ©cnicas, el modelado 3D usando redes neuronales ofrece ventajas debido a que la morfologĂa del tumor se codifica directamente sobre los pesos sinápticos de la red, no requiere ningĂşn conocimiento a priori y la representaciĂłn puede ser desarrollada en dos etapas: entrenamiento fuera de lĂnea y adaptaciĂłn en lĂnea. Se realizan pruebas experimentales con maniquĂes mĂ©dicos 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
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