207 research outputs found
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DEVICE AND METHOD FOR PRESSURE-DRIVEN PLUG TRANSPORT
The present invention provides microfabricated substrates and methods of conducting reactions within these substrates. The reactions occur in plugs transported in the flow of a carrier-fluid
Recommended from our members
DEVICE AND METHOD FOR PRESSURE-DRIVEN PLUG TRANSPORT
The present invention provides microfabricated substrates and methods of conducting reactions within these substrates. The reactions occur in plugs transported in the flow of a carrier-fluid
Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance
Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms.
In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time.
Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance.
Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed.
In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)
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DEVICE AND METHOD FOR PRESSURE-DRIVEN PLUG TRANSPORT
The present invention provides microfabricated substrates and methods of conducting reactions within these substrates. The reactions occur in plugs transported in the flow of a carrier-fluid
Recommended from our members
DEVICE AND METHOD FOR PRESSURE-DRIVEN PLUG TRANSPORT
The present invention provides microfabricated substrates and methods of conducting reactions within these substrates. The reactions occur in plugs transported in the flow of a carrier-fluid
Vessel recognition in ultrasound images using machine learning techniques
Purpose: Ultrasound is an imaging modality that is commonly used during cardiovascular surgeries globally. The purpose of this thesis is to investigate how machine learning techniques can be used to identify vessel properties and probe orientation in cardiac ultrasound images. The ultimate goal is developing a machine learning algorithm that can automatically recognize vessels in the region of interest with high mean average precision, identify vessel orientation, and run in near real-time. Method: This thesis present a thoroughly data exploration of ultrasound images acquired from a multicenter study. A pilot study of three different object detection models; Yolo, RetinaNet and EfficientDet, was done to find the best model fit for the dataset in the thesis. The three object detection models were trained, tuned and evaluated on the ultrasound data. The object detection model that performed the best after the pilot study was explored further. Yolo outperformed the other models and was therefore chosen as the object detection model for the final study. To overcome the dataset's class imbalance and size problem, data augmentation, resizing and upscaling of the ultrasound images were employed. The resulting data was used to train multiple yolo models with varying hyperparameter tunings. Model selection was then performed on these trained models, and the final model was evaluated on test data. Results: The final model achieved an overall mean average precision at 50\% at 71.77\%. The vessel orientation achieved a mean average precision at 64.6\% for the longitudinal orientation and 75.8\% for the transversal orientation. The model found it easier to locate the aorta compared to the anastomosis, which proved to be more challenging. The speed of the inference of all of these task was 5.6 milliseconds. Although the overall mean average precision was lower than the objective in this thesis, the model excelled in terms of speed. Conclusion: In conclusion, this thesis explored the application of machine learning techniques on ultrasound data for vessel recognition and orientation. Although the final model did not improve the state of the art, the research from this master thesis can serve as a starting point for future reasearch in the field. It represents pioneering work in utilizing a multicenter dataset for machine learning on ultrasound images, providing valuable groundwork and shedding light on the feasibility and potential of machine learning in intraoperative ultrasound.Masteroppgave i medisinsk teknologiMTEK39
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DEVICE AND METHOD FOR PRESSURE-DRIVEN PLUG TRANSPORT
The present invention provides microfabricated substrates and methods of conducting reactions within these substrates. The reactions occur in plugs transported in the flow of a carrier-fluid
Recommended from our members
DEVICE AND METHOD FOR PRESSURE-DRIVEN PLUG TRANSPORT
The present invention provides microfabricated substrates and methods of conducting reactions within these substrates. The reactions occur in plugs transported in the flow of a carrier-fluid
Acoustic-based Smart Tactile Sensing in Social Robots
Mención Internacional en el tÃtulo de doctorEl sentido del tacto es un componente crucial de la interacción social humana y es único
entre los cinco sentidos. Como único sentido proximal, el tacto requiere un contacto
fÃsico cercano o directo para registrar la información. Este hecho convierte al tacto en
una modalidad de interacción llena de posibilidades en cuanto a comunicación social. A través
del tacto, podemos conocer la intención de la otra persona y comunicar emociones. De esta
idea surge el concepto de social touch o tacto social como el acto de tocar a otra persona en
un contexto social. Puede servir para diversos fines, como saludar, mostrar afecto, persuadir
y regular el bienestar emocional y fÃsico.
Recientemente, el número de personas que interactúan con sistemas y agentes artificiales
ha aumentado, principalmente debido al auge de los dispositivos tecnológicos, como los smartphones
o los altavoces inteligentes. A pesar del auge de estos dispositivos, sus capacidades de
interacción son limitadas. Para paliar este problema, los recientes avances en robótica social han
mejorado las posibilidades de interacción para que los agentes funcionen de forma más fluida y
sean más útiles. En este sentido, los robots sociales están diseñados para facilitar interacciones
naturales entre humanos y agentes artificiales. El sentido del tacto en este contexto se revela
como un vehÃculo natural que puede mejorar la Human-Robot Interaction (HRI) debido a su
relevancia comunicativa en entornos sociales. Además de esto, para un robot social, la relación
entre el tacto social y su aspecto es directa, al disponer de un cuerpo fÃsico para aplicar o recibir
toques.
Desde un punto de vista técnico, los sistemas de detección táctil han sido objeto recientemente
de nuevas investigaciones, sobre todo dedicado a comprender este sentido para crear sistemas
inteligentes que puedan mejorar la vida de las personas. En este punto, los robots sociales
se han convertido en dispositivos muy populares que incluyen tecnologÃas para la detección
táctil. Esto está motivado por el hecho de que un robot puede esperada o inesperadamente
tener contacto fÃsico con una persona, lo que puede mejorar o interferir en la ejecución de sus
comportamientos. Por tanto, el sentido del tacto se antoja necesario para el desarrollo de aplicaciones
robóticas. Algunos métodos incluyen el reconocimiento de gestos táctiles, aunque
a menudo exigen importantes despliegues de hardware que requieren de múltiples sensores. Además, la fiabilidad de estas tecnologÃas de detección es limitada, ya que la mayorÃa de ellas
siguen teniendo problemas tales como falsos positivos o tasas de reconocimiento bajas. La detección
acústica, en este sentido, puede proporcionar un conjunto de caracterÃsticas capaces de
paliar las deficiencias anteriores. A pesar de que se trata de una tecnologÃa utilizada en diversos
campos de investigación, aún no se ha integrado en la interacción táctil entre humanos y robots.
Por ello, en este trabajo proponemos el sistema Acoustic Touch Recognition (ATR), un sistema
inteligente de detección táctil (smart tactile sensing system) basado en la detección acústica
y diseñado para mejorar la interacción social humano-robot. Nuestro sistema está desarrollado
para clasificar gestos táctiles y localizar su origen. Además de esto, se ha integrado en plataformas
robóticas sociales y se ha probado en aplicaciones reales con éxito. Nuestra propuesta
se ha enfocado desde dos puntos de vista: uno técnico y otro relacionado con el tacto social.
Por un lado, la propuesta tiene una motivación técnica centrada en conseguir un sistema táctil
rentable, modular y portátil. Para ello, en este trabajo se ha explorado el campo de las tecnologÃas
de detección táctil, los sistemas inteligentes de detección táctil y su aplicación en HRI. Por
otro lado, parte de la investigación se centra en el impacto afectivo del tacto social durante la
interacción humano-robot, lo que ha dado lugar a dos estudios que exploran esta idea.The sense of touch is a crucial component of human social interaction and is unique
among the five senses. As the only proximal sense, touch requires close or direct physical
contact to register information. This fact makes touch an interaction modality
full of possibilities regarding social communication. Through touch, we are able to ascertain
the other person’s intention and communicate emotions. From this idea emerges the concept
of social touch as the act of touching another person in a social context. It can serve various purposes,
such as greeting, showing affection, persuasion, and regulating emotional and physical
well-being.
Recently, the number of people interacting with artificial systems and agents has increased,
mainly due to the rise of technological devices, such as smartphones or smart speakers. Still,
these devices are limited in their interaction capabilities. To deal with this issue, recent developments
in social robotics have improved the interaction possibilities to make agents more seamless
and useful. In this sense, social robots are designed to facilitate natural interactions between
humans and artificial agents. In this context, the sense of touch is revealed as a natural interaction
vehicle that can improve HRI due to its communicative relevance. Moreover, for a social
robot, the relationship between social touch and its embodiment is direct, having a physical
body to apply or receive touches.
From a technical standpoint, tactile sensing systems have recently been the subject of further
research, mostly devoted to comprehending this sense to create intelligent systems that can
improve people’s lives. Currently, social robots are popular devices that include technologies
for touch sensing. This is motivated by the fact that robots may encounter expected or unexpected
physical contact with humans, which can either enhance or interfere with the execution
of their behaviours. There is, therefore, a need to detect human touch in robot applications.
Some methods even include touch-gesture recognition, although they often require significant
hardware deployments primarily that require multiple sensors. Additionally, the dependability
of those sensing technologies is constrained because the majority of them still struggle with issues
like false positives or poor recognition rates. Acoustic sensing, in this sense, can provide a
set of features that can alleviate the aforementioned shortcomings. Even though it is a technology that has been utilised in various research fields, it has yet to be integrated into human-robot
touch interaction.
Therefore, in thiswork,we propose theATRsystem, a smart tactile sensing system based on
acoustic sensing designed to improve human-robot social interaction. Our system is developed
to classify touch gestures and locate their source. It is also integrated into real social robotic platforms
and tested in real-world applications. Our proposal is approached from two standpoints,
one technical and the other related to social touch. Firstly, the technical motivation of thiswork
centred on achieving a cost-efficient, modular and portable tactile system. For that, we explore
the fields of touch sensing technologies, smart tactile sensing systems and their application in
HRI. On the other hand, part of the research is centred around the affective impact of touch
during human-robot interaction, resulting in two studies exploring this idea.Programa de Doctorado en IngenierÃa Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Pedro Manuel Urbano de Almeida Lima.- Secretaria: MarÃa Dolores Blanco Rojas.- Vocal: Antonio Fernández Caballer
Novel Magnetic Resonance Imaging-Compatible Mechatronic Needle Guidance System for Prostate Focal Laser Ablation Therapy
Advances in prostate cancer (PCa) screening techniques have led to diagnosis of many cases of low-grade and highly localized disease. Conventional whole-gland therapies often result in overtreatment in such cases and debate still surrounds the optimal method of oncologic control. MRI-guided prostate focal laser ablation (FLA) is a minimally invasive treatment option, which has demonstrated potential to destroy localized lesions while sparing healthy prostatic tissue, thereby reducing treatment-related side effects. Many challenges still exist in the development of FLA, including patient selection; tumour localization, visualization, and characterization; needle guidance; and evaluation of treatment efficacy. The objective of this thesis work was to advance and enhance techniques for needle guidance in MRI-guided focal laser ablation (FLA) therapy of PCa.
Several steps were taken in achieving this goal. Firstly, we evaluated the overlap between identified lesions and MRI-confirmed ablation regions using conventional needle guidance. Non-rigid thin-plate spline registration of pre-operative and intra-operative images was performed to align lesions with ablation boundaries and quantify the degree of coverage. Complete coverage of the lesion with the ablation zone is a clinically important metric of success for FLA therapy and we found it was not achieved in many cases. Therefore, our next step was to develop an MRI-compatible, remotely actuated mechatronic system for transperineal FLA of prostate cancer. The system allows physicians in the MRI scanner control room to accurately target lesions through 4 degrees of freedom while the patient remains in the scanner bore. To maintain compatibility with the MRI environment, piezoelectric motors were used to actuate the needle guidance templates, the device was constructed from non-ferromagnetic materials, and all cables were shielded from electromagnetic interference. The MR compatibility and needle placement accuracy of the device were evaluated with virtual and phantom targets.
The system should next be validated for accuracy and usefulness in a clinical trial where more complex tissue properties and potential patient motion will be encountered. Future advances in modeling the tissue properties and compensating for deformation of the prostate, as well as predicting needle deflection, will further bolster the potential of FLA as option for the management of PCa
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