193 research outputs found
Multimodal MR Prediction Models for Late-Life Depression and Treatment Response
Currently, depression diagnosis relies primarily on behavioral symptoms and signs, instead of underlying brain characteristics, and treatment is guided by trial and error instead of individual suitability associated with underlying brain characteristics. Also, previous brain-imaging studies attempting to resolve this issue have traditionally focused on mid-life depression using a single imaging modality and region-based approach, which may not fully explain the complexity of the underlying brain characteristics; especially for late-life depression. We aimed to evaluate and compare underlying brain characteristics of late-life depression diagnosis and treatment response by estimating accurate prediction models using multi-modal magnetic resonance imaging and non-imaging measures. Based on our finding, late-life depression diagnosis and treatment response predictors involve measures from different imaging modalities, which are indicative of differences in underlying brain characteristics
Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning
Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons
Quantifying atherosclerosis in vasculature using ultrasound imaging
Cerebrovascular disease accounts for approximately 30% of the global burden
associated with cardiovascular diseases [1]. According to the World Stroke
Organisation, there are approximately 13.7 million new stroke cases annually,
and just under six million people will die from stroke each year [2]. The
underlying cause of this disease is atherosclerosis – a vascular pathology
which is characterised by thickening and hardening of blood vessel walls.
When fatty substances such as cholesterol accumulate on the inner linings of
an artery, they cause a progressive narrowing of the lumen referred to as a
stenosis.
Localisation and grading of the severity of a stenosis, is important for
practitioners to assess the risk of rupture which leads to stroke. Ultrasound
imaging is popular for this purpose. It is low cost, non-invasive, and permits a
quick assessment of vessel geometry and stenosis by measuring the intima
media thickness. Research is showing that 3D monitoring of plaque
progression may provide a better indication of sites which are at risk of
rupture. Various metrics have been proposed. From these, the quantification
of plaques by measuring vessel wall volume (VWV) using the segmented
media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has
been shown to be sensitive to temporal changes in carotid plaque burden.
Thus, methods to segment these boundaries are required to help generate
VWV measurements with high accuracy, less user interaction and increased
robustness to variability in di↵erent user acquisition protocols.ii
This work proposes three novel methods to address these requirements, to
ultimately produce a highly accurate, fully automated segmentation algorithm
which works on intensity-invariant data. The first method proposed was that
of generating a novel, intensity-invariant representation of ultrasound data by
creating phase-congruency maps from raw unprocessed radio-frequency
ultrasound information. Experiments carried out showed that this
representation retained the necessary anatomical structural information to
facilitate segmentation, while concurrently being invariant to changes in
amplitude from the user. The second method proposed was the novel
application of Deep Convolutional Networks (DCN) to carotid ultrasound
images to achieve fully automatic delineation of the MAB boundaries, in
addition to the use of a novel fusion of amplitude and phase congruency data
as an image source. Experiments carried out showed that the DCN produces
highly accurate and automated results, and that the fusion of amplitude and
phase yield superior results to either one alone. The third method proposed
was a new geometrically constrained objective function for the network's
Stochastic Gradient Descent optimisation, thus tuning it to the segmentation
problem at hand, while also developing the network further to concurrently
delineate both the MAB and LIB to produce vessel wall contours. Experiments
carried out here also show that the novel geometric constraints improve the
segmentation results on both MAB and LIB contours.
In conclusion, the presented work provides significant novel contributions to
field of Carotid Ultrasound segmentation, and with future work, this could lead
to implementations which facilitate plaque progression analysis for the end�user
Advanced Applications of Rapid Prototyping Technology in Modern Engineering
Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems
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