9,571 research outputs found
Segmentation of the evolving left ventricle by learning the dynamics
We propose a method for recursive segmentation of the left ventricle
(LV) across a temporal sequence of magnetic resonance (MR) images.
The approach involves a technique for learning the LV boundary
dynamics together with a particle-based inference algorithm on
a loopy graphical model capturing the temporal periodicity of the
heart. The dynamic system state is a low-dimensional representation
of the boundary, and boundary estimation involves incorporating
curve evolution into state estimation. By formulating the problem
as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also
on predictions based on past and future boundary estimates. We assess
and demonstrate the effectiveness of the proposed framework
on a large data set of breath-hold cardiac MR image sequences
Learning the dynamics and time-recursive boundary detection of deformable objects
We propose a principled framework for recursively segmenting deformable objects across a sequence
of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac
cycle. The approach involves a technique for learning the system dynamics together with methods of
particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing
the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation
of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state
estimation. By formulating the problem as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also on predictions based on past and future
boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes
to temporally segmenting any deformable object
Energy-based Analysis of Biochemical Cycles using Bond Graphs
Thermodynamic aspects of chemical reactions have a long history in the
Physical Chemistry literature. In particular, biochemical cycles - the
building-blocks of biochemical systems - require a source of energy to
function. However, although fundamental, the role of chemical potential and
Gibb's free energy in the analysis of biochemical systems is often overlooked
leading to models which are physically impossible. The bond graph approach was
developed for modelling engineering systems where energy generation, storage
and transmission are fundamental. The method focuses on how power flows between
components and how energy is stored, transmitted or dissipated within
components. Based on early ideas of network thermodynamics, we have applied
this approach to biochemical systems to generate models which automatically
obey the laws of thermodynamics. We illustrate the method with examples of
biochemical cycles. We have found that thermodynamically compliant models of
simple biochemical cycles can easily be developed using this approach. In
particular, both stoichiometric information and simulation models can be
developed directly from the bond graph. Furthermore, model reduction and
approximation while retaining structural and thermodynamic properties is
facilitated. Because the bond graph approach is also modular and scaleable, we
believe that it provides a secure foundation for building thermodynamically
compliant models of large biochemical networks
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Systems and methods for physiological signal enhancement and biometric extraction using non-invasive optical sensors
A system and method for signal processing to remove unwanted noise components including: (i) wavelength-independent motion artifacts such as tissue, bone and skin effects, and (ii) wavelength-dependent motion artifact/noise components such as venous blood pulsation and movement due to various sources including muscle pump, respiratory pump and physical perturbation. Disclosed are methods, analytics, and their uses for reliable perfusion monitoring, arterial oxygen saturation monitoring, heart rate monitoring during daily activities and in hospital settings and for extraction of physiological parameters such as respiration information, hemodynamic parameters, venous capacity, and fluid responsiveness. The system and methods disclosed are extendable to include monitoring platforms for perfusion, hypoxia, arrhythmia detection, airway obstruction detection and sleep disorders including apnea.Board of Regents, University of Texas Syste
Total-liver-volume perfusion CT using 3-D image fusion to improve detection and characterization of liver metastases
The purpose of this study
was to evaluate the feasibility of a totalliver-
volume perfusion CT (CTP)
technique for the detection and characterization
of livermetastases. Twenty
patients underwent helical CT of the
total liver volume before and 11 times
after intravenous contrast-material
injection. To decrease distortion artifacts,
all phases were co-registered
using 3-D image fusion before creating
blood-flow maps. Lesion-based sensitivity
and specificity for liver metastases
of first the conventional four
phases (unenhanced, arterial, portal
venous, and equilibrium) and later all
12 phases including blood-flow maps
were determined as compared to intraoperative
ultrasound and surgical exploration.
Arterial and portal venous
perfusion was calculated for normalappearing
and metastatic liver tissue.
Total-liver-volume perfusion values
were comparable to studies using
single-level CTP. Compared to fourphase
CT, total -liver-volume CTP
increased sensitivity to 89.2 from
78.4% (P=0.046) and specificity to
82.6 from 78.3% (P=0.074). Total -
liver-volume CTP is a noninvasive,
quantitative, and feasible technique.
Preliminary results suggest an improved
detection of liver metastases for
CTP compared to four-phase CT
A multiscale model for collagen alignment in wound healing
It is thought that collagen alignment plays a significant part in scar tissue formation during dermal wound healing. We present a multiscale model for collagen deposition and alignment during this process. We consider fibroblasts as discrete units moving within an extracellular matrix of collagen and fibrin modelled as continua. Our model includes flux induced alignment of collagen by fibroblasts, and contact guidance of fibroblasts by collagen fibres. We can use the model to predict the effects of certain manipulations, such as varying fibroblast speed, or placing an aligned piece of tissue in the wound. We also simulate experiments which alter the TGF-β concentrations in a healing dermal wound and use the model to offer an explanation of the observed influence of this growth factor on scarring
Aortic Wave Dynamics and Its Influence on Left Ventricular Workload
The pumping mechanism of the heart is pulsatile, so the heart generates pulsatile flow that enters into the compliant aorta in the form of pressure and flow waves. We hypothesized that there exists a specific heart rate at which the external left ventricular (LV) power is minimized. To test this hypothesis, we used a computational model to explore the effects of heart rate (HR) and aortic rigidity on left ventricular (LV) power requirement. While both mean and pulsatile parts of the pressure play an important role in LV power requirement elevation, at higher rigidities the effect of pulsatility becomes more
dominant. For any given aortic rigidity, there exists an optimum HR that minimizes the LV power requirement at a given cardiac output. The optimum HR shifts to higher values as the aorta becomes more rigid. To conclude, there is an optimum condition for aortic waves that minimizes the LV pulsatile load and consequently the total LV workload
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