38,406 research outputs found
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
'Constant in gain Lead in phase' element - Application in precision motion control
This work presents a novel 'Constant in gain Lead in phase' (CgLp) element
using nonlinear reset technique. PID is the industrial workhorse even to this
day in high-tech precision positioning applications. However, Bode's gain phase
relationship and waterbed effect fundamentally limit performance of PID and
other linear controllers. This paper presents CgLp as a controlled nonlinear
element which can be introduced within the framework of PID allowing for wide
applicability and overcoming linear control limitations. Design of CgLp with
generalized first order reset element (GFORE) and generalized second order
reset element (GSORE) (introduced in this work) is presented using describing
function analysis. A more detailed analysis of reset elements in frequency
domain compared to existing literature is first carried out for this purpose.
Finally, CgLp is integrated with PID and tested on one of the DOFs of a planar
precision positioning stage. Performance improvement is shown in terms of
tracking, steady-state precision and bandwidth
An Extended Virtual Aperture Imaging Model for Through-the-wall Sensing and Its Environmental Parameters Estimation
Through-the-wall imaging (TWI) radar has been given increasing attention in recent years. However, prior knowledge about environmental parameters, such as wall thickness and dielectric constant, and the standoff distance between an array and a wall, is generally unavailable in real applications. Thus, targets behind the wall suffer from defocusing and displacement under the conventional imag¬ing operations. To solve this problem, in this paper, we first set up an extended imaging model of a virtual aperture obtained by a multiple-input-multiple-output array, which considers the array position to the wall and thus is more applicable for real situations. Then, we present a method to estimate the environmental parameters to calibrate the TWI, without multiple measurements or dominant scatter¬ers behind-the-wall to assist. Simulation and field experi¬ments were performed to illustrate the validity of the pro¬posed imaging model and the environmental parameters estimation method
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
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