7,436 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
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings
The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks.
In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner.
Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract
Acknowledgment
Contents
List of Figures
List of Tables
List of Abbreviations
List of Symbols
(1)Introduction
1.1)Background and Motivation
1.2)Aim of this Work
1.3)Dissertation Outline
1.4)Collaborators and Conflicts of Interest
(2)Clinical Background
2.1)Physiology
2.1.1)Changes in the maternal circulatory system
2.1.2)Intrauterine structures and feto-maternal connection
2.1.3)Fetal growth and presentation
2.1.4)Fetal circulatory system
2.1.5)Fetal autonomic nervous system
2.1.6)Fetal heart activity and underlying factors
2.2)Pathology
2.2.1)Premature rupture of membrane
2.2.2)Intrauterine growth restriction
2.2.3)Fetal anemia
2.3)Interpretation of Fetal Heart Activity
2.3.1)Summary of clinical studies on FHR/FHRV
2.3.2)Summary of studies on heart conduction
2.4)Chapter Summary
(3)Technical State of the Art
3.1)Prenatal Diagnostic and Measuring Technique
3.1.1)Fetal heart monitoring
3.1.2)Related metrics
3.2)Non-Invasive Fetal ECG Acquisition
3.2.1)Overview
3.2.2)Commercial equipment
3.2.3)Electrode configurations
3.2.4)Available NIFECG databases
3.2.5)Validity and usability of the non-invasive fetal ECG
3.3)Non-Invasive Fetal ECG Extraction Methods
3.3.1)Overview on the non-invasive fetal ECG extraction methods
3.3.2)Kalman filtering basics
3.3.3)Nonlinear Kalman filtering
3.3.4)Extended Kalman filter for FECG estimation
3.4)Fetal QRS Detection
3.4.1)Merging multichannel fetal QRS detections
3.4.2)Detection performance
3.5)Fetal Heart Rate Estimation
3.5.1)Preprocessing the fetal heart rate
3.5.2)Fetal heart rate statistics
3.6)Fetal ECG Morphological Analysis
3.7)Problem Description
3.8)Chapter Summary
(4)Novel Approaches for Fetal ECG Analysis
4.1)Preliminary Considerations
4.2)Fetal ECG Extraction by means of Kalman Filtering
4.2.1)Optimized Gaussian approximation
4.2.2)Time-varying covariance matrices
4.2.3)Extended Kalman filter with unknown inputs
4.2.4)Filter calibration
4.3)Accurate Fetal QRS and Heart Rate Detection
4.3.1)Multichannel evolutionary QRS correction
4.3.2)Multichannel fetal heart rate estimation using Kalman filters
4.4)Chapter Summary
(5)Data Material
5.1)Simulated Data
5.1.1)The FECG Synthetic Generator (FECGSYN)
5.1.2)The FECG Synthetic Database (FECGSYNDB)
5.2)Clinical Data
5.2.1)Clinical NIFECG recording
5.2.2)Scope and limitations of this study
5.2.3)Data annotation: signal quality and fetal amplitude
5.2.4)Data annotation: fetal QRS annotation
5.3)Chapter Summary
(6)Results for Data Analysis
6.1)Simulated Data
6.1.1)Fetal QRS detection
6.1.2)Morphological analysis
6.2)Own Clinical Data
6.2.1)FQRS correction using the evolutionary algorithm
6.2.2)FHR correction by means of Kalman filtering
(7)Discussion and Prospective
7.1)Data Availability
7.1.1)New measurement protocol
7.2)Signal Quality
7.3)Extraction Methods
7.4)FQRS and FHR Correction Algorithms
(8)Conclusion
References
(A)Appendix A - Signal Quality Annotation
(B)Appendix B - Fetal QRS Annotation
(C)Appendix C - Data Recording GU
Experimental multiphase estimation on a chip
Multiparameter estimation is a general problem that aims at measuring unknown
physical quantities, obtaining high precision in the process. In this context,
the adoption of quantum resources promises a substantial boost in the
achievable performances with respect to the classical case. However, several
open problems remain to be addressed in the multiparameter scenario. A crucial
requirement is the identification of suitable platforms to develop and
experimentally test novel efficient methodologies that can be employed in this
general framework. We report the experimental implementation of a
reconfigurable integrated multimode interferometer designed for the
simultaneous estimation of two optical phases. We verify the high-fidelity
operation of the implemented device, and demonstrate quantum-enhanced
performances in two-phase estimation with respect to the best classical case,
post-selected to the number of detected coincidences. This device can be
employed to test general adaptive multiphase protocols due to its high
reconfigurability level, and represents a powerful platform to investigate the
multiparameter estimation scenario.Comment: 10+7 pages, 7+4 figure
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