Noise and Speckle Reduction in Doppler Blood Flow Spectrograms Using an Adaptive Pulse-Coupled Neural Network

A novel method, called adaptive pulse coupled neural network (AD-PCNN) using a two-stage denoising strategy, is proposed to reduce noise and speckle in the spectrograms of Doppler blood flow signals. AD-PCNN contains an adaptive thresholding PCNN and a threshold decaying PCNN. Firstly, PCNN pulses based on the adaptive threshold filter a part of background noise in the spectrogram while isolating the remained noise and speckles. Subsequently, the speckles and noise of the denoised spectrogram are detected by the pulses generated through the threshold decaying PCNN and then are iteratively removed by the intensity variation to speckle or noise neurons. The relative root mean square (RRMS) error of the maximum frequency extracted from the AD-PCNN spectrogram of the simulated Doppler blood flow signals is decreased 25.2% on average compared to that extracted from the MPWD (matching pursuit with Wigner Distribution) spectrogram, and the RRMS error of the AD-PCNN spectrogram is decreased 10.8% on average compared to MPWD spectrogram. Experimental results of synthetic and clinical signals show that the proposed method is better than the MPWD in improving the accuracy of the spectrograms and their maximum frequency curves

A Self-Learning Neural Network Approach for RFI Detection and Removal in Radio Astronomy

We present a novel neural network (NN) method for the detection and removal of Radio Frequency Interference (RFI) from the raw digitized signal in the signal processing chain of a typical radio astronomy experiment. The main advantage of our method is that it does not require a training set. Instead, our method relies on the fact that the true signal of interest coming from astronomical sources is thermal and therefore described as a Gaussian random process, which cannot be compressed. We employ a variational encoder/decoder network to find the compressible information in the datastream that can explain the most variance with the fewest degrees of freedom. We demonstrate it on a set of toy problems and stored ringbuffers from the Baryon Mapping eXperiment (BMX) prototype. We find that the RFI subtraction is effective at cleaning simulated timestreams: while we find that the power spectra of the RFI-cleaned timestreams output by the NN suffer from extra signal consistent with additive noise, we find that it is generally around percent level across the band and sub 10 percent in contaminated spectral channels even when RFI power is an order of magnitude larger than the signal. We discuss advantages and limitations of this method and possible implementation in the front-end of future radio experiments.Comment: 16 pages, 6 figures, Accepted for publication in PAS

GENETIC FUZZY FILTER BASED ON MAD AND ROAD TO REMOVE MIXED IMPULSE NOISE

In this thesis, a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD) is proposed. The proposed method consists of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization using genetic algorithms (GA) to perform efficient and effective noise removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a pixel. Fuzzy inference system is used to justify the degree of which a pixel can be categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme that adopts median filter as the main estimator is applied to the filtering. The GA training aims to find the best parameters for the fuzzy sets in the fuzzy noise detection. From the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. It also surpasses the other methods, either classical or soft computing-based approaches to impulse noise removal, in MAE and PSNR evaluations. It can also remove salt-and-pepper and uniform impulse noise well

양성자 자기공명분광법을 사용한 딥러닝 기반 두뇌 대사체 정량화 기법

학위논문(박사) -- 서울대학교대학원 : 의과대학 의과학과, 2022.2. 김현진.Nonlinear-least-squares-fitting (NLSF) is widely used in proton magnetic resonance spectroscopy (MRS) for quantification of brain metabolites. However, it is known to subject to variability in the quantitative results depending on the prior knowledge. NLSF-based metabolite quantification is also sensitive to the quality of spectra. In combination with NLSF, Cramer-Rao lower Bounds (CRLB) are used as representing lower bounds of fit errors rather than actual errors. Consequently, a careful interpretation is required to avoid potential statistical bias. The purpose of this study was to develop more robust methods for metabolite quantification and uncertainty estimation in MRS by employing deep learning that has demonstrated its potential in a variety of different tasks including medical imaging. To achieve this goal, first, a convolutional neural network (CNN) was developed. It maps typical brain spectra that are degraded with noise, line-broadening and unknown baseline into noise-free, line-narrowed, baseline-removed spectra. Then, metabolites are quantified from the CNN-predicted spectra by a simple linear regression with more robustness against spectral degradation. Second, a CNN was developed that can isolate each individual metabolite signals from a typical brain spectrum. The CNN output is used not only for quantification but also for calculating signal-to-background-ratio (SBR) for each metabolite. Then, the SBR in combination with big training data are used for estimating measurement uncertainty heuristically. Finally, a Bayesian deep learning approach was employed for theory-oriented uncertainty estimation. In this approach, Monte Carlo dropout is performed for simultaneous estimation of metabolite content and associated uncertainty. These proposed methods were all tested on in vivo data and compared with the conventional approach based on NLSF and CRLB. The methods developed in this study should be tested more thoroughly on a larger amount of in vivo data. Nonetheless, the current results suggest that they may facilitate the applicability of MRS.두뇌 내 특정한 부위에 대한 대사체들의 종류와 농도 정보를 획득할 수 있는 자기공명분광 (MRS) 분야에서 일반적으로 활용하고 있는 비선형 최소제곱피팅 (Nonlinear least squares fitting; NSLF)은 주어진 사전 정보 (Prior knowledge)에 의존한 정량화 결과 변동 특성을 나타낸다. NLSF 기반한 두뇌 대사체 정량화는 MRS 신호품질에 민감하게 성능 변화를 나타낸다. 무엇 보다, NLSF를 통한 정량화 결과의 신뢰 지표인 크라메르-라오 하한 (Cramer-Rao lower Bound; CRLB)은 정량화 결과에 대한 오차정보를 반영하는 정확도가 아닌, 정밀도를 표현하므로, 이를 주의하여 활용하지 않으면 통계적 편향성을 나타낼 위험이 있다. 이러한 문제들로 인해 MRS는 현재까지도 제한적으로만 임상 활용되고 있는 상황이다. 따라서 본 연구는 자기공명분광법을 이용한 두뇌 대사체 정량화 과정에 있어서 딥 러닝 기술을 접목하여, 정량화 정확도를 개선하는 점에 주 목적을 두고 있다. 구체적으로 본 연구에서는 다음과 같이 두 부분에 대한 방법을 제안하였다. 첫번째로는 깊은 인공신경망을 통해 MRS 신호내의 두뇌 대사체 공명 신호만을 추출하여, 이를 간단한 선형 회귀 후처리를 통해 정량화를 할 수 있는 분석 기술을 개발하였다. 두번째로는 딥 러닝에서 예측하는 결과들에 대한 불확실성 지표를 표현하는 방법에 대해 개발하였다. 구체적으로는 빅데이터 기반의 경험적 불확실성 지표와, 베이지안 접근법에 기반한 정규분포를 따르는 불확실성 지표 표현 방법을 개발하였다. 결과적으로 제안된 방법들은 NLSF 대비 MRS 신호 품질에 덜 영향을 받으면서 낮은 정량화 결과 변동성을 나타내는 동시에, NLSF의 정량화 결과에 대한 신뢰지표인 CRLB에 비해 더 실제 오차와 상관성이 높은 불확실성 지표 성능을 보였다. 따라서 본 연구는, MRS를 활용한 두뇌 대사체 정량화에 대한 정확도 개선을 위해 딥 러닝 기술들을 활용한다면, MRS의 임상 적용 가능성을 높일 수 있음을 시사한다.Chapter 1. Introduction 1 1.1. Magnetic Resonance Spectroscopy 1 1.1.1. Nuclear Spin 1 1.1.2. Magnetization 4 1.1.3. MRS Signal 6 1.1.4. Chemical Shift 12 1.1.5. Indirect Spin-Spin Coupling 14 1.1.6. in vivo Metabolites 15 1.1.7. RF Pulses and Gradients 17 1.1.8. Water Suppression 19 1.1.9. Spatial Localized Methods in Single Voxel MRS 20 1.1.10. Metabolite Quantification 22 1.2. Deep Learning 24 1.2.1. Training for Regression Model 25 1.2.2. Training for Classification Model 27 1.2.3. Multilayer Perceptron 29 1.2.4. Model Evaluation and Selection 32 1.2.5. Training Stability and Initialization 35 1.2.6. Convolutional Neural Networks 36 1.3. Perpose of the Research 38 1.4. Preparation of MRS Spectra and Their Usage 40 Chapter 2. Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain 45 2.1. Introduction 45 2.2. Methods and Materials 46 2.2.1. Acquisition of in vivo Spectra 46 2.2.2. Acquisition of Metabolite Phantom Spectra 47 2.2.3. Simulation of Brain Spectra 47 2.2.4. Design and Optimization of CNN 52 2.2.5. Evaluation of the Reproducibility of the Optimized CNN 52 2.2.6. Metabolite Quantification from the Predicted Spectra 53 2.2.7. Evaluation of CNN in Metabolite Quantification 53 2.2.8. Statistical Analysis 54 2.3. Results 54 2.3.1. SNR Distribution of the Simulated Spectra 54 2.3.2. Optimized CNN 56 2.3.3. Representative Simulated and CNN-predicted Spectra 56 2.3.4. Metabolite Quantification in Simulated Spectra 57 2.3.5. Representative in vivo and CNN-predicted Spectra 61 2.3.6. Metabolite Quantification in in vivo Spectra 64 2.4. Discussions 67 2.4.1. Motivation of Study 67 2.4.2. Metabolite Quantification on Simulated and in vivo Brain Spectra 68 2.4.3. Metabolite Quantification Robustness against Low SNR 69 2.4.4. Study Limitation 70 Chapter 3. Deep learning-based target metabolite isolation and big data-driven measurement uncertainty estimation in proton magnetic resonance spectroscopy of the brain 79 3.1. Introduction 79 3.2. Methods and Materials 80 3.2.1. Acquisition and Analysis of in vivo Rat Brain Spectra 80 3.2.2. Simulation of Metabolite Basis set 81 3.2.3. Acquisition of Metabolite Basis set in Phantom 81 3.2.4. Simulation of Rat Brain Spectra using Simulated Metabolite and Baseline Basis Sets 82 3.2.5. Simulation of Rat Brain Spectra using Metabolite Phantom Spectra and in vivo Baseline 87 3.2.6. Design and Optimization of CNN 87 3.2.7. Metabolite Quantification from the CNN-predicted Spectra 90 3.2.8. Prediction of Quantitative Error 90 3.2.9. Evaluation of Proposed Method 93 3.2.10. Statistical Analysis 93 3.3. Results 94 3.3.1. Performance of Proposed Method on Simulated Spectra Set I 94 3.3.2. Performance of Proposed Method, LCModel, and jMRUI on Simulated Spectra Set II 99 3.3.3. Proposed Method Applied to in vivo Spectra 105 3.3.4. Processing Time 105 3.4. Discussions 109 3.4.1. Summary of the Study 109 3.4.2. Performance of Proposed Method on Simulated Spectra 110 3.4.3. Proposed Method Applied to in vivo Spectra 111 3.4.4. Robustness of CNNs against Different SNR 111 3.4.5. CRLB and Predicted Error 112 3.4.6. Study Limitation 113 Chapter 4. Bayesian deep learning-based proton magnetic resonance spectroscopy of the brain: metabolite quantification with uncertainty estimation using Monte Carlo dropout 118 4.1. Introduction 118 4.2. Methods and Materials 119 4.2.1. Theory 119 4.2.2. Preparation of Spectra 124 4.2.3. BCNN 125 4.2.4. Evaluation of Proposed Method 126 4.2.5. Statistical Analysis 127 4.3. Results 127 4.3.1. Metabolite Content and Uncertainty Estimation on the Simulated Spectra 127 4.3.2. BCNN and LCModel on Modified in vivo Spectra 136 4.4. Discussions 144 4.4.1. Motivation of Study 144 4.4.2. Metabolite Quantification on Simulated Brain Spectra 144 4.4.3. Uncertainty Estimation on Simulated Brain Spectra 145 4.4.4. Aleatoric, Epistemic and Total Uncertainty as a Function of SNR, Linewidth or Concentration of NAA 147 4.4.5. Robustness of BCNN against SNR and Linewidth Tested on Modified in vivo Spectra 148 4.4.6. Study Limitation 148 Chapter 5. Conclusion 160 5.1. Research Summary 160 5.2. Future Works 160 Bibliography 163 Abstract in Korean 173박

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Heart rates estimation using rPPG methods in challenging imaging conditions

Abstract. The cardiovascular system plays a crucial role in maintaining the body’s equilibrium by regulating blood flow and oxygen supply to different organs and tissues. While contact-based techniques like electrocardiography and photoplethysmography are commonly used in healthcare and clinical monitoring, they are not practical for everyday use due to their skin contact requirements. Therefore, non-contact alternatives like remote photoplethysmography (rPPG) have gained significant attention in recent years. However, extracting accurate heart rate information from rPPG signals under challenging imaging conditions, such as image degradation and occlusion, remains a significant challenge. Therefore, this thesis aims to investigate the effectiveness of rPPG methods in extracting heart rate information from rPPG signals in these imaging conditions. It evaluates the effectiveness of both traditional rPPG approaches and rPPG pre-trained deep learning models in the presence of real-world image transformations, such as occlusion of the faces by sunglasses or facemasks, as well as image degradation caused by noise artifacts and motion blur. The study also explores various image restoration techniques to enhance the performance of the selected rPPG methods and experiments with various fine-tuning methods of the best-performing pre-trained model. The research was conducted on three databases, namely UBFC-rPPG, UCLA-rPPG, and UBFC-Phys, and includes comprehensive experiments. The results of this study offer valuable insights into the efficacy of rPPG in practical scenarios and its potential as a non-contact alternative to traditional cardiovascular monitoring techniques

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV

Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation

This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined. The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies