Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

Objective: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen for training compared to the end-to-end deep learning method. Results: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. Conclusion: This study provides an intelligent, reliable and robust MRS quantification. Significance: QNet is the first LLS quantification aided by deep learning

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

A review of machine learning applications for the proton MR spectroscopy workflow

This literature review presents a comprehensive overview of machine learning (ML) applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the MR field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.</p

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure

Denoising single MR spectra by deep learning: Miracle or mirage?

PURPOSE The inherently poor SNR of MRS measurements presents a significant hurdle to its clinical application. Denoising by machine or deep learning (DL) was proposed as a remedy. It is investigated whether such denoising leads to lower estimate uncertainties or whether it essentially reduces noise in signal-free areas only. METHODS Noise removal based on supervised DL with U-nets was implemented using simulated 1 H MR spectra of human brain in two approaches: (1) via time-frequency domain spectrograms and (2) using 1D spectra as input. Quality of denoising was evaluated in three ways: (1) by an adapted fit quality score, (2) by traditional model fitting, and (3) by quantification via neural networks. RESULTS Visually appealing spectra were obtained; hinting that denoising is well-suited for MRS. However, an adapted denoising score showed that noise removal is inhomogeneous and more efficient for signal-free areas. This was confirmed by quantitative analysis of traditional fit results as well as DL quantitation following DL denoising. DL denoising, although apparently successful as judged by mean squared errors, led to substantially biased estimates in both implementations. CONCLUSION The implemented DL-based denoising techniques may be useful for display purposes, but do not help quantitative evaluations, confirming expectations based on estimation theory: Cramér Rao lower bounds defined by the original data and the appropriate fitting model cannot be circumvented in an unbiased way for single data sets, unless additional prior knowledge can be incurred in the form of parameter restrictions/relations or applicable substates