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

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

Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management

A Longitudinal Study of Tumour Metabolism Using Hyperpolarized Carbon-13 Magnetic Resonance Spectroscopic Imaging in a Preclinical Model of Glioma

Glioma is the most common and aggressive primary malignant brain tumour. Glioma is typically treated with surgery followed by radio/chemotherapy. Even with aggressive treatment, median survival time is expected to be ~12 to 15 months. Reoccurrence of glioma is almost inevitable, further threatening the well-being of patients who have already endured rigorous treatment. Therefore, it is paramount to choose the most effective therapy and to accurately determine outcome as early as possible to provide optimum end-of-life care. Tumours alter their metabolism in response to increasing energy demands, mainly through increased glycolysis and accompanying lactate production. This increases production of other acids and alters intracellular and extracellular pH. Hyperpolarized 13C magnetic resonance spectroscopic imaging, is capable of measuring in vivo metabolism. Increased lactate production in tumours can be probed by imaging the metabolism of hyperpolarized [1-13C]pyruvate after injection. Similarly, extracellular pH can be mapped after measuring the concentrations of H13CO3- and 13CO2 after injection of hyperpolarized 13C bicarbonate. The objective of this thesis is to investigate molecular changes in lactate production and pH gradient in a rat glioma model. To accomplish this objective, three related projects have been undertaken. For first project, a custom-made switch-tunable radiofrequency coil was designed and constructed. This radiofrequency coil facilitated imaging 1H and 13C nuclei without any registration issues producing high signal-to-noise ratio imaging data. In the second project, C6 glioma was implanted into brains of rats, which were imaged with hyperpolarized [1-13C]pyruvate at days 7, 12, 15, 18, 21 and 24 after implantation. Between days 10 and 15, rats received one of three therapies: radiotherapy, chemotherapy, combined therapy or none. Significant early therapeutic response, measured as a reduction in the lactate-to-pyruvate ratio, was observed for effective therapy. In the final project, the same tumour model was used to study cellular pH gradient in tumours. Animals were monitored at days 8, 12 and 15 after implantation using hyperpolarized 13C bicarbonate to measure intracellular pH and a chemical exchange saturation transfer method to measure intracellular pH. Measured pH gradient in tumours showed a higher intracellular pH than extracellular pH, which was the opposite of healthy brain tissue. These studies have demonstrated the potential of hyperpolarized 13C probes to promptly measure changes in tumour metabolism. Early response assessment is important for identifying effective therapies and eliminating the toxic effects of ineffective ones. This can potentially reduce treatment costs for expensive and ineffective therapies and improve the quality of life for patients

Interrogating Cancer Metabolism using Novel Imaging Techniques

Altered metabolism is one of the hallmarks of cancer and is known to enable the proliferation and survival of cancer cells in changing microenvironments. Metabolic changes can be interrogated using non-invasive metabolic imaging techniques, which enable the investigation of tumour metabolism in vivo and the identification of imaging biomarkers. Hyperpolarised 13C Magnetic Resonance Spectroscopic Imaging (MRSI) is a metabolic imaging technique that enables dynamic imaging of isotopically labelled metabolites in vivo, with [1-13C]pyruvate being the most widely used substrate in pre-clinical studies and the only substrate used so far in clinical studies. In the first part of this thesis, hyperpolarised 13C pyruvate MRSI was directly compared to mass spectrometry imaging in a murine lymphoma model, cross-validating both methods for providing information on the relative spatial distribution of 13C-labelled lactate. In the second part, the potential of hyperpolarised [1-13C]Ketoisocaproic Acid as a substrate for hyperpolarised imaging was investigated. Ketoisocaproic acid is a substrate for Branched Chain Aminotransferase 1 (BCAT1), the first enzyme in the catabolic pathway of Branched Chain Amino Acids. This enzyme is upregulated in many cancers and is linked to cancer progression. In this project, the role of BCAT1 in cell proliferation was confirmed in glioblastoma patient derived cells and tumours and BCAT1 was shown to regulate the cells’ metabolic and transcriptional profiles through regulation of alpha-ketoglutarate metabolism. In vivo imaging of BCAT1 activity is therefore suggested as a potential technique for patient stratification and potentially treatment monitoring. [1-13C]Ketoisocaproic acid was successfully hyperpolarised and showed fast uptake into subcutaneous EL4 lymphoma tumours, healthy rat brain and orthotopic gliomas in rat brain, following intravenous injection. Sufficient 13C leucine signal was generated in the lymphoma tumour models enabling in vivo imaging of BCAT activity but the reaction was found to be significantly slower in the healthy rat brain and in the glioma tumours. This was explained by low levels of glutamate, a co-substrate for the reaction, in the glioma tumours, which is a potential limiting factor for this reaction.Cancer Research UK Rosetrees Trus

Metabolic Signatures of Prostate Cancer and Renal Cell Carcinoma using High-Resolution NMR and Hyperpolarized 13C MRI

Non-invasive techniques to assess metabolic reprogramming during cancer progression can be used to improve therapeutic selection and provide an early assessment of therapeutic response or resistance in individual patients. Prior studies have shown that metabolic reprogramming plays a key role in the development of prostate cancer and renal cell carcinoma (RCC). This dissertation further elucidates the metabolic alterations that occur in treatment-resistant prostate cancer and in patient-derived models of RCC using high-resolution nuclear magnetic resonance (NMR) spectroscopy and hyperpolarized (HP) 13C magnetic resonance imaging (MRI), with the goal of identifying new non-invasive diagnostic imaging tools. Glycolysis, metabolism of pyruvate and glutamate via the tricarboxylic acid (TCA) cycle, glutaminolysis, and glutathione synthesis are upregulated in castration-resistant prostate cancer (CRPC) compared to their androgen-dependent counterparts, using human cell lines as well a treatment-driven transgenic murine model. These metabolic alterations were reversed in castration-resistant murine tumors by treatment with a secondary androgen pathway inhibitor, apalutamide, suggesting that early metabolic responses to treatment can be monitored using non-invasive imaging techniques. Furthermore, treatment-emergent small cell neuroendocrine prostate cancer, a consequence of protracted treatment with primary androgen deprivation therapy and secondary androgen pathway inhibitors, exhibits significantly upregulated glycolysis, TCA cycle metabolism of pyruvate and glutamate, and glutaminolysis, as well as significantly altered redox capacity compared to castration-resistant prostate adenocarcinoma using patient-derived xenograft models. Finally, the metabolic differences associated with the tumor microenvironment were compared between various patient-derived models of RCC, finding that RCC patient-derived xenografts (PDXs) displayed higher redox capacity and were more proliferative than cells and tissue slices derived from the PDXs and maintained ex vivo. The work presented in this dissertation suggests that a combination of HP [1-13C]pyruvate, [2-13C]pyruvate, [5-13C]glutamine, and [1-13C]dehydroascorbate can be used to distinguish advanced prostate cancer and RCC subtypes in future HP 13C MRI of patients for improved treatment selection and monitoring