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 in 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.

A Singular Spectrum Analysis-Based Data-Driven Technique for the Removal of Cardiogenic Oscillations in Esophageal Pressure Signals

Objective: Assessing the respiratory and lung mechanics of the patients in intensive care units is of utmost need in order to guide the management of ventilation support. The esophageal pressure (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document}) signal is a minimally invasive measure, which portrays the mechanics of the lung and the pattern of breathing. Because of the close proximity of the lung to the beating heart inside the thoracic cavity, the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document}) signal. However, the area of research addressing the removal of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document} from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal is still lagging behind. Methods and results: This paper presents a singular spectrum analysis-based high-efficient, adaptive and robust technique for the removal of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document} from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal utilizing the inherent periodicity and morphological property of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal. The performance of the proposed technique is tested on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signals. The efficiency of the proposed technique in removing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {CGO}$ \end{document} from the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal is quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\boldsymbol {P}_{ \boldsymbol {eso}}$ \end{document} signal fall under the categories ‘very good’ as per the subjective measure. Conclusion and clinical impact: The proposed technique: (1) does not follow any predefined mathematical model and hence, it is data-driven, (2) is adaptive to the sampling rate, and (3) can be adapted for denoising other biomedical signals which exhibit periodic or quasi-periodic nature.

A singular spectrum analysis-based model-free electrocardiogram denoising technique

BACKGROUND AND OBJECTIVE

An efficient and robust electrocardiogram (ECG) denoising technique caters three-fold benefits in the subsequent processing steps: first, it helps improving the accuracy of extracted features. Second, the improved accuracy in the extracted features enhances the performance as well as the reliability of computerised cardiovascular-disease diagnosis systems, and third, it also makes the interpretation task easier for the clinicians. Albeit a number of ECG denoising techniques are proposed in the literature, but most of these techniques suffer from one or more of the following drawbacks: i) model or function dependency, ii) sampling-rate dependency, or iii) high time-complexity.

METHODS

This paper presents a singular spectrum analysis (SSA)-based ECG denoising technique addressing most of these afore-mentioned shortcomings. First, a trajectory matrix of dimension K × L is formed using the original one-dimensional ECG signal of length N. In SSA operation the parameter L, which is denoted as the window-length, plays a very important role and is related to the sampling frequency of the signal. In this research the value of L is calculated dynamically based on the morphological property of the ECG signal. Then, the matrix is decomposed using singular value decomposition technique, and the principal components (PC) of the original ECG signal are computed. Next, the reconstructed components (RC) are calculated from the PCs, and all the RCs are filtered through Butterworth bandpass and notch filters. An optimum number of filtered RCs are retained based on their significance. Finally, these retained RCs are summed up to obtain the denoised ECG signal.

RESULTS

Evaluation result shows that the proposed technique outperforms state-of-the-art ECG denoising methods; in particular, the mean opinion score of the denoised signal falls under the category 'very good' as per the gold standard subjective measure.

CONCLUSIONS

Both the quantitative and qualitative distortion measure metrics show that the proposed ECG denoising technique is robust enough to filter various noises present in the signal without jeopardizing the clinical content. The proposed technique could be adapted for denoising other biomedical signals exhibiting periodic or quasi-periodic nature such as photoplethysmogram and esophageal pressure signal.