Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS

• Published in: Medical physics (Lancaster) Vol. 50; no. 12; pp. 7955 - 7966
• Main Authors: Wang, Jing, Ji, Bing, Lei, Yang, Liu, Tian, Mao, Hui, Yang, Xiaofeng
• Format: Journal Article
• Language: English
• Published: United States 01.12.2023
• Subjects: Brain - diagnostic imaging; Brain - metabolism; denoising; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Magnetic resonance spectroscopy (MRS); Magnetic Resonance Spectroscopy - methods; metabolite quantification; signal to noise ratio (SNR); Signal-To-Noise Ratio; sparse representation; stack auto‐encoder (SAE); denoising; signal to noise ratio (SNR); metabolite quantification; Magnetic resonance spectroscopy (MRS); sparse representation; stack auto-encoder (SAE)
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.16831

Background While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non‐invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal‐to‐noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time. Purpose We propose to use deep‐learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS. Methods The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto‐encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA = 192), which were also used to obtain the ground truth. We applied both self‐supervised and fully‐supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch‐based manner. We then tested the denoising methods on noise‐containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR “ground truth”, as well as the value of chemical shift of N‐acetyl‐aspartate (NAA) before and after denoising. Results With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. Conclusions The reported SAE denoising method is a model‐free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.