Denoising Repeatedly Sampled In Vivo Magnetic Resonance Spectroscopy with Deep Learning
Respondent： Wanqi Hu
Supervisor： Xiaobo Qu Professor/Doctoral supervisor
Chairman of the Reply Committee：
Jiyang Dong, Professor Doctora/supervisor, Department of Electronic Science, Xiamen University
Member of the Defense Committee：
Lijun Bao, Associate Professor/Master supervisor, Department of Electronic Science, Xiamen University
Jianzhong Lin, Chief Physician/Master supervisor, Department of Imaging, Zhongshan Hospital, Xiamen University
Secretary of Defense:
Yu Yang, Assistant professor, Department of Electronic Science, Xiamen University
Time：June 30, 2021 at 9:00 AM
Place：Xiamen University Haiyun Park Research-II Building 313
磁共振波谱(Magnetic Resonance Spectroscopy，MRS)是一种无创检测活体代谢产物的方法。在实际应用中，低浓度的代谢物通常会导致相对较低的信噪比，导致从MRS获得可靠的量化信息存在挑战。因此，提高MRS信噪比成为了磁共振学术界和工业界的共同目标。
Magnetic resonance spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. However, due to the low concentration of metabolites, the low signal-to-noise ratio (SNR) of MRS in application prevents it from providing reliable quantitative information. Therefore, achieving fast acquisition of MRS with high SNR has become a common goal of magnetic resonance research in academia and industry.
To improve the SNR, the basic method is to average the repeated samplings. However, the data acquisition time is correspondingly increased, making the scanned subject uncomfortable. This greatly limits the clinical application of MRS. In recent years, deep learning has been introduced into the field of magnetic resonance. Due to the lack of in vivo MRS data, these methods are usually used the simulated data for neural network training. However, the MRS acquisition system is not ideal which makes it difficult to carry out comprehensive mathematical modelling. This paper explores a novel way to use repeated sampling, which is widely existed in MRS acquisition, to increase the number of training data, trying to use the limited in vivo MRS to get a denoising network with certain robustness.
The proposed deep learning MRS denoising method is called Causal Long Short Term Memory. First, the low SNR MRS with fewer averages of repeated sampling is mapped to the high SNR MRS with more averages of repeated sampling. Then, MRS with different SNR levels are formed by the arrangement and combination of repeated sampling data as the input and output of the network, which solves the bottleneck problem that the network training needs a large number of training data. Results on simulated data and in vivo data showed that the proposed method can obtain high quality MRS with the reduction of acquisition time by 80%. When compared with the Low Rank denoising method, the corresponding metabolite quantization results are closer to the reference high SNR spectra.
Moreover, a new method, Average Smoothing Singular Value Decomposition, is proposed to obtain higher SNR signal by combining multi-channel coil data. Based on the preliminary results of singular value decomposition, the sensitivity matrix of the coil is optimized by two-dimensional convolution in the repeated sampling dimension. Experimental results of phantom and in vivo spectra show that, compared with the traditional method, the proposed method further improves the SNR of the averaged spectra of repeated samplings.
Keywords：Magnetic Resonance Spectroscopy, In Vivo, Denoising, Deep Learning, Singular Value Decomposition.