ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Diabetes: Molecular Mechanisms
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1581328
This article is part of the Research TopicIn Vivo Magnetic Resonance Imaging of Metabolic DisordersView all 4 articles
Assessment of reconstruction accuracy for under-sampled 31P-MRS data using compressed sensing and a low rank Hankel Matrix completion approach
Provisionally accepted
- 1Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, México, Mexico
- 2Department of Electrical and Computer Engineering, Faculty of Engineering, McMaster University, Hamilton, Ontario, Canada
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Phosphorus magnetic resonance spectroscopy and spectroscopic imaging ( 31 P-MRS/MRSI) are techniques to evaluate energy metabolism in vivo, they are capable of measuring metabolites such as phosphocreatine and inorganic phosphate in muscle and brain tissue. Despite their capability, these techniques are not very often used in clinical settings due to the long acquisition times required. In recent years, compressed sensing has been widely used as an acceleration method for MRI signal acquisition and translated to MRS. In order to use it, one of the main criteria states that the aliasing resulting from the undersampling scheme must be incoherent, which is achieved using a pseudo-random sampling strategy. However, when a set of pseudo-random sampling patterns are applied for the same acceleration factor, there is significant variability in the quality of the reconstructed signal. We present an evaluation of the influence of the undersampling pattern in the quality of the signal reconstruction through a series of experiments in 31 P-MRS data using the low rank Hankel matrix completion as the reconstruction method. Our results demonstrate that the reconstruction accuracy is heavily influenced by the selection of specific samples rather than the undersampling factor. Furthermore, the noise level in the signal has a more pronounced impact on reconstruction quality. Additionally, reconstruction accuracy is significantly correlated with the density of samples collected at early sampling times, making it possible to set large time values to zero without producing any statistical difference in the error distribution means for some cases.
Keywords: 31 P-MRS, Energy Metabolism, compressed sensing, Low rank Hankel matrix completion, Reconstruction accuracy
Received: 22 Feb 2025; Accepted: 26 May 2025.
Copyright: © 2025 Garcia, Noseworthy and Santos-Díaz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alejandro Santos-Díaz, Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, México, Mexico
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