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Diagnostics
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Clinical Applications and Potential of Magnetic Resonance Spectroscopy
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Clinical Applications and Potential of Magnetic Resonance Spectroscopy

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 4341

Special Issue Editor

Dr. Jimin Ren

E-Mail Website
Guest Editor
Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, United States
Interests: Magnetic resonance spectroscopy; brain metabolism; skeletal muscle metabolism; chemical exchange

Special Issue Information

Dear Colleagues,

Magnetic resonance spectroscopy has been increasingly used as a non-invasive modality in clinical applications. This Special Issue will collect recent development of MRS and MRSI in clinical research including, but not limited to, the assessment of ATP energy metabolism, redox state, (de)myelination, neurodegeneration, inflammation, ischemia and oxygenation, cellularity and atrophy, fat infiltration, mineral metabolism, kinetic and dynamic processes, metabolic pathways and intermediates, organ preservation and transplant viability, sex hormonal effects, exercise and fatigue, muscle fiber typing, transmembrane processes, membrane fluidity, mitochondrial (dys)function, and biomarkers of various diseases. Major body organs include brain, heart, liver, kidney, prostate, bone marrow, subcutaneous tissue, and skeletal muscle. Original work on 31P and 13C MRS and collaborative contributions from multiple institutes and using different modalities are especially encouraged.

Dr. Jimin Ren
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • magnetic resonance spectroscopy
  • metabolite
  • lipids and phospholipids
  • brain
  • skeletal muscle

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Published Papers (4 papers)

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Research

17 pages, 2430 KiB  
Article
PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data
by Jia Xu, Michael Vaeggemose, Rolf F. Schulte, Baolian Yang, Chu-Yu Lee, Christoffer Laustsen and Vincent A. Magnotta
Diagnostics 2024, 14(23), 2668; https://doi.org/10.3390/diagnostics14232668 - 27 Nov 2024
Viewed by 629
Abstract
Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source [...] Read more.
Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications. Full article
(This article belongs to the Special Issue Clinical Applications and Potential of Magnetic Resonance Spectroscopy)
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16 pages, 5276 KiB  
Article
Multimodal MRI and 1H-MRS for Preoperative Stratification of High-Risk Molecular Subtype in Adult-Type Diffuse Gliomas
by Xin Han, Kai Xiao, Jie Bai, Fengqi Li, Bixiao Cui, Ye Cheng, Huawei Liu and Jie Lu
Diagnostics 2024, 14(22), 2569; https://doi.org/10.3390/diagnostics14222569 - 15 Nov 2024
Viewed by 614
Abstract
Isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT) genes are critical molecular markers in determining treatment options and predicting the prognosis of adult-type diffuse gliomas. Objectives: this study aimed to investigate whether multimodal MRI enables the differentiation of genotypes in adult-type [...] Read more.
Isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT) genes are critical molecular markers in determining treatment options and predicting the prognosis of adult-type diffuse gliomas. Objectives: this study aimed to investigate whether multimodal MRI enables the differentiation of genotypes in adult-type diffuse gliomas. Methods: a total of 116 adult-type diffuse glioma patients (61 males, 51.5 (37, 62) years old) who underwent multimodal MRI before surgery were retrospectively analysed. Multimodal MRI included conventional MRI, proton magnetic resonance spectroscopy (1H-MRS), and diffusion tensor imaging (DTI). Conventional visual features, N-acetyl-aspartate (NAA)/Creatine (Cr), Choline (Cho)/Cr, Cho/NAA, fractional anisotropy (FA), mean diffusivity (MD), and diffusion histogram parameters were extracted on the whole tumour. Multimodal MRI parameters of IDH-mutant and IDH-wildtype gliomas were compared using the Mann–Whitney U test, Student’s t-test, or Pearson chi-square tests. Logistic regression was used to select the MRI parameters to predict IDH-mutant gliomas. Furthermore, multimodal MRI parameters were selected to establish models for predicting MGMT methylation in the IDH-wildtype gliomas. The performance of models was evaluated by the receiver operating characteristics curve. Results: a total of 56 patients with IDH-mutant gliomas and 60 patients with IDH-wildtype glioblastomas (GBM) (37 with methylated MGMT and 17 with unmethylated MGMT) were diagnosed by 2021 WHO classification criteria. The enhancement degree (OR = 4.298, p < 0.001), necrosis/cyst (OR = 5.381, p = 0.011), NAA/Cr (OR = 0.497, p = 0.037), FA-Skewness (OR = 0.497, p = 0.033), MD-Skewness (OR = 1.849, p = 0.035), FAmean (OR = 1.924, p = 0.049) were independent factors for the multimodal combined prediction model in predicting IDH-mutant gliomas. The combined modal based on conventional MRI, 1H-MRS, DTI parameters, and histogram performed best in predicting IDH-wildtype status (AUC = 0.890). However, only NAA/Cr (OR = 0.17, p = 0.043) and FA (OR = 0.38, p = 0.015) were associated with MGMT methylated in IDH-wildtype GBM. The combination of NAA/Cr and FA-Median is more accurate for predicting MGMT methylation levels than using these elements alone (AUC, 0.847 vs. 0.695/0.684). Conclusions: multimodal MRI based on conventional MRI, 1H-MRS, and DTI can provide compound imaging markers for stratified individual diagnosis of IDH mutant and MGMT promoter methylation in adult-type diffuse gliomas. Full article
(This article belongs to the Special Issue Clinical Applications and Potential of Magnetic Resonance Spectroscopy)
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22 pages, 3389 KiB  
Article
A Magnetic Resonance Spectroscopy Study on Polarity Subphenotypes in Bipolar Disorder
by Georgios D. Argyropoulos, Foteini Christidi, Efstratios Karavasilis, Peter Bede, Georgios Velonakis, Anastasia Antoniou, Ioannis Seimenis, Nikolaos Kelekis, Nikolaos Smyrnis, Olympia Papakonstantinou, Efstathios Efstathopoulos and Panagiotis Ferentinos
Diagnostics 2024, 14(11), 1170; https://doi.org/10.3390/diagnostics14111170 - 31 May 2024
Viewed by 856
Abstract
Although magnetic resonance spectroscopy (MRS) has provided in vivo measurements of brain chemical profiles in bipolar disorder (BD), there are no data on clinically and therapeutically important onset polarity (OP) and predominant polarity (PP). We conducted a proton MRS study in BD polarity [...] Read more.
Although magnetic resonance spectroscopy (MRS) has provided in vivo measurements of brain chemical profiles in bipolar disorder (BD), there are no data on clinically and therapeutically important onset polarity (OP) and predominant polarity (PP). We conducted a proton MRS study in BD polarity subphenotypes, focusing on emotion regulation brain regions. Forty-one euthymic BD patients stratified according to OP and PP and sixteen healthy controls (HC) were compared. 1H-MRS spectra of the anterior and posterior cingulate cortex (ACC, PCC), left and right hippocampus (LHIPPO, RHIPPO) were acquired at 3.0T to determine metabolite concentrations. We found significant main effects of OP in ACC mI, mI/tNAA, mI/tCr, mI/tCho, PCC tCho, and RHIPPO tNAA/tCho and tCho/tCr. Although PP had no significant main effects, several medium and large effect sizes emerged. Compared to HC, manic subphenotypes (i.e., manic-OP, manic-PP) showed greater differences in RHIPPO and PCC, whereas depressive suphenotypes (i.e., depressive-OP, depressive-PP) in ACC. Effect sizes were consistent between OP and PP as high intraclass correlation coefficients (ICC) were confirmed. Our findings support the utility of MRS in the study of the neurobiological underpinnings of OP and PP, highlighting that the regional specificity of metabolite changes within the emotion regulation network consistently marks both polarity subphenotypes. Full article
(This article belongs to the Special Issue Clinical Applications and Potential of Magnetic Resonance Spectroscopy)
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16 pages, 2777 KiB  
Article
17β-Estradiol Effects in Skeletal Muscle: A 31P MR Spectroscopic Imaging (MRSI) Study of Young Females during Early Follicular (EF) and Peri-Ovulation (PO) Phases
by Jimin Ren, Luis Rodriguez II, Talon Johnson, Anke Henning and Yasin Y. Dhaher
Diagnostics 2024, 14(3), 235; https://doi.org/10.3390/diagnostics14030235 - 23 Jan 2024
Viewed by 1619
Abstract
The natural variation in estrogen secretion throughout the female menstrual cycle impacts various organs, including estrogen receptor (ER)-expressed skeletal muscle. Many women commonly experience increased fatigue or reduced energy levels in the days leading up to and during menstruation, when blood estrogen levels [...] Read more.
The natural variation in estrogen secretion throughout the female menstrual cycle impacts various organs, including estrogen receptor (ER)-expressed skeletal muscle. Many women commonly experience increased fatigue or reduced energy levels in the days leading up to and during menstruation, when blood estrogen levels decline. Yet, it remains unclear whether endogenous 17β-estradiol, a major estrogen component, directly affects the energy metabolism in skeletal muscle due to the intricate and fluctuating nature of female hormones. In this study, we employed 2D 31P FID-MRSI at 7T to investigate phosphoryl metabolites in the soleus muscle of a cohort of young females (average age: 28 ± 6 years, n = 7) during the early follicular (EF) and peri-ovulation (PO) phases, when their blood 17β-estradiol levels differ significantly (EF: 28 ± 18 pg/mL vs. PO: 71 ± 30 pg/mL, p < 0.05), while the levels of other potentially interfering hormones remain relatively invariant. Our findings reveal a reduction in ATP-referenced phosphocreatine (PCr) levels in the EF phase compared to the PO phase for all participants (5.4 ± 4.3%). Furthermore, we observe a linear correlation between muscle PCr levels and blood 17β-estradiol concentrations (r = 0.64, p = 0.014). Conversely, inorganic phosphate Pi and phospholipid metabolite GPC levels remain independent of 17β-estradiol but display a high correlation between the EF and PO phases (p = 0.015 for Pi and p = 0.0008 for GPC). The robust association we have identified between ATP-referenced PCr and 17β-estradiol suggests that 17β-estradiol plays a modulatory role in the energy metabolism of skeletal muscle. Full article
(This article belongs to the Special Issue Clinical Applications and Potential of Magnetic Resonance Spectroscopy)
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