scholarly journals Comparison of Convolutional Neural Networks Based Method and LCmodel on the Quantification of in Vivo Magnetic Resonance Spectroscopy

2022 ◽  
Author(s):  
Yu-Long Huang ◽  
Yi-Ru Lin ◽  
Shang-Yueh Tsai
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Brain Regions ◽  
Resonance Spectroscopy ◽  
Metabolite Concentrations ◽  
Similar Range ◽  
Error Index

Abstract Quantification of metabolites concentrations in institutional unit (IU) for between subject and long-term comparison is considered important strategy in the applications of magnetic resonance spectroscopy (MRS). The aim of this study is to investigate if metabolite concentrations quantified by convolutional neuronal network (CNN) based method associated with a proposed scaling procedure can reflect variations of the metabolite concentrations in institution unit (IU) at different brain regions with different signal-to-noise-ratio (SNR) and linewidth (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels on the prediction for metabolites. In vivo MRS spectra were collected at 3 brain regions from 44 subjects at 3T system. Metabolite concentrations in IU quantified by LCModel and CNN from 44 subjects were compared. For in vivo spectra characterized under different spectral quality in terms of SNR and LW, line narrowing and noise free spectra were successfully exported by CNN. Concentrations of five metabolites quantified by CNN and LCModel are in similar range with statistically significant Pearson’s correlation coefficients (0.28~0.70). SE of the metabolites show positive correlation with Cramer-Rao lower bound (CRLB) (r=0.60) and with absolute CRLB (r=0.84). In conclusion, the CNN based method with the proposed scaling procedures can be used to quantify in vivo MRS spectra. The concentrations of five major metabolites were reported in IU, which are in the same range as those quantified using a routine MRS quantification procedures by LCModel. The SE can be used as error index indicating predicted uncertainties for metabolites with the information similar to the absolute CRLB.

Magnetic Resonance Spectroscopy and its Applications in Psychiatry

2002 ◽  
Vol 36 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Gin S. Malhi ◽  
Michael Valenzuela ◽  
Wei Wen ◽  
Perminder Sachdev
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Regions ◽  
Response To Treatment ◽  
Resonance Spectroscopy ◽  
Biochemical Basis ◽  
Non Invasive ◽  
Investigative Technique ◽  
Neurochemical Changes

Objective: This paper briefly describes neuroimaging using magnetic resonance spectroscopy (MRS) and provides a systematic review of its application to psychiatric disorders. Method: A literature review ( Index Medicus/ Medline) was carried out, as well as a review of other relevant papers and data known to the authors. Results: Magnetic resonance spectroscopy is a complex and sophisticated neuroimaging technique that allows reliable and reproducible quantification of brain neurochemistry provided its limitations are respected. In some branches of medicine it is already used clinically, for instance, to diagnose tumours and in psychiatry its applications are gradually extending beyond research. Neurochemical changes have been found in a variety of brain regions in dementia, schizophrenia and affective disorders and promising discoveries have also been made in anxiety disorders. Conclusions: Magnetic resonance spectroscopy is a non-invasive investigative technique that has provided useful insights into the biochemical basis of many neuropsychiatric disorders. It allows direct measurement, in vivo, of medication levels within the brain and has made it possible to track the neurochemical changes that occur as a consequence of disease and ageing or in response to treatment. It is an extremely useful advance in neuroimaging technology and one that will undoubtedly have many clinical uses in the near future.

scholarly journals Region-Specific Cerebral Metabolic Alterations in Streptozotocin-Induced Type 1 Diabetic Rats: An in vivo Proton Magnetic Resonance Spectroscopy Study

2015 ◽  
Vol 35 (11) ◽  
pp. 1738-1745 ◽  
Author(s):  
Hui Zhang ◽  
Mingming Huang ◽  
Lifeng Gao ◽  
Hao Lei
Keyword(s):  
Visual Cortex ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Brain Regions ◽  
Resonance Spectroscopy ◽  
Metabolic Disturbances ◽  
Metabolic Alterations ◽  
Neuronal Metabolism

Clinical and experimental in vivo1H-magnetic resonance spectroscopy (1H-MRS) studies have demonstrated that type 1 diabetes mellitus (T1DM) is associated with cerebral metabolic abnormalities. However, less is known whether T1DM induces different metabolic disturbances in different brain regions. In this study, in vivo1H-MRS was used to measure metabolic alterations in the visual cortex, striatum, and hippocampus of streptozotocin (STZ)-induced uncontrolled T1DM rats at 4 days and 4 weeks after induction. It was observed that altered neuronal metabolism occurred in STZ-treated rats as early as 4 days after induction. At 4 weeks, T1DM-related metabolic disturbances were clearly region specific. The diabetic visual cortex had more or less normal-appearing metabolic profile; while the striatum and hippocampus showed similar abnormalities in neuronal metabolism involving N-acetyl aspartate and glutamate; but only the hippocampus exhibited significant changes in glial markers such as taurine and myo-inositol. It is concluded that cerebral metabolic perturbations in STZ-induced T1DM rats are region specific at 4 weeks after induction, perhaps as a manifestation of varied vulnerability among the brain regions to sustained hyperglycemia.

scholarly journals Monitoring the Neurotransmitter Response to Glycemic Changes Using an Advanced Magnetic Resonance Spectroscopy Protocol at 7T

2021 ◽  
Vol 12 ◽  
Author(s):  
Young Woo Park ◽  
Dinesh K. Deelchand ◽  
James M. Joers ◽  
Anjali Kumar ◽  
Alison Bunio Alvear ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Signal To Noise Ratio ◽  
Occipital Cortex ◽  
Quality Data ◽  
Resonance Spectroscopy ◽  
Gamma Aminobutyric Acid ◽  
Signal To Noise ◽  
Noise Ratio

The primary excitatory and inhibitory neurotransmitters glutamate (Glu) and gamma-aminobutyric acid (GABA) are thought to be involved in the response of the brain to changes in glycemia. Therefore, their reliable measurement is critical for understanding the dynamics of these responses. The concentrations of Glu and GABA, as well as glucose (Glc) in brain tissue, can be measured in vivo using proton (1H) magnetic resonance spectroscopy (MRS). Advanced MRS methodology at ultrahigh field allows reliable monitoring of these metabolites under changing metabolic states. However, the long acquisition times needed for these experiments while maintaining blood Glc levels at predetermined targets present many challenges. We present an advanced MRS acquisition protocol that combines commercial 7T hardware (Siemens Scanner and Nova Medical head coil), BaTiO3 dielectric padding, optical motion tracking, and dynamic frequency and B0 shim updates to ensure the acquisition of reproducibly high-quality data. Data were acquired with a semi-LASER sequence [repetition time/echo time (TR/TE) = 5,000/26 ms] from volumes of interest (VOIs) in the prefrontal cortex (PFC) and hypothalamus (HTL). Five healthy volunteers were scanned to evaluate the effect of the BaTiO3 pads on B1+ distribution. Use of BaTiO3 padding resulted in a 60% gain in signal-to-noise ratio in the PFC VOI over the acquisition without the pad. The protocol was tested in six patients with type 1 diabetes during a clamp study where euglycemic (~100 mg/dL) and hypoglycemic (~50 mg/dL) blood Glc levels were maintained in the scanner. The new protocol allowed retention of all HTL data compared with our prior experience of having to exclude approximately half of the HTL data in similar clamp experiments in the 7T scanner due to subject motion. The advanced MRS protocol showed excellent data quality (reliable quantification of 11–12 metabolites) and stability (p > 0.05 for both signal-to-noise ratio and water linewidths) between euglycemia and hypoglycemia. Decreased brain Glc levels under hypoglycemia were reliably detected in both VOIs. In addition, mean Glu level trended lower at hypoglycemia than euglycemia for both VOIs, consistent with prior observations in the occipital cortex. This protocol will allow robust mechanistic investigations of the primary neurotransmitters, Glu and GABA, under changing glycemic conditions.

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