Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: 1) accelerate the L-COSY experiment and 2) quantify L-COSY spectra. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) ¹H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

Magnetic resonance spectroscopic imaging (MRSI) is a promising technique in both experimental and clinical settings. However, to date, MRSI has been hampered by prohibitively long acquisition times and artifacts caused by subject motion and hardware-related frequency drift. In the present study, we demonstrate that density weighted concentric ring trajectory (DW-CRT) k-space sampling in combination with semi-LASER excitation and metabolite-cycling enables high-resolution MRSI data to be rapidly acquired at 3 Tesla. Single-slice full-intensity MRSI data (short echo time (TE) semi-LASER TE = 32 ms) were acquired from 6 healthy volunteers with an in-plane resolution of 5 × 5 mm in 13 min 30 sec using this approach. Using LCModel analysis, we found that the acquired spectra allowed for the mapping of total N-acetylaspartate (median Cramer-Rao Lower Bound [CRLB] = 3%), glutamate+glutamine (8%), and glutathione (13%). In addition, we demonstrate potential clinical utility of this technique by optimizing the TE to detect 2-hydroxyglutarate (long TE semi-LASER, TE = 110 ms), to produce relevant high-resolution metabolite maps of grade III IDH-mutant oligodendroglioma in a single patient. This study demonstrates the potential utility of MRSI in the clinical setting at 3 Tesla.

The purpose of this study was to evaluate adipose tissue distributions and hepatic and pancreatic fat contents using a 6-point Dixon MRI technique in type 2 diabetes mellitus (T2DM), and to assess associations between fat distributions and biochemical markers of insulin resistance. Intra-abdominal MRI was investigated in 14 T2DM patients, 13 age- and sex-matched healthy controls (HC) and 11 young HC using a 3 T Prisma MRI scanner. All T2DM subjects completed a fasting comprehensive metabolic panel, and demographic measurements were taken according to standardized methodologies. We observed excellent correlation (R² = 0.94) between hepatic fat fraction quantified using 6-point Dixon MRI and gold standard MRS, establishing the accuracy and reliability of the Dixon technique. Significantly increased visceral adipose tissue (VAT) volumes were found in T2DM patients compared to age-matched HC (1569.81 ± 670.62 cm³ vs. 1106.60 ± 566.85 cm³, p = .04). We also observed a trend of increasing subcutaneous adipose tissues (SAT), and total abdominal fat (TAT) volumes in T2DM compared to age-matched HC. Hepatic fat fraction percentage (HFF%) was 44.6% higher in T2DM compared to age-matched HC and 64.4% higher compared to young HC. Pancreatic fat fractions in the head and body/tail were higher in T2DM patients compared to both healthy cohorts. We also observed correlations between fat contents of the liver and pancreas in T2DM patients, and association between biochemical markers of T2DM with HFF, indicating a risk for non-alcoholic fatty liver disease among T2DM. In summary, this study provides evidence of T2DM patients having increased liver and pancreatic fat, as well as increased adipose tissues.