Fast water concentration mapping to normalize (1)H MR spectroscopic imaging

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy

BACKGROUND

Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful.

MATERIALS AND METHODS

This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system.

RESULTS

The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and  absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content.

CONCLUSION

In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.

A Comparison of 7 Tesla MR Spectroscopic Imaging and 3 Tesla MR Fingerprinting for Tumor Localization in Glioma Patients

This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen-Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.

Repeatability of multinuclear interleaved acquisitions with nuclear Overhauser enhancement effect in dynamic experiments in the calf muscle at 3T

PURPOSE

To evaluate the repeatability of multinuclear interleaved ¹ H/³¹ P NMR dynamic acquisitions in skeletal muscle and the impact of nuclear Overhauser enhancement (nOe) on the ³¹ P results at 3T in exercise-recovery and ischemia-hyperemia paradigms.

METHODS

A ¹ H/³¹ P interleaved pulse sequence was used to measure every 2.5 s a perfusion-weighted image, a T 2 ∗ map, a ³¹ P spectrum and 32 ¹ H spectra sensitive to deoxymyoglobin. 21 subjects performed a plantar flexion exercise and after recovery underwent an 8-min lower leg ischemia. The procedure was repeated in visit 2 with 12 subjects. An additional exercise bout without ¹ H excitation was appended to visit 1. Individual ¹ H RF pulse nOe was measured at rest in every visit.

RESULTS

Repeatability scores (coefficient of variation, Bland-Altman analysis) were similar to those found in the literature using similar mono-nuclear acquisitions. |Pi]/[PCr], pH drop, creatine rephosphorylation rate (τ_PCr ), maximum perfusion, time to peak perfusion, and blood flow post-exercise showed high reliability (intraclass correlation coefficient > 0.7), whereas hemodynamic results from reactive hyperemia showed higher repeatability. After accounting for nOe, which increased Pi and PCr signal-to-noise ratio by 30%, no differences in ³¹ P results were observed between interleaved and ³¹ P MRS-only acquisitions. τ_PCr was unaffected by nOe.

CONCLUSION

The method shows good repeatability for both paradigms while simultaneously providing multiple dynamic data sets on a clinical scanner. The nOe effects were accounted for on a per-subject and per-visit basis using a short ³¹ P reference scan. This multiparametric approach has a multitude of applications for the study of oxygen utilization and ATP turnover in the muscle.

Reduction of variance in measurements of average metabolite concentration in anatomically-defined brain regions

Multiple methods have been proposed for using Magnetic Resonance Spectroscopy Imaging (MRSI) to measure representative metabolite concentrations of anatomically-defined brain regions. Generally these methods require spectral analysis, quantitation of the signal, and reconciliation with anatomical brain regions. However, to simplify processing pipelines, it is practical to only include those corrections that significantly improve data quality. Of particular importance for cross-sectional studies is knowledge about how much each correction lowers the inter-subject variance of the measurement, thereby increasing statistical power. Here we use a data set of 72 subjects to calculate the reduction in inter-subject variance produced by several corrections that are commonly used to process MRSI data. Our results demonstrate that significant reductions of variance can be achieved by performing water scaling, accounting for tissue type, and integrating MRSI data over anatomical regions rather than simply assigning MRSI voxels with anatomical region labels.

Whole-brain quantitative mapping of metabolites using short echo three-dimensional proton MRSI

BACKGROUND

To improve the extent over which whole brain quantitative three-dimensional (3D) magnetic resonance spectroscopic imaging (MRSI) maps can be obtained and be used to explore brain metabolism in a population of healthy volunteers.

METHODS

Two short echo time (20 ms) acquisitions of 3D echo planar spectroscopic imaging at two orientations, one in the anterior commissure-posterior commissure (AC-PC) plane and the second tilted in the AC-PC +15° plane were obtained at 3 Tesla in a group of 10 healthy volunteers. B1 (+) , B1 (-) , and B0 correction procedures and normalization of metabolite signals with quantitative water proton density measurements were performed. A combination of the two spatially normalized 3D-MRSI, using a weighted mean based on the pixel wise standard deviation metabolic maps of each orientation obtained from the whole group, provided metabolite maps for each subject allowing regional metabolic profiles of all parcels of the automated anatomical labeling (AAL) atlas to be obtained.

RESULTS

The combined metabolite maps derived from the two acquisitions reduced the regional intersubject variance. The numbers of AAL regions showing N-acetyl aspartate (NAA) SD/Mean ratios lower than 30% increased from 17 in the AC-PC orientation and 41 in the AC-PC+15° orientation, to a value of 76 regions of 116 for the combined NAA maps. Quantitatively, regional differences in absolute metabolite concentrations (mM) over the whole brain were depicted such as in the GM of frontal lobes (cNAA = 10.03 + 1.71; cCho = 1.78 ± 0.55; cCr = 7.29 ± 1.69; cmIns = 5.30 ± 2.67) and in cerebellum (cNAA = 5.28 ± 1.77; cCho = 1.60 ± 0.41; cCr = 6.95 ± 2.15; cmIns = 3.60 ± 0.74).

CONCLUSION

A double-angulation acquisition enables improved metabolic characterization over a wide volume of the brain.