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Water removal in MR spectroscopic imaging with L2 regularization.

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TLDR
An L2‐regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T.
Abstract
PURPOSE An L2-regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T. METHODS Water signals are removed by implementation of the L2 regularization using a synthesized water-basis matrix that is orthogonal to metabolite signals of interest in the spectral dimension. Simulated spectra with variable water amplitude and in vivo brain MRSI datasets were used to demonstrate the proposed method. Results were compared with two commonly-used postprocessing methods for removing water signals. RESULTS The L2 method yielded metabolite signals that were close to true values for the simulated spectra. Residual/unsuppressed water signals in human brain short- and long-echo time MRSI datasets were efficiently removed by the proposed method allowing good quality metabolite maps to be reconstructed with minimized contamination from water signals. Significant differences of the creatine signal were observed between brain long-echo time MRSI without and with water saturation, attributable to the previously described magnetization transfer effect. CONCLUSIONS With usage of a synthesized water matrix generated based on reasonable prior knowledge about water and metabolite resonances, the L2 method is shown to be an effective way to remove water signals from MRSI of the human brain.

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Journal ArticleDOI

Emerging methods and applications of ultra-high field MR spectroscopic imaging in the human brain.

TL;DR: In this paper, the authors focus on the challenges MRSI of the human brain faces at ultra-high field strength, as well as the possible applications to this date, and propose to exploit the advantages of ultrahigh field strengths.
Journal ArticleDOI

Accelerated J‐resolved 1H‐MRSI with limited and sparse sampling of (‐space

TL;DR: To accelerate the acquisition of J‐resolved proton magnetic resonance spectroscopic imaging (1H‐MRSI) data for high‐resolution mapping of brain metabolites and neurotransmitters.
Journal ArticleDOI

Test-retest reproducibility of human brain multi-slice 1H FID-MRSI data at 9.4 T after optimization of lipid regularization, macromolecular model and spline baseline stiffness

TL;DR: L2-regularization provided effective suppression of lipid-artifacts, but should be avoided if no artifacts are detected, and results from the second measurements of the same subjects show that slice positioning and data quality correlate significantly to the first measurement.
Proceedings ArticleDOI

A novel method for Magnetic Resonance Spectroscopy lipid signal suppression using Semi-classical signal analysis and Bidirectional Long short-term memory

TL;DR: A novel method for lipid suppression for MRS signal is proposed that combines a semi-classical signal analysis method and a bidirectional long short term memory technique and is validated using simulated data that mimics real MRS data.
Posted ContentDOI

The AGNOSTIC MRS Benchmark Dataset: Deep Learning for Out-of-voxel Artifacts

TL;DR: The AGNOSTIC dataset as discussed by the authors contains 259,200 synthetic 1H MRS examples for training and testing neural networks, which is intended to be acquisition-agnostic by using 270 basis sets that were simulated across 18 field strengths, 15 echo times, and a range of dwell times.
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TL;DR: The addition of 3D MR spectroscopic imaging to MR imaging provides better detection and localization of prostate cancer in a sextant of the prostate than does use of MR imaging alone.
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