Dataset Information

Application of a ¹H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

ABSTRACT: Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic ¹H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log₁₀ normed-MSE of -1.79, an improvement of almost two orders of magnitude.

SUBMITTER: Gudmundson AT

PROVIDER: S-EPMC10197548 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

Gudmundson Aaron T AT Davies-Jenkins Christopher W CW Özdemir İpek İ Murali-Manohar Saipavitra S Zöllner Helge J HJ Song Yulu Y Hupfeld Kathleen E KE Schnitzler Alfons A Oeltzschner Georg G Stark Craig E L CEL Edden Richard A E RAE

bioRxiv : the preprint server for biology 20230901

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residu ...[more]

PMID: 37215030

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Project description:Out-of-voxel (OOV) signals are common spurious echo artifacts in MRS. These signals often manifest in the spectrum as very strong "ripples," which interfere with spectral quantification by overlapping with targeted metabolite resonances. Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient schemes are algorithmically optimized to suppress all potential alternative coherence transfer pathways (CTPs), and should suppress unwanted OOV echoes. In addition, second-order shimming uses non-linear gradient fields to maximize field homogeneity inside the voxel, which unfortunately increases the diversity of local gradient fields outside of the voxel. Given that strong local spatial B0 gradients can refocus unintended CTPs, it is possible that OOVs are less prevalent when only linear first-order shimming is applied. Here we compare the size of unwanted OOV signals in Hadamard-edited (HERMES) data acquired with either a local gradient scheme (which we refer to here as "Shared") or DOTCOPS, and with first- or second-order shimming. We collected data from 15 healthy volunteers in two brain regions (voxel size 30 × 26 × 26 mm3 ) from which it is challenging to acquire MRS data: medial prefrontal cortex and left temporal cortex. Characteristic OOV echoes were seen in both GABA- and GSH-edited spectra for both brain regions, gradient schemes, and shimming approaches. A linear mixed-effect model revealed a statistically significant difference in the average residual based on the gradient scheme in both GABA- (p < 0.001) and GSH-edited (p < 0.001) spectra: that is, the DOTCOPS gradient scheme resulted in smaller OOV artifacts compared with the Shared scheme. There were no significant differences in OOV artifacts associated with shimming method. Thus, these results suggest that the DOTCOPS gradient scheme for J-difference-edited PRESS acquisitions yields spectra with smaller OOV echo artifacts than the Shared gradient scheme implemented in a widely disseminated editing sequence.

Dataset Information

Application of a ¹H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

Publications

Application of a <sup>1</sup>H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

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Dataset Information

Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

Publications

Application of a &lt;sup&gt;1&lt;/sup&gt;H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Application of a ¹H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

Application of a <sup>1</sup>H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.