| Popis: |
PurposeNeural networks are potentially valuable for many challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic MRS examples. To demonstrate the utility, we use 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.MethodsAGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemblein vivobrain data with combinations of metabolite, macromolecule, and residual water signals, and noise.Complex OOV signals were mixed into 85% of synthetic examples to train two separate U-net CNNs for the detection and prediction of OOV signals.ResultsAGNOSTIC 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 log10normed-MSE of –1.79, an improvement of almost two orders of magnitude.ConclusionThe AGNOSTIC benchmark dataset for MRS is introduced and various dataset features are described. As an exemplar use of AGNOSTIC, two CNNs were developed to detect and predict OOV echoes. |
