DOI: 10.7280/d1rx1t
title: Data from: Application of a 1H brain MRS benchmark dataset to deep learning for out-of-voxel artifacts

description: 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.
description: 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, and residual water signals, and noise. All of the
 parameters (i.e., amplitudes, relaxation decays, etc.) are included in
 each of the NumPy zipped archive file.
description: NumPy archive files can be opened using Python and NumPy.
description: # AGNOSTIC: Adaptable Generalized Neural-Network Open-source Spectroscopy
 Training dataset of Individual Components #### Published in Imaging
 Neuroscience: [Application of a 1H brain MRS benchmark dataset to deep
 learning for out-of-voxel artifacts](https://doi.org/10.1162/imag_a_00025)
 --- * **Aaron T. Gudmundson**, Johns Hopkins School of Medicine, Kennedy
 Krieger Institute, [ORCID:
 0000-0001-5104-0959](https://orcid.org/0000-0001-5104-0959) *
 **Christopher W. Davies-Jenkins**, Johns Hopkins School of Medicine,
 Kennedy Krieger Institute, [ORCID:
 0000-0002-6015-762X](https://orcid.org/0000-0002-6015-762X) * **İpek
 Özdemir**, Johns Hopkins School of Medicine, Kennedy Krieger Institute,
 [ORCID: 0000-0001-6807-9390](https://orcid.org/0000-0001-6807-9390) *
 **Saipavitra Murali-Manohar**, Johns Hopkins School of Medicine, Kennedy
 Krieger Institute, [ORCID:
 0000-0002-4978-0736](https://orcid.org/0000-0002-4978-0736) * **Helge J.
 Zöllner**, Johns Hopkins School of Medicine, Kennedy Krieger Institute,
 [ORCID: 0000-0002-7148-292X](https://orcid.org/0000-0002-7148-292X) *
 **Yulu Song**, Johns Hopkins School of Medicine, Kennedy Krieger
 Institute, [ORCID:
 0000-0002-4416-7959](https://orcid.org/0000-0002-4416-7959) * **Kathleen
 E. Hupfeld**, Johns Hopkins School of Medicine, Kennedy Krieger Institute,
 [ORCID: 0000-0001-5086-4841](https://orcid.org/0000-0001-5086-4841) *
 **Alfons Schnitzler**, Heinrich-Heine-University Düsseldorf, [ORCID:
 0000-0002-6414-7939](https://orcid.org/0000-0002-6414-7939) * **Georg
 Oeltzschner**, Johns Hopkins School of Medicine, Kennedy Krieger
 Institute, [ORCID:
 0000-0003-3083-9811](https://orcid.org/0000-0003-3083-9811) * **Craig E.
 L. Stark**, University of California, Irvine, [ORCID:
 0000-0002-9334-8502](https://orcid.org/0000-0002-9334-8502) * **Richard A.
 E. Edden**, Johns Hopkins School of Medicine, Kennedy Krieger Institute,
 [ORCID: 0000-0002-0671-7374](https://orcid.org/0000-0002-0671-7374) ##
 Publication Information Citation &gt; Gudmundson, A. T., Davies-Jenkins,
 C. W., Özdemir, İ., Murali-Manohar, S., Zöllner, H. J., Song, Y., ...
 & Edden, R. A. (2023). Application of a 1H brain MRS benchmark dataset
 to deep learning for out-of-voxel artifacts. Imaging Neuroscience, 1,
 1-15. ## Dataset Description
 The Adaptable Generalized Neural-Network Open-source Spectroscopy Training
 dataset of Individual Components (AGNOSTIC), is a dataset consisting of
 259,200 synthetic MRS examples. The synthetic examples contained within
 the dataset were produced to resemble in vivo brain data with metabolite,
 macromolecule, residual water signals, and noise. The parameter space that
 AGNOSTIC spans is wide-reaching, comprising: 18 field strengths; 15 echo
 times; broad distributions of metabolite, MM, and water amplitudes; and
 densely sampled time-domain to allow down-sampling. ## 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. ## Description of the Data
 and File Structure The dataset is structured as a zipped NumPy archive
 file (.npz). The zipped NumPy archive file contains complex-valued NumPy
 arrays of time-domain (4096 timepoints) data corresponding to the
 metabolite, macromolecule, water, and noise components which can be
 combined in different ways depending on the users goal or objective.
 Within the file, all the acquisition parameters (field strength, echo
 time, spectral width, etc.), simulation parameters (signal to noise,
 full-width half-max, concentrations, T2 relaxation, etc.), and data
 augmentation options are specified. ## Sharing/Access Information Data was
 derived from the following sources: *
 ## Code/Software The
 following Python 3 scripts, found at
 , were used to generate
 AGNOSTIC: * 00_simulation.py (Density Matrix Simulation Functions) *
 01_deep_sim.py (Acquisition Setting and Metabolite Simulations) *
 02_metab_matrix_py (Restructuring and Normalizing Basis Set) *
 03_gen_data.py (Synthetic Dataset) * 04_randomize.py (Randomizing Field
 Strengths and Echo Times) These scripts primarily rely upon NumPy, SciPy,
 and standard built-in Python libaries (os, glob, subprocess, etc.) ##
 Dataset Contains: | Column Name | Datatype | Shape | Description | | |
 ----------- | -------- | ------------ |
 -------------------------------------------------------------------------------------------- | :------------------ | | Dataset | String | 1 | Dataset used in Data Simulations for Concentrations | | | Field\_Str | Array | Batch x 1 | Field Strength Used (Tesla) | | | Echo\_Times | Array | Batch x 1 | Echo Times Used (ms) | | | sw | Array | Batch x 1 | Spectral Widths Available (Hz) (Dependent on Subsampling and Field Strength) | | | subsample | Array | Batch x 1 | Subsampling Stride Corresponding to SpecWidth | | | nPoints | Array | Batch x 1 | Number of Points (i.e. 512,1024,2048) | | | Metab | Array | Batch x 4096 | Full Metabolite Signal (w/ Concentration, Lorentzian LB, and Gaussian LB) | | | MM | Array | Batch x 4096 | Full Macromolecule Signal (w/ Concentration, Lorentzian LB, and Gaussian LB) | | | water | Array | Batch x 4096 | Full Water Signal (w/ &lt;=5Components, Lorentzian LB, Gaussian LB, Scaling 5x-20x Metabolites) | | | noise | Array | Batch x 4096 | Normal Distributed Noise | | | time | Array | Batch x 4096 | Time Axis (seconds) | | | ppm | Array | Batch x 4096 | Frequency Axis (ppm) | | | Amplitude | Array | Batch x 182 | Concentration Used for Each Spin (Metabolite & MM) | | | water\_pos | Array | Batch x 1 | Water is Positive or Negative (0=Pos; 1=Neg) | | | water\_comp | Array | Batch x 5 | PPM value of Each Component | | | waterNcomp | Array | Batch x 5 | Components Included | | | water\_amp | Array | Batch x 5 | Water Scaling | | | noise\_amp | Array | Batch x 1 | Noise Scaling (Equivalent SNR Range Across SpecWidth and Npoints) | | | freq\_shift | Array | Batch x 1 | Frequency Shifts (default is not applied) | | | phase0 | Array | Batch x 1 | 0th Order Phase (default is not applied) | | | phase1 | Array | Batch x 1 | 1st Order Phase (default is not applied) | | | phase1\_piv | Array | Batch x 1 | 1st Order Phase Pivot Point | | | SNR | Array | Batch x 1 | SNR (NAA\_Amp / StdDev\_Noise) | | | LBL | Array | Batch x 182 | Lorentzian Line Broadening Metab/MM | | | LBG | Array | Batch x 182 | Gaussian Line Broadening Metab/MM | | | m\_mult | Array | Batch x 2 | Norm Metab (Metab --&gt; 1; Column0 = No Phase & Column2 = with Phase) | | | w\_mult | Array | Batch x 2 | Norm Water (Water --&gt; 5x-20x) Correctly Scales Water Relative to Metab | | | LBL\_Water | Array | Batch x 5 | Lorentzian Line Broadening Water | | | LBG\_Water | Array | Batch x 5 | Gaussian Line Broadening Water | | | FWHM\_MM | Array | Batch x 14 | Target FWHM of Macromolecules (14 Macromolecules) | | | FWHM\_Metab | Array | Batch x 182 | Target FWHM of Metaboites (FWHM of NAA) | | | Healthy | Array | Batch x 1 | Healthy = 0; Clinical = 1 | | | Clinical | Array | Batch x 1 | Healthy = 0; Clinical &gt; 0 (See Clin\_Names) | | | Clin\_Names | List | 21 | Corresponding Names of Population Number from 'Clinical' | Note\* 0 is Healthy | | Drop\_Sig | Array | Batch x 4096 | Some/All Metab/MM Signal to be **Subtracted** (See Batch\_Drop and dIdx\_Drop) | | | Batch\_Drop | Array | Batch x 1 | Randomly Leave Off Some/All Metabolites/Macromolecules - Indicates Which Index was Selected | | | dIdx\_Drop | Array | Batch x 1 | Randomly Leave Off Some/All Metabolites/Macromolecules - Index of the 182 Spins to Drop | | ## Funding This work has been supported by The Henry L. Guenther Foundation, Sonderforschungsbereich (SFB) 974 (TP B07) of the German Research foundation, and the National Institute of Health, grants T32 AG00096, R00 AG062230, R21 EB033516, R01 EB016089, R01 EB023963, K00AG068440, P30 AG066519, R21 AG053040, R01 AG076942, P30 AG066519, and P41 EB031771.