Bugler Hanna, Shamaei Amirmohammad, Souza Roberto, Harris Ashley D
Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.
Department of Radiology, University of Calgary, Calgary, Alberta, Canada.
Magn Reson Med. 2025 Nov;94(5):1826-1839. doi: 10.1002/mrm.30597. Epub 2025 Jun 8.
To create a Python-based toolbox to simulate commonly occurring artifacts for single voxel gamma-aminobutyric acid (GABA)-edited MRS data.
The toolbox was designed to maximize user flexibility and contains artifact, applied, input/output (I/O), and support functions. The artifact functions can produce spurious echoes, eddy currents, nuisance peaks, line broadening, baseline contamination, linear frequency drifts, and frequency and phase shift artifacts. Applied functions combine or apply specific parameter values to produce recognizable effects such as lipid peak and motion contamination. I/O and support functions provide additional functionality to accommodate different kinds of input data (MATLAB FID-A.mat files, NIfTI-MRS files), which vary by domain (time vs. frequency), MRS data type (e.g., edited vs. non-edited) and scale. A frequency and phase correction machine learning model experiment trained on corrupted simulated data and validated on in vivo data is shown to highlight the utility of our toolbox.
Data simulated from the toolbox are complementary for research applications, as demonstrated by training a frequency and phase correction deep learning model that is applied to in vivo data containing artifacts. Visual assessment also confirms the resemblance of simulated artifacts compared to artifacts found in in vivo data.
Our easy to install Python artifact simulated toolbox SMART_MRS is useful to enhance the diversity and quality of existing simulated edited-MRS data and is complementary to existing MRS simulation software.
创建一个基于Python的工具箱，用于模拟单像素γ-氨基丁酸（GABA）编辑磁共振波谱（MRS）数据中常见的伪影。
该工具箱旨在最大限度地提高用户的灵活性，包含伪影、应用、输入/输出（I/O）和支持函数。伪影函数可以产生虚假回波、涡流、干扰峰、线展宽、基线污染、线性频率漂移以及频率和相移伪影。应用函数组合或应用特定参数值以产生可识别的效果，如脂质峰和运动污染。I/O和支持函数提供额外功能，以适应不同类型的输入数据（MATLAB FID - A.mat文件、NIfTI - MRS文件），这些数据因域（时间与频率）、MRS数据类型（例如，编辑后与未编辑）和比例而异。展示了一个在 corrupted 模拟数据上训练并在体内数据上验证的频率和相位校正机器学习模型实验，以突出我们工具箱的实用性。
从工具箱模拟的数据对研究应用具有补充作用，这通过训练一个应用于包含伪影的体内数据的频率和相位校正深度学习模型得到证明。视觉评估也证实了模拟伪影与体内数据中发现的伪影相似。
我们易于安装的Python伪影模拟工具箱SMART_MRS有助于提高现有模拟编辑MRS数据的多样性和质量，并且是现有MRS模拟软件的补充。
