Xu Jia, Vaeggemose Michael, Schulte Rolf F, Yang Baolian, Lee Chu-Yu, Laustsen Christoffer, Magnotta Vincent A
Department of Radiology, University of Iowa, Iowa City, IA 52242, USA.
GE HealthCare, 2605 Brondby, Denmark.
Diagnostics (Basel). 2024 Nov 27;14(23):2668. doi: 10.3390/diagnostics14232668.
: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. : PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. : PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. : PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications.
磁共振波谱学（MRS）是研究体内代谢过程的一种有价值的工具。虽然存在众多定量方法，但先进的准确、稳健且高效的光谱拟合方法（AMARES）是最常用的方法之一。本研究介绍了pyAMARES，这是AMARES的一种开源Python实现，满足了在Python生态系统中对灵活、用户友好且通用的MRS定量工具的需求。：PyAMARES是作为一个Python库开发的，实现了AMARES算法，并具有诸如多处理能力和可定制目标函数等附加功能。该软件使用模拟和体内MRS数据，针对已建立的AMARES实现（OXSA和jMRUI）进行了验证。进行了蒙特卡罗模拟，以评估在各种信噪比和参数扰动下的稳健性和准确性。：PyAMARES利用基于电子表格的先验知识和拟合参数设置，增强了灵活性和易用性。在准确性、精度和计算效率方面，它表现出与现有软件相当的性能。除了传统的AMARES拟合外，pyAMARES还支持无先验知识的拟合、频率选择性AMARES以及从移动大分子（MM）光谱中去除代谢物残留。利用多个CPU核心可显著提高pyAMARES的性能。：PyAMARES为Python生态系统中的MRS定量提供了一个稳健、灵活且用户友好的解决方案。其开源性质、全面的文档以及与流行数据科学工具的集成增强了MRS研究中的可重复性和协作性。PyAMARES弥合了传统MRS拟合方法与现代机器学习框架之间的差距，有可能加速代谢研究和临床应用的进展。
