Magnetic resonance spectroscopy (MRS) is an established technique for studying tissue metabolism, particularly in central nervous system disorders. While powerful and versatile, MRS is often limited by challenges associated with data quality, processing, and quantification. Existing MRS quantification methods face difficulties in balancing model complexity and reproducibility during spectral modeling, often falling into the trap of either oversimplification or over-parameterization. To address these limitations, this study introduces a deep learning (DL) framework that employs transfer learning, in which the model is pre-trained on simulated datasets before it undergoes fine-tuning on in vivo data. The proposed framework showed promising performance when applied to the Philips dataset from the BIG GABA repository and represents an exciting advancement in MRS data analysis.

翻译：磁共振波谱学（MRS）是研究组织代谢，特别是中枢神经系统疾病的成熟技术。尽管功能强大且用途广泛，MRS通常受限于与数据质量、处理及量化相关的挑战。现有的MRS量化方法在谱建模过程中难以平衡模型复杂性与可复现性，常陷入过度简化或过度参数化的困境。为应对这些局限，本研究引入了一种采用迁移学习的深度学习（DL）框架，该模型先在模拟数据集上进行预训练，再对体内数据进行微调。所提出的框架在应用于BIG GABA存储库的Philips数据集时表现出良好性能，代表了MRS数据分析领域一项令人振奋的进展。