在活體腦部之核磁共振頻譜中，包含了細胞蛋白之大分子頻譜，若沒有適當的參數化大分子頻譜且加入代謝物基底函數會造成代謝物定量之可靠度與再現性降低，過去許多團隊藉由獲得純大分子頻譜或是改變基線自由度，以研究大分子及基線對於代謝物定量之影響。本研究方法為使用較完整之大分子模型以及不同程度之基線自由度，並且提出一個在 3T 磁場下對於健康受試者而言較泛用之參數化 LCModel 模型，以克服大分子頻譜以及磁場不均勻所形成的基底頻譜問題。我們發現使用 LCModel 大分子模型加上適當自由度之基線擁有較高的再現性與穩定度。

The broad resonances which underlie the whole 1H spectrum are mainly contributed by macromolecules. It makes metabolite quantification 1H-MRS more challenging. Therefore, many previous researches have made a great amount of efforts to deal with baseline distortion problem through obtaining metabolite nulled spectrum and using different degrees of the spline baseline during fit. In this study, we tried to apply a more complete macromolecule model and control the flexibility of the spline baseline in order to provide a relatively robust module for healthy subjects using at 3T. At the end, using LCModel macromolecule model with a moderate spline was the most reproducible and stable.

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