用超网络快速精准量化脑部磁共振代谢物，支持多参数自适应。
HyperFitS -- Hypernetwork Fitting Spectra for metabolic quantification of ${}^1$H MR spectroscopic imaging
- 基于超网络设计，可灵活适配不同基线校正和水抑制条件。
- 处理速度从数小时缩短至几秒，与传统方法结果一致误差<30%。
- 无需重训练即可兼容多种扫描协议和场强，适合临床部署。
质子磁共振波谱成像（$^1$H MRSI）可实现活体全脑代谢物浓度映射，但代谢物定量长期面临谱图拟合耗时问题。近年深度学习方法可在数秒内完成全脑定量，但现有神经网络配置灵活性差，需重训练才能调整参数。本文提出HyperFitS，一种用于全脑$^1$H MRSI代谢物定量的超网络谱图拟合方法，可灵活适应多种基线校正和水抑制因子。对3T与7T下各向同性分辨率分别为10 mm、3.4 mm和2 mm的水抑制与非水抑制MRSI数据进行定量，结果表明：HyperFitS与标准LCModel方法结果高度一致，且拟合时间显著缩短。定量分析显示基线参数设定对结果影响可达30%。结论：HyperFitS在保持与先进传统方法相当精度的同时，将处理时间从数小时降至数秒；相比以往深度学习方法，其具备广泛可配置性，无需重训练即可适配多协议、多场强数据。
原文摘要 · Abstract (English)
Purpose: Proton magnetic resonance spectroscopic imaging ($^1$H MRSI) enables the mapping of whole-brain metabolites concentrations in-vivo. However, a long-standing problem for its clinical applicability is the metabolic quantification, which can require extensive time for spectral fitting. Recently, deep learning methods have been able to provide whole-brain metabolic quantification in only a few seconds. However, neural network implementations often lack configurability and require retraining to change predefined parameter settings. Methods: We introduce HyperFitS, a hypernetwork for spectral fitting for metabolite quantification in whole-brain $^1$H MRSI that flexibly adapts to a broad range of baseline corrections and water suppression factors. Metabolite maps of human subjects acquired at 3T and 7T with isotropic resolutions of 10 mm, 3.4 mm and 2 mm by water-suppressed and water-unsuppressed MRSI were quantified with HyperFitS and compared to conventional LCModel fitting. Results: Metabolic maps show a substantial agreement between the new and gold-standard methods, with significantly faster fitting times by HyperFitS. Quantitative results further highlight the impact of baseline parametrization on metabolic quantification, which can alter results by up to 30%. Conclusion: HyperFitS shows strong agreement with state-of-the-art conventional methods, while reducing processing times from hours to a few seconds. Compared to prior deep learning based spectral fitting methods, HyperFitS enables a wide range of configurability and can adapt to data quality acquired with multiple protocols and field strengths without retraining.
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