发表机构
Technical University of Munich; Eindhoven University of Technology; University Medical Center Utrecht; Duke University Medical Center; Medical University of Vienna; Yale School of Medicine（慕尼黑工业大学; 埃因霍温理工大学; 乌得勒支大学医学中心; 杜克大学医学中心; 维也纳医科大学; 耶鲁大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建了2024年MRSI数据处理与定量挑战合成数据集，作为MRSI方法开发与评估的受控测试平台，含24组训练、8组测试数据，支持相关方法的开发、比较与严格评估。
AI 中文摘要
合成数据是磁共振波谱（MRS）方法开发的核心，因为它能为软件验证、可重复基准测试以及机器学习和深度学习训练提供真实值。在MRS成像（MRSI）中，合成数据必须捕捉空间变化的解剖结构、场不均匀性、干扰信号以及测量效应。2024年MRSI数据处理与定量挑战合成数据集是一款模拟的3T脑FID-MRSI资源，带有真实值代谢物图谱，被开发为用于MRSI处理和定量方法的受控测试平台。针对特定受试者的模拟使用了人类连接组项目受试者的解剖图像和场图。组织掩模、量子力学模拟的代谢物基函数、体内衍生的大分子组分、Bloch模拟的WET后残留水、配准的体内脂质信号、光谱基线、依赖于B0的频移、Voigt线形变化以及复高斯噪声被整合到正向模型中。水和脂质信号在高分辨率网格上合成，并经傅里叶截断至最终回波平面光谱成像网格，以模拟有限的空间点扩散函数。该资源包含24个训练数据集和8个测试数据集，涵盖受污染的FID-MRSI数据、解剖图像、B0图、元数据以及真实值代谢物和组分信号。正向模型参数已记录，包括组织特异性代谢物浓度和弛豫时间、大分子振幅比、水模型、噪声以及场不均匀性范围。该合成基准支持MRSI处理、干扰信号去除和代谢物定量方法的开发与比较，同时实现方法评估，表明当难以或无法获取真实值时，特征化的合成数据可如何支持严格评估。
英文摘要
Synthetic data is central to magnetic resonance spectroscopy method development because they provide ground truths for software validation, reproducible benchmarking, and machine- and deep-learning training. In MRSI, synthetic data must capture spatially varying anatomy, field inhomogeneity, nuisance signals, and measurement effects. The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset is a simulated 3T brain FID-MRSI resource with ground-truth metabolite maps developed as a controlled testbed for MRSI processing and quantification methods. Subject-specific simulations used anatomical images and field maps from Human Connectome Project subjects. Tissue masks, quantum-mechanically simulated metabolite basis functions, in vivo-derived macromolecular components, Bloch-simulated post-WET residual water, registered in vivo lipid signals, spectral baseline, $B_0$-dependent frequency shifts, Voigt lineshape variations, and complex Gaussian noise were combined in a forward model. Water and lipid signals were synthesized on a high-resolution grid and Fourier-truncated to the final echo-planar spectroscopic imaging grid to model the finite spatial point spread function. This resource has 24 training and 8 testing datasets containing contaminated FID-MRSI data, anatomical images, $B_0$ maps, metadata, and ground-truth metabolite and component signals. Forward-model parameters are documented, including tissue-specific metabolite concentrations and relaxation times, macromolecular amplitude ratios, the water model, noise, and field-inhomogeneity ranges. This synthetic benchmark supports development and comparison of MRSI processing, nuisance-signal removal, and metabolite-quantification methods, while enabling method evaluation. It illustrates how characterized synthetic data can support rigorous evaluation when ground truth is difficult or impossible to obtain.
Comments21 pages, 9 figures, 9 tables
