Liebeskind Alexander, Schüre Jan Rüdiger, Fabian Moritz Simon, Weinmüller Simon, Schünke Patrick, Golkov Vladimir, Cremers Daniel, Zaiss Moritz
Computer Vision Group, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching bei München, Germany.
Munich Center for Machine Learning (MCML), Oettingenstraße 67, 80538, Munich, Germany.
MAGMA. 2025 Mar 27. doi: 10.1007/s10334-025-01242-6.
Despite prevalent use of chemical exchange saturation transfer (CEST) MRI, standardization remains elusive. Imaging depends heavily on parameters dictating radiofrequency (RF) events, gradients, and apparent diffusion coefficient (ADC). We present the Pulseq-CEST Library, a repository of CEST preparation and simulation definitions, including example data and evaluations, that provides a common basis for reproducible research, rapid prototyping, and in silico deep learning training data generation.
A Pulseq-CEST experiment requires (i) a CEST preparation sequence, (ii) a Bloch-McConnell parameter set, (iii) a Bloch-McConnell simulation, and (iv) an evaluation script. Pulseq-CEST utilizes the Bloch-McConnell equations to model in vitro and in vivo conditions. Using this model, a candidate sequence or environment can be held constant while varying other inputs, enabling robust testing.
Data were compared for amide proton transfer weighted (APTw) and water shift and B1 (WASABI) protocols using a five-tube phantom and simulated environments. Real and simulated data matched anticipated spectral shapes and local peak characteristics. The Pulseq-CEST Library supports similar experiments with common sequences and environments to assess new protocols and sample data.
The Pulseq-CEST Library provides a flexible mechanism for standardizing and prototyping CEST sequences, facilitating collaborative development. With the capability for expansion, including open-source incorporation of new sequences and environments, the library accelerates the invention and spread of novel CEST and other saturation transfer approaches, such as relayed NOEs (rNOEs) and semisolid magnetization transfer contrast (MTC) methods.
尽管化学交换饱和转移（CEST）磁共振成像（MRI）已被广泛应用，但标准化问题仍然难以解决。成像在很大程度上取决于决定射频（RF）事件、梯度和表观扩散系数（ADC）的参数。我们展示了Pulseq-CEST库，这是一个CEST制备和模拟定义的存储库，包括示例数据和评估，为可重复研究、快速原型制作和计算机深度学习训练数据生成提供了一个共同基础。
一个Pulseq-CEST实验需要（i）一个CEST制备序列，（ii）一个Bloch-McConnell参数集，（iii）一个Bloch-McConnell模拟，以及（iv）一个评估脚本。Pulseq-CEST利用Bloch-McConnell方程对体外和体内条件进行建模。使用该模型，可以在保持候选序列或环境不变的同时改变其他输入，从而实现可靠的测试。
使用五管模型和模拟环境对酰胺质子转移加权（APTw）以及水位移和B1（WASABI）协议的数据进行了比较。真实数据和模拟数据与预期的光谱形状和局部峰值特征相匹配。Pulseq-CEST库支持使用常见序列和环境进行类似实验，以评估新协议和样本数据。
Pulseq-CEST库为标准化和制作CEST序列原型提供了一种灵活机制，促进了协作开发。由于具有扩展能力，包括开源纳入新序列和环境，该库加速了新型CEST及其他饱和转移方法（如中继核Overhauser效应（rNOE）和半固体磁化传递对比（MTC）方法）的发明和传播。
