Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel.
Siemens Healthcare Ltd., Rosh Ha'ayeen, Israel.
Magn Reson Med. 2023 Sep;90(3):839-851. doi: 10.1002/mrm.29688. Epub 2023 May 8.
Conventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time.
We implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T , which was used to guide the selection of sequence parameters in real time.
Computer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T for n-acetyl-aspartate by a factor of 2.5.
Adaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.
常规序列本质上是静态的，提前固定测量参数，以预期广泛的预期组织参数值。我们着手设计和基准测试一种新的、个性化的方法 - 称为自适应 MR - 其中传入的主体数据用于实时更新和微调脉冲序列参数。
我们为估计 T s 实现了一种自适应的实时多回波（MTE）实验。我们的方法将贝叶斯框架与基于模型的重建相结合。它维护并不断更新所需组织参数的先验分布，包括 T ，用于实时指导序列参数的选择。
计算机模拟预测自适应多回波序列相对于静态序列的加速比为 1.7 至 3.3 倍。这些预测在幻影实验中得到了证实。在健康志愿者中，我们的自适应框架将 n-乙酰天冬氨酸的 T 测量加速了 2.5 倍。
实时改变激发的自适应脉冲序列可以大大减少采集时间。鉴于我们提出的框架的通用性，我们的结果促使对其他基于自适应模型的 MRI 和 MRS 方法进行进一步研究。
