Gradient Response Harvesting for Continuous System Characterization During MR Sequences

梯度分析 校准 计算机科学 图像质量 波形 领域(数学) 形态梯度 图像渐变 物理 数学 人工智能 图像处理 图像(数学) 电信 机器学习 彩色图像 排序 量子力学 二值图像 纯数学 雷达
作者
Bertram J. Wilm,Benjamin E. Dietrich,Jonas Reber,S. Johanna Vannesjo,Klaas P. Pruessmann
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:39 (3): 806-815 被引量:10
标识
DOI:10.1109/tmi.2019.2936107
摘要

MRI gradient systems are required to generate magnetic field gradient waveforms with very high fidelity. This is commonly implemented by gradient system calibration and pre-emphasis. However, a number of mechanisms, particularly thermal changes, cause variation in the gradient response over time, which cannot be addressed by calibration approaches. To overcome this limitation, we present a novel method termed gradient response harvesting, where the gradient response is continuously characterized during the course of a normal MR sequence. Snippets of field measurements are repeatedly acquired during an MR sequence, and from these multiple field measurements and the known nominal MR sequence gradients, the gradient response and gradient/field offsets are calculated. The calculation is implemented in a model-based and a model-free variant. The method is demonstrated for EPI with high gradient duty-cycle, where the continuous gradient characterization is used to obtain k-space trajectory estimates that are employed in the subsequent image reconstruction. During the course of the MR sequence, changes in both the envelope and the phase of the gradient response functions were observed, including shifts of mechanical resonances. The gradient response changes were also reflected in the calculated uninterrupted gradient waveforms and thus in the k-space trajectories. Using the updated encoding information in the image reconstruction removed ghosting artifacts, that otherwise impaired the image quality. We introduced the concept of gradient response harvesting and demonstrated its feasibility. The obtained gradient response functions may be used for quality assurance/preventive maintenance, real-time adaptation of gradient pre-emphasis or to calculate uninterrupted gradient field evolutions.
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