用多谱段数据提升核磁波谱成像灵敏度估计精度
Estimating Sensitivity Maps for X-Nuclei Magnetic Resonance Spectroscopic Imaging
- 通过最小二乘法整合多谱段、多时相数据估算线圈灵敏度
- 在人体脑、胰腺、心脏成像中信噪比提升显著
- 适合高极化代谢物成像,尤其灵敏度分布不均场景
本研究旨在对未在视野内均匀分布的X-核进行磁共振波谱成像时估计线圈灵敏度。提出一种基于最小二乘法的L2最优方法,每个体素的灵敏度由多个谱段、动态成像时相或频域激发下的多组测量共同估计。该方法与常用参考峰值(RefPeak)法对比,后者仅使用能量最高的谱段。在数值模拟中,L2最优法更准确;在以高极化丙酮酸为对比剂的脑、胰腺、心脏成像中,该方法实现了更高的信噪比。其优势在于从测量数据中提取了更多有效信息,提升了灵敏度估计的鲁棒性。
原文摘要 · Abstract (English)
The purpose of this research is to estimate sensitivity maps when imaging X-nuclei that may not have a significant presence throughout the field of view. We propose to estimate the coil's sensitivities by solving a least-squares problem where each row corresponds to an individual estimate of the sensitivity for a given voxel. Multiple estimates come from the multiple bins of the spectrum with spectroscopy, multiple times with dynamic imaging, or multiple frequencies when utilizing spectral excitation. The method presented in this manuscript, called the L2 optimal method, is compared to the commonly used RefPeak method which uses the spectral bin with the highest energy to estimate the sensitivity maps. The L2 optimal method yields more accurate sensitivity maps when imaging a numerical phantom and is shown to yield a higher signal-to-noise ratio when imaging the brain, pancreas, and heart with hyperpolarized pyruvate as the contrast agent with hyperpolarized MRI. The L2 optimal method is able to better estimate the sensitivity by extracting more information from the measurements.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。