IEEE Trans Med Imaging. 2013 Oct;32(10):1853-63. doi: 10.1109/TMI.2013.2266259. Epub 2013 Jun 4.
Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool capable of providing spatially localized maps of metabolite concentrations. Its utility, however, is often depreciated by spectral leakage artifacts resulting from low spatial resolution measurements through an effort to reduce acquisition times. Though model-based techniques can help circumvent these drawbacks, they require strong prior knowledge, and can introduce additional artifacts when the underlying models are inaccurate. We introduce a novel scheme in which a generative model is estimated from the raw MRSI data via a regularized variational framework that minimizes the model approximation error within a measurement-prescribed subspace. As additional a priori information, our approach relies only upon a measured field inhomogeneity map at high spatial resolution. We demonstrate the feasibility of our approach on both synthetic and experimental data.
磁共振波谱成像（MRSI）是一种强大的工具，能够提供代谢物浓度的空间定位图谱。然而，由于通过降低采集时间来实现低空间分辨率测量，其会产生光谱泄漏伪影，从而降低其效用。虽然基于模型的技术可以帮助克服这些缺点，但它们需要很强的先验知识，并且当基础模型不准确时，会引入额外的伪影。我们引入了一种新的方案，该方案通过正则化变分框架从原始 MRSI 数据中估计生成模型，该框架通过在测量规定的子空间内最小化模型逼近误差来实现。作为附加的先验信息，我们的方法仅依赖于高空间分辨率下测量的磁场不均匀性图。我们在合成数据和实验数据上证明了我们方法的可行性。
