Temporal interleaving artifacts in spiral MRSI: characterization and retrospective correction

Purpose: In Spiral Magnetic Resonance Spectroscopic Imaging (MRSI), achieving suitable spatio-temporal resolutions requires interleaving. Sampling inconsistencies occurring between temporally interleaved signals, -whether due to time-interleaved ADCs or phase/frequency mismatches in the excitation and demodulation stages-, result in artifacts in the recombined data. This paper proposes a mathematical description of these unavoidable artifacts and a postprocessing solution to circumvent the issues and mitigate them. Methods: The study proposed a detailed description and explanation of these artifacts using data acquired with MRSI interleaved sequence with gradients off, as well as standard interleaved FID sequence. The random signal mismatch between the interleaves was described as additional artifactual signals $ψ$(t) at each temporal interleave. A model was further proposed to match the acquired signal in relation to the ideal, artifact-free signal. Considering M interleaves, a correction method was derived relying on estimating 4 parameters in the $ψ$(t) model for each interleave by minimizing a multivariable cost function. This correction method was evaluated on spectra acquired from phantoms and in vivo acquisitions obtained in healthy volunteers. Results: The proposed correction method significantly reduced artifacts by a factor of 4 in the phantom and 1.8 in the healthy volunteers. This correction ensured accurate spectral quantification in regions where artifacts overlapped with the content of interest, such as lipids in subcutaneous fat. Additionally, it was demonstrated that appropriately selecting the number of temporal interleaves can shift the artifact away from the frequency of interest, leveraging the periodic and limited frequency span of the observed artifacts. Conclusion: In spiral-MRSI, temporal interleaves, while enhancing spectral bandwidth, introduced spurious content characterized by distinctive resonance frequencies and nonreproducible amplitudes and phases. The study proposes two solutions: manipulating the number of interleaves to control artifact frequency localization or using a retrospective correction method to approximate and attenuate artifacts.

Topological and Statistical Behavior Classifiers for Tracking Applications

We introduce the first unified theory for target tracking using Multiple Hypothesis Tracking, Topological Data Analysis, and machine learning. Our string of innovations are 1) robust topological features are used to encode behavioral information, 2) statistical models are fitted to distributions over these topological features, and 3) the target type classification methods of Wigren and Bar Shalom et al. are employed to exploit the resulting likelihoods for topological features inside of the tracking procedure. To demonstrate the efficacy of our approach, we test our procedure on synthetic vehicular data generated by the Simulation of Urban Mobility package.