arXiv:2611.13056v1 Announce Type: new Abstract: For self-navigation and self-gating in MRI, self-navigators are extracted from the complex values of the k-space center, called DC-signal, through a temporal frequency analysis. However, this signal often carries artifacts. This paper introduces a Self-Navigator Intelligent Filter (SNIF) that models the physical origin of the k-space center artifacts to properly estimate them and thus more accurately remove them. The artifact affecting the DC-signal $S_{DC}$ generally has two main origins: the jitter of the k-space center (''off-center artifact'') and the cumulative history of the spins (''spin-history artifact''). Both add noise-like artifacts to the signal. The off-center effect can be modeled as a perturbative signal $S_0$ and the spin-history effect as a convolution kernel $h$ so that the measured DC-signal is $S_{DC} = S_m + S_0 \star h$, where $S_m$ is the motion-related component which effectively contains the expected self-navigators. Methods to compute $h$ and $S_0$ were developed, which gives the possibility to extract $S_m$ from $S_{DC}$. They were implemented and evaluated on thoracic UTE datasets acquired on a healthy volunteer. Qualitative visual comparison and quantitative spectral analysis of the DC-signal before and after SNIF demonstrate that the method improves both the accuracy and the precision of the extracted respiratory and cardiac self-navigators. By considering the physical origin of the DC-signal artifacts, SNIF makes both respiratory and cardiac self-navigators more sensitive to motion, which supports finer MRI motion tracking.