LAUSR

Homodyne filtering is a standard preprocessing step in the estimation of SWI. Unfortunately, SWI is not quantitative, and QSM cannot be accurately estimated from filtered phase images. Compared with gradient‐echo sequences suitable for computing QSM, SWI is more readily available and is often the only susceptibility‐sensitive sequence acquired in the clinical setting. In this project, we aimed to quantify susceptibility from the homodyne‐filtered phase (HFP), acquired for computing susceptibility‐weighted images, using convolutional neural networks to solve the compounded problem of (1) computing the solution to the inverse dipole problem, and (2) compensating for the effects of the homodyne filtering.