Preprocessing for intravoxel incoherent motion analysis in the brain - Signal drift correction

Abstract

Diffusion magnetic resonance imaging (dMRI) is a diagnostic imaging technique that is sensitized to the movement of water molecules in tissues. Intravoxel incoherent motion (IVIM) analysis can be used to estimate blood flow in capillaries from weakly diffusion-weighted data. dMRI images can be impacted by confounding factors and are usually preprocessed to improve their quality. This study reviewed the preprocessing pipeline for dMRI in relation to IVIM analysis in the brain. The results showed that the necessary preprocessing steps for IVIM analysis can be challenging due to a lack of consensus in this field, and may vary depending on the data and circumstances. While various tools for preprocessing dMRI exist, most of these tools are designed for datasets that have stronger diffusion-weighted data and more diffusion-encoding gradient directions than those typically used in IVIM. The results showed that only a limited number of these preprocessing tools can be directly applied to IVIM datasets. Based on observerations in previously acquired data, an in-depth analysis of the effect of signal drift on IVIM data was also investigated, and two correction methods were evaluated: temporal correction and spatio-temporal correction. Results from the signal drift study showed that the temporal and the spatio-temporal correction methods can reduce or amplify the effects of signal drift in IVIM data. This may indicate that additional methods may be needed to fully correct for this issue and obtain reliable results from IVIM imaging. In conclusion, it is crucial to have a thorough understanding of the data and desired results in order to accurately correct data.