Characterization of small liver lesions using a partial volume corrected T2 mapping technique

Motivation: T2 mapping is an effective way to classify benign and malignant lesions within the liver, but existing methods that do not account for partial volume systematically underestimate lesion T2, leading to false positives. Goal(s): To develop a signal model that accounts for partial volume effects to more accurately quantify T2 of liver lesions. Approach: Use a two-compartment signal model tailored for turbo spin-echo acquisitions taking into account the varying lesion fraction across the imaging slice. Results: The proposed method leads to more accurate T2 estimation in the presence of partial volume as demonstrated in simulations, physical phantoms, and subjects with focal liver lesions. Impact: This work presents a turbo spin-echo signal model that accounts for partial volume effects to estimate T2 values of focal liver lesions more accurately. The proposed method improves the accuracy of classifying benign and malignant liver lesions.