PURPOSE: T2 mapping provides a quantitative approach for focal liver lesion characterization. For small lesions, a biexponential model should be used to account for partial volume effects (PVE). However, conventional biexponential fitting suffers from large uncertainty of the fitted parameters when noise is present. The purpose of this work is to develop a more robust method to correct for PVE affecting small lesions. METHODS: We developed a region of interest-based joint biexponential fitting (JBF) algorithm to estimate the T2 of lesions affected by PVE. JBF takes advantage of the lesion fraction variation among voxels within a region of interest. JBF is compared to conventional approaches using Cramér-Rao lower bound analysis, numerical simulations, phantom, and in vivo data. RESULTS: JBF provides more accurate and precise T2 estimates in the presence of PVE. Furthermore, JBF is less sensitive to region of interest drawing. Phantom and in vivo results show that JBF can be combined with a reconstruction method for highly undersampled data, enabling the characterization of small abdominal lesions from data acquired in a single breath hold. CONCLUSION: The JBF algorithm provides more accurate and stable T2 estimates for small structures than conventional techniques when PVE is present. It should be particularly useful for the characterization of small abdominal lesions.
PURPOSE:T2 mapping provides a quantitative approach for focal liver lesion characterization. For small lesions, a biexponential model should be used to account for partial volume effects (PVE). However, conventional biexponential fitting suffers from large uncertainty of the fitted parameters when noise is present. The purpose of this work is to develop a more robust method to correct for PVE affecting small lesions. METHODS: We developed a region of interest-based joint biexponential fitting (JBF) algorithm to estimate the T2 of lesions affected by PVE. JBF takes advantage of the lesion fraction variation among voxels within a region of interest. JBF is compared to conventional approaches using Cramér-Rao lower bound analysis, numerical simulations, phantom, and in vivo data. RESULTS: JBF provides more accurate and precise T2 estimates in the presence of PVE. Furthermore, JBF is less sensitive to region of interest drawing. Phantom and in vivo results show that JBF can be combined with a reconstruction method for highly undersampled data, enabling the characterization of small abdominal lesions from data acquired in a single breath hold. CONCLUSION: The JBF algorithm provides more accurate and stable T2 estimates for small structures than conventional techniques when PVE is present. It should be particularly useful for the characterization of small abdominal lesions.
Authors: Maria I Altbach; Eric K Outwater; Theodore P Trouard; Elizabeth A Krupinski; Rebecca J Theilmann; Alison T Stopeck; Mitsuko Kono; Arthur F Gmitro Journal: J Magn Reson Imaging Date: 2002-08 Impact factor: 4.813
Authors: Young H Kim; Sanjay Saini; Michael A Blake; Mukesh Harisinghani; Yi-You Chiou; Won Jin Lee; Jeong-Sik Yu; Peter F Hahn Journal: J Comput Assist Tomogr Date: 2005 Sep-Oct Impact factor: 1.826