Slice-stacking T2-weighted MRI for fast determination of internal target volume for liver tumor.

Abstract

To investigate the feasibility of generating maximum intensity projection (MIP) images to determine internal target volume (ITV) using slice-stacking MRI (SS-MRI) technique. Slice-stacking is a technique which applies a multi-slice MRI acquisition to generate a 3D MIP for ITV contouring, without reconstructing 4D-MRI. 4D digital extended cardiac-torso (XCAT) phantom was used to generate MIP images with sequential 2D HASTE sequence, with different tumor diameters (10, 30 and 50 mm) and with simulated regular and irregular (patient) breathing motions. A reference MIP was generated using all acquisition images. Consecutive repetitions were then used to generate MIP to analyze the relationship between Dice's similarity coefficient (DSC) and the number of repetitions, and the relationship between the relative ITV volume difference and the number of repetitions. Images from XCAT phantom and from three hepatic carcinoma patients were collected in this study to demonstrate the feasibility of this technique. For both regular and irregular breathing motion, the average DSC of ITV is >0.94 and the average relative ITV volume difference is <10% (approximately 0.15 cm3) when using 5 repeated scanning images to reconstruct MIP for tumor diameter of 10 mm. As tumor diameter increases, the DSC of ITV is >0.97 and the relative ITV volume difference is <5% for regular breathing motion, and the DSC of ITV is >0.97 and the relative ITV volume difference is <5.5% for irregular breathing motion when using 5 repeated scanning images to reconstruct MIP. In patient image study, the mean relative ITV volume difference is <3% and the mean DSC is 0.99 when using 5 repeated scanning images to reconstruct MIP. The number of scans required to generate tumor ITV for slice-stacking method (5-7 repetition) is 3-4 times less than that of 4D-MRI (15-20 repetitions). It is feasible to generate a fast clinically acceptable ITV using slice-stacking method with sequential 2D MR images.