A super-resolution framework for the reconstruction of T2-weighted (T2w) time-resolved (TR) 4DMRI using T1w TR-4DMRI as the guidance.

Purpose The purpose of this study was to develop T2-weighted (T2w) time-resolved (TR) four-dimensional magnetic resonance imaging (4DMRI) reconstruction technique with higher soft-tissue contrast for multiple breathing cycle motion assessment by building a super-resolution (SR) framework using the T1w TR-4DMRI reconstruction as guidance. Methods The multi-breath T1w TR-4DMRI was reconstructed by deforming a high-resolution (HR: 2 x 2 x 2 mm(3)) volumetric breath-hold (BH, 20s) three-dimensional magnetic resonance imaging (3DMRI) image to a series of low-resolution (LR: 5 x 5 x 5 mm(3)) 3D cine images at a 2Hz frame rate in free-breathing (FB, 40 s) using an enhanced Demons algorithm, namely [T1(BH)-> FB] reconstruction. Within the same imaging session, respiratory-correlated (RC) T2w 4DMRI (2 x 2 x 2 mm(3)) was acquired based on an internal navigator to gain HR T2w (T2(HR)) in three states (full exhalation and mid and full inhalation) in similar to 5 min. Minor binning artifacts in the RC-4DMRI were automatically identified based on voxel intensity correlation (VIC) between consecutive slices as outliers (VIC < VICmean-sigma) and corrected by deforming the artifact slices to interpolated slices from the adjacent slices iteratively until no outliers were identified. A T2(HR) image with minimal deformation (<1 cm at the diaphragm) from the T1(BH) image was selected for multi-modal B-Spline deformable image registration (DIR) to establish the T2(HR)-T1(BH) voxel correspondence. Two approaches to reconstruct T2w TR-4DMRI were investigated: (A) T2(HR)->[T1(BH)-> FB]: to deform T2w HR to T1w BH only as T1w TR-4DMRI was reconstructed, and combine the two displacement vector fields (DVFs) to reconstruct T2w TR-4DMRI, and (B) [T2(HR)<- T1(BH)]-> FB: to deform T1w BH to T2w HR first and apply the deformed T1w BH to reconstruct T2w TR-4DMRI. The reconstruction times were similar, 8-12 min per volume. To validate the two methods, T2w- and T1w-mapped 4D XCAT digital phantoms were utilized with three synthetic spherical tumors (phi = 2.0, 3.0, and 4.0 cm) in the lower or mid lobes as the ground truth to evaluate the tumor location (the center of mass, COM), size (volume ratio, %V), and shape (Dice index). Six lung cancer patients were scanned under an IRB-approved protocol and the T2w TR-4DMRI images reconstructed from the two methods were compared based on the preservation of the three tumor characteristics. The local tumor-contained image quality was also characterized using the VIC and structure similarity (SSIM) indexes. Results In the 4D digital phantom, excellent tumor alignment after T2(HR)-T1(HR) DIR is achieved: increment COM = 0.8 +/- 0.5 mm, %V = 1.06 +/- 0.02, and Dice = 0.91 +/- 0.03, in both deformation directions using the DIR-target image as the reference. In patients, binning artifacts are corrected with improved image quality: average VIC increases from 0.92 +/- 0.03 to 0.95 +/- 0.01. Both T2w TR-4DMRI reconstruction methods produce similar tumor alignment errors increment COM = 2.9 +/- 0.6 mm. However, method B ([T2(HR)<- T1(BH)]-> FB) produces superior results in preserving more T2w tumor features with a higher %V = 0.99 +/- 0.03, Dice = 0.81 +/- 0.06, VIC = 0.85 +/- 0.06, and SSIM = 0.65 +/- 0.10 in the T2w TR-4DMRI images. Conclusions This study has demonstrated the feasibility of T2w TR-4DMRI reconstruction with high soft-tissue contrast and adequately-preserved tumor position, size, and shape in multiple breathing cycles. The T2w-centric DIR (method B) produces a superior solution for the SR-based framework of T2w TR-4DMRI reconstruction with highly preserved tumor characteristics and local image features, which are useful for tumor delineation and motion management in radiation therapy.