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

ABSTRACT

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 × 2 × 2 mm3 ) volumetric breath-hold (BH, 20s) three-dimensional magnetic resonance imaging (3DMRI) image to a series of low-resolution (LR 5 × 5 × 5 mm3 ) 3D cine images at a 2Hz frame rate in free-breathing (FB, 40 s) using an enhanced Demons algorithm, namely [T1BH →FB] reconstruction. Within the same imaging session, respiratory-correlated (RC) T2w 4DMRI (2 × 2 × 2 mm3 ) was acquired based on an internal navigator to gain HR T2w (T2HR ) in three states (full exhalation and mid and full inhalation) in ~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 -σ) and corrected by deforming the artifact slices to interpolated slices from the adjacent slices iteratively until no outliers were identified. A T2HR image with minimal deformation (<1 cm at the diaphragm) from the T1BH image was selected for multi-modal B-Spline deformable image registration (DIR) to establish the T2HR -T1BH voxel correspondence. Two approaches to reconstruct T2w TR-4DMRI were investigated (A) T2HR →[T1BH →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) [T2HR ←T1BH ]→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 (ϕ = 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 T2HR -T1HR DIR is achieved ∆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 ∆COM = 2.9 ± 0.6 mm. However, method B ([T2HR ←T1BH ]→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.