A super‐resolution framework for the reconstruction of T2‐weighted (T2w) time‐resolved (TR) 4DMRI using T1w TR‐4DMRI as the guidance. Issue 7 (18th April 2020) - Nie, Xingyu; Saleh, Ziad; Kadbi, Mo; Zakian, Kristen; Deasy, Joseph; Rimner, Andreas; Li, Guang
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 mm 3 ) volumetric breath‐hold (BH, 20s) three‐dimensional magnetic resonance imaging (3DMRI) image to a series of low‐resolution (LR: 5 × 5 × 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 × 2 × 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 5 min. Minor binning artifacts in the RC‐4DMRI were automatically identified based on voxel intensity correlation (VIC) between consecutive slices as outliers (VIC < VIC mean ‐σ) 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 (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 T2 HR ‐T1 HR 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 ([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.