A dual-supervised deformation estimation model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy

Abstract

Background: Most available four-dimensional (4D)-magnetic resonance imaging (MRI) techniques are limited by insufficient image quality and long acquisition times or require specially designed sequences or hardware that are not available in the clinic. These limitations have greatly hindered the clinical implementation of 4D-MRI.

Purpose: This study aims to develop a fast ultra-quality (UQ) 4D-MRI reconstruction method using a commercially available 4D-MRI sequence and dual-supervised deformation estimation model (DDEM).

Methods: Thirty-nine patients receiving radiotherapy for liver tumors were included. Each patient was scanned using a time-resolved imaging with interleaved stochastic trajectories (TWIST)-lumetric interpolated breath-hold examination (VIBE) MRI sequence to acquire 4D-magnetic resonance (MR) images. They also received 3D T1-/T2-weighted MRI scans as prior images, and UQ 4D-MRI at any instant was considered a deformation of them. A DDEM was developed to obtain a 4D deformable vector field (DVF) from 4D-MRI data, and the prior images were deformed using this 4D-DVF to generate UQ 4D-MR images. The registration accuracies of the DDEM, VoxelMorph (normalized cross-correlation [NCC] supervised), VoxelMorph (end-to-end point error [EPE] supervised), and the parametric total variation (pTV) algorithm were compared. Tumor motion on UQ 4D-MRI was evaluated quantitatively using region of interest (ROI) tracking errors, while image quality was evaluated using the contrast-to-noise ratio (CNR), lung-liver edge sharpness, and perceptual blur metric (PBM).

Results: The registration accuracy of the DDEM was significantly better than those of VoxelMorph (NCC supervised), VoxelMorph (EPE supervised), and the pTV algorithm (all, p < 0.001), with an inference time of 69.3 ± 5.9 ms. UQ 4D-MRI yielded ROI tracking errors of 0.79 ± 0.65, 0.50 ± 0.55, and 0.51 ± 0.58 mm in the superior-inferior, anterior-posterior, and mid-lateral directions, respectively. From the original 4D-MRI to UQ 4D-MRI, the CNR increased from 7.25 ± 4.89 to 18.86 ± 15.81; the lung-liver edge full-width-at-half-maximum decreased from 8.22 ± 3.17 to 3.65 ± 1.66 mm in the in-plane direction and from 8.79 ± 2.78 to 5.04 ± 1.67 mm in the cross-plane direction, and the PBM decreased from 0.68 ± 0.07 to 0.38 ± 0.01.

Conclusion: This novel DDEM method successfully generated UQ 4D-MR images based on a commercial 4D-MRI sequence. It shows great promise for improving liver tumor motion management during radiation therapy.

Keywords: 4D-MRI; deep learning; deformable image registration; motion management.

Figure 1:

The overall study design of the proposed UQ 4D-MRI technique. (a) The original 4D-MRI frames whose respiratory phases were closest to that of the 3D-MR images were selected, and Elastix was used to fine-tune the residual mismatching. (b) A 4D-DVF between the selected frames and other frames was obtained via DDEM and applied to the aligned 3D-MR image to reconstruct the UQ 4D-MR images using corresponding frames. 4D: four-dimensional; DDEM: dual-supervised deformation estimation model; MRI: magnetic resonance imaging; DVF: displacement vector field UQ: ultra-quality.

Figure 2:

Architecture of the proposed DDEM. The moving volume (V_m), fixed volume (V_f), and map of differences map between these volumes (V_d) were input to the network. The output consisted of a predicted deformation (DVF_p) and warped volume (V_w). The training was dually supervised by the reference deformation (DVF_r) and V_f. 3D: three-dimensional; Conv: convolution; EPE: end-to-end point error; DVF: displacement vector field; NCC: normalized cross correlation.

Figure 3:

Reconstruction of an image from a sample patient using 6 consecutive frames. (a, b) Original 4D-MR (top), UQ T1w 4D-MR (mid), and UQ T2w 4D-MR images (bottom) of the patient in sagittal and axial views, respectively. Arrows indicate the tumor in the first frame. The images represent 3D volumes; only 2D slices are shown here for demonstration. (c) The tumor motion trajectory of this patient in the SI, AP, and ML directions. UQ 4D-MR images show good matching of tumor motion with the original 4D-MR data. 4D-MRI: four-dimensional magnetic resonance imaging; AP: anterior-posterior; ML: mid-lateral; SI: superior-inferior; T1w: T1-weighted; T2w: T2-weighted; UQ: ultra-quality.

Figure 4:

Statistical analysis of the patient data. (a, b) Relative ROI motion error of the UQ T1w and T2w 4D-MR images in the SI, AP, and ML directions, respectively. (c) CNR of the original 4D-MR and reconstructed UQ T1w and T2w 4D-MR images. (d, e) Lung–liver edge FWHM of the original 4D-MR and reconstructed UQ T1w and T2w 4D-MR images in the in-plane and cross-plane directions, respectively. (f) PBM of the original 4D-MR and reconstructed UQ T1w and T2w 4D-MR images. *: P-value < 0.05; **: P-value < 0.001; 4D-MRI: four-dimensional magnetic resonance imaging; AP: anterior-posterior; CNR: contrast-to-noise ratio; FWHM: full width at half maximum; ML: mid-lateral; PMB: perceptual blur metric; ROI: region of interest; SI: superior-inferior; T1w: T1-weighted; T2w: T2-weighted; UQ: ultra-quality.