A multisource adaptive magnetic resonance image fusion technique for versatile contrast magnetic resonance imaging

Zhang, Lei; Yin, Fang‐Fang; Moore, B; Han, Silu; Cai, Jing

doi:10.4103/ctm.ctm_21_18

Cited by 6 publications

(1 citation statement)

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“…More importantly, all methods above rely on continuous steady-state acquisitions for reference or template images, thus resulting in one single and fixed tissue contrast weighting (commonly T1-weighting) in the final real-time images. This contrast weighting is unsuitable for some tumor targets (Zhang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation

Han¹,

Chen²,

Xiao³

et al. 2022

Phys. Med. Biol.

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Objective. To develop and test the feasibility of a novel Single ProjectIon DrivEn Real-time Multi-contrast (SPIDERM) MR imaging technique that can generate real-time 3D images on-the-fly with flexible contrast weightings and a low latency. Approach. In SPIDERM, a “prep” scan is first performed, with sparse k-space sampling periodically interleaved with the central k-space line (navigator data), to learn a subject-specific model, incorporating a spatial subspace and a linear transformation between navigator data and subspace coordinates. A “live” scan is then performed by repeatedly acquiring the central k-space line only to dynamically determine subspace coordinates. With the “prep”-learned subspace and “live” coordinates, real-time 3D images are generated on-the-fly with computationally efficient matrix multiplication. When implemented based on a multi-contrast pulse sequence, SPIDERM further allows for data-driven image contrast regeneration to convert real-time contrast-varying images into contrast-frozen images at user’s discretion while maintaining motion states. Both digital phantom and in-vivo experiments were performed to evaluate the technical feasibility of SPIDERM. Main results. The elapsed time from the input of the central k-space line to the generation of real-time contrast-frozen 3D images was approximately 45 ms, permitting a latency of 55 ms or less. Motion displacement measured from SPIDERM and reference images showed excellent correlation (R^2≥0.983). Geometric variation from the ground truth in the digital phantom was acceptable as demonstrated by pancreas contour analysis (Dice≥0.84, mean surface distance≤0.95 mm). Quantitative image quality metrics showed good consistency between reference images and contrast-varying SPIDREM images in in-vivo studies (mean NMRSE=0.141, PSNR=30.12, SSIM=0.88). Significance. SPIDERM is capable of generating real-time multi-contrast 3D images with a low latency. An imaging framework based on SPIDERM has the potential to serve as a standalone package for MR guided radiation therapy by offering adaptive simulation through a “prep” scan and real-time image guidance through a “live” scan.

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Section: Introductionmentioning

confidence: 99%

Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation

Han¹,

Chen²,

Xiao³

et al. 2022

Phys. Med. Biol.

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Multi‐contrast four‐dimensional magnetic resonance imaging (MC‐4D‐MRI): Development and initial evaluation in liver tumor patients

Zhang

Yin

et al. 2021

Medical Physics

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To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. Methods and materials:The MC-4D-MRI technique integrates multiparametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image preprocessing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of -inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume. Results: MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1-w MRI, to -4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV interobserver mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1-w 4D-MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior-inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5 mm in SI, AP, and left-right (LR) directions. Conclusion:We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial 7984

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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

Xiao

Zhi

et al. 2022

Medical Physics

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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.

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A multisource adaptive magnetic resonance image fusion technique for versatile contrast magnetic resonance imaging

Cited by 6 publications

References 1 publication

Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation

Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation

Multi‐contrast four‐dimensional magnetic resonance imaging (MC‐4D‐MRI): Development and initial evaluation in liver tumor patients

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

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