Research

4D MRI reconstruction

4D volumetric MRI has shown clear advantages in radiotherapy for soft-tissue tumor delineation and mobile target localization/tracking with zero ionization radiation hazard. However, 4D volumetric MRI has not been fully established in current clinical practice, mostly due to the physical constraints that lead to the challenging task of achieving high temporal resolution imaging while maintaining adequate spatial resolution. The popular retrospective sorting approach used in 4D-MRI generation leads to k-space under-sampling, and causes aliasing artifacts in the resulting MR images. This project proposes to develop a simultaneous k-space-driven motion estimation and compensation (SK-MEC) method for 4D-MRI reconstruction. SK-MEC explores the feasibility of combining k-space-driven inter-respiratory-phase motion estimation with motion-compensated reconstruction for 4D-MRI reconstruction from under-sampled k-space data. By motion-compensated reconstruction, SK-MEC uses the entire k-space dataset and a motion model to reconstruct a reference (respiratory) phase MRI, which substantially reduces the aliasing artifacts from under-sampling. By k-space-driven motion-estimation, SK-MEC directly solves the motion fields between the reference (respiratory) phase and the other (respiratory) phases through k-space data matching, thus eliminating the uncertainties from solving motion by registering two reconstructed MRIs of varying image quality and incoherent aliasing artifacts. Our preliminary study on digitally simulated k-space data has yielded encouraging results for the proposed technique. In this project we propose to develop and optimize the SK-MEC method using raw k-space data of phantoms and in vivo data from clinical/research Philips MRI scanners. We will initiate the study using raw k-space data of static phantoms (without motion), to evaluate the general integrity of our reconstruction algorithm. After the initial step, we will acquire mobile phantom raw k-space data using the same scanner, to evaluate, optimize and test the performance of the proposed SK-MEC algorithm to generate 4D-MRI images. We will evaluate different k-space sparsity strategy, and quantify the acceleration potential and the accuracy of SK-MEC in 4D-MRI acquisition. We will evaluate and choose a corresponding pulse-sequence that works best with our algorithm, with potential modifications and developments to currently-available pulse sequences. In the end, an in-vivo dataset will be used to further optimize, validate, and evaluate the SK-MEC algorithm. The successful completion of the study will pave the way for a further MRI-guided radiation therapy study which aims to reduce the treatment damages to normal liver tissues, and improve the efficacy of liver tumor treatment.

Figure 1. Reconstructed image comparison between different techniques, using k-space data simulated from available MR images. Phase X: respiratory phase X. X spokes: the number (X) of spokes of each respiratory phase used in 4D-MRI reconstruction. A golden-angle, radial sampling strategy was employed. NUFFT: non-uniform fast Fourier transform algorithm. CG-TV: conjugate-gradient algorithm with total variation regularization. XD-GRASP: eXtra dimensional golden-angle radial sparse parallel algorithm, a published advanced reconstruction technique for 4D-MRI. The SK-MEC technique offers superior image quality compared to the other techniques, even for severely under-sampled cases (5 spokes per respiratory phase).

  • Y. Zhang, Z. Iqbal, C. Shen, C. Wang, S. Jiang, J. Wang, “ Dynamic MRI Reconstruction Using Simultaneous K-Space-Driven Motion Estimation and Compensation (SK-MEC) ”, AAPM Annual Meeting, 2019