Jang-Yeon Park | Searcharxiv

arXiv2023

Three-Dimensional Variable Slab-Selective Projection Acquisition Imaging

Three-dimensional (3D) projection acquisition (PA) imaging has recently gained attention because of its advantages, such as achievability of very short echo time, less sensitivity to motion, and undersampled acquisition of projections without sacrificing spatial resolution. However, larger subjects require a stronger Nyquist criterion and are more likely to be affected by outer-volume signals outside the field of view (FOV), which significantly degrades the image quality. Here, we proposed a variable slab-selective projection acquisition (VSS-PA) method to mitigate the Nyquist criterion and effectively suppress aliasing streak artifacts in 3D PA imaging. The proposed method involves maintaining the vertical orientation of the slab-selective gradient for frequency-selective spin excitation and the readout gradient for data acquisition. As VSS-PA can selectively excite spins only in the width of the desired FOV in the projection direction during data acquisition, the effective size of the scanned object that determines the Nyquist criterion can be reduced. Additionally, unwanted signals originating from outside the FOV (e.g., aliasing streak artifacts) can be effectively avoided. The mitigation of the Nyquist criterion owing to VSS-PA was theoretically described and confirmed through numerical simulations and phantom and human lung experiments. These experiments further showed that the aliasing streak artifacts were nearly suppressed.

physics.med-ph↗

arXiv2019

k-Space Deep Learning for Reference-free EPI Ghost Correction

Nyquist ghost artifacts in EPI are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the non-linear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k-space interpolation problem that can be solved using structured low-rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k-space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non-accelerated EPI acquisitions. To take advantage of the even and odd-phase directional redundancy, the k-space data is divided into two channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi-coil k-space data into additional input channels. Then, our k-space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k-space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k-space data from both ghost and subsampling. Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster.The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.

cs.CV↗