School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Bionics Research Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea.
Med Phys. 2024 Jun;51(6):4143-4157. doi: 10.1002/mp.17066. Epub 2024 Apr 10.
Reducing Magnetic resonance imaging (MRI) scan time has been an important issue for clinical applications. In order to reduce MRI scan time, imaging acceleration was made possible by undersampling k-space data. This is achieved by leveraging additional spatial information from multiple, independent receiver coils, thereby reducing the number of sampled k-space lines.
The aim of this study is to develop a deep-learning method for parallel imaging with a reduced number of auto-calibration signals (ACS) lines in noisy environments.
A cycle interpolator network is developed for robust reconstruction of parallel MRI with a small number of ACS lines in noisy environments. The network estimates missing (unsampled) lines of each coil data, and these estimated missing lines are then utilized to re-estimate the sampled k-space lines. In addition, a slice aware reconstruction technique is developed for noise-robust reconstruction while reducing the number of ACS lines. We conducted an evaluation study using retrospectively subsampled data obtained from three healthy volunteers at 3T MRI, involving three different slice thicknesses (1.5, 3.0, and 4.5 mm) and three different image contrasts (T1w, T2w, and FLAIR).
Despite the challenges posed by substantial noise in cases with a limited number of ACS lines and thinner slices, the slice aware cycle interpolator network reconstructs the enhanced parallel images. It outperforms RAKI, effectively eliminating aliasing artifacts. Moreover, the proposed network outperforms GRAPPA and demonstrates the ability to successfully reconstruct brain images even under severe noisy conditions.
The slice aware cycle interpolator network has the potential to improve reconstruction accuracy for a reduced number of ACS lines in noisy environments.
减少磁共振成像（MRI）扫描时间一直是临床应用中的一个重要问题。为了减少 MRI 扫描时间，可以通过欠采样 k 空间数据来实现成像加速。这是通过利用来自多个独立接收线圈的额外空间信息来实现的，从而减少了采样的 k 空间线的数量。
本研究旨在开发一种在噪声环境下使用较少自动校准信号（ACS）线的并行成像深度学习方法。
为了在噪声环境下使用较少的 ACS 线进行稳健的并行 MRI 重建，我们开发了一种循环内插网络。该网络估计每个线圈数据中缺失（未采样）的线，并利用这些估计的缺失线重新估计采样的 k 空间线。此外，还开发了一种基于切片的重建技术，用于在减少 ACS 线数量的同时进行噪声稳健重建。我们在 3T MRI 上对三名健康志愿者的回顾性亚采样数据进行了评估研究，涉及三种不同的切片厚度（1.5、3.0 和 4.5 毫米）和三种不同的图像对比度（T1w、T2w 和 FLAIR）。
尽管在 ACS 线数量有限和切片较薄的情况下存在大量噪声的挑战，但基于切片的循环内插网络仍能重建增强的并行图像。它优于 RAKI，有效地消除了混叠伪影。此外，该网络还优于 GRAPPA，并且即使在严重噪声的情况下，也能够成功重建大脑图像。
基于切片的循环内插网络有可能提高噪声环境下使用较少 ACS 线的重建准确性。
