  Purpose: Long scan time in phase encoding for forming complete K-space
matrices is a critical drawback of MRI, making patients uncomfortable and
wasting important time for diagnosing emergent diseases. This paper aims to
reducing the scan time by actively and sequentially selecting partial phases in
a short time so that a slice can be accurately reconstructed from the resultant
slice-specific incomplete K-space matrix. Methods: A transformer based deep
reinforcement learning framework is proposed for actively determining a
sequence of partial phases according to reconstruction-quality based Q-value (a
function of reward), where the reward is the improvement degree of
reconstructed image quality. The Q-value is efficiently predicted from binary
phase-indicator vectors, incomplete K-space matrices and their corresponding
undersampled images with a light-weight transformer so that the sequential
information of phases and global relationship in images can be used. The
inverse Fourier transform is employed for efficiently computing the
undersampled images and hence gaining the rewards of selecting phases. Results:
Experimental results on the fastMRI dataset with original K-space data
accessible demonstrate the efficiency and accuracy superiorities of proposed
method. Compared with the state-of-the-art reinforcement learning based method
proposed by Pineda et al., the proposed method is roughly 150 times faster and
achieves significant improvement in reconstruction accuracy. Conclusions: We
have proposed a light-weight transformer based deep reinforcement learning
framework for generating high-quality slice-specific trajectory consisting of a
small number of phases. The proposed method, called TITLE (Transformer Involved
Trajectory LEarning), has remarkable superiority in phase-encode selection
efficiency and image reconstruction accuracy.
