Real-time MR-based 3D motion monitoring using raw k-space data

Marius Krusen, Floris Ernst

Keywords: MR-guided radiotherapy, motion estimation, real-time MRI, recurrent neural network, XCAT phantom, MR-linac, undersampling

Abstract:

Due to its great soft-tissue contrast and non-invasive nature, magnetic resonance imaging (MRI) is uniquely qualified for motion monitoring during radiotherapy.
However, real-time capabilities are limited by its long acquisition times, particularly in 3D, and require highly undersampling k-space resulting in lower image resolution and image artifacts.
In this paper, we propose a simple recurrent neural network (RNN) architecture to continually estimate target motion from single k-space spokes.
By directly using the incoming k-space data, additional image reconstruction steps are avoided and less data is required between estimations achieving a latency of only a few milliseconds.
The 4D XCAT phantom was used to generate realistic data of the abdomen affected by respiratory and cardiac motion and a simulated lesion inserted into the liver acted as the target.
We show that using a Kooshball trajectory to sample 3D k-space gives superior results compared to a stack-of-stars (SoS) trajectory.
The RNN quickly learns the motion pattern and can give new motion estimations at a frequency of more than 230 Hz, demonstrating the feasibility of drastically improving latency of MR-based motion monitoring systems.

Submission Number: 257