2024-12-23-krusen24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Real-time MR-based 3D motion monitoring using raw k-space data	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	krusen24a	0	Real-time MR-based 3D motion monitoring using raw k-space data	768	781	768-781	768	false	Krusen, Marius and Ernst, Floris	given family Marius Krusen given family Floris Ernst	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/krusen24a/krusen24a.pdf