SEP/BA/MA/IDP: Patient Position Detection using Machine Learning Techniques

Supervision by: Christian Wachinger, Tobias Blum
Advisor: Prof. Dr. Nassir Navab
Due date:

Abstract

Although magnetic resonance imaging is considered to be non-invasive, there is at least one effect on the patient which has to be monitored: The heating which is generated by absorbed radio frequency (RF) power. It is described using the specific absorption rate (SAR). In order to obey legal limits for these SAR values, the scanner's duty cycle has to be adjusted. The limiting factor depends on the patient's position with respect to the scanner. Detection of this position allows a better adjustment of the RF power resulting in an improved scan performance and image quality.

In this thesis, different machine learning techniques have to be researched and evaluated. This may include PCA, ICA, neural networks, Haar features, ... but the student is also encouraged to propose own approaches. This thesis would perfect for students who are interested in medical imaging and machine learning. Previous knowledge in those domains is helpful but not mandatory.

Resources

Please take a look at our project page SAR Optimization.

Literature

	C. Wachinger, D. Mateus, A. Keil, N. Navab Manifold Learning for Patient Position Detection in MRI IEEE International Symposium on Biomedical Imaging (ISBI), Rotterdam, The Netherlands, April 14 - 17 2010. (bib)
	A. Keil, C. Wachinger, G. Brinker, S. Thesen, N. Navab Patient Position Detection for SAR Optimization in Magnetic Resonance Imaging Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI 2006), Copenhagen, Denmark, October 2006. The original publication is available online at www.springerlink.com. (bib)