Deep learning-based respiratory navigation for abdominal MRI

In Magnetic Resonance Imaging (MRI) of the abdomen, breathing motion
and cardiac motion are the main confounding factors introducing
artifacts and causing diminished image quality. Different strategies to
minimize the susceptibility to (breathing) motion-related artifacts have
been developed over the last decades, the most routinely used ones being
breath-held acquisitions, retrospective gating, and prospective
triggering. Breath-held techniques are sampling efficient but may not be
applicable in seriously ill patients and pediatric patients. In
addition, MRI techniques such as 3D high-resolution Magnetic Resonance
Cholangiopancreatography (MRCP) require parameter sets making it extremely
difficult to perform the exam in a single breath-hold or multi
breath-hold fashion. Under the assumption that breathing patterns are
stable and regular, triggered acquisition schemes aim to acquire data in
certain states/sectors of the breathing cycle, typically during the
relatively stable end-expiratory phase. This technique is less sampling
efficient and has additional challenges in irregular breathers.
The aim of this thesis is to analyze existing techniques for breathing
trigger point detection and breathing pattern analysis and to explore if
neural networks are suitable to derive optimal trigger points, adapt
triggering schemes to changing patient conditions, and investigate
whether breathing irregularities can be predicted from previous
breathing cycles.