Seeing sleep: real-time MRI methods for the evaluation of sleep apnea

Magnetic Resonance Imaging (MRI) is a non‐invasive medical imaging technique that can visualize static anatomical structures as well as dynamic changes in arbitrary orientations. This dissertation is focused on one particular application of MRI—imaging the upper airway dynamics for the evaluation of sleep apnea. It targets the three biggest challenges faced by this particular application: slow acquisition speed, loud acoustic noise, and high financial cost. ❧ First, I introduce a real‐time imaging method that I developed for the upper airway. It can significantly reduce acquisition time by up to 33 times than conventional 2DFT imaging. It combines several acceleration techniques: simultaneous multi‐slice excitation, non‐Cartesian (radial) sampling trajectory, parallel imaging, and compressed sensing reconstruction. The saved acquisition time is used for better spatial coverage, spatial and temporal resolutions that are needed for accurate airway collapsibility measurements. Important clinical findings enabled by the proposed approach are also discussed. ❧ Second, I describe the design and implementation of a golden‐angle based radial sampling strategy that supports anisotropic FOV. The benefit of this method increases with FOV asymmetry and can reduce 33% scan time when the major‐to‐minor ratio is 2:1 for elliptical FOV. Phantom and in vivo cardiac experiments have confirmed the effect of reduced streaking artifacts compared to conventional GA sampling. ❧ Next, I present the evaluation and improvement of an independent linear model for gradient‐induced MRI acoustic noise. The results show that the errors are less than 3% for all physical gradient axes. A new method that synchronizes measured acoustic impulse response for the three axes have reduced prediction error from 32% to 4% when all gradients are on simultaneously. The gradient‐sound transfer functions are found to be highly dependent on body habitus and location beyond 1 KHz. Implications for reducing acoustic noise based on the model are discussed. ❧ Finally, I illustrate the framework for simulating lower‐field acquisitions based on data acquired at a higher field. To date, standard clinical MRI (1.5/3 T) has proven to be cost‐prohibitive for many applications, including imaging sleep apnea, to benefit large‐scale population. The simulation framework has demonstrated that comparable diagnostic values can be achieved at B₀ as low as 0.2 T with the methods we used for imaging sleep apnea. ❧ A classic description of the principles of MRI, as well as the summary and future remarks about this dissertation are also presented.