|Delaram Behnami graduated from the Biomedical Engineering Program with a Master of Applied Science in November 2016. Below is a short summary of her thesis that she successfully defended in October 2016.|
Joint multimodal registration of medical images to a statistical model of the lumbar spine for guiding anesthesia
Facet joint injections and epidural needle insertions are widely used for spine anesthesia. Needle guidance is usually performed by fluoroscopy or palpation, resulting in radiation exposure and multiple needle re-insertions. Several ultrasound (US)-based guidance approaches have been proposed to eliminate such issues. However, they have not widely accepted in clinics due to difficulties in interpretation of the complex spinal anatomy in US, which leads to clinicians’ lack of confidence in relying only on information derived from US for needle guidance. In this thesis, a model-based multi-modal joint registration framework is introduced, where a statistical model of the lumbar spine (shape+pose or shape+pose+scale) is concurrently registered to intraprocedure US and easy-to-interpret preprocedure images. The goal is to take advantage of the complementary features visible in US and preprocedure images, namely Computed Topography (CT) and Magnetic Resonance (MR) scans. The underlying assumption is that the shape and size of the spine of a given subject are common amongst all imaging modalities. However, the pose of the spine depends on the patient’s position during acquisition of different imaging modalities. We successfully validated the proposed method on two datasets: (i) 10 pairs of US and CT scans and (ii) nine US and MR images of the lumbar spine. It was shown the joint registration allows for more accurate augmentation of important anatomical landmarks in both intraprocedure and preprocedure modalities. Furthermore, observing the patient-specific model in preprocedure domains allows the clinicians to assess the local registration accuracy qualitatively. This can help increase their confidence in using the US model for deriving needle guidance decisions.