Spine needle injections are widely applied to alleviate pain and to remove nerve sensation through anesthesia. Current treatment is performed either blindly having no image guidance or using fluoroscopy or computed tomography (CT). Both CT and fluoroscopy guidance expose patients to ionizing radiation. Alternatively, ultrasound (US) guidance for spine needle procedures is becoming more prevalent since US is a non-ionizing and more accessible image modality. An inherent challenge to US imaging of the spine is the acoustic shadows created by the bony structures of the vertebra limiting visibility. It is challenging to use US as the sole imaging modality for intraoperative guidance of spine needle injections. However, it is possible to enhance the anatomical information through a preoperative diagnostic CT. To achieve this, image registration between the CT and the US images is proposed in this thesis. Image registration integrates the anatomical information from the CT with the US images. The aligned CT augments anatomical visualization for the clinician during spinal interventions. To align the preoperative CT and intraoperative US, a novel registration pipeline is presented that involves automatic global and multi-vertebrae registration. The registration pipeline is composed of two distinct phases: preoperative and intraoperative. Preoperatively, artificial spring points are selected between adjacent vertebrae. Intraoperatively, the lumbar spine is first aligned between the CT and US followed by a multi-vertebrae registration. The artificial springs are used to constrain the movement of the individually transformed vertebrae to ensure the optimal alignment is a pose of the lumbar spine that is physically possible. Validation of the algorithm is performed on five clinical patient datasets. A protocol for US data collection was created to eliminate variability in the quality of acquired US images. The registration pipeline was able to register the datasets from initial misalignments of up to 25 mm with a mean TRE of 1.17 mm. From these results, it is evident that the proposed registration pipeline offers a robust registration between clinical CT and US data. / Thesis (Master, Computing) -- Queen's University, 2013-08-19 12:50:54.521