Total Hip Replacement (THR) has become a common surgical procedure in recent years, as a result of increasing
aging population with osteoarthritis of the hip joint. Localization of the pelvic anatomical coordinate system
(PaCS) is a critical step in accurate placement of the femur prosthesis in the acetabulum in THR. Intra-operative
ultrasound (US) imaging can provide a radiation-free navigation system for localization of the PaCS. However,
US images are noisy and cannot provide any anatomical information beneath the bone surface due to the total
reflection of US beam at the bone-soft tissue interface. A solution to this problem is to fuse intra-operative
US with pre-operative imaging or a statistical shape model (SSM) of the pelvis. Here, we propose a multi-slice
to volume intensity-based registration of the pelvic SSM to a sparse set of 2D US images in order to localize
the PaCS in the US. In this registration technique, a set of 2D slices are extracted from a pelvic SSM using
the approximate location and orientation of their corresponding 2D US images. During the registration, the
comparison between the SSM slices and the US images is made using an ultrasound simulation technique and
a correlation-based similarity metric. We demonstrate the feasibility of our proposed approach in localizing
the PaCS on five patient-based phantoms. These results indicate the necessity of including pubic symphysis
landmarks in the 2D US slices in order to have a precise estimation of the PaCS.
Identifying the proper orientation of the pelvis is a critical step in accurate placement of the femur prosthesis in the
acetabulum in Total Hip Replacement (THR) surgeries. The general approach to localize the orientation of the pelvis
coordinate system is to use X-ray fluoroscopy to guide the procedure. An alternative can be employing intra-operative
ultrasound (US) imaging with pre-operative CT scan or fluoroscopy imaging. In this paper, we propose to replace the
need of pre-operative imaging by using a statistical shape model of the pelvis, constructed from several CT images. We
then propose an automatic deformable intensity-based registration of the anatomical atlas to a sparse set of 2D
ultrasound images of the pelvis in order to localize its anatomical coordinate system. In this registration technique, we
first extract a set of 2D slices from a single instance of the pelvic atlas. Each individual 2D slice is generated based on
the location of a corresponding 2D ultrasound image. Next, we create simulated ultrasound images out of the 2D atlas
slices and calculate a similarity metric between the simulated images and the actual ultrasound images. The similarity
metric guides an optimizer to generate an instance of the atlas that best matches the ultrasound data. We demonstrated
the feasibility of our proposed approach on two male human cadaver data. The registration was able to localize a
patient-specific pelvic coordinate system with origin translation error of 2 mm and 3.45 mm, and average axes rotation
error of 3.5 degrees and 3.9 degrees for the two cadavers, respectively.
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