3D ultrasound-CT registration in orthopaedic trauma using GMM registration with optimized particle simulation-based data reduction

Three-Dimensional Registration of Freehand-Tracked Ultrasound to CT Images of the Talocrural Joint

A rigid surface⁻volume registration scheme is presented in this study to register computed tomography (CT) and free-hand tracked ultrasound (US) images of the talocrural joint. Prior to registration, bone surfaces expected to be visible in US are extracted from the CT volume and bone contours in 2D US data are enhanced based on monogenic signal representation of 2D US images. A 3D monogenic signal data is reconstructed from the 2D data using the position of the US probe recorded with an optical tracking system. When registering the surface extracted from the CT scan to the monogenic signal feature volume, six transformation parameters are estimated so as to optimize the sum of monogenic signal features over the transformed surface. The robustness of the registration algorithm was tested on a dataset collected from 12 cadaveric ankles. The proposed method was used in a clinical case study to investigate the potential of US imaging for pre-operative planning of arthroscopic access to talar (osteo)chondral defects (OCDs). The results suggest that registrations with a registration error of 2 mm and less is achievable, and US has the potential to be used in assessment of an OCD' arthroscopic accessibility, given the fact that 51% of the talar surface could be visualized.

Fast and automatic bone segmentation and registration of 3D ultrasound to CT for the full pelvic anatomy: a comparative study

PURPOSE

Ultrasound (US) is a safer alternative to X-rays for bone imaging, and its popularity for orthopedic surgical navigation is growing. Routine use of intraoperative US for navigation requires fast, accurate and automatic alignment of tracked US to preoperative computed tomography (CT) patient models. Our group previously investigated image segmentation and registration to align untracked US to CT of only the partial pelvic anatomy. In this paper, we extend this to study the performance of these previously published techniques over the full pelvis in a tracked framework, to characterize their suitability in more realistic scenarios, along with an additional simplified segmentation method and similarity metric for registration.

METHOD

We evaluated phase symmetry segmentation, and Gaussian mixture model (GMM) and coherent point drift (CPD) registration methods on a pelvic phantom augmented with human soft tissue images. Additionally, we proposed and evaluated a simplified 3D bone segmentation algorithm we call Shadow-Peak (SP), which uses acoustic shadowing and peak intensities to detect bone surfaces. We paired this with a registration pipeline that optimizes the normalized cross-correlation (NCC) between distance maps of the segmented US-CT images.

RESULTS

SP segmentation combined with the proposed NCC registration successfully aligned tracked US volumes to the preoperative CT model in all trials, in contrast to the other techniques. SP with NCC achieved a median target registration error (TRE) of 2.44 mm (maximum 4.06 mm), when imaging all three anterior pelvic structures, and a mean runtime of 27.3 s. SP segmentation with CPD registration was the next most accurate combination: median TRE of 3.19 mm (maximum 6.07 mm), though a much faster runtime of 4.2 s.

CONCLUSION

We demonstrate an accurate, automatic image processing pipeline for intraoperative alignment of US-CT over the full pelvis and compare its performance with the state-of-the-art methods. The proposed methods are amenable to clinical implementation due to their high accuracy on realistic data and acceptably low runtimes.

Automatic bone detection and soft tissue aware ultrasound-CT registration for computer-aided orthopedic surgery

PURPOSE

The transfer of preoperative CT data into the tracking system coordinates within an operating room is of high interest for computer-aided orthopedic surgery. In this work, we introduce a solution for intra-operative ultrasound-CT registration of bones.

METHODS

We have developed methods for fully automatic real-time bone detection in ultrasound images and global automatic registration to CT. The bone detection algorithm uses a novel bone-specific feature descriptor and was thoroughly evaluated on both in-vivo and ex-vivo data. A global optimization strategy aligns the bone surface, followed by a soft tissue aware intensity-based registration to provide higher local registration accuracy.

RESULTS

We evaluated the system on femur, tibia and fibula anatomy in a cadaver study with human legs, where magnetically tracked bone markers were implanted to yield ground truth information. An overall median system error of 3.7 mm was achieved on 11 datasets.

CONCLUSION

Global and fully automatic registration of bones aquired with ultrasound to CT is feasible, with bone detection and tracking operating in real time for immediate feedback to the surgeon.