We present a parallel implementation of a statistical shape model registration to 3D ultrasound images of the
lumbar vertebrae (L2-L4). Covariance Matrix Adaptation Evolution Strategy optimization technique, along
with Linear Correlation of Linear Combination similarity metric have been used, to improve the robustness and
capture range of the registration approach. Instantiation and ultrasound simulation have been implemented on
a graphics processing unit for a faster registration. Phantom studies show a mean target registration error of 3.2
mm, while 80% of all the cases yield target registration error of below 3.5 mm.
3D ultrasound (US) to computed tomography (CT) registration is a topic of significant interest because it
can potentially improve many minimally invasive procedures such as laparoscopic partial nephrectomy. Partial
nephrectomy patients often receive preoperative CT angiography, which helps define the important structures
of the kidney such as the vasculature. Intraoperatively, dynamic real-time imaging information can be captured
using ultrasound and compared with the preoperative data. Providing accurate registration between the two
modalities would enhance navigation and guidance for the surgeon. However, one of the major problems of
developing and evaluating registration techniques is obtaining sufficiently accurate and realistic phantom data
especially for soft tissue. We present a detailed procedure for constructing tissue phantoms using porcine kidneys,
which incorporates contrast agent into the tissue such that the kidneys appear representative of in vivo human CT
angiography. These phantoms are also imaged with US and resemble US images from human patients. We then
perform registration on corresponding CT and US datasets using a simulation-based algorithm. The method
simulates an US image from the CT, generating an intermediate modality that resembles ultrasound. This
simulated US is then registered to the original US dataset. Embedded fiducial markers provide a gold standard
for registration. Being able to test our registration method on realistic datasets facilitates the development of
novel CT to US registration techniques such that we can generate an effective method for human studies.
We developed an algorithm for tracking prostate motion during MRI-guided prostatic needle placement, with the
primary application in prostate biopsy. Our algorithm has been tested on simulated patient and phantom data. The
algorithm features a robust automatic restart and a 12-core biopsy error validation scheme. Simulation tests were
performed on four patient MRI pre-operative volumes. Three orthogonal slices were extracted from the pre-operative
volume to simulate the intra-operative volume and a volume of interest was defined to isolate the prostate. Phantom tests
used six datasets, each representing the phantom at a known perturbed position. These volumes were registered to their
corresponding reference volume (the phantom at its home position). Convergence tests on the phantom data showed that
the algorithm demonstrated accurate results at 100% confidence level for initial misalignments of less than 5mm and at
73% confidence level for initial misalignments less than 10mm. Our algorithm converged in 95% of the cases for the
simulated patient data with 0.66mm error and the six phantom registration tests resulted in 1.64mm error.
An ultrasound (US) guided, CT augmented, spine needle insertion navigational system is introduced. The
system consists of an electromagnetic (EM) sensor, an US machine, and a preoperative CT volume of the patient
anatomy. Three-dimensional (3D) US volume is reconstructed intraoperatively from a set of two-dimensional
(2D) freehand US slices, and is coregistered with the preoperative CT. This allows the preoperative CT volume to
be used in the intraoperative clinical coordinate. The spatial relationship between the patient anatomy, surgical
tools, and the US transducer are tracked using the EM sensor, and are displayed with respect to the CT volume.
The pose of the US transducer is used to interpolate the CT volume, providing the physician with a 2D "x-ray
vision" to guide the needle insertion. Many of the system software components are GPU-accelerated, allowing
real-time performance of the guidance system in a clinical setting.
Automatic registration of ultrasound (US) to computed tomography (CT) datasets is a challenge of considerable interest,
particularly in orthopaedic and percutaneous interventions. We propose an algorithm for group-wise volume-to-volume
registration of US to CT images of the lumbar spine. Each vertebra in CT is treated as a sub-volume and transformed
individually. The sub-volumes are then reconstructed into a single volume. The algorithm dynamically combines
simulated US reflections from the vertebrae surfaces and surrounding soft tissue in the reconstructed CT, with scaled CT
data to simulate US images of the spine anatomy. The simulated US data is used to register preoperative CT data to
intra-operative US images. Covariance Matrix Adaption - Evolution Strategy (CMA-ES) is utilized as the optimization
strategy. The registration is tested using a phantom of the lumbar spine (L3-L5). Initial misalignments of up to 8 mm
were registered with a mean target registration error of 1.87±0.73 mm for L3, 2.79±0.93 mm for L4, 1.72±0.70 mm for
L5, and 2.08±0.55 mm across the entire volume. To select an appropriate optimization strategy, we performed a volume-to-
volume registration of US to CT of the lumbar spine, allowing no relative motion between vertebrae. We compare the
results of this registration using three optimization strategies: simplex, gradient descent and CMA-ES. CMA-ES was
found to converge slower than gradient descent and simplex, but was more robust for rigid volume-to-volume
registration for initial misalignments up to 20 mm.
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