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.
We report a quantitative evaluation of the clinical accuracy of a MRI-guided robotic prostate biopsy system that has
been in use for over five years at the U.S. National Cancer Institute. A two-step rigid volume registration using mutual
information between the pre and post needle insertion images was performed. Contour overlays of the prostate before
and after registration were used to validate the registration. A total of 20 biopsies from 5 patients were evaluated. The
maximum registration error was 2 mm. The mean biopsy target displacement, needle placement error, and biopsy error
was 5.4 mm, 2.2 mm, and 5.1 mm respectively. The results show that the pre-planned biopsy target did dislocate during
the procedure and therefore causing biopsy errors.
Multi-parametric MRI is a new imaging modality superior in quality to Ultrasound (US) which is currently used in
standard prostate biopsy procedures. Surface-based registration of the pre-operative and intra-operative prostate volumes
is a simple alternative to side-step the challenges involved with deformable registration. However, segmentation errors
inevitably introduced during prostate contouring spoil the registration and biopsy targeting accuracies. For the crucial
purpose of validating this procedure, we introduce a fully interactive and customizable simulator which determines the
resulting targeting errors of simulated registrations between prostate volumes given user-provided parameters for organ
deformation, segmentation, and targeting. We present the workflow executed by the simulator in detail and discuss the
parameters involved. We also present a segmentation error introduction algorithm, based on polar curves and natural
cubic spline interpolation, which introduces statistically realistic contouring errors. One simulation, including all I/O and
preparation for rendering, takes approximately 1 minute and 40 seconds to complete on a system with 3 GB of RAM and
four Intel Core 2 Quad CPUs each with a speed of 2.40 GHz. Preliminary results of our simulation suggest the maximum
tolerable segmentation error given the presence of a 5.0 mm wide small tumor is between 4-5 mm. We intend to validate
these results via clinical trials as part of our ongoing work.
With MRI possibly becoming a modality of choice for detection and staging of prostate cancer, fast and accurate
outlining of the prostate is required in the volume of clinical interest. We present a semi-automatic algorithm that uses a
priori knowledge of prostate shape to arrive at the final prostate contour. The contour of one slice is then used as initial
estimate in the neighboring slices. Thus we propagate the contour in 3D through steps of refinement in each slice. The
algorithm makes only minimum assumptions about the prostate shape. A statistical shape model of prostate contour in
polar transform space is employed to narrow search space. Further, shape guidance is implicitly imposed by allowing
only plausible edge orientations using template matching. The algorithm does not require region-homogeneity,
discriminative edge force, or any particular edge profile. Likewise, it makes no assumption on the imaging coils and
pulse sequences used and it is robust to the patient's pose (supine, prone, etc.). The contour method was validated using
expert segmentation on clinical MRI data. We recorded a mean absolute distance of 2.0 ± 0.6 mm and dice similarity
coefficient of 0.93 ± 0.3 in midsection. The algorithm takes about 1 second per slice.
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