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ABSTRACT: 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.
Proc SPIE 02/2010; 7625(8):76251A.
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ABSTRACT: 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.
Proc SPIE 02/2010; 7625(2010):762517-762518.
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ABSTRACT: Prostate cancer is a major health threat for men. For over five years, the U.S. National Cancer Institute has performed prostate biopsies with a magnetic resonance imaging (MRI)-guided robotic system.
A retrospective evaluation methodology and analysis of the clinical accuracy of this system is reported.
Using the pre and post-needle insertion image volumes, a registration algorithm that contains a two-step rigid registration followed by a deformable refinement was developed to capture prostate dislocation during the procedure. The method was validated by using three-dimensional contour overlays of the segmented prostates and the registrations were accurate up to 2 mm.
It was found that tissue deformation was less of a factor than organ displacement. Out of the 82 biopsies from 21 patients, the mean target displacement, needle placement error, and clinical biopsy error was 5.9 mm, 2.3 mm, and 4 mm, respectively.
The results suggest that motion compensation for organ displacement should be used to improve targeting accuracy.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2010; 13(Pt 3):383-91.
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ABSTRACT: 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.
Proc SPIE 01/2010; 7625:76252V.
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ABSTRACT: Image-guided percutaneous (through the skin) needle-based surgery has become part of routine clinical practice in performing procedures such as biopsies, injections and therapeutic implants. A novice physician typically performs needle interventions under the supervision of a senior physician; a slow and inherently subjective training process that lacks objective, quantitative assessment of the surgical skill and performance. Shortening the learning curve and increasing procedural consistency are important factors in assuring high-quality medical care.
This paper describes a laboratory validation system, called Perk Station, for standardized training and performance measurement under different assistance techniques for needle-based surgical guidance systems. The initial goal of the Perk Station is to assess and compare different techniques: 2D image overlay, biplane laser guide, laser protractor and conventional freehand. The main focus of this manuscript is the planning and guidance software system developed on the 3D Slicer platform, a free, open source software package designed for visualization and analysis of medical image data.
The prototype Perk Station has been successfully developed, the associated needle insertion phantoms were built, and the graphical user interface was fully implemented. The system was inaugurated in undergraduate teaching and a wide array of outreach activities. Initial results, experiences, ongoing activities and future plans are reported.
Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society 07/2009; 34(1):19-32. · 1.04 Impact Factor
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ABSTRACT: 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.
Proc SPIE 03/2009; 7259:72594A.
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ABSTRACT: Infective keratitis is a major sight-threatening condition in developing countries like India. An early diagnosis of infective keratitis is critical to its treatment. Epidemiological trends, morphological features of corneal ulceration and presence of other risk factors often dictate choice of initial treatment. This work assesses the usefulness of classification of infective keratitis by artificial neural network (ANN).
Forty input variables from each of the sixty-three known bacterial or fungal ulcers provided the basis for training a three layer feed-forward neural network. The trained neural network classified another set of forty-three corneal ulcers.
Trained artificial neural network could classify correctly all sixty-three cornea ulcers in the training set. In the test set, the artificial neural network correctly classified 39 out of 43 cornea ulcers. Specificity for bacterial and fungal categories was 76.47% and 100% respectively. Accuracy of classification by neural network was 90.7% and compared significantly better than clinicians' prediction of 62.8% (p < 0.01).
ANN has the potential to help clinicians classify corneal ulcers more accurately.
Current Eye Research 09/2003; 27(2):111-6. · 1.28 Impact Factor
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ABSTRACT: Ultrasound has become a very important modality in image-guided therapy. At present, however, the collection, synchronization and transfer of ultrasonic images are more cumbersome than necessary. This paper presents a reusable solution to these problems. We propose a software package called SynchroGrab, which allows the collection of interventional ultrasound images as well as their synchronization with a stream of pose measurements. The software includes support for an open-interface ultrasound system, namely the Sonix RP, from Ultrasonix (Vancouver, Canada). Using an open-interface system like the Sonix RP allows customization of the imaging process and the capture of the ultrasound images directly from memory without the need for a frame-grabbing card. Pose measurement is currently performed with an Optotrak Certus by Northern Digital (Waterloo, Canada). However, extensibility was a primary goal in the design of this software, so the support of new devices can be achieved simply by sub-classing the relevant base class. SynchroGrab also performs reconstruction of 3D ultrasound volumes from synchronized data streams. Moreover, the recorded images, volumes and tracking information are available for visualization or further processing either directly from the file system or from a network connection compliant with the OpenIGTLink protocol, which is supported by Slicer 3. The authors wish to thank the Natural Sciences and Engineering Research Council (NSERC), the Canadian Institutes of Health Research (CIHR), and the National Alliance in Medical Image Computing (NIH 5U54EB005149-03) for funding this project.
