KEYWORDS: Kidney, Tumors, Image segmentation, Data modeling, Computed tomography, 3D scanning, Tumor growth modeling, 3D modeling, Surgery, Health sciences
CT-guided renal tumor ablations have been considered an alternative to treat small renal tumors, typically 4 cm in size or smaller, especially for patients who are ineligible to receive nephron-sparing surgery. For this procedure, the radiologist must compare the pre-operative with the post-operative CT to determine the presence of residual tumors. Distinguishing between malignant and benign kidney tumors poses a significant challenge. To automate this tumor coverage evaluation step and assist the radiologist in identifying kidney tumors, we proposed a coarse-to-fine U-Net-based model to segment kidneys and masses. We used the TotalSegmentator tool to obtain an approximate segmentation and region of interest of the kidneys, which was inputted into our 3D segmentation network trained using the nnUNet library to fully segment the kidneys and masses within them. Our model achieved an aggregated DICE score of 0.777 on testing data, and on local CT kidney data with tumors collected from the London Health Sciences University Hospital, our model achieved a DICE score of 0.7 for tumour segmentation. Our results indicate the model will be useful for tumour identification and evaluation.
Percutaneous thermal ablations are promising curative treatment techniques of focal liver tumors, particularly for those patients who are not eligible for surgical resection. Complete coverage of the targeted tumor by the thermal ablation zone and with a safety margin of 5-10 mm is required to ensure that complete tumor eradication will be achieved. 2D ultrasound (US) is a commonly used modality to guide this procedure; however, it has limitations in estimating the ablation tumor coverage due to the difficulty of evaluating tumor coverage using only one or multiple 2D US images. The use of intra-procedural 3D US is a promising approach to solve this unmet need. Although most of current approaches provide reformatted three orthogonal views to better evaluate the tumor coverage, comprehensive volumetric evaluation is rarely available. In this paper, for tumor-visible cases in US, we aim to investigate the ability of 3D US images to visualize the applicators and relevant surrounding structures, then assess the feasibility of evaluating the tumor coverage quantitatively using surface- and volume-based metrics. Using our previously developed 3D US liver ablation system, we collected 10 patients’ 3D US liver images in our clinical trial. The visibility of the applicator and relevant structures were assessed qualitatively. We then evaluated the surface error and volume accuracy of the tumor coverage. Results demonstrated that 3D US images allow visualization of the appropriate anatomical structures and applicators, and our volumetric evaluation can provide systematic knowledge of tumor coverage and an opportunity to correct the ablation applicator position intra-procedurally.
Image-guided percutaneous thermal ablations are promising techniques for the treatment of focal liver tumors. Conventionally, 2D ultrasound (US)-guidance is used extensively to assist liver tumor treatment. Recently, 3D US imaging has attracted much attention as its provided volumetric information can better help physicians interpret and localize liver structures. However, 3D US imaging still has the same limitation as conventional 2D US imaging in visualizing ultrasonically invisible cases. To address this issue, the current mainstream solution is to provide real-time 2D US-CT/MRI registered images by leveraging external tracking systems, such as electromagnetic (EM) or optical approaches. Due to their inevitable constraints, such as the presence of ferromagnetic structures in the case of EM tracking systems, or the line-of-sight limitation for optical systems, whether these solutions can be readily applied to the clinic has been under investigation. In this paper, we aim to investigate the feasibility of a 3D US-based ablation paradigm using our developed 2D/3D US/CT-guided liver ablation system. To achieve this goal and provide accurate guidance for ablation procedures, we proposed a local-and- global calibration method to track our mechatronic guidance arm. We also used a fiducial-based registration method to align 3D US with diagnostic CT images and implemented the re-slicing function to display the CT image corresponding to the US transducer’s pose. Results demonstrated the feasibility of our system to visualize the complementary information from multiple image modalities in real time. Our calibration method can provide accurate tracking with an unsigned error of 1.6 mm ± 0.4 mm. This work is a step towards providing a system to guide the liver ablation procedure, including cases with ultrasonically invisible or poorly visible tumors.
Prostatic adenocarcinoma is one of the most commonly occurring cancers among men in the world, and it also the most curable cancer when it is detected early. Multiparametric MRI (mpMRI) combines anatomic and functional prostate imaging techniques, which have been shown to produce high sensitivity and specificity in cancer localization, which is important in planning biopsies and focal therapies. However, in previous investigations, lesion localization was achieved mainly by manual segmentation, which is time-consuming and prone to observer variability. Here, we developed an algorithm based on locality alignment discriminant analysis (LADA) technique, which can be considered as a version of linear discriminant analysis (LDA) localized to patches in the feature space. Sensitivity, specificity and accuracy generated by the proposed algorithm in five prostates by LADA were 52.2%, 89.1% and 85.1% respectively, compared to 31.3%, 85.3% and 80.9% generated by LDA. The delineation accuracy attainable by this tool has a potential in increasing the cancer detection rate in biopsies and in minimizing collateral damage of surrounding tissues in focal therapies.
KEYWORDS: Image segmentation, Prostate, Magnetic resonance imaging, Error analysis, Bladder, Image processing algorithms and systems, Principal component analysis, 3D modeling, Cancer, Prostate cancer
Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre- and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328, and 393 s for manual, semiautomatic, and automatic segmentation respectively, while an average fully manual segmentation time of 564 s was recorded. The reduced measured postediting interoperator variability of semiautomatic and automatic segmentations compared to the manual approach indicates the potential of computer-assisted segmentation for generating a clinically acceptable segmentation faster with higher consistency. The lack of strong correlation between editing time and the values of typically used error metrics (ρ<0.5) implies that the necessary postsegmentation editing time needs to be measured directly in order to evaluate an algorithm’s suitability for clinical translation.
KEYWORDS: Ultrasonography, Magnetic resonance imaging, Prostate cancer, Principal component analysis, In vivo imaging, Cancer, Image fusion, Image registration, Receivers, Tissues, Data modeling, Tumor growth modeling
Recently, multi-parametric Magnetic Resonance Imaging (mp-MRI) has been used to improve the sensitivity of detecting high-risk prostate cancer (PCa). Prior to biopsy, primary and secondary cancer lesions are identified on mp-MRI. The lesions are then targeted using TRUS guidance. In this paper, for the first time, we present a fused mp-MRI-temporal-ultrasound framework for characterization of PCa, in vivo. Cancer classification results obtained using temporal ultrasound are fused with those achieved using consolidated mp-MRI maps determined by multiple observers. We verify the outcome of our study using histopathology following deformable registration of ultrasound and histology images. Fusion of temporal ultrasound and mp-MRI for characterization of the PCa results in an area under the receiver operating characteristic curve (AUC) of 0.86 for cancerous regions with Gleason scores (GSs)≥3+3, and AUC of 0.89 for those with GSs≥3+4.
KEYWORDS: Biopsy, Cancer, Prostate, Probability theory, Prostate cancer, Pathology, Magnetic resonance imaging, 3D acquisition, 3D image processing, Surveillance, Tumors, Monte Carlo methods, Principal component analysis, Error analysis
Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided "fusion" prostate biopsy aims to reduce the 21–47% false negative rate of clinical 2D TRUS-guided sextant biopsy, but still has a substantial false negative rate. This could be improved via biopsy needle target optimization, accounting for uncertainties due to guidance system errors, image registration errors, and irregular tumor shapes. As an initial step toward the broader goal of optimized prostate biopsy targeting, in this study we elucidated the impact of biopsy needle delivery error on the probability of obtaining a tumor sample, and on the core involvement. These are both important parameters to patient risk stratification and the decision for active surveillance vs. definitive therapy. We addressed these questions for cancer of all grades, and separately for high grade (≥ Gleason 4+3) cancer. We used expert-contoured gold-standard prostatectomy histology to simulate targeted biopsies using an isotropic Gaussian needle delivery error from 1 to 6 mm, and investigated the amount of cancer obtained in each biopsy core as determined by histology. Needle delivery error resulted in variability in core involvement that could influence treatment decisions; the presence or absence of cancer in 1/3 or more of each needle core can be attributed to a needle delivery error of 4 mm. However, our data showed that by making multiple biopsy attempts at selected tumor foci, we may increase the probability of correctly characterizing the extent and grade of the cancer.
KEYWORDS: Tumors, Biopsy, Error analysis, 3D acquisition, Prostate, Anisotropy, Magnetic resonance imaging, Principal component analysis, 3D image processing, Ultrasonography
Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided “fusion” prostate biopsy aims to reduce the 21–47% false negative rate of clinical 2D TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsy still has a substantial false negative rate. Therefore, we propose optimization of biopsy targeting to meet the clinician’s desired tumor sampling probability, optimizing needle targets within each tumor and accounting for uncertainties due to guidance system errors, image registration errors, and irregular tumor shapes. As a step toward this optimization, we obtained multiparametric MRI (mpMRI) and 3D TRUS images from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D surfaces that were registered to 3D TRUS. We estimated the probability, P, of obtaining a tumor sample with a single biopsy, and investigated the effects of systematic errors and anisotropy on P. Our experiments indicated that a biopsy system’s lateral and elevational errors have a much greater effect on sampling probabilities, relative to its axial error. We have also determined that for a system with RMS error of 3.5 mm, tumors of volume 1.9 cm3 and smaller may require more than one biopsy core to ensure 95% probability of a sample with 50% core involvement, and tumors 1.0 cm3 and smaller may require more than two cores.
Eli Gibson, Mena Gaed, Thomas Hrinivich, José Gómez, Madeleine Moussa, Cesare Romagnoli, Jonathan Mandel, Matthew Bastian-Jordan, Derek Cool, Suha Ghoul, Stephen Pautler, Joseph Chin, Cathie Crukley, Glenn Bauman, Aaron Fenster, Aaron Ward
KEYWORDS: Tumors, Tissues, Image registration, Cancer, Magnetic resonance imaging, Prostate cancer, Image fusion, In vivo imaging, 3D image reconstruction, 3D image processing
Purpose: Multiparametric magnetic resonance imaging (MPMRI) supports detection and staging of prostate cancer, but the image characteristics needed for tumor boundary delineation to support focal therapy have not been widely investigated. We quantified the detectability (image contrast between tumor and non-cancerous contralateral tissue) and the localizability (image contrast between tumor and non-cancerous neighboring tissue) of Gleason score 7 (GS7) peripheral zone (PZ) tumors on MPMRI using tumor contours mapped from histology using accurate 2D–3D registration.
Methods: MPMRI [comprising T2-weighted (T2W), dynamic-contrast-enhanced (DCE), apparent diffusion coefficient (ADC) and contrast transfer coefficient images] and post-prostatectomy digitized histology images were acquired for 6 subjects. Histology contouring and grading (approved by a genitourinary pathologist) identified 7 GS7 PZ tumors. Contours were mapped to MPMRI images using semi-automated registration algorithms (combined target registration error: 2 mm). For each focus, three measurements of mean ± standard deviation of image intensity were taken on each image: tumor tissue (mT±sT), non-cancerous PZ tissue < 5 mm from the tumor (mN±sN), and non-cancerous contralateral PZ tissue (mC±sC). Detectability [D, defined as mT-mC normalized by sT and sC added in quadrature] and localizability [L, defined as mT-mN normalized by sT and sN added in quadrature] were quantified for each focus on each image.
Results: T2W images showed the strongest detectability, although detectability |D|≥1 was observed on either ADC or DCE images, or both, for all foci. Localizability on all modalities was variable; however, ADC images showed localizability |L|≥1 for 3 foci.
Conclusions: Delineation of GS7 PZ tumors on individual MPMRI images faces challenges; however, images may contain complementary information, suggesting a role for fusion of information across MPMRI images for delineation.
Measurement of prostate tumour volume can inform prognosis and treatment selection, including an assessment of the
suitability and feasibility of focal therapy, which can potentially spare patients the deleterious side effects of radical
treatment. Prostate biopsy is the clinical standard for diagnosis but provides limited information regarding tumour
volume due to sparse tissue sampling. A non-invasive means for accurate determination of tumour burden could be of
clinical value and an important step toward reduction of overtreatment. Multi-parametric magnetic resonance imaging
(MPMRI) is showing promise for prostate cancer diagnosis. However, the accuracy and inter-observer variability of
prostate tumour volume estimation based on separate expert contouring of T2-weighted (T2W), dynamic contrastenhanced
(DCE), and diffusion-weighted (DW) MRI sequences acquired using an endorectal coil at 3T is currently
unknown. We investigated this question using a histologic reference standard based on a highly accurate MPMRIhistology
image registration and a smooth interpolation of planimetric tumour measurements on histology. Our results
showed that prostate tumour volumes estimated based on MPMRI consistently overestimated histological reference
tumour volumes. The variability of tumour volume estimates across the different pulse sequences exceeded interobserver
variability within any sequence. Tumour volume estimates on DCE MRI provided the lowest inter-observer
variability and the highest correlation with histology tumour volumes, whereas the apparent diffusion coefficient (ADC)
maps provided the lowest volume estimation error. If validated on a larger data set, the observed correlations could
support the development of automated prostate tumour volume segmentation algorithms as well as correction schemes
for tumour burden estimation on MPMRI.
Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided “fusion” prostate biopsy aims to reduce the ~23% false negative rate of clinical 2D TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsy still yields false negatives. Therefore, we propose optimization of biopsy targeting to meet the clinician’s desired tumor sampling probability, optimizing needle targets within each tumor and accounting for uncertainties due to guidance system errors, image registration errors, and irregular tumor shapes. We obtained multiparametric MRI and 3D TRUS images from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D surfaces that were registered to 3D TRUS. We estimated the probability, P, of obtaining a tumor sample with a single biopsy. Given an RMS needle delivery error of 3.5 mm for a contemporary fusion biopsy system, P ≥ 95% for 21 out of 81 tumors when the point of optimal sampling probability was targeted. Therefore, more than one biopsy core must be taken from 74% of the tumors to achieve P ≥ 95% for a biopsy system with an error of 3.5 mm. Our experiments indicated that the effect of error along the needle axis on the percentage of core involvement (and thus the measured tumor burden) was mitigated by the 18 mm core length.
In targeted 3D transrectal ultrasound (TRUS)-guided biopsy, patient and prostate movement during the procedure can cause target misalignments that hinder accurate sampling of pre-planned suspicious tissue locations. Multiple solutions have been proposed for motion compensation via registration of intra-procedural TRUS images to a baseline 3D TRUS image acquired at the beginning of the biopsy procedure. While 2D TRUS images are widely used for intra-procedural guidance, some solutions utilize richer intra-procedural images such as bi- or multi-planar TRUS or 3D TRUS, acquired by specialized probes. In this work, we measured the impact of such richer intra-procedural imaging on motion compensation accuracy, to evaluate the tradeoff between cost and complexity of intra-procedural imaging versus improved motion compensation. We acquired baseline and intra-procedural 3D TRUS images from 29 patients at standard sextant-template biopsy locations. We used the planes extracted from the 3D intra-procedural scans to simulate 2D and 3D information available in different clinically relevant scenarios for registration. The registration accuracy was evaluated by calculating the target registration error (TRE) using manually identified homologous fiducial markers (micro-calcifications). Our results indicate that TRE improves gradually when the number of intra-procedural imaging planes used in registration is increased. Full 3D TRUS information helps the registration algorithm to robustly converge to more accurate solutions. These results can also inform the design of a fail-safe workflow during motion compensation in a system using a tracked 2D TRUS probe, by prescribing rotational acquisitions that can be performed quickly and easily by the physician immediately prior to needle targeting.
KEYWORDS: 3D image processing, Tumors, Computed tomography, 3D metrology, Liver, Transducers, 3D scanning, 3D modeling, Image segmentation, Medium wave
Two-dimensional ultrasound (2D US) imaging is commonly used for diagnostic and intraoperative guidance of
interventional abdominal procedures including percutaneous thermal ablation of focal liver tumors with radiofrequency (RF) or microwave (MW) induced energy. However, in many situations 2D US may not provide enough anatomical detail and guidance information. Therefore, intra-procedural CT or MR imaging are used in many centers for guidance purposes. These modalities are costly and are mainly utilized to confirm tool placement rather than guiding the insertion. Three-dimensional ultrasound (3D US) has been introduced to address these issues. In this paper, we present our integrated solution to provide 3D US images using a newly developed mechanical transducer with a large field-ofview and without the need for external tracking devices to combine diagnostic and planning information of different modalities for intraoperative guidance. The system provides tools to segment the target(s), plan the treatment, and detect the ablation applicators during the procedure for guiding purposes. We present experimental results used to ensure that our system generates accurate measurements and our early clinical evaluation results. The results suggest that 3D US used for focal liver ablation can provide a more reliable planning and guidance tool compared to 2D US only, and in many cases offers comparable measurements to other alternatives at significantly lower cost, faster time and with no harmful radiation.
KEYWORDS: Image segmentation, Prostate, Magnetic resonance imaging, 3D image processing, 3D modeling, Cancer, Medical imaging, Data modeling, Magnetism, Shape analysis
3D segmentation of the prostate in medical images is useful to prostate cancer diagnosis and therapy guidance, but is time-consuming to perform manually. Clinical translation of computer-assisted segmentation algorithms for this purpose requires a comprehensive and complementary set of evaluation metrics that are informative to the clinical end user. We have developed an interactive 3D prostate segmentation method for 1.5T and 3.0T T2-weighted magnetic resonance imaging (T2W MRI) acquired using an endorectal coil. We evaluated our method against manual segmentations of 36 3D images using complementary boundary-based (mean absolute distance; MAD), regional overlap (Dice similarity coefficient; DSC) and volume difference (ΔV) metrics. Our technique is based on inter-subject prostate shape and local boundary appearance similarity. In the training phase, we calculated a point distribution model (PDM) and a set of local mean intensity patches centered on the prostate border to capture shape and appearance variability. To segment an unseen image, we defined a set of rays – one corresponding to each of the mean intensity patches computed in training – emanating from the prostate centre. We used a radial-based search strategy and translated each mean intensity patch along its corresponding ray, selecting as a candidate the boundary point with the highest normalized cross correlation along each ray. These boundary points were then regularized using the PDM. For the whole gland, we measured a mean±std MAD of 2.5±0.7 mm, DSC of 80±4%, and ΔV of 1.1±8.8 cc. We also provided an anatomic breakdown of these metrics within the prostatic base, mid-gland, and apex.
In order to obtain a definitive diagnosis of prostate cancer, over one million men undergo prostate biopsies every year.
Currently, biopsies are performed under two-dimensional (2D) transrectal ultrasound (TRUS) guidance with manual
stabilization of a hand-held end- or side-firing transducer probe. With this method, it is challenging to precisely guide a
needle to its target due to a potentially unstable ultrasound probe and limited anatomic information, and it is impossible
to obtain a 3D record of biopsy locations. We have developed a mechanically-stabilized, 3-dimensional (3D) TRUSguided
prostate biopsy system, which provides additional anatomic information and permits a 3D record of biopsies. A
critical step in this system's performance is the registration of 3D-TRUS images obtained during the procedure, which
compensates for intra-session motion and deformation of the prostate. We evaluated the accuracy and variability of
surface-based 3D-TRUS to 3D-TRUS rigid and non-rigid registration by measuring the target registration (TRE) error as
the post-registration misalignment of manually marked, corresponding, intrinsic fiducials. We also measured the fiducial
localization error (FLE), to measure its contribution to the TRE. Our results yielded mean TRE values of 2.13 mm and
2.09 mm for rigid and non-rigid techniques, respectively. Our FLE of 0.21 mm did not dominate the overall TRE. These
results compare favorably with a clinical need for a TRE of less than 2.5 mm.
KEYWORDS: Biopsy, Prostate, 3D acquisition, 3D image processing, 3D modeling, Ultrasonography, 3D displays, Transducers, Image segmentation, Imaging systems
Prostate biopsy procedures are currently limited to using 2D transrectal ultrasound (TRUS) imaging to guide the biopsy
needle. Being limited to 2D causes ambiguity in needle guidance and provides an insufficient record to allow guidance
to the same suspicious locations or avoid regions that are negative during previous biopsy sessions. We have developed
a mechanically assisted 3D ultrasound imaging and needle tracking system, which supports a commercially available
TRUS probe and integrated needle guide for prostate biopsy. The mechanical device is fixed to a cart and the
mechanical tracking linkage allows its joints to be manually manipulated while fully supporting the weight of the
ultrasound probe. The computer interface is provided in order to track the needle trajectory and display its path on a
corresponding 3D TRUS image, allowing the physician to aim the needle-guide at predefined targets within the prostate.
The system has been designed for use with several end-fired transducers that can be rotated about the longitudinal axis
of the probe in order to generate 3D image for 3D navigation. Using the system, 3D TRUS prostate images can be
generated in approximately 10 seconds. The system reduces most of the user variability from conventional hand-held
probes, which make them unsuitable for precision biopsy, while preserving some of the user familiarity and procedural
workflow. In this paper, we describe the 3D TRUS guided biopsy system and report on the initial clinical use of this
system for prostate biopsy.
Prostate biopsy procedures are generally limited to 2D transrectal ultrasound (TRUS) imaging for biopsy needle
guidance. This limitation results in needle position ambiguity and an insufficient record of biopsy core locations in cases
of prostate re-biopsy. We have developed a multi-jointed mechanical device that supports a commercially available
TRUS probe with an integrated needle guide for precision prostate biopsy. The device is fixed at the base, allowing the
joints to be manually manipulated while fully supporting its weight throughout its full range of motion. Means are
provided to track the needle trajectory and display this trajectory on a corresponding TRUS image. This allows the
physician to aim the needle-guide at predefined targets within the prostate, providing true 3D navigation. The tracker has
been designed for use with several end-fired transducers that can be rotated about the longitudinal axis of the probe to
generate 3D images. The tracker reduces the variability associated with conventional hand-held probes, while preserving
user familiarity and procedural workflow. In a prostate phantom, biopsy needles were guided to within 2 mm of their
targets, and the 3D location of the biopsy core was accurate to within 3 mm. The 3D navigation system is validated in
the presence of prostate motion in a preliminary patient study.
KEYWORDS: Biopsy, Prostate, 3D modeling, 3D acquisition, 3D image processing, Image segmentation, Gold, Computed tomography, Imaging systems, Magnetic tracking
Biopsy of the prostate using ultrasound guidance is the clinical gold standard for diagnosis of prostate adenocarinoma.
However, because early stage tumors are rarely visible under US, the procedure carries high false-negative
rates and often patients require multiple biopsies before cancer is detected. To improve cancer detection, it is
imperative that throughout the biopsy procedure, physicians know where they are within the prostate and where
they have sampled during prior biopsies. The current biopsy procedure is limited to using only 2D ultrasound
images to find and record target biopsy core sample sites. This information leaves ambiguity as the physician
tries to interpret the 2D information and apply it to their 3D workspace. We have developed a 3D ultrasound-guided
prostate biopsy system that provides 3D intra-biopsy information to physicians for needle guidance and
biopsy location recording. The system is designed to conform to the workflow of the current prostate biopsy
procedure, making it easier for clinical integration. In this paper, we describe the system design and validate its
accuracy by performing an in vitro biopsy procedure on US/CT multi-modal patient-specific prostate phantoms.
A clinical sextant biopsy was performed by a urologist on the phantoms and the 3D models of the prostates were
generated with volume errors less than 4% and mean boundary errors of less than 1 mm. Using the 3D biopsy
system, needles were guided to within 1.36 ± 0.83 mm of 3D targets and the position of the biopsy sites were
accurately localized to 1.06 ± 0.89 mm for the two prostates.
Real-time 3D optical tracking of free-hand imaging devices or surgical tools has been studied and employed for object localization in many minimally invasive interventions. However, the surgical workspace for many interventional procedures is often sub-dermal with tool access through ports from surgical incisions or anatomical orifices. To maintain the optical line-of-sight criterion, external extensions of inserted imaging devices and rigid surgical tools must be tracked to localize the internal tool tips. Unfortunately, tracking by this form of correspondence is very susceptible to noise as orientation errors on the external tracked end compound into both rotational and translational errors on the internal, workspace position. These translational errors are proportional to the length of the probe and the sine of the angulation error, so small angulation errors can quickly compromise the accuracy of the tool tip localization. We propose a real-time tracking correction technique that uses the rotational fulcrum created by the device entry port to minimize the effect of translational and rotational noise errors for tool tip localization. Our technique could apply to many types of interventions, but we focus on the application to the prostate biopsy procedure for tracking a transrectal ultrasound (TRUS) probe commonly used for prostate biopsies. In vitro studies were performed using the Claron Technology MicronTracker 2 to track a TRUS probe in a fixed rotational device. Our experimental results showed an order of magnitude improvement in RMS localization of the internal TRUS probe tip using fulcrum correction over the raw tracking information.
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