Biomedical subjects

Aaron Fenster

Publications and source records attributed to Aaron Fenster.

At least 37 records · Page 2Linked to original sources

PubMed2005

Quantification of carotid plaque volume measurements using 3D ultrasound imaging.

An accurate and reliable technique used to quantify carotid plaque volume has practical importance in research and patient management. In this study, we develop and investigate a theoretical description of carotid plaque volume measurements made using three-dimensional (3D) ultrasound (US) images and compare it with experimental results. Multiple observers measured 48 3D US patient images of carotid plaque (13.2 to 544.0 mm(3)) by manual planimetry. Coefficients of variation in the measurement of plaque volume were found to decrease with increasing plaque size for both inter- (90.8 to 3.9%) and intraobserver (70.2 to 3.1%) measurements. Plaque volume measurement variability was found to increase with interslice distance (ISD), while the relative measurement accuracy remained constant for ISDs between 1.0 and 3.0 mm and then decreased. Root-mean-square (RMS) difference between our theoretical description of plaque volume measurement variance and the experimental results was 5.7%. Thus, our results support the clinical utility of measuring carotid plaque volume by manual planimetry noninvasively using 3D US.

Aged↗

PubMed2005

Three-dimensional ultrasound biomicroscopy for xenograft growth analysis.

We reported the use of high-frequency ultrasound biomicroscopy (UBM) in the quantitative analysis of early tumor growth in mice bearing melanoma xenografts in a noninvasive longitudinal assay. Initially, measurements of tumor width, depth and length were obtained using on-screen UBM calipers in real time and tumor volume was calculated with the standard ellipsoid formula w d l pi/6. We were able to detect initiating minute tumor nodules, with the lower limit of detection at approximately 0.01 mm(3) in volume. Successive parallel cross-sectional UBM images (33 microm step) encompassing the complete length of these tumors were also obtained and reconstructed into 3-D representations. Subsequent segmentational volumetric analysis provided a measure of tumor volume. Volume measurements using the two techniques were highly correlated when all 33 xenografts were studied (r = 0.9813, p < 0.0001) and a lower degree of correlation was measured with a subset of early small tumors (r = 0.7973, n = 16, p = 0.0004). Further analysis demonstrated that 3-D segmentational volumetric analysis yielded volume estimates that were often smaller than the caliper-and-formula calculation for most early developing xenografts. Thus, 3-D UBM imaging and segmentation is expected to be especially valuable for small tumors that were observed to grow in irregular shapes other than ellipsoids.

Animals↗

PubMed2005

Brachytherapy needle deflection evaluation and correction.

In prostate brachytherapy, an 18-gauge needle is used to implant radioactive seeds. This thin needle can be deflected from the preplanned trajectory in the prostate, potentially resulting in a suboptimum dose pattern and at times requiring repeated needle insertion to achieve optimal dosimetry. In this paper, we report on the evaluation of brachytherapy needle deflection and bending in test phantoms and two approaches to overcome the problem. First we tested the relationship between needle deflection and insertion depth as well as whether needle bending occurred. Targeting accuracy was tested by inserting a brachytherapy needle to target 16 points in chicken tissue phantoms. By implanting dummy seeds into chicken tissue phantoms under 3D ultrasound guidance, the overall accuracy of seed implantation was determined. We evaluated methods to overcome brachytherapy needle deflection with three different insertion methods: constant orientation, constant rotation, and orientation reversal at half of the insertion depth. Our results showed that needle deflection is linear with needle insertion depth, and that no noticeable bending occurs with needle insertion into the tissue and agar phantoms. A 3D principal component analysis was performed to obtain the population distribution of needle tip and seed position relative to the target positions. Our results showed that with the constant orientation insertion method, the mean needle targeting error was 2.8 mm and the mean seed implantation error was 2.9 mm. Using the constant rotation and orientation reversal at half insertion depth methods, the deflection error was reduced. The mean needle targeting errors were 0.8 and 1.2 mm for the constant rotation and orientation reversal methods, respectively, and the seed implantation errors were 0.9 and 1.5 mm for constant rotation insertion and orientation reversal methods, respectively.

Agar↗

PubMed2005

Oblique needle segmentation and tracking for 3D TRUS guided prostate brachytherapy.

An algorithm was developed in order to segment and track brachytherapy needles inserted along oblique trajectories. Three-dimensional (3D) transrectal ultrasound (TRUS) images of the rigid rod simulating the needle inserted into the tissue-mimicking agar and chicken breast phantoms were obtained to test the accuracy of the algorithm under ideal conditions. Because the robot possesses high positioning and angulation accuracies, we used the robot as a "gold standard," and compared the results of algorithm segmentation to the values measured by the robot. Our testing results showed that the accuracy of the needle segmentation algorithm depends on the needle insertion distance into the 3D TRUS image and the angulations with respect to the TRUS transducer, e.g., at a 10 degrees insertion anglulation in agar phantoms, the error of the algorithm in determining the needle tip position was less than 1 mm when the insertion distance was greater than 15 mm. Near real-time needle tracking was achieved by scanning a small volume containing the needle. Our tests also showed that, the segmentation time was less than 60 ms, and the scanning time was less than 1.2 s, when the insertion distance into the 3D TRUS image was less than 55 mm. In our needle tracking tests in chicken breast phantoms, the errors in determining the needle orientation were less than 2 degrees in robot yaw and 0.7 degrees in robot pitch orientations, for up to 20 degrees needle insertion angles with the TRUS transducer in the horizontal plane when the needle insertion distance was greater than 15 mm.

Agar↗

PubMed2004

Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour.

Knowing the location and the volume of the prostate is important for ultrasound-guided prostate brachytherapy, a commonly used prostate cancer treatment method. The prostate boundary must be segmented before a dose plan can be obtained. However, manual segmentation is arduous and time consuming. This paper introduces a semi-automatic segmentation algorithm based on the dyadic wavelet transform (DWT) and the discrete dynamic contour (DDC). A spline interpolation method is used to determine the initial contour based on four user-defined initial points. The DDC model then refines the initial contour based on the approximate coefficients and the wavelet coefficients generated using the DWT. The DDC model is executed under two settings. The coefficients used in these two settings are derived using smoothing functions with different sizes. A selection rule is used to choose the best contour based on the contours produced in these two settings. The accuracy of the final contour produced by the proposed algorithm is evaluated by comparing it with the manual contour outlined by an expert observer. A total of 114 2D TRUS images taken for six different patients scheduled for brachytherapy were segmented using the proposed algorithm. The average difference between the contour segmented using the proposed algorithm and the manually outlined contour is less than 3 pixels.

Algorithms↗

PubMed2004

Genetic variation in PPARG encoding peroxisome proliferator-activated receptor gamma associated with carotid atherosclerosis.

BACKGROUND AND PURPOSE: Peroxisome proliferator-activated receptor gamma is a crucial molecule in atherogenesis because it is associated with metabolic risk factors such as obesity and diabetes and also plays a key role in subcellular metabolism of arterial wall macrophage foam cells. Genetic variation in PPARG has been associated with metabolic and cardiovascular end points. METHODS: We investigated the relationship between 2 common PPARG polymorphisms, namely P12A and c.1431C>T, and carotid atherosclerosis in a sample of 161 Canadian aboriginal people. Dependent variables were carotid intima media thickness (IMT), assessed using B-mode ultrasonography, and total carotid plaque volume (TPV), assessed using 3D ultrasound. RESULTS: Using multivariate analysis, we found that subjects with > or =1 PPARG A12 allele had less carotid IMT than others (0.72+/-0.03 versus 0.80+/-0.02 mm; P=0.0045), with no between-genotype difference in TPV. In contrast, subjects with the PPARG c.1431T allele had greater TPV than others (124+/-18.4 versus 65.1+/-23.7 mm3; P=0.0079), with no between-genotype difference in IMT. CONCLUSIONS: The findings show an association between PPARG genotypes and carotid arterial phenotypes, and further reflect the prevailing view that the PPARG A12 allele protects against deleterious phenotypes. Also, whereas IMT and TPV are somewhat correlated with each other, they might also represent distinct traits with discrete determinants representing different stages of atherogenesis.

Adult↗

PubMed2004

Measurement of carotid plaque volume by 3-dimensional ultrasound.

BACKGROUND AND PURPOSE: Measurement of carotid plaque volume and its progression are important tools for research and patient management. In this study, we investigate the observer variability in the measurement of plaque volume as determined by 3-dimensional (3D) ultrasound (US). We also investigate the effect of interslice distances (ISD) and repeated 3D US scans on measurement variability. Materials and Methods Forty 3D US patient images of plaques (range, 37.43 to 604.1 mm3) were measured by manual planimetry. We applied ANOVA to determine plaque volume measurement variability and reliability. Plaque volumes were measured with 9 ISDs to determine the effect of ISD on measurement variability. Additional plaque volumes were also measured from multiple 3D US scans to investigate repeated scan acquisition variability. Results Intraobserver and interobserver measurement reliabilities were 94% and 93.2%, respectively. Plaque volume measurement variability decreased with increasing plaque volume (range, 27.1% to 2.2%). Measurement precision was constant for ISDs between 1.0 and 3.0 mm, whereas plaque volume measurement variability increased with ISD. Repeated 3D US scan measurements were not different from single-scan measurements (P=0.867). Conclusions The coefficient of variation in the measurement of plaque volume decreased with plaque size. The volumetric change that must be observed to establish with 95% confidence that a plaque has undergone change is approximately 20% to 35% for plaques <100 mm3 and approximately 10% to 20% for plaques >100 mm3. Measurement precision was unchanged for ISDs <3.0 mm, whereas measurement variability increased with ISD. Repeated 3D US scans did not affect plaque volume measurement variability.

Aged↗

PubMed2004

Robot-assisted 3D-TRUS guided prostate brachytherapy: system integration and validation.

Current transperineal prostate brachytherapy uses transrectal ultrasound (TRUS) guidance and a template at a fixed position to guide needles along parallel trajectories. However, pubic arch interference (PAI) with the implant path obstructs part of the prostate from being targeted by the brachytherapy needles along parallel trajectories. To solve the PAI problem, some investigators have explored other insertion trajectories than parallel, i.e., oblique. However, parallel trajectory constraints in current brachytherapy procedure do not allow oblique insertion. In this paper, we describe a robot-assisted, three-dimensional (3D) TRUS guided approach to solve this problem. Our prototype consists of a commercial robot, and a 3D TRUS imaging system including an ultrasound machine, image acquisition apparatus and 3D TRUS image reconstruction, and display software. In our approach, we use the robot as a movable needle guide, i.e., the robot positions the needle before insertion, but the physician inserts the needle into the patient's prostate. In a later phase of our work, we will include robot insertion. By unifying the robot, ultrasound transducer, and the 3D TRUS image coordinate systems, the position of the template hole can be accurately related to 3D TRUS image coordinate system, allowing accurate and consistent insertion of the needle via the template hole into the targeted position in the prostate. The unification of the various coordinate systems includes two steps, i.e., 3D image calibration and robot calibration. Our testing of the system showed that the needle placement accuracy of the robot system at the "patient's" skin position was 0.15 mm+/-0.06 mm, and the mean needle angulation error was 0.07 degrees. The fiducial localization error (FLE) in localizing the intersections of the nylon strings for image calibration was 0.13 mm, and the FLE in localizing the divots for robot calibration was 0.37 mm. The fiducial registration error for image calibration was 0.12 mm and 0.52 mm for robot calibration. The target registration error for image calibration was 0.23 mm, and 0.68 mm for robot calibration. Evaluation of the complete system showed that needles can be used to target positions in agar phantoms with a mean error of 0.79 mm+/-0.32 mm.

Automation↗

PubMed2004

3D ultrasound imaging of the carotid arteries.

Although ultrasonography is an important cost-effective imaging modality, technical improvements are needed before its full potential is realized for accurate and reproducible monitoring of carotid disease and plaque burden. 2D viewing of 3D anatomy, using conventional ultrasonography limits our ability to quantify and visualize carotid disease and is partly responsible for the reported variability in diagnosis and monitoring of disease progression. Efforts of investigators have focused on overcoming these deficiencies by developing 3D ultrasound imaging techniques that are capable of acquiring B-mode, color Doppler and power Doppler images of the carotid arteries using existing conventional ultrasound systems, reconstructing the information into 3D images, and then allowing interactive viewing of the 3D images on inexpensive desktop computers. In addition, the availability of 3D ultrasound images of the carotid arteries has allowed the development of techniques to quantify plaque volume and surface morphology as well as allowing registration with other 3D imaging modalities. This paper describes 3D ultrasound imaging techniques used to image the carotid arteries and summarizes some of the developments aimed at quantifying plaque volume and morphology.

Carotid Arteries↗

PubMed2004

Projection-based needle segmentation in 3D ultrasound images.

Needles are used extensively in interventional procedures such as biopsy and brachytherapy. To deliver radioactive seeds to pre-planned positions or sample lesions from the region that may contain cancer cells, the 3D position of the needle must be determined accurately and quickly. Three-dimensional ultrasound (US) image guidance is an efficient technique used to perform this task. In this paper, we describe the development of a projection-based needle segmentation method comprising three steps. First, the 3D image is projected along an initial direction perpendicular to the approximate needle direction determined from the 3D imaging system. The needle is then segmented in a projected 2D image. Using the projection direction and the detected 2D needle direction, a plane containing the needle--called the needle plane--is determined. Secondly, the 3D image is re-projected in the direction perpendicular to the normal of the needle plane and step 1 is repeated. If the needle direction in the projected 2D image is horizontal, the needle plane is correct; otherwise, steps 1 and 2 are repeated until a correct needle plane is found. Thirdly, the 3D image is projected along the normal direction of the needle plane and the needle endpoints in the projected 2D image are determined. Using the relationship between the 3D projection and the 3D volume coordinate systems, the coordinates of the endpoints of the needle in the 3D US coordinate system are determined. Experiments with agar and turkey phantom 3D US images demonstrated that our method could segment the needle from 3D US images with an average accuracy of 0.7 mm in position and 1.2 degrees in orientation with a speed of 13 fps on a 1.3-GHz PC. In addition, experiments illustrated that our method is robust to variations in the initial estimated needle direction, the size of the cropped volume, and the ray-casting transfer function parameters used in pre-processing.

Algorithms↗

PubMed2003

Three-dimensional ultrasound imaging and its use in quantifying organ and pathology volumes.

Although ultrasonography is an important cost-effective imaging modality, technical improvements are needed before its full potential is realized for accurate and reproducible monitoring of disease progression or regression. Two-dimensional viewing of three-dimensional anatomy, using conventional ultrasonography, limits our ability to quantify and visualize pathology and is partly responsible for the reported variability in diagnosis and monitoring of disease progression. Efforts of investigators have focused on overcoming these deficiencies by developing 3D ultrasound imaging techniques that are capable of acquiring B-mode images using existing conventional ultrasound systems, reconstructing the information into 3D images, and then allowing interactive viewing of the 3D images on inexpensive desktop computers. In addition, the availability of 3D ultrasound images has allowed the development of manual and semi-automated techniques to quantify normal and abnormal anatomical volumes. In this paper we review our semi-automated 3D segmentation approaches for segmenting the surface of the carotid arteries and plaques, and segmenting the prostate. These techniques demonstrate that efficient segmentation techniques can be used with 3D ultrasound images to quantify anatomical organ volumes and morphology.

Carotid Arteries↗

PubMed2003

MP3 compression of Doppler ultrasound signals.

The effect of lossy, MP3 compression on spectral parameters derived from Doppler ultrasound (US) signals was investigated. Compression was tested on signals acquired from two sources: 1. phase quadrature and 2. stereo audio directional output. A total of 11, 10-s acquisitions of Doppler US signal were collected from each source at three sites in a flow phantom. Doppler signals were digitized at 44.1 kHz and compressed using four grades of MP3 compression (in kilobits per second, kbps; compression ratios in brackets): 1400 kbps (uncompressed), 128 kbps (11:1), 64 kbps (22:1) and 32 kbps (44:1). Doppler spectra were characterized by peak velocity, mean velocity, spectral width, integrated power and ratio of spectral power between negative and positive velocities. The results suggest that MP3 compression on digital Doppler US signals is feasible at 128 kbps, with a resulting 11:1 compression ratio, without compromising clinically relevant information. Higher compression ratios led to significant differences for both signal sources when compared with the uncompressed signals.

Algorithms↗

PubMed2003

A pulsating coronary vessel phantom for two- and three-dimensional intravascular ultrasound studies.

The evaluation of new techniques for 2-D and 3-D intravascular ultrasound (US) imaging (IVUS) often requires the use of a pulsating coronary phantom. This study describes the design, construction and evaluation of a phantom simulating the pulsation of a human coronary artery for IVUS studies. Polyvinyl alcohol (PVA) cryogel was used as a tissue mimic for the coronary vessel, which was incorporated in a custom-built assembly. The phantom was programmed to pulsate under servomotor control, to model the pulsation of a normal coronary artery and 2-D IVUS images were obtained using an IVUS imaging catheter. To evaluate the performance of the phantom, the lumen area variation of the phantom was determined and compared with the programmed pulsation waveforms. Our results showed that phantom pulsation correlated well with the programmed pulsation waveform (r = 0.97). The deviation of the least squares line from the line of identity was calculated to be < 4%.

Coronary Vessels↗

PubMed2003

Automatic needle segmentation in three-dimensional ultrasound images using two orthogonal two-dimensional image projections.

In this paper, we describe an algorithm to segment a needle from a three-dimensional (3D) ultrasound image by using two orthogonal two-dimensional (2D) image projections. Not only is the needle more conspicuous in a projected (volume-rendered) image, but its direction in 3D lies in the plane defined by the projection direction and the needle direction in the projected 2D image. Hence, using two such projections, the 3D vector describing the needle direction lies along the intersection of the two corresponding planes. Thus, the task of 3D needle segmentation is reduced to two 2D needle segmentations. For improved accuracy and robustness, we use orthogonal projection directions (both orthogonal to a given a priori estimate of the needle direction), and use volume cropping and Gaussian transfer functions to remove complex background from the 2D projection images. To evaluate our algorithm, we tested it with 3D ultrasound images of agar and turkey breast phantoms. Using a 500 MHz personal computer equipped with a commercial volume-rendering card, we found that our 3D needle segmentation algorithm performed in near real time (about 10 fps) with a root-mean-square accuracy in needle length and endpoint coordinates of better than 0.8 mm, and about 0.5 mm on average, for needles lengths in the 3D image from 4.0 mm to 36.7 mm.

Algorithms↗

PubMed2003

Semiautomatic three-dimensional segmentation of the prostate using two-dimensional ultrasound images.

In this paper, we report on two methods for semiautomatic three-dimensional (3-D) prostate boundary segmentation using 2-D ultrasound images. For each method, a 3-D ultrasound prostate image was sliced into the series of contiguous 2-D images, either in a parallel manner, with a uniform slice spacing of 1 mm, or in a rotational manner, about an axis approximately through the center of the prostate, with a uniform angular spacing of 5 degrees. The segmentation process was initiated by manually placing four points on the boundary of a selected slice, from which an initial prostate boundary was determined. This initial boundary was refined using the Discrete Dynamic Contour until it fit the actual prostate boundary. The remaining slices were then segmented by iteratively propagating this result to an adjacent slice and repeating the refinement, pausing the process when necessary to manually edit the boundary. The two methods were tested with six 3-D prostate images. The results showed that the parallel and rotational methods had mean editing rates of 20% and 14%, and mean (mean absolute) volume errors of -5.4% (6.5%) and -1.7% (3.1%), respectively. Based on these results, as well as the relative difficulty in editing, we conclude that the rotational segmentation method is superior.

Algorithms↗

PubMed2003

Statistical analysis of decorrelation-based transducer tracking for three-dimensional ultrasound.

The use of speckle decorrelation techniques to calculate the displacement of a moving transducer has shown promise. We describe a technique to estimate displacement between pairs of parallel planes without assuming that plane separation in the scan is uniform. We perform theoretical and empirical analyses of the bias and uncertainty in plane spacing estimates as a function of speckle size, patch size, and the number of planes used for normalization. Practically, only the central, linear region of the autocovariance curves can be used in this decorrelation method, which implies that distance between acquired image planes should be approximately half the speckle size. In this region, the uncertainty in estimated plane spacing was less than 15% for a 8.1 mm (axial) by 9.1 mm (lateral) patch and increased to 33% for an 8.1 mm (axial) by 1.5 mm (lateral) patch. The number of planes, Nz, used to calculate the normalization factors (averages of brightness and- squared brightness) was a major source of bias. Optimum Nz was found to be five to ten planes, depending on distance between acquired image planes, with a poor choice of Nz resulting in a bias of 10% or greater. A second source of bias is brightness gradients which, although they appear very slight on intensity images, can cause a large bias is the plane spacing estimates made using linearized data.

Algorithms↗

PubMed2003

Testing and optimization of a semiautomatic prostate boundary segmentation algorithm using virtual operators.

Image analysis tasks such as size measurement and landmark-based registration require the user to select control points in an image. The output of such algorithms depends on the choice of control points. Since the choice of points varies from one user to the next, the requirement for user input introduces variability into the output of the algorithm. In order to test and/or optimize such algorithms, it is necessary to assess the multiplicity of outputs generated by the algorithm in response to a large set of inputs; however, the input of data requires substantial time and effort from multiple users. In this paper we describe a method to automate the testing and optimization of algorithms using "virtual operators," which consist of a set of spatial distributions describing how actual users select control points in an image. In order to construct the virtual operator, multiple users must repeatedly select control points in the image on which testing is to be performed. Once virtual operators are generated, control points for initializing the algorithm can be generated from them using a random number generator. Although an initial investment of time is required from the users in order to construct the virtual operator, testing and optimization of the algorithm can be done without further user interaction. We illustrate the construction and use of virtual operators by testing and optimizing our prostate boundary segmentation algorithm. The algorithm requires the user to select four control points on the prostate as input.

Algorithms↗

PubMed2003

Prostate boundary segmentation from 3D ultrasound images.

Segmenting, or outlining the prostate boundary is an important task in the management of patients with prostate cancer. In this paper, an algorithm is described for semiautomatic segmentation of the prostate from 3D ultrasound images. The algorithm uses model-based initialization and mesh refinement using an efficient deformable model. Initialization requires the user to select only six points from which the outline of the prostate is estimated using shape information. The estimated outline is then automatically deformed to better fit the prostate boundary. An editing tool allows the user to edit the boundary in problematic regions and then deform the model again to improve the final results. The algorithm requires less than 1 min on a Pentium III 400 MHz PC. The accuracy of the algorithm was assessed by comparing the algorithm results, obtained from both local and global analysis, to the manual segmentations on six prostates. The local difference was mapped on the surface of the algorithm boundary to produce a visual representation. Global error analysis showed that the average difference between manual and algorithm boundaries was -0.20 +/- 0.28 mm, the average absolute difference was 1.19 +/- 0.14 mm, the average maximum difference was 7.01 +/- 1.04 mm, and the average volume difference was 7.16% +/- 3.45%. Variability in manual and algorithm segmentation was also assessed: Visual representations of local variability were generated by mapping variability on the segmentation mesh. The mean variability in manual segmentation was 0.98 mm and in algorithm segmentation was 0.63 mm and the differences of about 51.5% of the points comprising the average algorithm boundary are insignificant (P < or = 0.01) to the manual average boundary.

Algorithms↗