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Purpose: The purpose of the study was to assess the feasibility of performing intraoperative dosimetry for permanent prostate brachytherapy by combining transrectal ultrasound (TRUS) and fluoroscopy/cone beam CT [CBCT] images and accounting for the effect of prostate deformation., Methods and Materials: 13 patients underwent TRUS and multiview two-dimensional fluoroscopic imaging partway through the implant, as well as repeat fluoroscopic imaging with the TRUS probe inserted and retracted, and finally three-dimensional CBCT imaging at the end of the implant. The locations of all the implanted seeds were obtained from the fluoroscopy/CBCT images and were registered to prostate contours delineated on the TRUS images based on a common subset of seeds identified on both image sets. Prostate contours were also deformed, using a finite-element model, to take into account the effect of the TRUS probe pressure. Prostate dosimetry parameters were obtained for fluoroscopic and CBCT-dosimetry approaches and compared with the standard-of-care Day-0 postimplant CT dosimetry., Results: High linear correlation (R 2  > 0.8) was observed in the measured values of prostate D 90% , V 100% , and V 150% , between the two intraoperative dosimetry approaches. The prostate D 90% and V 100% obtained from intraoperative dosimetry methods were in agreement with the postimplant CT dosimetry. Only the prostate V 150% was on average 4.1% (p-value <0.05) higher in the CBCT-dosimetry approach and 6.7% (p-value <0.05) higher in postimplant CT dosimetry compared with the fluoroscopic dosimetry approach. Deformation of the prostate by the ultrasound probe appeared to have a minimal effect on prostate dosimetry., Conclusions: The results of this study have shown that both of the proposed dosimetric evaluation approaches have potential for real-time intraoperative dosimetry., (Copyright © 2020 American Brachytherapy Society. All rights reserved.)

Purpose: Eye gaze tracking is proving to be beneficial in many biomedical applications. The performance of systems based on eye gaze tracking is very much dependent on how accurate their calibration is. It has been reported that the gaze tracking accuracy deteriorates cumulatively and significantly with usage time. This impedes the wide use of gaze tracking in user interfaces., Methods: Explicit re-calibration, typically requiring the user's active attention, is time-consuming and can interfere with the user's main activity. Therefore, we propose an implicit re-calibration method, which can rectify the deterioration of the gaze tracking accuracy without bringing about the user's deliberate attention. We make use of hand-eye coordination, with the reasonable assumption that the eye gaze follows the pointer during a selection task, to acquire additional calibration points during normal usage of a gaze-contingent system. We construct a statistical model for the calibration and the hand-eye coordination and apply the Gaussian process regression framework to perform the re-calibration., Results: To validate our model and method, we performed a user study on ultrasonography tasks on a gaze-contingent interface for ultrasound machines. Results suggest that our method can rectify the tracking accuracy deterioration for [Formula: see text] of all cases where deterioration occurs in our user study. With another benchmark dataset, our method can redress tracking accuracy to a level comparable to the initial calibration in more than [Formula: see text] of the cases., Conclusions: Our implicit re-calibration method is a practical and convenient fix for tracking accuracy deterioration in gaze-contingent user interfaces, and in particular for gaze-contingent ultrasound machines.

A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm, is presented. 3D radiofrequency ultrasound signals were collected from 13 patients using the linear array of the TRUS probe during the brachytherapy procedure in which needles are used for insertion of stranded seeds. Gold standard for the location of seeds on TRUS data were obtained with the guidance of the complete reconstruction of the seed locations from multiple C-arm fluoroscopy views and used in the creation of the training set. We designed and trained a convolutional neural network (CNN) model that worked on 3D cubical sub-regions of the TRUS images, that will be referred to as patches, representing seed, non-seed within a needle track and non-seed elsewhere in the images. The models were trained with these patches to detect the needle track first and then the individual seeds within the needle track. A leave-one-out cross validation approach was used to test the model on the data from eight of the patients, for whom accurate seed locations were available from fluoroscopic imaging. The total inference time was about 7 min for needle track detection in each patient's image and approximately 1 min for seed detection in each needle, leading to a total seed detection time of less than 15 min. Our seed detection algorithm achieved [Formula: see text] precision, [Formula: see text] recall and [Formula: see text] F1&#95;score. The results from our CNN-based method were compared to manual seed localization performed by an expert. The CNN model yielded higher precision (lower false discovery rate) compared to the manual method. The automated approach requires little modification to the current clinical setups and offers the prospect of application in real time intraoperative dosimetric analysis of the implant.

The goal of this work was to develop a method for accurate and robust automatic segmentation of the prostate clinical target volume in transrectal ultrasound (TRUS) images for brachytherapy. These images can be difficult to segment because of weak or insufficient landmarks or strong artifacts. We devise a method, based on convolutional neural networks (CNNs), that produces accurate segmentations on easy and difficult images alike. We propose two strategies to achieve improved segmentation accuracy on difficult images. First, for CNN training we adopt an adaptive sampling strategy, whereby the training process is encouraged to pay more attention to images that are difficult to segment. Secondly, we train a CNN ensemble and use the disagreement among this ensemble to identify uncertain segmentations and to estimate a segmentation uncertainty map. We improve uncertain segmentations by utilizing the prior shape information in the form of a statistical shape model. Our method achieves Hausdorff distance of 2.7 ± 2.3 mm and Dice score of 93.9 ± 3.5%. Comparisons with several competing methods show that our method achieves significantly better results and reduces the likelihood of committing large segmentation errors. Furthermore, our experiments show that our approach to estimating segmentation uncertainty is better than or on par with recent methods for estimation of prediction uncertainty in deep learning models. Our study demonstrates that estimation of model uncertainty and use of prior shape information can significantly improve the performance of CNN-based medical image segmentation methods, especially on difficult images., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Purpose: Conventional ultrasound (US) machines employ a physical control panel (PCP) as the primary user interface for machine control. This panel is adjacent to the main machine display that requires the operator's constant attention. The switch of attention to the control panel can lead to interruptions in the flow of the medical examination. Some ultraportable machines also lack many physical controls. Furthermore, the need to both control the US machine and observe the US image may lead the practitioners to adopt unergonomic postures and repetitive motions that can lead to work-related injuries. Therefore, there is a need for a more efficient human-computer interaction method on US machines., Methods: To tackle some of the limitations with the PCP, we propose to merge the PCP into the main screen of the US machines. We propose to use gaze tracking and a handheld controller so that machine control can be achieved via a multimodal human-computer interaction (HCI) method that does not require one to touch the screen or look away from the US image. As a first step, a pop-up menu and measurement tool were designed on top of the US image based on gaze position for efficient machine control., Results: A comparative study was performed on the BK Medical SonixTOUCH US machine. Participants were asked to complete the task of measuring the area of an ellipse-shaped tumor in a phantom using our gaze-supported HCI method as well as the traditional method. The user study indicates that the task completion time can be reduced by [Formula: see text] when using our gaze-supported HCI, while no extra workload is imposed on the operators., Conclusions: Our preliminary study suggests that, when combined with a simple handheld controller, eye gaze tracking can be integrated into the US machine HCI for more efficient machine control.

Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration. In this paper, first, a theoretical formulation for TeUS is presented. Next, a series of simulations are carried out to investigate micro vibration as a source of tissue characterizing information in TeUS. The simulations include finite element modeling of micro vibration in synthetic phantoms, followed by US image generation during TeUS imaging. The simulations are performed on two media, a sparse array of scatterers and a medium with pathology mimicking scatterers that match nuclei distribution extracted from a prostate digital pathology data set. Statistical analysis of the simulated TeUS data shows its ability to accurately classify tissue types. Our experiments suggest that TeUS can capture the microstructural differences, including scatterer density, in tissues as they react to micro vibrations.

Purpose: This paper presents a new micro-motion-based approach to track a needle in ultrasound images captured by a handheld transducer., Methods: We propose a novel learning-based framework to track a handheld needle by detecting microscale variations of motion dynamics over time. The current state of the art on using motion analysis for needle detection uses absolute motion and hence work well only when the transducer is static. We have introduced and evaluated novel spatiotemporal and spectral features, obtained from the phase image, in a self-supervised tracking framework to improve the detection accuracy in the subsequent frames using incremental training. Our proposed tracking method involves volumetric feature selection and differential flow analysis to incorporate the neighboring pixels and mitigate the effects of the subtle tremor motion of a handheld transducer. To evaluate the detection accuracy, the method is tested on porcine tissue in-vivo, during the needle insertion in the biceps femoris muscle., Results: Experimental results show the mean, standard deviation and root-mean-square errors of [Formula: see text], [Formula: see text] and [Formula: see text] in the insertion angle, and 0.82, 1.21, 1.47 mm, in the needle tip, respectively., Conclusions: Compared to the appearance-based detection approaches, the proposed method is especially suitable for needles with ultrasonic characteristics that are imperceptible in the static image and to the naked eye.

Purpose: This paper presents a new approach to detect a standard handheld needle in ultrasound-guided interventions., Methods: Our proposal is to use natural hand tremor, which causes minute displacement of the needle, to detect the needle in ultrasound B-mode images. Subtle displacements arising from tremor motion have a periodic pattern which is usually imperceptible to the naked eye in the B-mode image. We use these displacement measurements in a spatiotemporal framework to detect linear structures with periodic pattern among a sequence of frames. The needle trajectory is estimated as a linear path in the image having maximum spectral correlation with the time trace of displacement due to tremor. A coarse estimation process is followed by a fine estimation step, where the motion pattern is analyzed along spatiotemporal linear paths with various angles originating from the estimated puncture site, within the trajectory channel. Spectral coherency is derived for each sample path versus the reference path, and the needle trajectory is identified as the mean of the sample paths with the maximum coherence within the tremor frequency range., Results: To evaluate the detection accuracy, we tested the method in vivo on porcine tissue, where the needle was inserted into the biceps femoris muscle. To understand whether tremor itself affects needle position, the maximum angular change due to tremor was calculated: mean, standard deviation (SD) and root-mean-square (RMS) measurement of [Formula: see text] and [Formula: see text]. The accuracy of the needle trajectory was calculated by comparing to an expert manual segmentation, averaged over the captured data and presented in mean, SD and RMS error of [Formula: see text] and [Formula: see text], respectively., Conclusion: Results demonstrate that natural tremor motion creates minute coherent motion along the needle, which could be used to localize the needle trajectory within the acceptable accuracy. This method is suitable for standard needles used clinically.

Tracking the position and orientation of a 3-D ultrasound transducer has many clinical applications. Tracking requires calibration to find the transformation between the tracking sensor and the ultrasound coordinates. Typically the set of image slice data are scan converted to a Cartesian volume using assumed motor geometry and a single transformation to the sensor. We propose, instead, the calibration of individual slices using a 2-D calibration technique. A best fit to a subset of slices is performed to decrease data collection time compared with that for calibration of all slices, and to reduce the influence of random errors in individual calibrations. We compare our technique with four scan conversion-based techniques: 2-D N-wire on the center slice, N-wire using a 3-D volume, N-wire using a 3-D volume including the edge points and a new closed-form planar method using a 3-D volume. The proposed multi-slice technique produced the smallest point reconstruction error (0.82 mm using a tracked stylus)., (Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Delineation of the prostate from transrectal ultrasound images is a necessary step in several computer-assisted clinical interventions, such as low dose rate brachytherapy. Current approaches to user segmentation require user intervention and therefore it is subject to user errors. It is desirable to have a fully automatic segmentation for improved segmentation consistency and speed. In this paper, we propose a multi-atlas fusion framework to automatically segment prostate transrectal ultrasound images. The framework initially registers a dataset of a priori segmented ultrasound images to a target image. Subsequently, it uses the pairwise similarity of registered prostate shapes, which is independent of the image-similarity metric optimized during the registration process, to prune the dataset prior to the fusion and consensus segmentation step. A leave-one-out cross-validation of the proposed framework on a dataset of 50 transrectal ultrasound volumes obtained from patients undergoing brachytherapy treatment shows that the proposed is clinically robust, accurate and reproducible.

In this study we evaluated a new method for registering three-dimensional ultrasound (3DUS) data to external coordinate systems. First, 3DUS was registered to the stereo endoscope of a da Vinci Surgical System by placing a registration tool against an air-tissue boundary so that the 3DUS could image ultrasound fiducials while the stereo endoscope could image camera markers on the same tool. The common points were used to solve the registration between the 3DUS and camera coordinate systems. The target registration error (TRE) when imaging through a PVC tissue phantom ranged from 3.85 1.76 mm to 1.82 1.03 mm using one to four registration tool positions. TRE when imaging through an ex-vivo liver tissue sample ranged from 2.36 1.01 mm to 1.51 0.70 mm using one to four registration tool positions. Second, using a similar method, 3DUS was registered to the kinematic coordinate system of a da Vinci Surgical System by using the da Vinci surgical manipulators to identify common points on an air-tissue boundary. TRE when imaging through a PVC tissue phantom was 0.95 0.38 mm. This registration method is simpler and potentially more accurate than methods using commercial motion tracking systems. This method may be useful in the future in augmented reality systems for laparoscopic and robotic-assisted surgery.

In this paper, we propose an interpolation-based method for simulating rigid needles in B-mode ultrasound images in real time. We parameterize the needle B-mode image as a function of needle position and orientation. We collect needle images under various spatial configurations in a water-tank using a needle guidance robot. Then we use multidimensional tensor-product interpolation to simulate images of needles with arbitrary poses and positions using collected images. After further processing, the interpolated needle and seed images are superimposed on top of phantom or tissue image backgrounds. The similarity between the simulated and the real images is measured using a correlation metric. A comparison is also performed with in vivo images obtained during prostate brachytherapy. Our results, carried out for both the convex (transverse plane) and linear (sagittal/para-sagittal plane) arrays of a trans-rectal transducer indicate that our interpolation method produces good results while requiring modest computing resources. The needle simulation method we present can be extended to the simulation of ultrasound images of other wire-like objects. In particular, we have shown that the proposed approach can be used to simulate brachytherapy seeds.

A widely used time-domain technique for motion or delay estimation between digitized ultrasound RF signals involves the maximization of a discrete pattern-matching function, usually the cross-correlation. To achieve sub-sample accuracy, the discrete pattern-matching function is interpolated using the values at the discrete maximizer and adjacent samples. In prior work, only 1-D fit, applied separately along the axial, lateral, and elevational axes, has been used to estimate the sub-sample motion in 1-D, 2-D, and 3-D. In this paper, we explore the use of 2-D and 3-D polynomial fitting for this purpose. We quantify the estimation error in noise-free simulations using Field II and experiments with a commercial ultrasound machine. In simulated 2-D translational motions, function fitting with quartic spline polynomials leads to maximum bias of 0.2% of the sample spacing in the axial direction and 0.4% of the sample spacing in the lateral direction, corresponding to 38 nm and 1.31 μm, respectively. The maximum standard deviations were approximately 1% of the sample spacing in both the axial and the lateral directions, corresponding to 193 nm axially and 4.43 μm laterally. In simulated 1% axial strain, the same function fitting leads to mean absolute displacement estimation errors of 255 nm in the axial direction and 4.77 μm in the lateral direction. In experiments with a linear array transducer, 2-D quartic spline fitting leads to maximum bias of 458 nm and 6.27 μm in the axial and the lateral directions, respectively. These results are more than one order of magnitude smaller than those obtained with separate 1-D fit when applied to the same data set. Simulations and experiments in 3-D yield similar results when comparing 3-D polynomial fitting with 1-D fitting along the axial, lateral, and elevational directions.

A novel registration method between 3D ultrasound and stereoscopic cameras is proposed based on tracking a registration tool featuring both ultrasound fiducials and optical markers. The registration tool is pressed against an air-tissue boundary where it can be seen both in ultrasound and in the camera view. By localizing the fiducials in the ultrasound volume, knowing the registration tool geometry, and tracking the tool with the cameras, a registration is found. This method eliminates the need for external tracking, requires minimal setup, and may be suitable for a range of minimally invasive surgeries. A study of the appearance of ultrasound fiducials on an air-tissue boundary is presented, and an initial assessment of the ability to localize the fiducials in ultrasound with sub-millimeter accuracy is provided. The overall accuracy of registration (1.69 +/- 0.60 mm) is a noticeable improvement over other reported methods and warrants patient studies.

This paper presents an algorithm for fast image synthesis inside deformed volumes. Given the node displacements of a mesh and a reference 3-D image dataset of a predeformed volume, the method first maps the image pixels that need to be synthesized from the deformed configuration to the nominal predeformed configuration, where the pixel intensities are obtained easily through interpolation in the regular-grid structure of the reference voxel volume. This mapping requires the identification of the mesh element enclosing each pixel for every image frame. To accelerate this point location operation, a fast method of projecting the deformed mesh on image pixels is introduced in this paper. The method presented was implemented for ultrasound B-mode image simulation of a synthetic tissue phantom. The phantom deformation as a result of ultrasound probe motion was modeled using the finite element method. Experimental images of the phantom under deformation were then compared with the corresponding synthesized images using sum of squared differences and mutual information metrics. Both this quantitative comparison and a qualitative assessment show that realistic images can be synthesized using the proposed technique. An ultrasound examination system was also implemented to demonstrate that real-time image synthesis with the proposed technique can be successfully integrated into a haptic simulation.

The performance of many signal-processing applications in ultrasound medical imaging, including elastography, blood flow imaging, tissue velocity imaging, and acoustic radiation force impulse imaging, depends on the performance of their time-delay estimators. In this paper, we present a new time-delay estimator based on the tracking of individual samples using a continuous representation of the echo signal. We also present the use of the same interpolation approach to improve the performance of the zero-crossing tracking delay estimator. Simulation results show that sample tracking algorithms significantly outperform commonly used window-based algorithms in terms of bias and standard deviation. Sample tracking algorithms also have higher sensitivity and resolution compared with traditional delay estimators, including recently introduced spline-based continuous time-delay estimators, because they provide the displacement of individual samples. Experimental results demonstrating the viability of sample tracking delay estimation are also presented.

Motion estimation in sequences of ultrasound echo signals is essential for a wide range of modern ultrasound-based signal processing applications. Pattern matching algorithms are generally used for finding the motion within sampling accuracy. Subsequently, 1D interpolation techniques are employed to find any motion smaller than sampling accuracy. In this paper, we propose several interpolation schemes that are suited for 2D motion estimation. Simulations using Field II ultrasound simulation software and experiments using an Ultrasonix Sonix RP imaging system with a L9-4/38 linear array transducer are used to study these techniques and compare their performance to other methods reported in the literature. The results show that the proposed interpolation methods outperform other common techniques in terms of bias and jitter. The axial and the lateral bias of the proposed methods are measured to be less than 1 microm and 5 microm, respectively, in both the simulated and the experimental data.

This paper presents a 3D semi-automatic prostate segmentation method for B-mode trans-rectal ultrasound (TRUS) images. Segmentation is based on prior knowledge of the prostate transversal section shape which is assumed to be a tapered ellipse. The approach consists of an initial untapering and warping of the image to make the shape of the prostate approximately elliptical. The prostate contour is found in the untapered, warped images by edge detection and 2D/3D ellipsoidal curve fitting, performed by solving convex optimization problem. This leads to an average total segmentation duration of 7.42s which is more than 40 times faster than manual segmentation. Clinical studies carried out on 58 prostate volume studies show a volume sensitivity and accuracy of approximately 94% and 73% respectively.

The performances of various signal-processing applications in ultrasound medical imaging, including elastography, blood flow imaging, tissue velocity imaging, and acoustic radiation force impulse imaging, depend on their time-delay estimators. In this paper, we introduced a new class of time-delay estimator based on the tracking of individual samples using a continuous representation of the echo signal. Simulation results show that the sample tracking algorithms significantly outperformed the conventional window-based algorithms in terms of bias and jitter when individual estimates from the samples are averaged over the same window length. Simulations further showed similar performance to that of recently introduced spline-based continuous time-delay estimators only if all the samples in the window had identical delays. When the samples inside a window experienced different delays, sample tracking algorithms considerably outperformed the continuous delay estimators. In addition, the sample tracking algorithms showed to have much higher resolution and sensitivity when compared to all the other window-based techniques, including the continuous delay estimators.

A new method is proposed to estimate the motion and relative local compression between two successive ultrasound RF signals under different compression states. The algorithm uses the continuous wavelet transform to locate the peaks in the RF signals. The estimated peaks in the pre- and post-compression signals are assigned to each other by a peak matching technique with the goal of minimizing the number of false matches. The method allows local shifts of the tissue to be estimated. The method has been tested in one-dimensional simulations and phantom experiments. The signal-to-noise ratio and the rms error are shown to be better than for the standard cross-correlation method (CC). The new estimator remains unbiased for up to 10% strain which is a larger range than that of CC. The maximum signal-to-noise ratio is 3 times as high as that of the CC method, showing higher sensitivity as well. The method is computationally efficient, achieving 0.7 msec/RF line on a standard personal computer.

Motion estimation in sequences of ultrasound echo signals is essential for a wide range of applications. In time domain cross correlation, which is a common motion estimation technique, the displacements are typically not integral multiples of the sampling period. Therefore, to estimate the motion with sub-sample accuracy, 1D and 2D interpolation methods such as parabolic, cosine, and ellipsoid fitting have been introduced in the literature. In this paper, a simulation framework is presented in order to compare the performance of currently available techniques. First, the tissue deformation is modeled using the finite element method (FEM) and then the corresponding pre-/post-deformation radio-frequency (RF) signals are generated using Field II ultrasound simulation software. Using these simulated RF data of deformation, both axial and lateral tissue motion are estimated with sub-sample accuracy. The estimated displacements are then evaluated by comparing them to the known displacements computed by the FEM. This simulation approach was used to evaluate three different lateral motion estimation techniques employing (i) two separate 1D sub-sampling, (ii) two consecutive 1D sub-sampling, and (iii) 2D joint sub-sampling estimators. The estimation errors during two different tissue compression tests are presented with and without spatial filtering. Results show that RF signal processing methods involving tissue deformation can be evaluated using the proposed simulation technique, which employs accurate models.

This paper presents a fast image synthesis procedure for elastic volumes under deformation. Given the node displacements of a mesh and the 3D image voxel data of an undeformed volume, the method maps the image plane pixels to be synthesized from the deformed configuration back to the nominal pre-deformed configuration, where the pixel intensities are obtained easily through interpolation in the regular-grid structure of the voxel volume. For smooth interpolation, this mapping requires the identification of the mesh element enclosing each image pixel. To accelerate this point location procedure, a fast method of marking the image pixels is employed by finding the intersection of the mesh and the image, and marking this intersection on the image pixels using Bresenham's line drawing algorithm. A deformable tissue phantom was constructed, it was modeled using the finite element method, and its 3D ultrasound volume was acquired in its undeformed state. Actual B-mode images of the phantom under deformation by the ultrasound probe were then compared with the corresponding synthesized images simulated for the same deformations. Results show that realistic images can be synthesized in real-time using the proposed technique.

This paper presents a fast image synthesis procedure inside elastic volumes under deformation simulated by the finite element method (FEM). Given the node displacements of a mesh and the 3D image voxel data of a volume prior to deformation, the method maps the image pixels, to be synthesized, from the deformed configuration back to the nominal pre-deformed configuration, where the pixel intensities are obtained easily through interpolation in the regular-grid structure of the voxel volume. This mapping requires the identification of the mesh element enclosing each image pixel, in order to use its corresponding shape function for smooth interpolation. To accelerate this point location operation, a fast method of marking the projection of the deformed mesh on the image pixels at every frame is introduced. In order to evaluate our method, a deformable tissue phantom was constructed and its 3D ultrasound volume was acquired in its nominal state. B-mode images of the phantom were then synthesized under the simulated deformation of an ultrasound probe. Results show that realistic B-mode images can be simulated in real-time with the proposed technique, even under large deformations. The technique is also implemented on a real-time system for ultrasound exploration with deformation.

In this paper we introduce a new speckle tracking method that is based on the standard time-domain cross correlation strain estimation (TDE). We call this method time-domain cross-correlation with prior estimates (TDPE), because it uses prior displacement estimates of neighboring windows to speed up computation. TDPE has all the advantages of TDE, but is much faster. Simulations, as well as experiments with phantoms and tissue, indicate that TDPE is capable of reliably estimating tissue displacement and strain over a large range of displacements in real time. The computational efficiency of TDPE is compared with current time-efficient methods that have been used in real time strain imaging systems. The results show that TDPE is the most time efficient algorithm to date, and is roughly 10 times faster than the TDE. The implementation of TDPE on an Ultrasonix RP500 ultrasound machine runs at 30 fps for strain images of 16000 pixels.

Ultrasound-guided percutaneous needle insertions are widely used techniques in current clinical practice. Some of these procedures have a high degree of difficulty because of poor observability of the needle in the ultrasound image. There have been recent efforts to improve guidance by computer assisted needle detection. These software techniques are often limited by not representing needle curvature. We present two methods to detect the needle in 2D ultrasound that specifically address needle curvature. Firstly, we demonstrate a real-time needle segmentation algorithm based on the Hough transform which detects the needle and represents its curved shape. Secondly, we demonstrate how a new coordinate transformation can transform detection of a curved needle to a linear fit. These methods are demonstrated on ultrasound and photographic images.

A system for objective vessel compression assessment for deep venous thrombosis characterization using ultrasound image data and a sensorized ultrasound probe is presented. Two new objective measures calculated from applied force and transverse vessel area are also presented and used to describe vessel compressibility. A modified star-Kalman algorithm is used for feature detection in acquired ultrasound images, and objective measures of vessel compressibility are calculated from the detected features and acquired force and location data from the sensorized probe. A three-dimensional shape model of the examined vessel that includes compressibility measures mapped as colors to its surface is presented on the user interface, as well as a virtual representation of the image plane. The compressibility measures were validated using expert segmentation of healthy and diseased vessels and compared using paired t-tests, which showed a significant difference between healthy and diseased cases for both measures. 100% sensitivity and specificity were obtained for both measures. The system was implemented in real-time (16 Hz) and evaluated using a tissue phantom and on healthy human subjects. Sensitivity was 100% and 60%, while specificity was 97% for both measures when implemented. The initial results for the system and its components are promising.

This paper presents a new algorithm for the semi-automatic segmentation of the prostate from B-mode trans-rectal ultrasound (TRUS) images. The segmentation algorithm first uses image warping to make the prostate shape elliptical. Measurement points along the prostate boundary, obtained from an edge-detector, are then used to find the best elliptical fit to the warped prostate. The final segmentation result is obtained by applying a reverse warping algorithm to the elliptical fit. This algorithm was validated using manual segmentation by an expert observer on 17 midgland, pre-operative, TRUS images. Distance-based metrics between the manual and semi-automatic contours showed a mean absolute difference of 0.67 +/- 0.18 mm, which is significantly lower than inter-observer variability. Area-based metrics showed an average sensitivity greater than 97% and average accuracy greater than 93%. The proposed algorithm was almost two times faster than manual segmentation and has potential for real-time applications.

Purpose To provide unencumbered real-time ultrasound image guidance during robot-assisted laparoscopic radical prostatectomy, we developed a robotic transrectal ultrasound system that tracks the da Vinci® Surgical System instruments. We describe our initial clinical experience with this system. Materials and Methods After an evaluation in a canine model, 20 patients were enrolled in the study. During each procedure the transrectal ultrasound transducer was manually positioned using a brachytherapy stabilizer to provide good imaging of the prostate. Then the transrectal ultrasound was registered to the da Vinci robot by a previously validated procedure. Finally, automatic rotation of the transrectal ultrasound was enabled such that the transrectal ultrasound imaging plane safely tracked the tip of the da Vinci instrument controlled by the surgeon, while real-time transrectal ultrasound images were relayed to the surgeon at the da Vinci console. Tracking was activated during all critical stages of the surgery. Results The transrectal ultrasound robot was easy to set up and use, adding 7 minutes (range 5 to 14) to the procedure. It did not require an assistant or additional control devices. Qualitative feedback was acquired from the surgeons, who found transrectal ultrasound useful in identifying the urethra while passing the dorsal venous complex suture, defining the prostate-bladder interface during bladder neck dissection, identifying the seminal vesicles and their location with respect to the rectal wall, and identifying the distal prostate boundary at the apex. Conclusions Real-time, registered robotic transrectal ultrasound guidance with automatic instrument tracking during robot-assisted laparoscopic radical prostatectomy is feasible and potentially useful. The results justify further studies to establish whether the approach can improve procedure outcomes. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method for detecting prostate cancer using a vibroelastography (VE) system developed in our group and a tissue classification approach based on texture analysis of VE images. Methods: The VE method applies wide-band mechanical vibrations to the tissue. Here, the authors report on the use of this system for cancer detection and show that the texture of VE images characterized by the first and the second order statistics of the pixel intensities form a promising set of features for tissue typing to detect prostate cancer. The system was used to image patients prior to radical surgery. The removed specimens were sectioned and studied by an experienced histopathologist. The authors registered the whole-mount histology sections to the ultrasound images using an automatic registration algorithm. This enabled the quantitative evaluation of the performance of the authors' imaging method in cancer detection in an unbiased manner. The authors used support vector machine (SVM) classification to measure the cancer detection performance of the VE method. Regions of tissue of size 5 × 5 mm, labeled as cancer and noncancer based on automatic registration to histology slides, were classified using SVM. Results: The authors report an area under ROC of 0.81 ± 0.10 in cancer detection on 1066 tissue regions from 203 images. All cancer tumors in all zones were included in this analysis and were classified versus the noncancer tissue in the peripheral zone. This outcome was obtained in leaveone- patient-out validation. Conclusions: The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method. [ABSTRACT FROM AUTHOR]