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Soft tissue needle guidance and steering for clinical applications has been an active topic of research in the past decade. Although dynamic feedback control of needle insertion systems is expected to provide more accurate target tracking, it has received little attention due to the fact that most available models for needle-tissue interaction do not incorporate the dynamics of motions. In this paper, we study the controllability of rigid or flexible needles inside soft tissues using mechanical-based dynamic models. The results have significant implications on the design of suitable feedback controllers for different types of needle insertion systems.

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.

Prostate segmentation from trans-rectal transverse B-mode ultrasound images is required for radiation treatment of prostate cancer. Manual segmentation is a time-consuming task, the results of which are dependent on image quality and physicians' experience. This paper introduces a semi-automatic 3D method based on super-ellipsoidal shapes. It produces a 3D segmentation in less than 15 seconds using a warped, tapered ellipsoid fit to the prostate. A study of patient images shows good performance and repeatability. This method is currently in clinical use at the Vancouver Cancer Center where it has become the standard segmentation procedure for low dose-rate brachytherapy treatment.

We show that vibro-elastography, an ultrasound-based method that creates images of tissue viscoelasticity contrast, can be used for visualization and segmentation of the prostate. We use MRI as the gold standard and show that VE images yield more accurate 3D volumes of the prostate gland than conventional B-mode imaging. Furthermore, we propose two novel measures characterizing the strength and continuity of edges in noisy images. These measures, as well as contrast to noise ratio, demonstrate the utility of VE as a prostate imaging modality. The results of our study show that in addition to mapping the visco-elastic properties of tissue, VE can play a central role in improving the anatomic visualization of the prostate region and become an integral component of interventional procedures such as brachytherapy.

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.

An iterative solution to the inverse problem of elasticity and viscosity is proposed in this paper. A new dynamic finite element model that is consistent with known rheological models has been derived to account for the viscoelastic changes in soft tissue. The model assumes known lumped masses at the nodes, and comprises two vectors of elasticity and viscosity parameters that depend on the material elasticity and viscosity distribution, respectively. Using this deformation model and the observed dynamic data for harmonic excitation, the inverse problem is solved to reconstruct the viscosity and elasticity in the medium by using a Gauss-Newton-based approach. As in other inverse problems, previous knowledge of the parameters on the boundaries of the medium is necessary to assure uniqueness and convergence and to obtain an accurate map of the viscoelastic properties. The sensitivity of the solutions to noise, model and boundary conditions has been studied through numerical simulations. Experimental results are also presented. The viscosity and elasticity of a gelatin-based phantom with inclusion of known properties have been reconstructed and have been shown to be close to the values obtained using standard rheometry.

Needle insertion simulators find use in a number of medical interventions, such as prostate brachytherapy. A needle insertion simulator has three main components: the needle model, the tissue model, and the model of interaction between the needle and the tissue. In this paper, a new methodology is introduced for the joint modeling of tissue and needle-tissue interactions. The approach consists of the measurement of tissue motion using ultrasound, and of the needle position and base force. Tissue motion is determined using a correlation-based algorithm that processes the ultrasound radiofrequency data. The tissue elastic parameters and the parameters of the tissue-needle interaction model are determined by using numerical optimization to match the response of the needle insertion model to the measured data. Phantom experiments were carried out in which a brachytherapy needle was inserted into a two-layer non-homogeneous phantom mimicking a prostate and its surrounding tissue. Experimental results show good agreement with the model obtained. In particular, the parameters of a three-parameter force model were identified for each layer of the phantom to fit the measured force to the simulated one. Also, the Young's modulus of each layer was identified to match the measured and simulated nodal axial displacements. This is the first report of the use of ultrasound radiofrequency data to characterize tissue motion during needle insertion. As the method is non-invasive and does not involve ionizing radiation, its application in patient studies is feasible.

An iterative solution to the inverse problem of elasticity and viscosity is proposed in this paper. A finite elements model has been derived to account for the viscoelastic changes in soft tissue that is consistent with known rheological models. Using this deformation model and the observed dynamic data, the viscosity and elasticity have been reconstructed in the medium by using a Gauss-Newton based approach. Previous knowledge of the parameters on the boundaries of the medium are necessary to assure uniqueness and convergence and to obtain an accurate map of the viscoelastic properties. The methods have been studied in FEM simulations and also tested through experiments, where the viscosity and elasticity of a gelatin based phantom with an inclusion of known properties have been reconstructed successfully.

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.

Needle insertion simulators are in demand to train physicians for surgical procedures such as prostate brachytherapy. In order to design a needle insertion simulator, a needle-tissue interaction model is necessary. Such a model can be identified using force and displacement data measured during the insertion of the needle. In this paper, an experiment is conducted in which a needle is inserted into a two-layered PVC phantom, while the needle position and insertion forces are measured and the tissue is imaged using a trans-rectal probe in the sagittal plane. A 2D block matching algorithm is used to estimate tissue deformation from the envelope of the recorded radio-frequency signals. This algorithm can be used to estimate the rotation and displacement of the prostate during prostate brachytherapy. The block matching method was validated in an independent experiment. With the measured force, needle position and tissue displacements, a finite element simulation was conducted to identify the parameters of a needle-tissue force distribution model and Young's moduli of each part of the tissue phantom.

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 method for vessel segmentation and tracking in ultrasound images using Kalman filters is presented. A modified Star-Kalman algorithm is used to determine vessel contours and ellipse parameters using an extended Kalman filter with an elliptical model. The parameters can be used to easily calculate the transverse vessel area which is of clinical use. A temporal Kalman filter is used for tracking the vessel center over several frames, using location measurements from a handheld sensorized ultrasound probe. The segmentation and tracking have been implemented in real-time and validated using simulated ultrasound data with known features and real data, for which expert segmentation was performed. Results indicate that mean errors between segmented contours and expert tracings are on the order of 1%-2% of the maximum feature dimension, and that the transverse cross-sectional vessel area as computed from estimated ellipse parameters a, b as determined by our algorithm is within 10% of that determined by experts. The location of the vessel center was tracked accurately for a range of speeds from 1.4 to 11.2 mm/s.

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.

A needle-tissue interaction model is an essential part of every needle insertion simulator. In this paper, a new experimental method for the modeling of needle-tissue interaction is presented. The method consists of measuring needle and tissue displacements with ultrasound, measuring needle base forces, and using a deformation simulation model to identify the parameters of a needle-tissue interaction model. The feasibility of this non-invasive approach was demonstrated in an experiment in which a brachytherapy needle was inserted into a prostate phantom. Ultrasound radio-frequency data and the time-domain cross-correlation method, often used in ultrasound elastography, were used to generate the tissue displacement field during needle insertion. A three-parameter force density model was assumed for the needle-tissue interaction. With the needle displacement, tissue displacement and needle base forces as input data, finite element simulations were carried out to adjust the model parameters to achieve a good fit between simulated and measured data.

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.

In this work, needle-tissue interaction forces are modeled by a three parameter force distribution composed of two step functions with variable amplitudes and spacing. A finite element based simulation is used to adjust the parameters and fit the simulation results to the experimental data. In experiments, needle displacements and needle base forces were measured along with tissue displacements. A real-time version of the time-domain cross-correlation method was employed in this study to estimate the tissue displacements from ultrasound radio-frequency data, as done in elastography. In addition to the force model parameters, the elastic parameters of the tissue were adjusted to match the simulated and measured displacements.

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.

This paper presents a needle-tissue interaction model that is a 3D extension of prior work based on needle and tissue models discretized using the Finite Element Method. The use of flexible needles necessitates remeshing the tissue during insertion, since simple mesh-node snapping to the tip can be detrimental to the simulation. In this paper, node repositioning and node addition are the two methods of mesh modification examined for coarse meshes. Our focus is on numerical approaches for fast implementation of these techniques. Although the two approaches compared, namely the Woodbury formula (matrix inversion lemma) and the boundary condition switches, have the same computational complexity, the Woodbury formula is shown to perform faster due to its cache-efficient order of operations. Furthermore, node addition is applied in constant time for both approaches, whereas node repositioning requires longer and variable computational times. A method for rendering the needle forces during simulated insertions into a 3D prostate model has been implemented. Combined with a detailed anatomical segmentation, this will be useful in teaching the practice of prostate brachytherapy. Issues related to discretization of such coupled (e.g., needle-tissue) models are also discussed.

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.

Power Doppler ultrasound is used to localize the tip of a needle by detecting physical vibrations. Two types of vibrations are investigated, lateral and axial. The lateral vibrations are created by rotating a stylet, whose tip is slightly bent, inside a stationary cannula while the stylet is completely within the cannula. The minute deflection at the needle tip when rotated causes tissue motion. The axial vibration is induced by extending and retracting a straight stylet inside a stationary cannula. The stylet's tip makes contact with the tissue and causes it to move. The lateral vibration method was found to perform approximately the same under a variety of configurations (e.g., different insertion angles and depths) and better than the axial vibration method. Tissue stiffness affects the performance of the lateral vibration method, but good images can be obtained through proper tuning of the ultrasound machine.

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.

Modeling the deflection of flexible needles is an essential part of needle insertion simulation and path planning. In this paper, three models are compared in terms of accuracy in simulating the bending of a prostate brachytherapy needle. The first two utilize the finite element method, one using geometric non-linearity and triangular plane elements, the other using non-linear beam elements. The third model uses angular springs to model cantilever deflection. The simulations are compared with the experimental bent needle configurations. The models are assessed in terms of geometric conformity using independently identified and pre-identified model parameters. The results show that the angular spring model, which is also the simplest, simulates the needle more accurately than the others.

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.

A novel interactive virtual needle insertion simulation is presented. The simulation models are based on measured planar tissue deformations and needle insertion forces. Since the force-displacement relationship is only of interest along the needle shaft, a condensation technique is shown to reduce the computational complexity of linear simulation models significantly. As the needle penetrates or is withdrawn from the tissue model, the boundary conditions that determine the tissue and needle motion change. Boundary condition and local material coordinate changes are facilitated by fast low-rank matrix updates. A large-strain elastic needle model is coupled to the tissue models to account for needle deflection and bending during simulated insertion. A haptic environment, based on these novel interactive simulation techniques, allows users to manipulate a three-degree-of-freedom virtual needle as it penetrates virtual tissue models, while experiencing steering torques and lateral needle forces through a planar haptic interface.

This paper presents a needle-tissue interaction model that is a 3D extension of a prior work based on the finite element method. The model is also adapted to accommodate arbitrary meshes so that the anatomy can effectively be meshed using third-party algorithms. Using this model a prostate brachytherapy simulator is designed to help medical residents acquire needle steering skills. This simulation uses a prostate mesh generated from clinical data segmented as contours on parallel slices. Node repositioning and addition, which are methods for achieving needle-tissue coupling, are discussed. In order to achieve realtime haptic rates, computational approaches to these methods are compared. Specifically, the benefit of using the Woodbury formula (matrix inversion lemma) is studied. Our simulation of needle insertion into a prostate is shown to run faster than 1 kHz.