Liner orientation change of dual mobility cup determined via 3D ultrasound imaging and motion analysis: A cadaver study.

BACKGROUND: A mobile polyethylene liner enables the dual mobility cup (DMC) to contribute to restoring hip joint range-of-motion, decreasing wear and increasing implant stability. However, more data is required on how liner orientation changes with hip joint movement. As a first step towards better understanding liner orientation change in vivo, this cadaver study focuses on quantifying DMC liner orientation change after different hip passive movements, using ultrasound imaging and motion analysis. HYPOTHESIS: The liner does not always go back to its initial orientation and its final orientation depends mainly on hip movement amplitude. METHODS: 3D ultrasound imaging and motion analysis were used to define liner and hip movements for four fresh post-mortem human subjects with six implanted DMC. Abduction and anteversion angles of the liner plane relative to the pelvis were measured before and after hip flexion, internal rotation, external rotation, abduction, adduction. RESULTS: Liner orientation changes were generally defined by angle variation smaller than 5°, with the liner nearly going back to its initial orientation. However, hip flexion caused liner abduction and anteversion angle variations greater than 15°. Except for hip adduction, only weak or no correlation was found between the final angle of the liner and the maximal hip joint movement amplitude. DISCUSSION: This study is the first attempt to quantify liner orientation change for implanted DMC via ultrasound imaging and constitutes a step forward in the understanding of liner orientation change and its relationship with hip joint movement. The hypothesis that the final liner abduction and anteversion angles depend mainly on hip movement amplitude was not confirmed, even if hip flexion was the movement generating the most liner orientation changes over 15°. This approach should be extended to in vivo clinical investigations, as measured liner angle variation could provide important support for the wear and stability claims made for DMC. LEVEL OF EVIDENCE: IV; cadaveric study.

Cancer characterization using light backscattering spectroscopy and quantitative ultrasound: an ex vivo study on sarcoma subtypes.

Histological analysis is the gold standard method for cancer diagnosis. However, it is prone to subjectivity and sampling bias. In response to these limitations, we introduce a quantitative bimodal approach that aims to provide non-invasive guidance towards suspicious regions. Light backscattering spectroscopy and quantitative ultrasound techniques were combined to characterize two different bone tumor types from animal models: chondrosarcomas and osteosarcomas. Two different cell lines were used to induce osteosarcoma growth. Histological analyses were conducted to serve as references. Three ultrasound parameters and intensities of the light reflectance profiles showed significant differences between chondrosarcomas and osteosarcomas at the 5% level. Likewise, variations in the same biomarkers were reported for the two types of osteosarcoma, despite their similar morphology observed in the histological examinations. These observations show the sensitivity of our techniques in probing fine tissue properties. Secondly, the ultrasound spectral-based technique identified the mean size of chondrosarcoma cells and nuclei with relative errors of about 22% and 9% respectively. The optical equivalent technique correctly extracted scatterer size distributions that encompass nuclei and cells for chondrosarcomas and osteosarcomas ([Formula: see text] and [Formula: see text] respectively). The optical scattering contributions of nuclei were estimated at 52% for the chondrosarcomas and 69% for the osteosarcomas, probably indicating the abundant and the absent extracellular matrix respectively. Thus, the ultrasound and the optical methods brought complementary parameters. They successfully estimated morphological parameters at the cellular and the nuclear scales, making our bimodal technique promising for tumor characterization.

Optimized Virtual Sources Distributions for 3-D Ultrafast Diverging Wave Compounding Imaging: A Simulation Study.

Ultrafast ultrasound imaging allows observing rapid phenomena; combined with 3-D imaging it has the potential to provide a more accurate analysis of organs which leads, in the end, to better diagnosis. Coherent compounding using diverging waves is commonly used to reconstruct high-quality images on large volumes while keeping the frame rate high enough to allow dynamic analysis. In practice, the virtual sources (VSs) that drive the diverging waves are often distributed in a deterministic way: following a regular grid, concentric rings, and spirals. Even though those deterministic distributions can offer various tradeoffs in terms of imaging performance, other distributions can be considered to improve imaging performance. It is herein suggested to look at alternative VSs distributions for optimizing the lateral resolution and the secondary lobes level (SLL) on several point spread functions (PSFs) by means of a multiobjective genetic algorithm. The optimization framework has led to seven pseudo-irregular distributions of VSs distributions that have not yet been found in the literature. An analysis of the imaging performance with a simulated phantom shows that these new distributions offer different tradeoffs between lateral resolution and contrast, respectively, measured on point-like reflectors and anechoic cysts. As an example, one of these optimized distributions improves the lateral resolution by 16% and gives equivalent contrast values on cysts and PSF isotropy properties, when compared to a concentric-rings-based distribution.

Evaluation and comparison of current biopsy needle localization and tracking methods using 3D ultrasound.

This article compares four different biopsy needle localization algorithms in both 3D and 4D situations to evaluate their accuracy and execution time. The localization algorithms were: Principle component analysis (PCA), random Hough transform (RHT), parallel integral projection (PIP) and ROI-RK (ROI based RANSAC and Kalman filter). To enhance the contrast of the biopsy needle and background tissue, a line filtering pre-processing step was implemented. To make the PCA, RHT and PIP algorithms comparable with the ROI-RK method, a region of interest (ROI) strategy was added. Simulated and ex-vivo data were used to evaluate the performance of the different biopsy needle localization algorithms. The resolutions of the sectorial and cylindrical volumes were 0.3mm×0.4mm×0.6mmand0.1mm×0.1mm×0.2mm (axial×lateral×azimuthal) respectively. In so far as the simulation and experimental results show, the ROI-RK method successfully located and tracked the biopsy needle in both 3D and 4D situations. The tip localization error was within 1.5mm and the axis accuracy was within 1.6mm. To the best of our knowledge, considering both localization accuracy and execution time, the ROI-RK was the most stable and time-saving method. Normally, accuracy comes at the expense of time. However, the ROI-RK method was able to locate the biopsy needle with high accuracy in real time, which makes it a promising method for clinical applications.

2-D Ultrasound Sparse Arrays Multidepth Radiation Optimization Using Simulated Annealing and Spiral-Array Inspired Energy Functions.

Full matrix arrays are excellent tools for 3-D ultrasound imaging, but the required number of active elements is too high to be individually controlled by an equal number of scanner channels. The number of active elements is significantly reduced by the sparse array techniques, but the position of the remaining elements must be carefully optimized. This issue is faced here by introducing novel energy functions in the simulated annealing (SA) algorithm. At each iteration step of the optimization process, one element is freely translated and the associated radiated pattern is simulated. To control the pressure field behavior at multiple depths, three energy functions inspired by the pressure field radiated by a Blackman-tapered spiral array are introduced. Such energy functions aim at limiting the main lobe width while lowering the side lobe and grating lobe levels at multiple depths. Numerical optimization results illustrate the influence of the number of iterations, pressure measurement points, and depths, as well as the influence of the energy function definition on the optimized layout. It is also shown that performance close to or even better than the one provided by a spiral array, here assumed as reference, may be obtained. The finite-time convergence properties of SA allow the duration of the optimization process to be set in advance.

Semiautomatic registration of 3D transabdominal ultrasound images for patient repositioning during postprostatectomy radiotherapy.

PURPOSE: The aim of the present work is to propose and evaluate registration algorithms of three-dimensional (3D) transabdominal (TA) ultrasound (US) images to setup postprostatectomy patients during radiation therapy. METHODS: Three registration methods have been developed and evaluated to register a reference 3D-TA-US image acquired during the planning CT session and a 3D-TA-US image acquired before each treatment session. The first method (method A) uses only gray value information, whereas the second one (method B) uses only gradient information. The third one (method C) combines both sets of information. All methods restrict the comparison to a region of interest computed from the dilated reference positioning volume drawn on the reference image and use mutual information as a similarity measure. The considered geometric transformations are translations and have been optimized by using the adaptive stochastic gradient descent algorithm. Validation has been carried out using manual registration by three operators of the same set of image pairs as the algorithms. Sixty-two treatment US images of seven patients irradiated after a prostatectomy have been registered to their corresponding reference US image. The reference registration has been defined as the average of the manual registration values. Registration error has been calculated by subtracting the reference registration from the algorithm result. For each session, the method has been considered a failure if the registration error was above both the interoperator variability of the session and a global threshold of 3.0 mm. RESULTS: All proposed registration algorithms have no systematic bias. Method B leads to the best results with mean errors of -0.6, 0.7, and -0.2 mm in left-right (LR), superior-inferior (SI), and anterior-posterior (AP) directions, respectively. With this method, the standard deviations of the mean error are of 1.7, 2.4, and 2.6 mm in LR, SI, and AP directions, respectively. The latter are inferior to the interoperator registration variabilities which are of 2.5, 2.5, and 3.5 mm in LR, SI, and AP directions, respectively. Failures occur in 5%, 18%, and 10% of cases in LR, SI, and AP directions, respectively. 69% of the sessions have no failure. CONCLUSIONS: Results of the best proposed registration algorithm of 3D-TA-US images for postprostatectomy treatment have no bias and are in the same variability range as manual registration. As the algorithm requires a short computation time, it could be used in clinical practice provided that a visual review is performed.

Impact of probe pressure variability on prostate localization for ultrasound-based image-guided radiotherapy.

PURPOSE: To evaluate the impact of transabdominal probe pressure on prostate positioning with an intramodality ultrasound (US) image-guided-radiotherapy system and to quantify pressure variability over the treatment course. MATERIAL AND METHODS: 8 prostate cancer patients (group A) and 17 healthy volunteers underwent 3 consecutive US images with increasing probe pressure levels, and 1 CT acquisition for the group A only. Prostate positions were compared after manual registration of the first US image contour projected on 2 others. Group A's pressure levels were quantified by measuring skin-to-skin distances between corresponding CT-US images. The same methodology was used on paired CT/CBCT-US images acquired during treatments of 18 prostate cancer patients to determine whether the different pressure levels applied to the group A were close to the clinical practices and to quantify pressure variability along the treatment course. RESULTS: 84% of 3D prostate displacements were above 2mm for at least one pressure level. Probe pressures deliberately applied were similar to the ones observed clinically. The latter drastically varied between sessions. CONCLUSION: Even with an intramodality system, probe pressure can impact prostate localization because of the pressure variability along the treatment course. Therefore, margins should be expanded from 0.5 to 1.2mm to ensure treatment accuracy.

Line filtering for surgical tool localization in 3D ultrasound images.

We present a method for automatic surgical tool localization in 3D ultrasound images based on line filtering, voxel classification and model fitting. This could possibly provide assistance for biopsy needle or micro-electrode insertion, or a robotic system performing this insertion. The line-filtering method is first used to enhance the contrast of the 3D ultrasound image, then a classifier is chosen to separate the tool voxels, in order to reduce the number of outliers. The last step is Random Sample Consensus (RANSAC) model fitting. Experimental results on several different polyvinyl alcohol (PVA) cryogel data sets demonstrate that the failure rate of the method proposed herein is improved by at least 86% compared to the model-fitting RANSAC algorithm with axis accuracy better than 1mm, at the expense of only a modest increase in computational effort. The results of this experiment show that this system could be useful for clinical applications.

Automatic needle detection and tracking in 3D ultrasound using an ROI-based RANSAC and Kalman method.

This article proposes a robust technique for needle detection and tracking using three-dimensional ultrasound (3D US). It is difficult for radiologists to detect and follow the position of micro tools, such as biopsy needles, that are inserted in human tissues under 3D US guidance. To overcome this difficulty, we propose a method that automatically reduces the processed volume to a limited region of interest (ROI), increasing at the same time the calculation speed and the robustness of the proposed technique. First, a line filter method that enhances the contrast of the needle against the background is used to facilitate the initialization of ROI using the tubularness information of the complete US volume. Then, the random sample consensus (RANSAC) and Kalman filter (RK) algorithm is used in the ROI to detect and track the precise position of the needle. A series of numerical inhomogeneous phantoms with a needle simulated from real 3D US volumes are used to evaluate our method. The results show that the proposed method is much more robust than the RANSAC algorithm when detecting the needle, regardless of whether or not the insertion axis corresponds to an acquisition plane in the 3D US volume. The possibility of failure is also discussed in this article.

Design of optimal 2-D nongrid sparse arrays for medical ultrasound.

Three-dimensional imaging with 2-D matrix probes is one of the most exciting recent ultrasound innovations. Unfortunately, the number of elements of a 2-D matrix probe is often very high, and reducing this number deteriorates the beam properties. In this paper, we propose a new sparse-array design technique with irregular element positioning, which significantly reduces the number of active elements as well as the grating-lobe level. In particular, we introduce a new cost function for optimizing the weighting coefficients of the elements and a new annealing-based algorithm to compute the lowest cost solutions. Numerical simulations show substantial improvements over standard sparse arrays.

Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing.

Compressive sensing (CS) theory makes it possible - under certain assumptions - to recover a signal or an image sampled below the Nyquist sampling limit. In medical ultrasound imaging, CS could allow lowering the amount of acquired data needed to reconstruct the echographic image. CS thus offers the perspective of speeding up echographic acquisitions and could have many applications, e.g. triplex acquisitions for CFM/B-mode/Doppler imaging, high-frame-rate echocardiography, 3D imaging using matrix probes, etc. The objective of this paper is to study the feasibility of CS for the reconstruction of channel RF data, i.e. the 2D set of raw RF lines gathered at the receive elements. Successful application of CS implies selecting a representation basis where the data to be reconstructed have a sparse expansion. Because they consist mainly in warped oscillatory patterns, channel RF data do not easily lend themselves to a sparse representation and thus represent a specific challenge. Within this perspective, we propose to perform and assess CS reconstruction of channel RF data using the recently introduced wave atoms [1] representation, which exhibit advantageous properties for sparsely representing such oscillatory patterns. Reconstructions obtained using wave atoms are compared with the reconstruction performed with two conventional representation bases, namely Fourier and Daubechies wavelets. The first experiment was conducted on simulated channel RF data acquired from a numerical cyst phantom. The quality of the reconstructions was quantified through the mean absolute error at varying subsampling rates by removing 50-90% of the original samples. The results obtained for channel RF data reconstruction yield error ranges of [0.6-3.0]×10(-2), [0.2-2.6]×10(-2), [0.1-1.5]×10(-2), for wavelets, Fourier and wave atoms respectively. The error ranges observed for the associated beamformed log-envelope images are [2.4-20.6]dB, [1.1-12.2]dB, and [0.5-8.8dB] using wavelets, Fourier, and wave atoms, respectively. These results thus show the superiority of the wave atom representation and the feasibility of CS for the reconstruction of US RF data. The second experiment aimed at showing the experimental feasibility of the method proposed using a data set acquired by imaging a general-purpose phantom (CIRS Model 054GS) using an Ultrasonix MDP scanner. The reconstruction was performed by removing 80% of the initial samples and using wave atoms. The reconstructed image was found to reliably preserve the speckle structure and was associated with an error of 5.5dB.

Model fitting using RANSAC for surgical tool localization in 3-D ultrasound images.

Ultrasound guidance is used for many surgical interventions such as biopsy and electrode insertion. We present a method to localize a thin surgical tool such as a biopsy needle or a microelectrode in a 3-D ultrasound image. The proposed method starts with thresholding and model fitting using random sample consensus for robust localization of the axis. Subsequent local optimization refines its position. Two different tool image models are presented: one is simple and fast and the second uses learned a priori information about the tool's voxel intensities and the background. Finally, the tip of the tool is localized by finding an intensity drop along the axis. The simulation study shows that our algorithm can localize the tool at nearly real-time speed, even using a MATLAB implementation, with accuracy better than 1 mm. In an experimental comparison with several alternative localization methods, our method appears to be the fastest and the most robust one. We also show the results on real 3-D ultrasound data from a PVA cryogel phantom, turkey breast, and breast biopsy.

A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease.

Ultrasound elastography is a promising imaging technique that can assist in diagnosis of thyroid cancer. However, the complexity of the tissue movements under freehand compression requires the use of a parametric displacement model and a specific estimation method adapted to sub-pixel motion. Therefore, the aim of this study was to develop a motion estimation method for ultrasound elastography and test its performances compared to a classical block matching technique. The proposed method, referred to as Bilinear Deformable Block Matching (BDBM), uses a bilinear model with eight parameters for controlling the local mesh deformation. In addition, a technique of motion initialization based on a triangle scan of the images adapted to ultrasound elastography is proposed. The BDBM method includes an iterative multi-scale process. This iterative approach is shown to decrease the absolute error of the displacement estimation by a factor of 1.4 when passing from 1 to 2 iterations. The method was tested on simulated images and the results show that absolute displacement estimation error was reduced by a factor of 4 compared to classical block matching. We applied the BDBM method on three experimental sets of data. In the first data set, a phantom designed for ultrasound elastography was used. The two other sets of data involve the thyroid gland and were acquired using freehand tissue compression by ultrasound probe of a clinical ultrasound scanner modified for research. A similarity measurement based on local cross-correlation shows that, for experimental data, the BDBM method outperforms the usual block matching.