Showing papers by "Septimiu E. Salcudean published in 2015"

Ultrasound RF Time Series for Classification of Breast Lesions

Abstract: This work reports the use of ultrasound radio frequency (RF) time series analysis as a method for ultrasound-based classification of malignant breast lesions. The RF time series method is versatile and requires only a few seconds of raw ultrasound data with no need for additional instrumentation. Using the RF time series features, and a machine learning framework, we have generated malignancy maps, from the estimated cancer likelihood, for decision support in biopsy recommendation. These maps depict the likelihood of malignancy for regions of size $1~{\hbox {mm}}^{2}$ within the suspicious lesions. We report an area under receiver operating characteristics curve of 0.86 (95% confidence interval [CI]: 0.84%–0.90%) using support vector machines and 0.81 (95% CI: 0.78–0.85) using Random Forests classification algorithms, on 22 subjects with leave-one-subject-out cross-validation. Changing the classification method yielded consistent results which indicates the robustness of this tissue typing method. The findings of this report suggest that ultrasound RF time series, along with the developed machine learning framework, can help in differentiating malignant from benign breast lesions, subsequently reducing the number of unnecessary biopsies after mammography screening.

Projection-Based Phase Features for Localization of a Needle Tip in 2D Curvilinear Ultrasound

Abstract: Localization of a needle's tip in ultrasound images is often a challenge during percutaneous procedures due to the inherent limitations of ultrasound imaging. A new method is proposed for tip localization with curvilinear arrays using local image statistics over a region extended from the partially visible needle shaft. First, local phase-based image projections are extracted using orientation-tuned Log-Gabor filters to coarsely estimate the needle trajectory. The trajectory estimation is then improved using a best fit iterative method. To account for the typically discontinuous needle shaft appearance, a geometric optimization is then performed that connects the extracted inliers of the point cloud. In the final stage, the enhanced needle trajectory points are passed to a feature extraction method that uses a combination of spatially distributed image statistics to enhance the needle tip. The needle tip is localized using the enhanced images and calculated trajectory. Validation results obtained from 150 ex vivo ultrasound scans show an accuracy of 0.43 ±0.31 mm for needle tip localization.

Needle detection in curvilinear ultrasound images based on the reflection pattern of circular ultrasound waves.

Abstract: Purpose: Ultrasound imaging provides a low-cost, real-time modality to guide needle insertion procedures, but localizing the needle using conventional ultrasound images is often challenging. Estimating the needle trajectory can increase the success rate of ultrasound-guided needle interventions and improve patient comfort. In this study, a novel method is introduced to localize the needle trajectory in curvilinear ultrasound images based on the needle reflection pattern of circular ultrasound waves. Methods: A circular ultrasound wave was synthesized by sequentially firing the elements of a curvilinear transducer and recording the radio-frequency signals received by each element. Two features, namely, the large amplitude and repetitive reflection pattern, were used to identify the needle echoes in the received signals. The trajectory of the needle was estimated by fitting the arrival times of needle echoes to an equation that describes needle reflection of circular waves. The method was employed to estimate the trajectories of needles inserted in agar phantom, beef muscle, and porcine tissue specimens. Results: The maximum error rates of estimating the needle trajectories were on the order of 1 mm and 3° for the radial and azimuth coordinates, respectively. Conclusions: These results suggest that the proposed method can improve the robustness and accuracy of needle segmentation methods by adding signature-based detection of the needle trajectory in curvilinear ultrasound images. The method can be implemented on conventional ultrasound imaging systems.

On the feasibility of heart motion compensation on the daVinci® surgical robot for coronary artery bypass surgery: Implementation and user studies

Abstract: This paper describes the implementation of a heart motion compensation system on the da Vinci surgical system (Intuitive Surgical Inc.) for coronary artery bypass surgery. By introducing a robot-assisted solution, this surgery could be performed completely minimally invasively and on a beating heart. In this work we describe the development of open loop controllers based on spectral line decomposition and the assumption of a periodic trajectory. This allows the da Vinci patient-side manipulators to track an actual heart trajectory with sub-millimetre error. Further, to simulate a virtually stabilized environment, we present the novel concept of maintaining the camera fixed relative to the heart target, effectively decoupling the vision tracking and arm tracking challenges. Finally, we executed preliminary experiments to evaluate surgeons' ability to perform simulated suturing and peg transfer tasks on a moving target. Performance for the simulated suturing was evaluated based on task completion time, accuracy of needle placement, and number of errors. For the suture task, the number of missed targets decreased from 37% to 13% when compensation was enabled, the number of hit targets increased from 26% to 41%, and completion time decreased. For the peg transfer tasks, again completion time and number of errors were measured. Though the margin for error was larger, there was less perceived difficulty of the task when compensation was enabled.

Direct vibro-elastography FEM inversion in Cartesian and cylindrical coordinate systems without the local homogeneity assumption

Abstract: To produce images of tissue elasticity, the vibro-elastography technique involves applying a steady-state multi-frequency vibration to tissue, estimating displacements from ultrasound echo data, and using the estimated displacements in an inverse elasticity problem with the shear modulus spatial distribution as the unknown. In order to fully solve the inverse problem, all three displacement components are required. However, using ultrasound, the axial component of the displacement is measured much more accurately than the other directions. Therefore, simplifying assumptions must be used in this case. Usually, the equations of motion are transformed into a Helmholtz equation by assuming tissue incompressibility and local homogeneity. The local homogeneity assumption causes significant imaging artifacts in areas of varying elasticity. In this paper, we remove the local homogeneity assumption. In particular we introduce a new finite element based direct inversion technique in which only the coupling terms in the equation of motion are ignored, so it can be used with only one component of the displacement. Both Cartesian and cylindrical coordinate systems are considered. The use of multi-frequency excitation also allows us to obtain multiple measurements and reduce artifacts in areas where the displacement of one frequency is close to zero. The proposed method was tested in simulations and experiments against a conventional approach in which the local homogeneity is used. The results show significant improvements in elasticity imaging with the new method compared to previous methods that assumes local homogeneity. For example in simulations, the contrast to noise ratio (CNR) for the region with spherical inclusion increases from an average value of 1.5-17 after using the proposed method instead of the local inversion with homogeneity assumption, and similarly in the prostate phantom experiment, the CNR improved from an average value of 1.6 to about 20.

Vibro-elastography: Absolute elasticity from motorized 3D ultrasound measurements of harmonic motion vectors

Abstract: Absolute elasticity images can be generated from harmonic motion vector measurements over a volume, usually with MRI, called MR elastography (MRE). This work aims to apply the same method using motorized 3D ultrasound. Previous work has demonstrated measurement of motion vectors over ultrasound volumes, however measuring dynamic motion on the order of hundreds of cycles per second remains a challenge. A solution is proposed based on synchronizing the ultrasound acquisition with the mechanical exciter producing the waves. Measured motion is smoothed using a constraint on the divergence. An elasticity reconstruction algorithm used in MRE is applied to ultrasound data captured on a tissue mimicking phantom. The reconstructed values are within 20% and 8% of previous MRE measurements of hard and soft regions, respectively.

A retrofit eye gaze tracker for the da Vinci and its integration in task execution using the da Vinci Research Kit

Abstract: The integration of eye-gaze tracking at the console of surgical robots has the potential to add both speed and functionality to the human-robot interface. In this paper, we present a novel eye gaze tracker that can be integrated as a simple retrofit to the da Vinci console. In particular, the eye tracker can be used with the da Vinci Research Kit (dVRK) to control the patient side manipulators. We present the eye-tracker design and calibration. First, a 2D calibration is carried out to estimate the gaze on the da Vinci's stereoscopic display, followed by a 3D “hand-eye” calibration to estimate the gaze in the surgical scene. Using the dVRK, we demonstrated the use of the eye-tracker to perform a gaze-assisted task, in which the estimated gaze is used as a set point for the robot and the user is guided towards the point of gaze through haptic feedback. The task performed was peg placement and was evaluated in a preliminary user study with five users.

A framework for optimization-based design of motion encoding in magnetic resonance elastography.

Abstract: Purpose In conventional three-dimensional magnetic resonance elastography, motion encoding gradients (MEGs) synchronized to a mechanical excitation are applied separately in each direction to encode tissue displacement generated by the corresponding waves. This requires long acquisition times that introduce errors due to patient motion and may hinder clinical deployment of magnetic resonance elastography. In this article, a framework for MEGs sequence design is proposed to reduce scanning time and increase signal-to-noise ratio. Theory and Methods The approach is based on applying MEGs in all three directions simultaneously with varying parameters, and formulation of the problem as a linear estimation of the wave properties. Multidirectional MEGs sequences are derived by setting the problem in an experimental design framework. Such designs are implemented and evaluated on simulation and phantom data. Results Estimation error of the displacement using the proposed MEGs designs is reduced up to a factor of two in comparison with a unidirectional design with a same number of acquisitions. Alternatively, for the same error, scanning time is reduced up to a factor of three using the multidirectional designs. Conclusion The proposed framework generalizes acquisition of magnetic resonance elastography, and allows quantification of design performance, and optimization-based derivation of designs. Magn Reson Med 73:1514–1525, 2015. © 2014 Wiley Periodicals, Inc.

Prostate vibro-elastography: Multi-frequency 1D over 3D steady-state shear wave imaging for quantitative elastic modulus measurement

Abstract: Background, Motivation and Objective: This article describes a state of the art ultrasound system that is used to quantify tissue elasticity of the prostate. Statement of Contribution/Methods: The system involves the measurement of the steady-state multi-frequency response of tissue to transperineal excitation while imaging the gland using the sagittal array of the BK Medical 8848 trans-rectal ultrasound transducer. A roll motor rotates the transducer in discrete steps to create a volume sector of images. GPU Processing of raw in-phase/quadrature data streamed from the ultrasound machine is used to measure the axial (radial) spatial distribution of tissue motion and compute displacement phasors. Bandpass sampling is used to perform the reconstruction since the sampling frequency is below the Nyquist rate. Finally, a local frequency estimator is used to compute the Youngs Modulus from the three dimensional local spatial wavelengths of the shear wave that is created at each of the excitation frequencies. Results/Discussion: The system has been used in several clinical studies but its design and function has not been published before. Some of the study results have been published and include an area under receiver operating characteristic curve of 0.820.01 with regards to prostate cancer identification in the peripheral zone. The accuracy of the elastic modulus measurement was validated using a CIRS elastography phantom. Numerical and visual correlations between our elasticity measurements and pathology results demonstrate its clinical potential. The system has been expanded to a generic platform that can be used to provide real-time quantitative elasticity measurements in other organs.

A System for MR-Ultrasound Guidance during Robot-Assisted Laparoscopic Radical Prostatectomy

Abstract: We describe a new ultrasound and magnetic resonance image guidance system for robot assisted radical prostatectomy and its first use in patients This system integrates previously developed and new components and presents to the surgeon preoperative magnetic resonance images MRI registered to real-time 2D ultrasound to inform the surgeon of anatomy and cancer location At the start of surgery, a trans-rectal ultrasound TRUS is manually positioned for prostate imaging using a standard brachytherapy stepper When the anterior prostate surface is exposed, the TRUS, which can be rotated under computer control, is registered to one of the da Vinci patient-side manipulators by recognizing the tip of the da Vinci instrument at multiple locations on the tissue surface A 3D TRUS volume is then taken, which is segmented semi-automatically A segmentation-based, biomechanically regularized deformable registration algorithm is used to register the 3D TRUS image to preoperatively acquired and annotated T2-weighted images, which are deformed to the patient MRI and TRUS images can then be pointed at and examined by the surgeon at the da Vinci console We outline the approaches used and present our experience with the system in the first two patients While this work is preliminary, the feasibility of fused MRI and TRUS during radical prostatectomy has not been demonstrated before Given the significant rates of positive surgical margins still reported in the literature, such a system has potentially significant clinical benefits

Automatic Prostate Brachytherapy Preplanning Using Joint Sparse Analysis

Abstract: Prostate brachytherapy preplanning is the process of determining treatment target volume and arrangement of radioactive seeds w.r.t. the target volume prior to the implantation. Although preplanning is typically performed by a trained expert using a strict set of guidelines, the process remains highly subjective, resulting in significant user-dependent variability in the plans. In this work, we aim to reduce the preplanning variability by automating the seed arrangement process. We propose a novel framework which uses a retrospective treatment dataset to extract common radioactive seed patterns. The framework captures the inter-relation between the treatment volume delineation and seed arrangements through a joint sparse representation of retrospective data. This representation is used to estimate an initial seed arrangement for a new treatment volume, followed by a novel optimization process which captures the clinical guidelines, to fine-tune the seed arrangement. The proposed framework is evaluated on a dataset of 590 brachytherapy treatment cases by 5-fold cross validation. It achieves 86% success rate, when compared to the clinical guidelines and the actual plans.

Multimodal classification of prostate tissue: a feasibility study on combining multiparametric MRI and ultrasound

Abstract: The common practice for biopsy guidance is through transrectal ultrasound, with the fusion of ultrasound and MRI-basedtargetswhenavailable. However,ultrasoundis onlyusedas aguidancemodalityinMR-targetedultrasound-guidedbiopsy,even though previous work has shown the potential utility of ultrasound, particularly ultrasound vibro-elastography, as atissue typingapproach. We arguethat multiparametricultrasound,whichincludesB-modeandvibro-elastographyimages,could contain information that is not captured using multiparametric MRI (mpMRI) and therefore play a role in ren ingthe biopsy and treatment strategies. In this work, we combine mpMRI with multiparametric ultrasound features fromregistered tissue areas to examine the potential improvement in cancer detection. All the images were acquired prior toradicalprostatectomyandcancerdetectionwas validatedbasedon 36wholemounthistologyslides. We calculateda set of24 texture features from vibro-elastographyand B-mode images, and v e features from mpMRI. Then we used recursivefeature elimination (RFE) and sparse regression through LASSO to n d an optimal set of features to be used for tissueclassic ation. We show that the set of these selected features increases the area under ROC curve from 0.87 with mpMRIalone to 0.94 with the selected mpMRI and multiparametric ultrasound features, when used with support vector machineclassic ation on features extracted from peripheral zone. For features extracted from the whole-gland, the area under thecurve was 0.75 and 0.82 for mpMRI and mpMRI along with ultrasound, respectively. These preliminary results provideevidence that ultrasound and ultrasound vibro-elastography could be used as modalities for improved cancer detection incombinationwith MRI.

Deep venous thrombosis identification from analysis of ultrasound data

Abstract: The purpose of this research was to determine whether combined ultrasound- and sensor-based compressibility and augmented blood flow measures yielded better results for DVT detection than for the individual measures alone. Twenty-six limbs from 19 patients were scanned using a sensorized ultrasound DVT screening system, and compressibility and flow measures were obtained at 125 locations. Results from conventional compression ultrasound examination were used as gold standard, with seven vessels (four patients) positive for DVT. A classification approach was used to combine the individual DVT measures per vessel and generate an optimal feature for every possible combination of individual measures. Sensitivity and specificity were calculated for the individual measures and for all combined measures, as was a usefulness criteria $$J$$ for measuring class separability. Seven optimal combined features were found with 100 % sensitivity and 100 % specificity, with the best combined feature having a $$J$$ value over two orders of magnitude greater than the best individual DVT measure. The proposed approach for DVT detection combines different aspects of thrombus detection in a novel way generating a quantifiable measure and outperforms any of the individual measures when used independently. All of the combined measures included the flow measure as well as the slope compressibility measure, which uses the magnitude of the force applied by the ultrasound probe, suggesting that these measurements provide important information when characterizing DVT.

Data fusion for planning target volume and isodose prediction in prostate brachytherapy

Abstract: In low-dose prostate brachytherapy treatment, a large number of radioactive seeds is implanted in and adjacent to the prostate gland. Planning of this treatment involves the determination of a Planning Target Volume (PTV), followed by defining the optimal number of seeds, needles and their coordinates for implantation. The two major planning tasks, i.e. PTV determination and seed definition, are associated with inter- and intra-expert variability. Moreover, since these two steps are performed in sequence, the variability is accumulated in the overall treatment plan. In this paper, we introduce a model based on a data fusion technique that enables joint determination of PTV and the minimum Prescribed Isodose (mPD) map. The model captures the correlation between different information modalities consisting of transrectal ultrasound (TRUS) volumes, PTV and isodose contours. We take advantage of joint Independent Component Analysis (jICA) as a linear decomposition technique to obtain a set of joint components that optimally describe such correlation. We perform a component stability analysis to generate a model with stable parameters that predicts the PTV and isodose contours solely based on a new patient TRUS volume. We propose a framework for both modeling and prediction processes and evaluate it on a dataset of 60 brachytherapy treatment records. We show PTV prediction error of 10:02±4:5% and the V100 isodose overlap of 97±3:55% with respect to the clinical gold standard.