Seyedeh Sara Mahdavi

Evaluation of visualization of the prostate gland in vibro-elastography images.

In this paper, vibro-elastography (VE), an ultrasound-based method that creates images of tissue viscoelasticity contrast, is evaluated as an imaging modality to visualize and segment the prostate. We report a clinical study to characterize the visibility of the prostate in VE images and the ability to detect the boundary of the gland. Measures for contrast, edge strength characterized by gradient and statistical intensity change at the edge, and the continuity of the edges are proposed and computed for VE and B-mode ultrasound images. Furthermore, using MRI as the gold standard, we compare the error in the computation of the volume of the gland from VE and B-mode images. The results demonstrate that VE images are superior to B-mode images in terms of contrast, with an approximately six fold improvement in contrast-to-noise ratio, and in terms of edge strength, with an approximately two fold improvement in the gradient in the direction normal to the edge. The computed volumes show that the VE images provide an accurate 3D visualization of the prostate with volume errors that are slightly lower than errors computed based on B-mode images. The total gland volume error is 8.8±2.5% for VE vs. MRI and 10.3±4.6% for B-mode vs. MRI, and the total gland volume difference is -4.6±11.1% for VE vs. MRI and -4.1±17.1% for B-mode vs. MRI, averaged over nine patients and three observers. Our results show that viscoelastic mapping of the prostate region using VE images can play an important role in improving the anatomic visualization of the prostate and has the potential of becoming an integral component of interventional procedures such as brachytherapy.

Wheel-Based Climbing Robot : Modeling and Control

This paper addresses the kinematics modeling and control of a novel nonholonomic wheel-based pole climbing robot called UT-PCR. This robot belongs to a challenging and less-studied class of wheel-based mobile robots in which the relative position of the wheels changes in a complex manner and the robot is constrained to maneuver on a closed geometric surface. The problem is formulated in terms of the kinematic model of the robot, which is derived using non-holonomic constraints imposed by the wheels on the motion. This model is an underactuated driftless nonlinear state space (control system) which is linear in its inputs. Feasibility of complex maneuvering is then proved by an analysis of controllability for this nonlinear system. It is shown that three orientations of the robot cannot be controlled independently. Therefore, three basic movements are introduced as the fundamental elements of the kinematic control strategy and stable controllers are designed to create those basic movements. Simulation and experimental results are provided to show the applicability of the proposed control system.

Ultrasound RF time series for tissue typing: First in vivo clinical results

The low diagnostic value of ultrasound in prostate cancer imaging has resulted in an effort to enhance the tumor contrast using ultrasound-based technologies that go beyond traditional B-mode imaging. Ultrasound RF time series, formed by echo samples originating from the same location over a few seconds of imaging, has been proposed and experimentally used for tissue typing with the goal of cancer detection. In this work, for the first time we report the preliminary results of in vivo clinical use of spectral parameters extracted from RF time series in prostate cancer detection. An image processing pipeline is designed to register the ultrasound data to wholemount histopathology references acquired from prostate specimens that are removed in radical prostatectomy after imaging. Support vector machine classification is used to detect cancer in 524 regions of interest of size 5×5 mm, each forming a feature vector of spectral RF time series parameters. Preliminary ROC curves acquired based on RF time series analysis for individual cases, with leave-one-patient-out cross validation, are presented and compared with B-mode texture analysis.

A robotic system for intra-operative trans-rectal ultrasound and ultrasound elastography in radical prostatectomy

A new robotic system for trans-rectal ultrasound (TRUS) imaging during robot-assisted laparoscopic radical prostatectomy is described. The system consists of three main parts: a robotic probe manipulator (robot), an ultrasound machine with a biplane TRUS probe, and control and image processing software. A review of prior use of TRUS during prostatectomy is provided in order to demonstrate the potential benefits of such intra-operative imaging. The ability of the system to capture two-dimensional and three-dimensional B-mode and elastography data is demonstrated using a prostate phantom. A registration method that can be used for instrument tracking in RALRP is described and tested. Initial patient images captured using the system are presented.

A Multi-Atlas-Based Segmentation Framework for Prostate Brachytherapy

Low-dose-rate brachytherapy is a radiation treatment method for localized prostate cancer. The standard of care for this treatment procedure is to acquire transrectal ultrasound images of the prostate in order to devise a plan to deliver sufficient radiation dose to the cancerous tissue. Brachytherapy planning involves delineation of contours in these images, which closely follow the prostate boundary, i.e., clinical target volume. This process is currently performed either manually or semi-automatically, which requires user interaction for landmark initialization. In this paper, we propose a multi-atlas fusion framework to automatically delineate the clinical target volume in ultrasound images. A dataset of a priori segmented ultrasound images, i.e., atlases, is registered to a target image. We introduce a pairwise atlas agreement factor that combines an image-similarity metric and similarity between a priori segmented contours. This factor is used in an atlas selection algorithm to prune the dataset before combining the atlas contours to produce a consensus segmentation. We evaluate the proposed segmentation approach on a set of 280 transrectal prostate volume studies. The proposed method produces segmentation results that are within the range of observer variability when compared to a semi-automatic segmentation technique that is routinely used in our cancer clinic.

Biomechanical Modeling of the Prostate for Procedure Guidance and Simulation

Biomechanical models of the prostate have a number of potential applications in the diagnosis and management of prostate cancer. Most importantly, it has been shown in several studies that cancerous prostate tissue has different viscoelastic properties than normal prostate tissue: it is typically stiffer (higher storage modulus) and more viscous (higher loss modulus). If a strong correlation can be obtained between malignant tissue and its viscoelastic properties, then all commonly practiced prostate cancer procedures—biopsies, surgery and radiation treatment—can be improved by elasticity imaging. The elastic properties of the prostate and peri-prostatic tissue can also be used in procedure planning, even if such elastic properties do not show strong correlation to cancer. This chapter starts with an introduction to the prostate anatomy, prostate cancer, and a description of the most common procedures and their clinical needs. It continues by presenting the potential impact of elasticity imaging on these procedures. A brief survey of elastography techniques is presented next, with a sampling of some prostate elastography results to date. We describe two of the systems that we developed for the acquisition of prostate ultrasound and magnetic resonance elastography images and summarize our results to date. We show that these elasticity images can be used for prostate segmentation and cross-modality image registration. Furthermore, we show how prostate region deformation models can be used in the development of a prostate brachytherapy simulator which can also be used in the planning of needle insertions that account for deformation.

An Automatic Multi-atlas Segmentation of the Prostate in Transrectal Ultrasound Images Using Pairwise Atlas Shape Similarity

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.

Vibro-Elastography for Visualization of the Prostate Region: Method Evaluation

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.

Automatic prostate segmentation using fused ultrasound B- mode and elastography images

In this paper we propose a fully automatic 2D prostate segmentation algorithm using fused ultrasound (US) and elastography images. We show that the addition of information from mechanical tissue properties acquired from elastography to acoustic information from B-mode ultrasound, can improve segmentation results. Gray level edge similarity and edge continuity in both US and elastography images deform an Active Shape Model. Comparison of automatic and manual contours on 107 transverse images of the prostate show a mean absolute error of 2.6 +/- 0.9 mm and a running time of 17.9 +/- 12.2 s. These results show that the combination of the high contrast elastography images with the more detailed but low contrast US images can lead to very promising results for developing an automatic 3D segmentation algorithm.

Clinical Target-Volume Delineation in Prostate Brachytherapy Using Residual Neural Networks

Low dose-rate prostate brachytherapy is commonly used to treat early stage prostate cancer. This intervention involves implanting radioactive seeds inside a volume containing the prostate. Planning the intervention requires obtaining a series of ultrasound images from the prostate. This is followed by delineation of a clinical target volume, which mostly traces the prostate boundary in the ultrasound data, but can be modified based on institution-specific clinical guidelines. Here, we aim to automate the delineation of clinical target volume by using a new deep learning network based on residual neural nets and dilated convolution at deeper layers. In addition, we propose to include an exponential weight map in the optimization to improve local prediction. We train the network on 4,284 expert-labeled transrectal ultrasound images and test it on an independent set of 1,081 ultrasound images. With respect to the gold-standard delineation, we achieve a mean Dice similarity coefficient of 94%, a mean surface distance error of 1.05 mm and a mean Hausdorff distance error of 3.0 mm. The obtained results are statistically significantly better than two previous state-of-the-art techniques.