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Purpose: To evaluate the feasibility and to report the early outcomes of focal treatment of prostate cancer using lowdose- rate brachytherapy (LDR-PB). Material and methods: Seventeen patients were screened with multi-parametric magnetic resonance imaging (mpMRI), 14 of whom proceeded to receive trans-perineal template mapping biopsy (TTMB). Focal LDR-PB was performed on five eligible patients using dual air kerma strength treatment plans based on planning target volumes derived from cancer locations and determined by TTMB. Patient follow-up includes prostate specific antigen (PSA) measurements, urinary and sexual function questionnaires, repeated imaging and TTMB at specific intervals post-treatment. Results: Feasibility of focal LDR-PB was shown and short-term outcomes are promising. While the detection rate of tumors, a majority of which were low grade GS 3 + 3, was found to be low on mpMRI (sensitivity of 37.5%), our results suggest the potential of mpMRI in detecting the presence of higher grade (GS ≥ 3 + 4), and bilateral disease indicating its usefulness as a screening tool for focal LDR-PB. Conclusions: Low-dose-rate brachytherapy is a favorable ablation option for focal treatment of prostate cancer, requiring minimal modification to the standard (whole gland) LDR-PB treatment, and appears to have a more favorable side effect profile. Further investigation, in the form of a larger study, is needed to assess the methods used and the long-term outcomes of focal LDR-PB. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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]

Prostate segmentation in B-mode images is a challenging task even when done manually by experts. In this paper, we propose a 3-D automatic prostate segmentation algorithm which makes use of information from both ultrasound B-mode and vibro-elastography data. We exploit the high contrast-to-noise ratio of vibro-elastography images of the prostate, in addition to the commonly used B-mode images, to implement a 2-D active shape model (ASM)-based segmentation algorithm on the mid-gland image. The prostate model is deformed by a combination of two measures: the gray level similarity and the continuity of the prostate edge in both image types. The automatically obtained mid-gland contour is then used to initialize a 3-D segmentation algorithm which models the prostate as a tapered and warped ellipsoid. Vibro-elastography images are used in addition to ultrasound images to improve boundary detection. We report a Dice similarity coefficient of 0.87 \pm 0.07 and 0.87 \pm 0.08 comparing the 2-D automatic contours with manual contours of two observers on 61 images. For 11 cases, a whole gland volume error of 10.2 \pm 2.2% and 13.5 \pm 4.1% and whole gland volume difference of -7.2\pm 9.1\% and -13.3\pm 12.6\% between 3-D automatic and manual surfaces of two observers is obtained. This is the first validated work showing the fusion of B-mode and vibro-elastography data for automatic 3-D segmentation of the prostate. [ABSTRACT FROM AUTHOR]

We propose a novel fiducial-free approach for the registration of C-arm fluoroscopy to 3-D ultrasound images of prostate brachytherapy implants to enable dosimetry. The approach involves the reliable detection of a subset of radioactive seeds from 3-D ultrasound, and the use of needle tracks in both ultrasound and fluoroscopy for registration. Seed detection in ultrasound is achieved through template matching in 3-D radio frequency ultrasound signals, followed by thresholding and spatial filtering. The resulting subset of seeds is registered to the complete reconstruction of the brachytherapy implant from multiple C-arm fluoroscopy views. To compensate for the deformation caused by the ultrasound probe, simulated warping is applied to the seed cloud from fluoroscopy. The magnitude of the applied warping is optimized within the registration process. The registration is performed in two stages. First, the needle track projections from fluoroscopy and ultrasound are matched. Only the seeds in the matched needles are then used as fiducials for point-based registration. We report results from a physical phantom with a realistic implant (average postregistration seed distance of 1.6 \pm 1.2 mm) and from five clinical patient datasets (average error: 2.8 \pm 1.5 mm over 128 detected seeds). We conclude that it is feasible to use RF ultrasound data, template matching, and spatial filtering to detect a reliable subset of brachytherapy seeds from ultrasound to enable registration to fluoroscopy for dosimetry. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Optimizing Prostate Cancer Treatment in Men with Advanced Local disease (OPTiMAL) is a single-arm phase II study targeted towards unfavourable risk patients. As a successor to the largest randomized clinical trial (ASCENDE) comparing low-dose-rate prostate brachytherapy (LDR-PB) boost to external beam radiation therapy (EBRT) boost for prostate cancer (PCa) treatment, this study is designed to continue the use of the LDR-PB boost that resulted in high levels of biochemical progression-free survival (b-PFS) in ASCENDE while also aiming to reduce adverse side effects by lowering the overall dose and also limiting high dose regions to identified sites of disease. The central treatment policy in OPTiMAL is to give 100% of the minimum prescribed brachytherapy dose (mpd) to the whole prostate gland while limiting regions with ≥150% of the mpd−as much as possible−to areas containing PCa as determined from transperineal template-guided mapping biopsy (TTMB). In addition, the mpd has been lowered to 100 Gy relative to the 115 Gy ASCENDE dose, and EBRT is delivered after the implant to enable the implanted seeds to be used for prostate localization on verification images. Treatment also includes androgen deprivation therapy (ADT) between TTMB and implant. An important additional aim of the OPTiMAL study is to use the TTMB results as a "ground truth" for investigating the potential of multi-modal advanced imaging of the prostate to better delineate tumours and target them appropriately during treatment. Imaging modalities included in the study are as follows: (1) multi-parametric magnetic resonance imaging (mpMRI) following a protocol compatible with PI-RADs V2, (2) magnetic resonance elastography (MRE) (in the same mpMRI session), (3) multi-parametric transrectal ultrasound (mpTRUS), including shear wave absolute vibro-elastography imaging (SWAVE), strain elastography, and time-series B-mode, all acquired immediately prior to and with the patient in position for the TTMB. Although OPTiMAL does not specifically use the findings from these multi-modal imaging studies for brachytherapy treatment planning, the potential of these multi-modal images for PCa localization is being studied by comparing the findings with the TTMB results. Biopsy core locations in the prostate are tracked during the procedure using ultrasound imaging and the VariPath module in VariSeed software (Varian Medical Systems, Palo Alto, CA, United States). Once available, pathology findings are allocated to the tracked cores within the VariPath software and then localized in the multi-modal images using intensity and surface-based image registration to the TTMB ultrasound images and labels. The OPTiMAL study is ongoing and 11 patients have been enrolled so far with 10 patients fulfilling all requirements for LDR-PB boost treatment. Advanced imaging data have been collected for 5 of the patients. Initial analysis of the elastography data (MRE, SWAVE) indicates a positive correlation with the TTMB ground truth. Fig 1 illustrates the correlation of the TTMB findings with the elastography images for P10. Initial analysis suggests that such multi-modal imaging data has the potential to localize tumours reliably which can eventually replace the invasive TTMB procedure altogether. We are currently recruiting more patients and with more imaging and follow-up (relapse rate, side effects endured) data available, a deeper analysis will be conducted. [ABSTRACT FROM AUTHOR]