Brachytherapy Needle Steering Guidance Using Image Overlay

2018 ◽  
pp. 191-204 ◽  
Author(s):  
Carlos Rossa ◽  
McNiel Inyani Keri ◽  
Mahdi Tavakoli
Keyword(s):  
Target Location ◽  
Interaction Model ◽  
Experimental Results ◽  
Ultrasound Images ◽  
Needle Steering ◽  
Robotic Assisted ◽  
Intended Target ◽  
Image Overlay ◽  
Phantom Tissue ◽  
Insertion Process

This chapter presents a physical simulator for needle steering in brachytherapy. As the user inserts the needle in a phantom tissue, images of the needle and prostate shape reconstructed from 2D transverse ultrasound images are displayed online in a semi-transparent mirror. During insertion, the user sees the images as if they were floating inside the phantom accounting for scale and orientation. The ultrasound images of the needle are combined with a needle-tissue interaction model that predicts the needle deflection further along the insertion process. The necessary manoeuvres that bring the needle towards its intended target location are displayed to the user along with the actual needle location. This platform allows the user to test different manual and robotic-assisted needle steering techniques. Reported experimental results confirm the accuracy of the system in reconstructing and overlaying images onto the phantom.

Obstacle negotiation analysis of track-legged robot based on terramechanics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhu Hongbiao ◽  
Yueming Liu ◽  
Weidong Wang ◽  
Zhijiang Du
Keyword(s):  
Kinematic Model ◽  
Interaction Model ◽  
Experimental Results ◽  
Physical Parameters ◽  
Negotiation Analysis ◽  
Content Type ◽  
Practical Applications ◽  
Loose Soil ◽  
Key Steps ◽  
Drawbar Pull

Purpose This paper aims to present a new method to analyze the robot’s obstacle negotiation based on the terramechanics, where the terrain physical parameters, the sinkage and the slippage of the robot are taken into account, to enhance the robot’s trafficability. Design/methodology/approach In this paper, terramechanics is used in motion planning for all-terrain obstacle negotiation. First, wheel/track-terrain interaction models are established and used to analyze traction performances in different locomotion modes of the reconfigurable robot. Next, several key steps of obstacle-climbing are reanalyzed and the sinkage, the slippage and the drawbar pull are obtained by the models in these steps. In addition, an obstacle negotiation analysis method on loose soil is proposed. Finally, experiments in different locomotion modes are conducted and the results demonstrate that the model is more suitable for practical applications than the center of gravity (CoG) kinematic model. Findings Using the traction performance experimental platform, the relationships between the drawbar pull and the slippage in different locomotion modes are obtained, and then the traction performances are obtained. The experimental results show that the relationships obtained by the models are in good agreement with the measured. The obstacle-climbing experiments are carried out to confirm the availability of the method, and the experimental results demonstrate that the model is more suitable for practical applications than the CoG kinematic model. Originality/value Comparing with the results without considering Terramechanics, obstacle-negotiation analysis based on the proposed track-terrain interaction model considering Terramechanics is much more accurate than without considering Terramechanics.

Ultrasound-Guided Model Predictive Control of Needle Steering in Biological Tissue

2016 ◽  
Vol 01 (01) ◽  
pp. 1640007 ◽  
Author(s):  
Mohsen Khadem ◽  
Carlos Rossa ◽  
Ron S. Sloboda ◽  
Nawaid Usmani ◽  
Mahdi Tavakoli
Keyword(s):  
Biological Tissue ◽  
Tracking System ◽  
Maximum Error ◽  
Needle Insertion ◽  
Steering System ◽  
Ultrasound Images ◽  
Ultrasound Guided ◽  
Needle Steering ◽  
Needle Deflection ◽  
Main Components

In needle-based medical procedures, beveled tip flexible needles are steered inside soft tissue to reach the desired target locations. In this paper, we have developed an autonomous image-guided needle steering system that enhances targeting accuracy in needle insertion while minimizing tissue trauma. The system has three main components. First is a novel mechanics-based needle steering model that predicts needle deflection and accepts needle tip rotation as an input for needle steering. The second is a needle tip tracking system that determines needle deflection from the ultrasound images. The needle steering model employs the estimated needle deflection at the present time to predict needle tip trajectory in the future steps. The third component is a nonlinear model predictive controller (NMPC) that steers the needle inside the tissue by rotating the needle beveled tip. The MPC controller calculates control decisions based on iterative optimization of the predictions of the needle steering model. To validate the proposed ultrasound-guided needle steering system, needle insertion experiments in biological tissue phantoms are performed in two cases–with and without obstacle. The results demonstrate that our needle steering strategy guides the needle to the desired targets with the maximum error of 2.85[Formula: see text]mm.

scholarly journals Cooperative Carrying Control for Multi-Evolutionary Mobile Robots in Unknown Environments

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 298 ◽  
Author(s):  
Jyun-Yu Jhang ◽  
Cheng-Jian Lin ◽  
Kuu-Young Young
Keyword(s):  
Differential Evolution ◽  
Mobile Robots ◽  
Target Location ◽  
Learning Strategy ◽  
Control Method ◽  
Behavioral Control ◽  
Differential Evolution Algorithm ◽  
Experimental Results ◽  
Unknown Environment ◽  
Navigation Control

This study provides an effective cooperative carrying and navigation control method for mobile robots in an unknown environment. The manager mode switches between two behavioral control modes—wall-following mode (WFM) and toward-goal mode (TGM)—based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller (IT2FNC) based on a dynamic group differential evolution (DGDE) is proposed to realize the carrying control and WFM control for mobile robots. The proposed DGDE uses a hybrid method that involves a group concept and an improved differential evolution to overcome the drawbacks of the traditional differential evolution algorithm. A reinforcement learning strategy was adopted to develop an adaptive WFM control and achieve cooperative carrying control for mobile robots. The experimental results demonstrated that the proposed DGDE is superior to other algorithms at using WFM control. Moreover, the experimental results demonstrate that the proposed method can complete the task of cooperative carrying, and can realize navigation control to enable the robot to reach the target location.

scholarly journals Efficient Real-Time Video-in-Video Insertion into a Pre-Encoded Video Stream

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Dan Grois ◽  
Evgeny Kaminsky ◽  
Ofer Hadar
Keyword(s):  
Real Time ◽  
Efficient Method ◽  
Video Sequence ◽  
Video Stream ◽  
Experimental Results ◽  
Second Step ◽  
Visual Content ◽  
Low Bit Rate ◽  
Computationally Intensive ◽  
Insertion Process

This work relates to the developing and implementing of an efficient method and system for the fast real-time Video-in-Video (ViV) insertion, thereby enabling efficiently inserting a video sequence into a predefined location within a pre-encoded video stream. The proposed method and system are based on dividing the video insertion process into two steps. The first step (i.e., the Video-in-Video Constrained Format (ViVCF) encoder) includes the modification of the conventional H.264/AVC video encoder to support the visual content insertion Constrained Format (CF), including generation of isolated regions without using the Frequent Macroblock Ordering (FMO) slicing, and to support the fast real-time insertion of overlays. Although, the first step is computationally intensive, it should to be performed only once even if different overlays have to be modified (e.g., for different users). The second step for performing the ViV insertion (i.e., the ViVCF inserter) is relatively simple (operating mostly in a bit-domain), and is performed separately for each different overlay. The performance of the presented method and system is demonstrated and compared with the H.264/AVC reference software (JM 12); according to our experimental results, there is a significantly low bit-rate overhead, while there is substantially no degradation in the PSNR quality.

scholarly journals Columnar Injection for Intracerebral Cell Therapy

2019 ◽  
Vol 18 (3) ◽  
pp. 321-328
Author(s):  
Jeffrey S Schweitzer ◽  
Bin Song ◽  
Pierre R Leblanc ◽  
Melissa Feitosa ◽  
Bob S Carter ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cell Therapy ◽  
Dopaminergic Neurons ◽  
Target Location ◽  
Embryonic Stem ◽  
Volume Ratio ◽  
Injection Technique ◽  
Graft Material ◽  
Intended Target ◽  
Injection Techniques

Abstract BACKGROUND Surgical implantation of cellular grafts into the brain is of increasing importance, as stem cell-based therapies for Parkinson and other diseases continue to develop. The effect of grafting technique on development and survival of the graft has received less attention. Rate and method of graft delivery may impact the cell viability and success of these therapies. Understanding the final location of the graft with respect to the intended target location is also critical. OBJECTIVE To describe a “columnar injection” technique designed to reduce damage to host tissue and result in a column of graft material with greater surface area to volume ratio than traditional injection techniques. METHODS Using a clinically relevant model system of human embryonic stem cell-derived dopaminergic progenitors injected into athymic rat host brain, we describe a novel device that allows separate control of syringe barrel and plunger, permitting precise deposition of the contents into the cannula tract during withdrawal. Controls consist of contralateral injection using traditional techniques. Graft histology was examined at graft maturity. RESULTS Bolus grafts were centered on the injection tract but were largely proximal to the “target” location. These grafts displayed a conspicuous peripheral distribution of cells, particularly of mature dopaminergic neurons. In contrast, column injections remained centered at the intended target, contained more evenly distributed cells, and had significantly more mature dopaminergic neurons. CONCLUSION We suggest that this columnar injection technique may allow better engraftment and development of intracerebral grafts, enhancing outcomes of cell therapy, compared to fixed-point injection techniques.

scholarly journals Magnetic Needle Steering in Soft Phantom Tissue

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mahdi Ilami ◽  
Reza James Ahmed ◽  
Alex Petras ◽  
Borhan Beigzadeh ◽  
Hamid Marvi
Keyword(s):  
Needle Steering ◽  
Phantom Tissue ◽  
Magnetic Needle

scholarly journals Efficient Deep Learning Architecture for Detection and Recognition of Thyroid Nodules

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jingzhe Ma ◽  
Shaobo Duan ◽  
Ye Zhang ◽  
Jing Wang ◽  
Zongmin Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Data Augmentation ◽  
Experimental Results ◽  
Local Health ◽  
Ultrasound Images ◽  
Detection Time ◽  
Average Precision ◽  
Medical Image Diagnosis ◽  
Detection And Recognition

Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of thyroid nodules have different appearances, interior features, and blurred borders that are difficult for a physician to diagnose into malignant or benign types merely through visual recognition. The development of artificial intelligence, especially deep learning, has led to great advances in the field of medical image diagnosis. However, there are some challenges to achieve precision and efficiency in the recognition of thyroid nodules. In this work, we propose a deep learning architecture, you only look once v3 dense multireceptive fields convolutional neural network (YOLOv3-DMRF), based on YOLOv3. It comprises a DMRF-CNN and multiscale detection layers. In DMRF-CNN, we integrate dilated convolution with different dilation rates to continue passing the edge and the texture features to deeper layers. Two different scale detection layers are deployed to recognize the different sizes of the thyroid nodules. We used two datasets to train and evaluate the YOLOv3-DMRF during the experiments. One dataset includes 699 original ultrasound images of thyroid nodules collected from a local health physical center. We obtained 10,485 images after data augmentation. Another dataset is an open-access dataset that includes ultrasound images of 111 malignant and 41 benign thyroid nodules. Average precision (AP) and mean average precision (mAP) are used as the metrics for quantitative and qualitative evaluations. We compared the proposed YOLOv3-DMRF with some state-of-the-art deep learning networks. The experimental results show that YOLOv3-DMRF outperforms others on mAP and detection time on both the datasets. Specifically, the values of mAP and detection time were 90.05 and 95.23% and 3.7 and 2.2 s, respectively, on the two test datasets. Experimental results demonstrate that the proposed YOLOv3-DMRF is efficient for detection and recognition of thyroid nodules for ultrasound images.

scholarly journals On the Replica of US Pulmonary Artifacts by Means of Physical Models

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1666
Author(s):  
Marcello Demi
Keyword(s):  
Visual Inspection ◽  
Lung Ultrasound ◽  
Diagnostic Value ◽  
Experimental Results ◽  
Physical Models ◽  
Ultrasound Images ◽  
Pulmonary Disorder ◽  
Visual Characteristics ◽  
Artifact Formation ◽  
B Lines

Currently, the diagnostic value of the artefactual information provided by lung ultrasound images is widely recognized by physicians. In particular, the existence of a correlation between the visual characteristics of the vertical artifacts, which arise from the pleura line, and the genesis (pneumogenic or cardiogenic) of a pulmonary disorder is commonly accepted. Physicians distinguish vertical artifacts from vertical artifacts which extend to the bottom of the screen (B-lines) and common vertical artifacts from well-structured artifacts (modulated B-lines). However, the link between these visual characteristics and the causes which determine them is still unclear. Moreover, the distinction between short and long artifacts and the distinction between common and structured artifacts are not on/off, and their classification can be critical. In order to derive further information from the visual inspection of the vertical artifacts, the mechanisms which control the artifact formation must be identified. In this paper, the link between the visual characteristics of the vertical artifacts (the observed effect) and the distribution of the aerated spaces at the pleural level (the cause) is addressed. Plausible mechanisms are suggested and illustrated through experimental results.

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