Reality-Based Estimation of Needle and Soft-Tissue Interaction for Accurate Haptic Feedback in Prostate Brachytherapy Simulation

2007 ◽  
pp. 34-48 ◽  
Author(s):  
James T. Hing ◽  
Ari D. Brooks ◽  
Jaydev P. Desai
Keyword(s):  
Soft Tissue ◽  
Haptic Feedback ◽  
Prostate Brachytherapy ◽  
Tissue Interaction

scholarly journals GPU-based real-time soft tissue deformation with cutting and haptic feedback

2010 ◽  
Vol 103 (2-3) ◽  
pp. 159-168 ◽  
Author(s):  
Hadrien Courtecuisse ◽  
Hoeryong Jung ◽  
Jérémie Allard ◽  
Christian Duriez ◽  
Doo Yong Lee ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Real Time ◽  
Haptic Feedback ◽  
Tissue Deformation ◽  
Soft Tissue Deformation

Laser-tissue interaction with a continuous wave 3-?m fibre laser: Preliminary studies with soft tissue

2000 ◽  
Vol 26 (5) ◽  
pp. 491-495 ◽  
Author(s):  
Mark C. Pierce ◽  
Stuart D. Jackson ◽  
Mark R. Dickinson ◽  
Terence A. King ◽  
Philip Sloan
Keyword(s):  
Soft Tissue ◽  
Continuous Wave ◽  
Fibre Laser ◽  
Tissue Interaction ◽  
Laser Tissue Interaction

scholarly journals Development of an MRI-Based Workflow for Post-implant Dosimetry of Prostate Low-Dose-Rate (Ldr) Brachytherapy

2021 ◽  
Author(s):  
Reyhaneh Nosrati
Keyword(s):  
Soft Tissue ◽  
Dose Rate ◽  
Low Dose ◽  
Positive Contrast ◽  
Machine Learning Algorithms ◽  
Prostate Brachytherapy ◽  
Soft Tissue Contrast ◽  
Low Dose Rate ◽  
Ldr Brachytherapy ◽  
Tissue Contrast

Permanent implantation of low-dose-rate (LDR) brachytherapy seeds is a well-established treatment modality for patients with localized prostate cancer. The quality of the implant is assessed within 30 days following implantation through post-implant dosimetry. The standard recommended procedure for post-implant dosimetry is based on computed tomography (CT). CT provides excellent seed visualization and localization; however, due to poor soft tissue contrast and challenging anatomical identificatio,n it leads to significant interobserver variabilities. The current MRI-CT fusion-based workflow for post-implant dosimetry LDR prostate brachytherapy takes advantage of the superior soft tissue contrast of MRI but still relies on CT for seed visualization and detection, and it suffers from image fusion uncertainties and extra cost and logistics. The lack of positive contrast from brachytherapy seeds in conventional MR images remains a major challenge towards an MRI-only workflow for post-implant dosimetry of Low- Dose-Rate (LDR) brachytherapy. In this thesis, a clinically feasible MRI-based workflow has been developed for brachytherapy seed visualization and localization. The seed visualization is based on a novel Quantitative Susceptibility Mapping (QSM) algorithm. The proposed seed localization on QSM utilizes machine learning algorithms. The reliability of the proposed workflow has been validated on 23 patients by comparing the seed positions and final dosimetric parameters between the proposed MRI-only workflow and the clinical CT-MRI fusion-based approach and there was excellent agreement between the two methods.

scholarly journals Development of an MRI-Based Workflow for Post-implant Dosimetry of Prostate Low-Dose-Rate (Ldr) Brachytherapy

2021 ◽  
Author(s):  
Reyhaneh Nosrati
Keyword(s):  
Soft Tissue ◽  
Dose Rate ◽  
Low Dose ◽  
Positive Contrast ◽  
Machine Learning Algorithms ◽  
Prostate Brachytherapy ◽  
Soft Tissue Contrast ◽  
Low Dose Rate ◽  
Ldr Brachytherapy ◽  
Tissue Contrast

Permanent implantation of low-dose-rate (LDR) brachytherapy seeds is a well-established treatment modality for patients with localized prostate cancer. The quality of the implant is assessed within 30 days following implantation through post-implant dosimetry. The standard recommended procedure for post-implant dosimetry is based on computed tomography (CT). CT provides excellent seed visualization and localization; however, due to poor soft tissue contrast and challenging anatomical identificatio,n it leads to significant interobserver variabilities. The current MRI-CT fusion-based workflow for post-implant dosimetry LDR prostate brachytherapy takes advantage of the superior soft tissue contrast of MRI but still relies on CT for seed visualization and detection, and it suffers from image fusion uncertainties and extra cost and logistics. The lack of positive contrast from brachytherapy seeds in conventional MR images remains a major challenge towards an MRI-only workflow for post-implant dosimetry of Low- Dose-Rate (LDR) brachytherapy. In this thesis, a clinically feasible MRI-based workflow has been developed for brachytherapy seed visualization and localization. The seed visualization is based on a novel Quantitative Susceptibility Mapping (QSM) algorithm. The proposed seed localization on QSM utilizes machine learning algorithms. The reliability of the proposed workflow has been validated on 23 patients by comparing the seed positions and final dosimetric parameters between the proposed MRI-only workflow and the clinical CT-MRI fusion-based approach and there was excellent agreement between the two methods.

scholarly journals Acoustic signal analysis of instrument–tissue interaction for minimally invasive interventions

2020 ◽  
Vol 15 (5) ◽  
pp. 771-779
Author(s):  
Daniel Ostler ◽  
Matthias Seibold ◽  
Jonas Fuchtmann ◽  
Nicole Samm ◽  
Hubertus Feussner ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Haptic Feedback ◽  
Automated Analysis ◽  
Acoustic Signals ◽  
Feature Representation ◽  
Fatty Tissue ◽  
Surgical Performance ◽  
Data Set ◽  
Tissue Interaction ◽  
Limited Range Of Motion

Abstract Purpose Minimally invasive surgery (MIS) has become the standard for many surgical procedures as it minimizes trauma, reduces infection rates and shortens hospitalization. However, the manipulation of objects in the surgical workspace can be difficult due to the unintuitive handling of instruments and limited range of motion. Apart from the advantages of robot-assisted systems such as augmented view or improved dexterity, both robotic and MIS techniques introduce drawbacks such as limited haptic perception and their major reliance on visual perception. Methods In order to address the above-mentioned limitations, a perception study was conducted to investigate whether the transmission of intra-abdominal acoustic signals can potentially improve the perception during MIS. To investigate whether these acoustic signals can be used as a basis for further automated analysis, a large audio data set capturing the application of electrosurgery on different types of porcine tissue was acquired. A sliding window technique was applied to compute log-mel-spectrograms, which were fed to a pre-trained convolutional neural network for feature extraction. A fully connected layer was trained on the intermediate feature representation to classify instrument–tissue interaction. Results The perception study revealed that acoustic feedback has potential to improve the perception during MIS and to serve as a basis for further automated analysis. The proposed classification pipeline yielded excellent performance for four types of instrument–tissue interaction (muscle, fascia, liver and fatty tissue) and achieved top-1 accuracies of up to 89.9%. Moreover, our model is able to distinguish electrosurgical operation modes with an overall classification accuracy of 86.40%. Conclusion Our proof-of-principle indicates great application potential for guidance systems in MIS, such as controlled tissue resection. Supported by a pilot perception study with surgeons, we believe that utilizing audio signals as an additional information channel has great potential to improve the surgical performance and to partly compensate the loss of haptic feedback.

Simulation of 3D needle-tissue interaction with application to image guided prostate brachytherapy

2010 ◽  
Vol 16 (1) ◽  
pp. 28-32
Author(s):  
Shan Jiang ◽  
Nobuhiko Hata ◽  
Bohan Xiao ◽  
Weijin An
Keyword(s):  
Prostate Brachytherapy ◽  
Tissue Interaction ◽  
Image Guided
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