added 3 research items
Updates
0 new
0
Recommendations
0 new
0
Followers
0 new
8
Reads
18 new
801
Project log
![[object Object]](https://i1.rgstatic.net/ii/profile.image/913523299323904-1594812335037_Q64/Moritz-Spiller.jpg)
![[object Object]](https://i1.rgstatic.net/ii/profile.image/916009854705664-1595405176032_Q64/Nazila-Esmaeili.jpg)
![[object Object]](https://c5.rgstatic.net/m/4671872220764/images/template/default/profile/profile_default_m.jpg)
![[object Object]](https://i1.rgstatic.net/ii/profile.image/677961913102336-1538650124555_Q64/Katarzyna-Heryan-bugajska.jpg)
![[object Object]](https://i1.rgstatic.net/ii/profile.image/535610903076864-1504710998804_Q64/Ivan-Maldonado-Zambrano.jpg)
In robot-assisted procedures, the surgeon controls the surgical instruments from a remote console, while visually monitoring the procedure through the endoscope. There is no haptic feedback available to the surgeon, which impedes the assessment of diseased tissue and the detection of hidden structures beneath the tissue, such as vessels. Only visual clues are available to the surgeon to control the force applied to the tissue by the instruments, which poses a risk for iatrogenic injuries. Additional information on haptic interactions of the employed instruments and the treated tissue that is provided to the surgeon during robotic surgery could compensate for this deficit. Acoustic emissions (AE) from the instrument/tissue interactions, transmitted by the instrument are a potential source of this information. AE can be recorded by audio sensors that do not have to be integrated into the instruments, but that can be modularly attached to the outside of the instruments shaft or enclosure. The location of the sensor on a robotic system is essential for the applicability of the concept in real situations. While the signal strength of the acoustic emissions decreases with distance from the point of interaction, an installation close to the patient would require sterilization measures. The aim of this work is to investigate whether it is feasible to install the audio sensor in non-sterile areas far away from the patient and still be able to receive useful AE signals. To determine whether signals can be recorded at different potential mounting locations, instrument/tissue interactions with different textures were simulated in an experimental setup. The results showed that meaningful and valuable AE can be recorded in the non-sterile area of a robotic surgical system despite the expected signal losses.
Arthroscopic surgery is a technically challenging but common minimally invasive procedure with a long learning curve and a high incidence of iatrogenic damage. These damages can occur due to the lack of feedback and supplementary information regarding tissue-instrument-contact during surgery. Deliberately performed interactions can be used however to obtain clinically relevant information, e.g. when a surgeon uses the tactile feedback to assess the condition of articular cartilage. Yet, the perception of such events is highly subjective. We propose a novel proximally attached sensing concept applied to arthroscopic surgery to allow an objective characterization and utilization of interactions. It is based on acoustic emissions which originate from tissue-instrument-contact, that propagate naturally via the instrument shaft and that can be obtained by a transducer setup outside of the body. The setup was tested on its ability to differentiate various conditions of articular cartilage. A femoral head with varying grades of osteoarthritic cartilage was tapped multiple times ex-vivo with a conventional Veress needle with a sound transducer attached at the outpatient end. A wavelet-based processing of the obtained signals and subsequent analysis of distribution of spectral energy showed the potential of tool-tissue-interactions to characterize different cartilage conditions. The proposed concept needs further evaluation with a dedicated design of the palpation tool and should be tested in realistic arthroscopic scenarios.
![[object Object]](https://i1.rgstatic.net/ii/profile.image/833926734028800-1575835035136_Q64/Christian-Hansen-9.jpg)
Accurate needle placement is highly relevant for puncture of anatomical structures. The clinician’s experience and medical imaging are essential to complete these procedures safely. However, imaging may come with inaccuracies due to image artifacts. Sensor-based solutions have been proposed for acquiring additional guidance information. These sensors typically require to be embedded in the instrument tip, leading to direct tissue contact, sterilization issues, and added device complexity, risk, and cost. Recently, an audio-based technique has been proposed for “listening” to needle tip-tissue interactions by an externally placed sensor. This technique has shown promising results for different applications. But the relation between the interaction event and the generated audio excitation is still not fully understood. This work aims to study this relationship, using a force sensor as a reference, by relating events and dynamical characteristics occurring in the audio signal with those occurring in the force signal. We want to show that dynamical information that a well-known sensor as force can provide could also be extracted from a low-cost and simple sensor such as audio. In this aim, the Pearson coefficient was used for signal-to-signal correlation between extracted audio and force indicators. Also, an event-to-event correlation between audio and force was performed by computing features from the indicators. Results show high values of correlation between audio and force indicators in the range of 0.53 to 0.72. These promising results demonstrate the usability of audio sensing for tissue-tool interaction and its potential to improve telemanipulated and robotic surgery in the future.
This work aims to demonstrate the feasibility that haptic information can be acquired from a da Vinci robotic tool using audio sensing according to sensor placement requirements in a real clinical scenario. For that, two potential audio sensor locations were studied using an experimental setup for performing, in a repeatable way, interactions of a da Vinci forceps with three different tissues. The obtained audio signals were assessed in terms of their resulting signal-to-noise-ratio (SNR) and their capability to distinguish between different tissues. A spectral energy distribution analysis using Discrete Wavelet Transformation was performed to extract signal signatures from the tested tissues. Results show that a high SNR was obtained in most of the audio recordings acquired from both studied positions. Additionally, evident spectral energy-related patterns could be extracted from the audio signals allowing us to distinguish between different palpated tissues.
![[object Object]](https://i1.rgstatic.net/ii/profile.image/786475905974272-1564521876056_Q64/Chien-Hsi-Chen.jpg)
![[object Object]](https://i1.rgstatic.net/ii/profile.image/905951666266113-1593007117600_Q64/Marco-Kalmar.jpg)
Robotic minimally invasive surgery (RMIS) has played an important role in the last decades. In traditional surgery, surgeons rely on palpation using their hands. However, during RMIS, surgeons use the visual-haptics technique to compensate the missing sense of touch. Various sensors have been widely used to retrieve this natural sense, but there are still issues like integration, costs, sterilization and the small sensing area that prevent such approaches from being applied. A new method based on acoustic emission has been recently proposed for acquiring audio information from tool-tissue interaction during minimally invasive procedures that provide user guidance feedback. In this work the concept was adapted for acquiring audio information from a RMIS grasper and a first proof of concept is presented. Interactions of the grasper with various artificial and biological texture samples were recorded and analyzed using advanced signal processing and a clear correlation between audio spectral components and the tested texture were identified.
https://doi.org/10.1016/j.compbiomed.2019.103370
Artery guidewire-induced perforation during coronary interventions is an uncommon but potentially serious complication with significant morbidity and mortality rates. For minimizing its impact it is crucial for the surgeon to early detect that a perforation has occurred. However, this is not always easy since perforation sometimes is not characterized by any symptom or sign. In this work a time-varying (TV) characterization of coronary artery perforation is proposed through a TV parametrical modelling of an audio signal acquired from the distal part of a guidewire.
A stethoscope equipped with a microphone connected to a computer has been attached to the distal part of a 0.014-inch guidewire using a coupling box allowing a direct contact between the distal part of the guidewire and the stethoscope membrane. Coronary arteries belonging to several pork hearts were perforated using the tip of the guidewire. During the procedure audio signals and time of perforation were recorded. An audio database has been implemented in order to evaluate the performances of the proposed characterization technique. It included 100 coronary artery perforations audio recordings, each one with a duration of 30 seconds and 200 recordings with different types of induced guidewire audio artifacts.
Each audio signals has been first decimated and then filtered using a wavelet based band-pass filter. The resulting signal has been modelled using a TV autoregressive (AR) model for estimating a TV power spectral density and TV poles. Finally different features has been computed from the AR spectrum and AR poles, based mainly on spectral energy dispersion and tracking of the pole of maximal energy.
Results show that that guidewire perforation leaves a characteristic TV trace which can be tracked through the TV poles and spectrum and that clear differentiating patterns can be extracted allowing 90% of correct perforation classification.
![[object Object]](https://i1.rgstatic.net/ii/profile.image/848244821213184-1579248733000_Q64/Naghmeh-Mahmoodian.jpg)
