Hyperspectral image-based analysis of thermal damage for ex-vivo bovine liver utilizing radiofrequency ablation

Aref, Mohamed Hisham; Youssef, Abou-Bakr M.; El-Sharkawy, Yasser H.

doi:10.1016/j.suronc.2021.101564

Cited by 9 publications

(6 citation statements)

References 66 publications

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“…Experimental studies were conducted during thermal ablation procedures on pigs, with the focus to detect thermal damage induced in vital organs (such as the liver, pancreas, and stomach) during laser thermal therapy. Other studies have been carried out in ex vivo models undergoing LA and RFA [8,9,20]. In all works, the visible and NIR ranges were considered.…”

Section: Hyperspectral Imaging For Thermal Effect Investigation In Bi...mentioning

confidence: 99%

The new thermometry with light: fiber optic sensors and novel imaging technologies for monitoring thermal-based therapies for localized tumors

Saccomandi

2024

Clinical Biophotonics III

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Thermal therapies are energy-based interventions that provide a minimally invasive treatment option for cancer patients who may not be surgical candidates. Being these therapies based on the increase of tissue temperature to achieve tumor necrosis, it is important to assure complete tumor destruction and simultaneous safety by minimizing collateral thermal damage. Thus, the availability of accurate tools for monitoring tissue temperature changes during the treatment and its control represents an important clinical need. This work presents an overview of two main optical tools (i.e., fiber optic sensors and hyperspectral imaging) which are under investigation for thermometry purposes in tumors and organs undergoing thermal therapies.

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Section: Hyperspectral Imaging For Thermal Effect Investigation In Bi...mentioning

confidence: 99%

The new thermometry with light: fiber optic sensors and novel imaging technologies for monitoring thermal-based therapies for localized tumors

Saccomandi

2024

Clinical Biophotonics III

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“…Therefore, optical technology is a promising tool for achieving safe ablation margins [ 26 , 27 ]. Among the optical approaches, hyperspectral imaging (HSI) recently gained importance in image-guided surgery [ 28 , 29 ] and in the ablative therapy field [ 30 , 31 , 32 ]. HSI provides a discrete three-dimensional image with two spatial dimensions (x,y) coupled with a third dimension (z), which gives the relative reflectance of each pixel of the image within a narrow spectral band, forming a hypercube.…”

Section: Introductionmentioning

confidence: 99%

Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning

Landro

Felli

Collins

et al. 2021

Sensors

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Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm “peak temperature prediction model” (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment.

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“…Aref et al used the HSI of ex vivo cow liver to evaluate the variations of the tissue optical properties with different responses to light (transmission, specular reflection, absorption and diffuse reflection) over the whole spectral range and selected the best wavelengths that can distinguish between the different regions (normal/thermally influenced/ablated), see Figure A2 in the Appendix. [58][59][60] This study aims to monitor the RFA of the investigated ex vivo liver samples to distinguish between the normal and the ablated regions, as well as the thermally affected regions by measuring the diffuse reflectance (R d ) with two different systems. Initially, a hyperspectral (HS) camera with a polychromatic light source (tungsten halogen lamp) within the spectral range 348-950 nm was employed.…”

Section: Introductionmentioning

confidence: 99%

Prospective study for commercial and low-cost hyperspectral imaging systems to evaluate thermal tissue effect on bovine liver samples

Aref¹,

Hussein²,

Youssef

et al. 2021

J. Spectral Imaging

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Thermal ablation modalities, for example radiofrequency ablation (RFA) and microwave ablation, are intended to prompt controlled tumour removal by raising tissue temperature. However, monitoring the size of the resulting tissue damage during the thermal removal procedures is a challenging task. The objective of this study was to evaluate the observation of RFA on an ex vivo liver sample with both a commercial and a low-cost system to distinguish between the normal and the ablated regions as well as the thermally affected regions. RFA trials were conducted on five different ex vivo normal bovine samples and monitored initially by a custom hyperspectral (HS) camera to measure the diffuse reflectance (Rd) utilising a polychromatic light source (tungsten halogen lamp) within the spectral range 348–950 nm. Next, the light source was replaced with monochromatic LEDs (415, 565 and 660 nm) and a commercial charge-coupled device (CCD) camera was used instead of the HS camera. The system algorithm comprises image enhancement (normalisation and moving average filter) and image segmentation with K-means clustering, combining spectral and spatial information to assess the variable responses to polychromatic light and monochromatic LEDs to highlight the differences in the Rd properties of thermally affected/normal tissue regions. The measured spectral signatures of the various regions, besides the calculation of the standard deviations (δ) between the generated six groups, guided us to select three optimal wavelengths (420, 540 and 660 nm) to discriminate between these various regions. Next, we selected six spectral images to apply the image processing to (at 450, 500, 550, 600, 650 and 700 nm). We noticed that the optimum image is the superimposed spectral images at 550, 600, 650 and 700 nm, which are capable of discriminating between the various regions. Later, we measured Rd with the CCD camera and commercially available monochromatic LED light sources at 415, 565 and 660 nm. Compared to the HS camera results, this system was more capable of identifying the ablated and the thermally affected regions of surface RFA than the side-penetration RFA of the investigated ex vivo liver samples. However, we succeeded in developing a low-cost system that provides satisfactory information to highlight the ablated and thermally affected region to improve the outcome of surgical tumour ablation with much shorter time for image capture and processing compared to the HS system.

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Hyperspectral image-based analysis of thermal damage for ex-vivo bovine liver utilizing radiofrequency ablation

Cited by 9 publications

References 66 publications

The new thermometry with light: fiber optic sensors and novel imaging technologies for monitoring thermal-based therapies for localized tumors

The new thermometry with light: fiber optic sensors and novel imaging technologies for monitoring thermal-based therapies for localized tumors

Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning

Prospective study for commercial and low-cost hyperspectral imaging systems to evaluate thermal tissue effect on bovine liver samples

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