Feedback-controlled laser ablation for cancer treatment: comparison of On-Off and PID control strategies

: Laser ablation is a rising technique used to induce a localized temperature increment for tumor ablation. The outcomes of the therapy depend on the tissue thermal history. Monitoring devices help to assess the tissue thermal response, and their combination with a control strategy can be used to promptly address unexpected temperature changes and thus reduce unwanted thermal effects. In this application, numerical simulations can drive the selection of the laser control settings (i.e., laser power and gain parameters) and allow evaluating the thermal effects of the control strategies. In this study, the influence of different control strategies (On-Off and PID-based controls) is quantified considering the treatment time and the thermal effect on the tissue. Finite element model-based simulations were implemented to model the laser-tissue interaction, the heat-transfer, and the consequent thermal damage in liver tissue with tumor. The laser power was modulated based on the temperature feedback provided within the tumor safety margin. Results show that the chosen control strategy does not have a major influence on the extent of thermal damage but on the treatment duration; the percentage of necrosis within the tumor domain is 100% with both strategies, while the treatment duration is 630 s and 786 s for On-Off and PID, respectively. The choice of the control strategy is a trade-off between treatment duration and unwanted temperature overshoot during closed-loop laser ablation. Clinical Relevance-This work establishes that different temperature-based control of the laser ablation procedure does not have a major influence on the extent of thermal damage but on the duration of treatment

Estimation of porcine pancreas optical properties in the 600–1100 nm wavelength range for light-based therapies

This work reports the optical properties of porcine pancreatic tissue in the broad wavelength range of 600–1100 nm. Absorption and reduced scattering coefficients (µ(a) and µ(s)′) of the ex vivo pancreas were obtained by means of Time-domain Diffuse Optical Spectroscopy. We have investigated different experimental conditions—including compression, repositioning, spatial sampling, temporal stability—the effect of the freezing procedure (fresh vs frozen-thawed pancreas), and finally inter-sample variability. Good repeatability under different experimental conditions was obtained (median coefficient of variation less than 8% and ~ 16% for µ(a) and µ(s)′, respectively). Freezing–thawing the samples caused an irreversible threefold reduction of µ(s)′ and no effect on µ(a). The absorption and reduced scattering spectra averaged over different samples were in the range of 0.12–0.74 cm(−1) and 12–21 cm(−1) with an inter-sample variation of ~ 10% and ~ 40% for µ(a) and µ(s)′, respectively. The calculated effective transport coefficient (µ(eff)) for fresh pancreatic tissue shows that regions between 800–900 nm and 1050–1100 nm are similar and offer the lowest tissue attenuation in the considered range (i.e., µ(eff) ranging from 2.4 to 2.7 cm(−1)). These data, describing specific light-pancreas interactions in the therapeutic optical window for the first time, provide pivotal information for planning of light-based thermotherapies (e.g., laser ablation) and instruction of light transport models for biophotonic applications involving this organ

Characterization of susceptibility artifacts in magnetic resonance thermometry images during laser interstitial thermal therapy: dimension analysis and temperature error estimation

Objective: Laser interstitial thermal therapy (LITT) is a minimally invasive procedure used to treat a lesion through light irradiation and consequent temperature increase. Magnetic Resonance Thermometry Imaging (MRTI) provides a multidimensional measurement of the temperature inside the target thus enabling accurate monitoring of the zone of damage during the procedure. In proton resonance frequency shift-based thermometry, artifacts in the images may strongly interfere with the estimated temperature maps. In our work, after noticing the formation of the dipolar-behavior artifact linkable to magnetic susceptibility changes during in vivo LITT, an investigation of susceptibility artifacts in tissue-mimicking phantoms was implemented. Approach: The artifact was characterized: (i) by measuring the area and total volume of error regions and their evolution during the treatment; and (ii) by comparison with temperature reference provided by three temperature sensing needles. Lastly, a strategy to avoid artifacts formation was devised by using the temperature-sensing needles to implement a temperature-controlled LITT. Main results: The artifact appearance was associated with gas bubble formation and with unwanted treatment effects producing magnetic susceptibility changes when 2 W laser power was set. The analysis of the artifact's dimension demonstrated that in the sagittal plane the dipolar-shape artifact may consistently spread following the temperature trend until reaching a volume 8 times bigger than the ablated one. Also, the artifact shape is quite symmetric with respect to the laser tip. An absolute temperature error showing a negative Gaussian profile in the area of susceptibility artifact with values up to 64.4 °C was estimated. Conversely, a maximum error of 2.8 °C is measured in the area not-affected by artifacts and far from the applicator tip. Finally, by regulating laser power, susceptibility artifacts formation was avoided, and appreciable thermal damage was induced. Significance: Such findings may help in improving the MRTI-based guidance of thermal therapies

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

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

Analysis of cavitation artifacts in Magnetic Resonance Imaging Thermometry during laser ablation monitoring

: Magnetic Resonance Thermometry Imaging (MRTI) holds great potential in laser ablation (LA) monitoring. It provides the real-time multidimensional visualization of the treatment effect inside the body, thus enabling accurate intraoperative prediction of the thermal damage induced. Despite its great potential., thermal maps obtained with MRTI may be affected by numerous artifacts. Among the sources of error producing artifacts in the images., the cavitation phenomena which could occur in the tissue during LA induces dipole-structured artifacts. In this work., an analysis of the cavitation artifacts occurring during LA in a gelatin phantom in terms of symmetry in space and symmetry of temperature values was performed. Results of 2 Wand 4 W laser power were compared finding higher symmetry for the 2 W case in terms of both dimensions of artifact-lobes and difference in temperature values extracted in specular pixels in the image. This preliminary investigation of artifact features may provide a step forward in the identification of the best strategy to correct and avoid artifact occurrence during thermal therapy monitoring. Clinical Relevance- This work presents an analysis of cavitation artifacts in MRTI from LA which must be corrected to avoid error in the prediction of thermal damage during LA monitoring

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

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