Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>We have developed an original method to measure the temperature dependence of the biological tissue electrical properties based on its heating by homogeneous optical radiation.The conventional approach in hyperthermia research involves heating due to a thermal conductivity through the surface of the sample. The novel technique is based on the emission of heat sources in the sample volume caused by the absorption of optical radiation.The method was verified using chicken liver and aloe parenchyma samples, which were uniformly irradiated in a special chamber with an optically scattering inner coating. The electrical impedance of the samples was measured using a 4-electrode technique in the frequency range 100 Hz−1 MHz. In order to approximate and analyze the electrical impedance module, an equivalent electrical circuit based on the Cole-Cole function was used and the dependences of the approximation parameters on time and temperature were obtained. Applying the Arrhenius formulation to the kinetics of low-frequency resistance, we obtained the parameters of the kinetics of degradation of the biological tissues (critical temperature <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{T}}}_{{\rm{cr}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">T</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">cr</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn1.gif" xlink:type="simple" /> </jats:inline-formula> and activation energy <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{E}}}_{{\rm{a}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">E</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">a</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn2.gif" xlink:type="simple" /> </jats:inline-formula>): <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{E}}}_{{\rm{a}}}=(16\pm 4){\rm{\cdot }}{10}^{5}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">E</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">a</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mo stretchy="false">(</mml:mo> <mml:mn>16</mml:mn> <mml:mo>±</mml:mo> <mml:mn>4</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mi mathvariant="normal">·</mml:mi> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn3.gif" xlink:type="simple" /> </jats:inline-formula> J <jats:inline-formula> <jats:tex-math> <?CDATA ${\rm{\cdot }}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="normal">·</mml:mi> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn4.gif" xlink:type="simple" /> </jats:inline-formula> mol<jats:sup>−1</jats:sup>, <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{T}}}_{{\rm{cr}}}=63\pm 1$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">T</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">cr</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>63</mml:mn> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn5.gif" xlink:type="simple" /> </jats:inline-formula> °C for the aloe parenchyma and <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{E}}}_{{\rm{a}}}=(4.5\pm 2){\rm{\cdot }}{10}^{5}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">E</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">a</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mo stretchy="false">(</mml:mo> <mml:mn>4.5</mml:mn> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mi mathvariant="normal">·</mml:mi> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn6.gif" xlink:type="simple" /> </jats:inline-formula> J <jats:inline-formula> <jats:tex-math> <?CDATA ${\rm{\cdot }}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="normal">·</mml:mi> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn7.gif" xlink:type="simple" /> </jats:inline-formula> mol<jats:sup>−1</jats:sup>, <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{T}}}_{{\rm{cr}}}=83\pm 1$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">T</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">cr</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>83</mml:mn> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn8.gif" xlink:type="simple" /> </jats:inline-formula> <jats:inline-formula> <jats:tex-math> <?CDATA ${\rm{^\circ }}{\rm{C}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="normal">°</mml:mi> <mml:mi mathvariant="normal">C</mml:mi> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac7eedieqn9.gif" xlink:type="simple" /> </jats:inline-formula> for the chicken liver.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> Tissue recognition is a critical process during a Robot-assisted minimally invasive surgery (RMIS) and it relies on the involvement of advanced sensing technology. <jats:italic>Approach.</jats:italic> In this paper, the concept of Robot Assisted Electrical Impedance Sensing (RAEIS) is utilized and further developed aiming to sense the electrical bioimpedance of target tissue directly based on the existing robotic instruments and control strategy. Specifically, we present a new sensing configuration called pseudo-tetrapolar method. With the help of robotic control, we can achieve a similar configuration as traditional tetrapolar, and with better accuracy. <jats:italic>Main results.</jats:italic> Five configurations including monopolar, bipolar, tripolar, tetrapolar and pseudo-tetrapolar are analyzed and compared through simulation experiments. Advantages and disadvantages of each configuration are thus discussed. <jats:italic>Significance.</jats:italic> This study investigates the measurement of tissue electrical property directly based on the existing robotic surgical instruments. Specifically, different sensing configurations can be realized through different connection and control strategies, making them suitable for different application scenarios.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Objective. To estimate organs’ absorbed dose from the two-phase CT of parathyroid glands, effective dose (ED) based on three different methods, and compare the dose values with those reported by other published protocols. Methods. Volumetric computed tomography dose index (CTDI<jats:sub>vol</jats:sub>), dose length product (DLP), and the corresponding scan length during each phase of a parathyroid protocol were recorded, for seventy-six patients. One k-factor, and two different k-factors for the neck and chest area were used to estimate the ED from DLP. A Monte Carlo software, VirtualDoseCT, was also used for the estimation of organs’ absorbed dose and ED. Results. Two-phase parathyroid CT resulted in a mean ED of 3.93 mSv, 4.29 mSv and 4.21 mSv according to the one k-factor, two k-factors, and VirtualDoseCT methods, respectively. The two k-factors method resulted in a slight overestimation of 1.9% in total ED compared to VirtualDoseCT. No statistically significant difference was found in ED values between these methods (Wilcoxon test, p &gt; 0.05), except for female patients in the pre-contrast phase. The organs inside the scanning field of view (SFOV) received the following doses: thymus 23.3 mGy, lungs 11.5 mGy, oesophagus 9.2 mGy, thyroid 6.9 mGy, and breast 6.3 mGy. The ED and organs’ dose (OD) values were significantly lower in the pre-contrast than in the arterial phase (Wilcoxon test, p &lt; 0.001). A statistically significant difference was observed between male and female patients for the pre-contrast phase (Mann-Whitney test, p &lt; 0.05), regarding the ED values obtained with the two k-factors method and VirtualDoseCT software. Conclusions. The two k-factors method could be applied for the ED estimation in clinical practice, if appropriate software is not available. An extensive range of ED values derived from the literature, mainly depending on the acquisition protocol parameters and the estimation method.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>X-ray phase-contrast imaging can display subtle differences in low-density materials (e.g. soft tissues) more readily than conventional x-ray imaging. However, producing x-ray phase images requires significant spatial coherence of the beam which in turn requires highly specialized sources such as synchrotrons, small and low power microfocus sources, or complex procedures, such as multiple exposures with several carefully stepped precision gratings. To find appropriate approaches for producing x-ray phase-contrast imaging in a clinically meaningful way, we employed a grating-free method that utilized a low-cost, coarse wire mesh and simple processing. This method relaxes the spatial coherence constraint and allows quantitative phase retrieval for not only monochromatic but also polychromatic beams. We also combined the mesh-based system with polycapillary optics to significantly improve the accuracy of quantitative phase retrieval.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Microwave imaging (MWI) systems are being investigated for breast cancer diagnostics as an alternative to conventional x-ray mammography and breast ultrasound. This work aims at a next generation of tissue-mimicking phantoms modelling the temperature-dependent dielectric properties of breast tissue over a large frequency bandwidth. Such phantoms can be used to develop a novel kind of MWI systems that exploit the temperature-dependent permittivity of tissue as a natural contrast agent. Due to the higher water content in tumor tissue, a temperature increase leads to a different change in the complex permittivity compared to surrounding tissue. This will generate a tumor dominated scattering response when the overall tissue temperature increases by a few degrees, e.g. through the use of microwave hyperthermia systems. In that case a differential diagnostic image can be calculated between microwave measurements at reference (around 37 °C) and elevated temperature conditions. This work proposes the design and characterization of agar-oil-glycerin phantoms for fatty, glandular, skin and tumor tissue. The characterization includes measurements with an open-ended coaxial probe and a network analyzer for the frequency range from 50 MHz to 20 GHz in a temperature-controlled environment covering the temperature range from 25 °C to 46 °C. The phantoms show an unique temperature response over the considered frequency bandwidth leading to significant changes in the real and imaginary part of the complex permittivity. Comparative studies with porcine skin and fat tissue show a qualitative agreement.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Purpose. Development of a simple, phantom-based methodology allowing for pilot applications for the Elements TPS cranio-vascular module and clinical implementation prior to AVM treatments. Methods. A customized phantom was developed to be visible in MRI and CT images. High resolution digital subtraction angiograms (DSAs) and CT images of the phantom were acquired and imported into the Brainlab Elements treatment planning system. A clinical treatment plan with 5 arcs was generated in cranial vascular planning module and delivered to the phantom using a Varian TrueBeam STx Linac equipped with HD-MLCs and Brainlab ExacTrac imaging system for non-coplanar setup verification. The delivered dose was verified using a calibrated ionization chamber placed in the phantom. Upon verification of the TPS workflow, three patients with AVM who have been treated to date at our center using the Brainlab’s cranial vascular module for AVM are presented here for retrospective review. Results. The difference between the planed and measured dose by the ionization chamber was found to be less than 1%. Following a successful dose verification study, a clinical workflow was created. Currently, three AVM patients have been treated successfully. Clinical aspects of imaging and treatment planning consideration are presented in retrospective setting. Conclusions. Dose verification of the Brainlab Elements cranial vascular planning module for intracranial SRS treatments of AVM on Varian TrueBeam was successfully implemented using a custom-made phantom with &lt;1% discrepancy. The Brainlab Elements’ cranial vascular module was successfully implemented in clinical workflow to treat patients with AVM. This manuscript provides a guideline for clinical implementation of frameless Linac-based AVM treatment using the Brainlab Elements TPS.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Digital droplet PCR (ddPCR) is classified as the third-generation PCR technology that enables absolute quantitative detection of nucleic acid molecules and has become an increasingly powerful tool for clinic diagnosis. We previously established a CLEAR-dPCR technique based on the combination of CLEAR droplets generated by micro-centrifuge-based microtubule arrays (MiCA) and <jats:italic>insitu</jats:italic> 3D readout by light-sheet fluorescence imaging. This CLEAR-dPCR technique attains very high readout speed and dynamic range. Meanwhile, it is free from sample loss and contamination, showing its advantages over commercial d-PCR technologies. However, a conventional orthogonal light-sheet imaging setup in CLEAR d-PCR cannot image multiple centrifuge tubes, thereby limiting its widespread application to large-scale, high-speed dd-PCR assays. Herein, we propose an in-parallel 3D dd-PCR readout technique based on an open-top light-sheet microscopy setup. This approach can continuously scan multiple centrifuge tubes which contain CLEAR emulsions with highly diverse concentrations, and thus further boost the scale and throughput of our 3D dd-PCR technique.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The purpose of this study was to develop a predictive model based on plan complexity metrics and linac log-files analysis to classify the dosimetric accuracy of VMAT plans. A total of 612 VMAT plans, corresponding to 1224 arcs, were analyzed. All VMAT arcs underwent pre-treatment verification that was performed by means of the dynamic log-files generated by the linac. The comparison of predicted (by TPS) and measured (by log-files) integral fluences was performed using <jats:italic>γ</jats:italic>-analysis in terms of the percentage of points with <jats:italic>γ</jats:italic>-value smaller than one (<jats:italic>γ</jats:italic>%) and using a stringent 2%(local)/2 mm criteria. This <jats:italic>γ</jats:italic>-analysis was performed by a commercial software LinacWatch. The action limits (AL) were derived from the mean values, standard deviations and the confidence limit (CL) of the <jats:italic>γ</jats:italic>% distribution. A plan complexity metric, the modulation complexity score (MCS), based on the aperture beam area variability and leaf sequence variability was used as input variable of the model. A binary logistic regression (LR) model was developed to classify QA results as ‘pass’ (<jats:italic>γ</jats:italic>% ≥ AL) or ‘fail’ (<jats:italic>γ</jats:italic>% &lt; AL). Receiver operator characteristics (ROC) curves were used to determine the optimal MCS threshold to flag ‘failed’ plans that need to be re-optimized. The model reliability was evaluated stratifying the plans in training, validation and testing groups. The confidence and action limits for <jats:italic>γ</jats:italic>% were found 20.1% and 79.9%, respectively. The accuracy of the model for the training and testing dataset was 97.4% and 98.0%, respectively. The optimal MCS threshold value for the identification of failed plans was 0.142, providing a true positive rate able to flag the plans failing QA of 91%. In clinical routine, the use of this MCS threshold may allow the prompt identification of overly modulated plans, then reducing the number of QA failures and improving the quality of VMAT plans used for treatment.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Transiently evoked otoacoustic emissions (TEOAEs) are routinely used in the hearing assessment of the auditory periphery. The major contribution of TEOAEs is the early detection of hearing losses in neonates, children, and adults. The evaluation of TEOAE responses by specific signal decomposition techniques offers numerous advantages for current and future research. One methodology, based on recurrence quantification analysis (RQA), can identify adult subjects presenting sensorineural hearing impairments. In two previous papers, the RQA-based approach was successfully applied in identifying and classifying cases presenting noise and age related hearing losses. The current work investigates further two aspects of the previously proposed RQA-based analysis for hearing loss detection: (i) the reliability of a Training set built from different numbers of ears with normal hearing, and (ii) the threshold set of values of the key hearing loss detecting parameter RAD2D. <jats:italic>Results:</jats:italic> The Training set built from 158 healthy ears was found to be quite reliable and a similar but slightly minor performance was observed for the training set of 118 normal subjects, used in the past; the proposed ROC-curve method, optimizing the values of RAD2D, shows improved sensibility and specificity in one class discrimination. <jats:italic>Conclusions.</jats:italic> A complete and simplified procedure, based on the combined use of the traditional TEOAE reproducibility value and on values from the RQA-based RAD2D parameter, is proposed as an improved automatic classifier, in terms of sensitivity and specificity, for different types of hearing losses.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Purpose</jats:italic>. Laboratory models of human arterial tissues are advantageous to examine the mechanical response of blood vessels in a simplified and controllable manner. In the present study, we investigated three silicone-based materials for replicating the mechanical properties of human arteries documented in the literature. <jats:italic>Methods</jats:italic>. We performed uniaxial tensile tests up to rupture on Sylgard184, Sylgard170 and DowsilEE-3200 under different curing conditions and obtained their True (Cauchy) stress-strain behavior and Poisson’s ratios by means of digital image correlation (DIC). For each formulation, we derived the constitutive parameters of the 3-term Ogden model and designed numerical simulations of tubular models under a radial pressure of 250 mmHg. <jats:italic>Results</jats:italic>. Each material exhibits evident non-linear hyperelasticity and dependence on the curing condition. Sylgard184 is the stiffest formulation, with the highest shear moduli and ultimate stresses at relative low strains (<jats:italic>μ</jats:italic> <jats:sub>184</jats:sub> = 0.52–0.88 MPa, <jats:italic>σ</jats:italic> <jats:sub>184</jats:sub> = 15.90–16.54 MPa, <jats:italic>ε</jats:italic> <jats:sub>184</jats:sub> = 0.72–0.96). Conversely, Sylgard170 and DowsilEE-3200 present significantly lower shear moduli and ultimate stresses that are closer to data reported for arterial tissues (<jats:italic>μ</jats:italic> <jats:sub>170</jats:sub> = 0.33–0.7 MPa <jats:italic>σ</jats:italic> <jats:sub>170</jats:sub> = 2.61–3.67 MPa, <jats:italic>ε</jats:italic> <jats:sub>170</jats:sub> = 0.69–0.81; <jats:italic>μ</jats:italic> <jats:sub>dow</jats:sub> = 0.02–0.09 MPa <jats:italic>σ</jats:italic> <jats:sub>dow</jats:sub> = 0.83–2.05 MPa, <jats:italic>ε</jats:italic> <jats:sub>dow</jats:sub> = 0.91–1.05). Under radial pressure, all formulations except DowsilEE-3200 at 1:1 curing ratio undergo circumferential stresses that remain in the elastic region with values ranging from 0.1 to 0.18 MPa. <jats:italic>Conclusion</jats:italic>. Sylgard170 and DowsilEE-3200 appear to better reproduce the rupture behavior of vascular tissues within their typical ultimate stress and strain range. Numerical models demonstrate that all three materials achieve circumferential stresses similar to human common carotid arteries (Sommer <jats:italic>et al</jats:italic> 2010), making these formulations suited for cylindrical laboratory models under physiological and supraphysiological loading.</jats:p>