Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>Several clinical conditions leading to traumatic brain injury can cause hematomas or edemas inside the cerebral tissue. If these are not properly treated in time, they are prone to produce long-term neurological disabilities, or even death. Low-cost, portable and easy-to-handle devices are desired for continuous monitoring of these conditions and Near Infrared Spectroscopy (NIRS) techniques represent an appropriate choice. In this work, we use Time-Resolved (TR) Monte Carlo simulations to present a study of NIR light propagation over a digital MRI phantom. Healthy and injured (hematoma/edema) situations are considered. TR Diffuse Reflectance simulations for different lesion volumes and interoptode distances are performed in the frontal area and the left parietal area. Results show that mean partial pathlengths, photon measurement density functions and time dependent contrasts are sensitive to the presence of lesions, allowing their detection mainly for intermediate optodes separations, which proves that these metrics represent robust means of diagnose and monitoring. Conventional Continuous Wave (CW) contrasts are also presented as a particular case of the time dependent ones, but they result less sensitive to the lesions, and have higher associated uncertainties.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The purpose of this work was to develop and manufacture a 3D range-modulator (3D RM) for a complex target contour for scanned proton therapy. The 3D RM is considered to be a viable technique for the very fast dose application in patient-specific tumors with only one fixed energy. The RM was developed based on a tumor from a patient CT and manufactured with high-quality 3D printing techniques with both polymer resin and aluminum. Monte Carlo simulations were utilized to investigate its modulating properties and the resulting dose distribution. Additionally, the simulation results were validated with measurements at the Marburg Ion-Beam Therapy Centre. For this purpose, a previously developed water phantom was used to conduct fast, automated high-resolution dose measurements. The results show a very good agreement between simulations and measurements and indicate that highly homogeneous dose distributions are possible. The delivered dose is conformed to the distal as well as to the proximal edge of the target. The 3D range-modulator concept combines a high degree of dose homogeneity and conformity, comparable to standard IMPT with very short irradiation times, promising clinically applicable dose distributions for lung and/or FLASH treatment, comparable and competitive to those from conventional irradiation techniques.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Covariance of reconstruction images are useful to analyze the magnitude and correlation of noise in the evaluation of systems and reconstruction algorithms. The covariance estimation requires a big number of image samples that are hard to acquire in reality. A covariance propagation method from projection by a few noisy realizations is studied in this work. Based on the property of convergent points of cost funtions, the proposed method is composed of three steps, (1) construct a relationship between the covariance of projection and corresponding reconstruction from cost functions at its convergent point, (2) simplify the covariance relationship constructed in (1) by introducing an approximate gradient of penalties, and (3) obtain an analytical covariance estimation according to the simplified relationship in (2). Three approximation methods for step (2) are studied: the linear approximation of the gradient of penalties (LAM), the Taylor apprximation (TAM), and the mixture of LAM and TAM (MAM). TV and qGGMRF penalized weighted least square methods are experimented on. Results from statistical methods are used as reference. Under the condition of unstable 2nd derivative of penalties such as TV, the covariance image estimated by LAM accords to reference well but of smaller values, while the covarianc estimation by TAM is quite off. Under the conditon of relatively stable 2nd derivative of penalties such as qGGMRF, TAM performs well and LAM is again with a negative bias in magnitude. MAM gives a best performance under both conditions by combining LAM and TAM. Results also show that only one noise realization is enough to obtain reasonable covariance estimation analytically, which is important for practical usage. This work suggests the necessity and a new way to estimate the covariance for non-quadratically penalized reconstructions. Currently, the proposed method is computationally expensive for large size reconstructions.Computational efficiency is our future work to focus.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The article presents a new medical device through an authorial and interdisciplinary approach. It consists of a flexible external fixator, whose flexible property may bring advantages over rigid mechanisms. Its design was inspired by the DNA biological mechanism of condensation, while the modeling was based on the pseudo-rigid modeling technique. From the models obtained, this study conducted prototyping and computational tests to obtain a proof-of-concept of the bioinspired theory and dynamic functioning effectiveness. The prototyping relied on hot glue manufacturing and the computational simulations consisted of linear static analysis. The experimental analysis concluded that the prototype with fewer beams and thinner beams delivered better results in all three parameters: flexibility, height variation and rotation arc. In the computational analysis, among the design models with the variation of the number of beams, the model with 8 beams performed better. Concerning thickness variation, the one whose beams measured 8 mm in thickness showed better results. Among the models with length variation, the design made with 100 mm long beams better equilibrated the parameters.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Purpose</jats:italic>. The aim of this study was to assess the feasibility of the development and training of a deep learning object detection model for automating the assessment of fiducial marker migration and tracking of the prostate in radiotherapy patients. <jats:italic>Methods and Materials</jats:italic>. A fiducial marker detection model was trained on the YOLO v2 detection framework using approximately 20,000 pelvis kV projection images with fiducial markers labelled. The ability of the trained model to detect marker positions was validated by tracking the motion of markers in a respiratory phantom and comparing detection data with the expected displacement from a reference position. Marker migration was then assessed in 14 prostate radiotherapy patients using the detector for comparison with previously conducted studies. This was done by determining variations in intermarker distance between the first and subsequent fractions in each patient. <jats:italic>Results</jats:italic>. On completion of training, a detection model was developed that operated at a 96% detection efficacy and with a root mean square error of 0.3 pixels. By determining the displacement from a reference position in a respiratory phantom, experimentally and with the detector it was found that the detector was able to compute displacements with a mean accuracy of 97.8% when compared to the actual values. Interfraction marker migration was measured in 14 patients and the average and maximum <jats:inline-formula> <jats:tex-math> <?CDATA $\pm $?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>±</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac34daieqn1.gif" xlink:type="simple" /> </jats:inline-formula> standard deviation marker migration were found to be <jats:inline-formula> <jats:tex-math> <?CDATA $2.0\pm 0.9\,{\rm{mm}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2.0</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.9</mml:mn> <mml:mspace width=".25em" /> <mml:mi mathvariant="normal">mm</mml:mi> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac34daieqn2.gif" xlink:type="simple" /> </jats:inline-formula> and <jats:inline-formula> <jats:tex-math> <?CDATA $2.3\pm 0.9\,{\rm{mm}},$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2.3</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.9</mml:mn> <mml:mspace width="0.25em" /> <mml:mi mathvariant="normal">mm</mml:mi> <mml:mo>,</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac34daieqn3.gif" xlink:type="simple" /> </jats:inline-formula> respectively. <jats:italic>Conclusion</jats:italic>. This study demonstrates the benefits of pairing deep learning object detection, and image-guided radiotherapy and how a workflow to automate the assessment of organ motion and seed migration during prostate radiotherapy can be developed. The high detection efficacy and low error make evident the advantages of using a pre-trained model to automate the assessment of the target volume positional variation and the migration of fiducial markers between fractions.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>A new analytical formalism, previously developed for estimating electron-absorbed fractions, was extended for estimating photon absorbed fractions in soft-tissue spheres, containing uniformly distributed photon-emitter. Analytical equations were formulated for calculating values of photon-absorbed fractions. The method involves a rescaling procedure with transformation of real biological sizes to unitless effective ones, combining information of photon energy, object’s size, and material. Rescaling was applied to large published datasets of photon absorbed fractions in soft-tissue spheres, computed with Monte Carlo codes. A new effect was demonstrated in which the rescaled data formed a single smooth ‘unified curve’ with saturation. The unified curve for photon absorbed fractions was described analytically, using simple equations without fitting parameters. The new method was tested for a wide range of spheres—from 1 mg up to 1000 kg, and wide range of photon energies—from 0.02 up to 5 MeV. For larger spheres, a close agreement between analytical values and Monte Carlo datasets was demonstrated. For small biovolumes, analytical equations predict higher values than available Monte Carlo data. The unified formalism is now available for direct calculating radiation absorbed fractions in soft-tissue spherical organs and organisms without Monte Carlo codes.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>This paper presents a pure element-free Galerkin method (EFGM) forward model for image reconstruction in 2D and 3D electrical impedance tomography (EIT) using an adaptive current injection method. In EIT systems with the adapting current injection method, both static and dynamic images can be reconstructed; however, determination of electrode contact impedances in the complete electrode model is difficult and the Gap model is used. In this paper, in the EIT forward problem a weak form functional based on the Gap model and a pure EFGM approach are developed, and in the EIT inverse problem, Jacobian matrix is computed by the EFGM, and a fast integration technique is introduced to calculate the entries of the Jacobian matrix within an adequate computation time. The influence of increasing the density of nodes at and near the electrodes with steep electric potential gradients on the accuracy of FEM and EFGM forward solutions is investigated, and the performance of the image reconstruction algorithm with the proposed fast integration technique is examined. The numerical results reveal that the proposed EFGM forward model with the fast integration technique has an efficient performance both in terms of mean relative imaging errors and computational time.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Despite advancements in tissue engineering, the methods used to generate three-dimensional (3D) <jats:italic>in vitro</jats:italic> models for rapid screening and characterization studies remain time and labor intensive. Bioprinting offers an opportunity to offset these limitations by providing a scalable, high-throughput method with precise control over biomaterial scaffold and cellular deposition. However, the process of formulating bioinks can be complex in terms of balancing the mechanical integrity of a bioscaffold and viability of cells. One key factor, especially in alginate-based bioinks, is the rate of bioscaffold dissolution. It must allow cells to replace the bioscaffold with extracellular matrix (ECM), yet remain durable during extended tissue culture. This study uses a Design of Experiments (DoE) approach to understand the dependencies of multiple variables involved in the formulation and processing of an alginate-based bioink. The focus of the DoE was to understand the effects of hydrogel composition on bioink durability while maintaining cell viability. Three ingredients were varied in all: alginate, nanocellulose, and fibrinogen. Their effects on the bioink were then measured with respect to extrudability, strength, and stiffness as determined by dynamic mechanical analysis (DMA). The DoE demonstrated that mechanical integrity increased with increasing alginate concentration. In contrast, fibrinogen and nanofibril concentration had no statistically significant effect. The optimized ink containing fibroblasts was printable using multiple nozzle sizes while also supporting fibroblast cell viability. DMA characterization further showed that the composition of the cell culture medium did not modulate the degradation rate of the hydrogel. Ultimately, the study outlines a methodology for formulating a bioink that will result in robust bioscaffolds for <jats:italic>in vitro</jats:italic> model development.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:bold>Purpose:</jats:bold> Since guidance based on x-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the x-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures. <jats:bold>Method:</jats:bold> We present a robust learning-based denoising strategy involving model-based simulations of low-dose x-ray images during the training phase. The method also utilizes a data-driven normalization step—based on an x-ray imaging model—to stabilize the mixed signal-dependent noise associated with x-ray images. We thoroughly analyze the method’s sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose x-ray images on the denoising results. <jats:bold>Results:</jats:bold> A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band coefficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy. <jats:bold>Conclusion:</jats:bold> The proposed learning-based denoising strategy provides scope for significant x-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training phase.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Purpose.</jats:italic> The paper describes the application of unlaminated Gafchromic EBT3 film dosimeter for quantification of dose enhancement using locally synthesized silver nanoparticle-embedded alginate film (AgNPs-Alg film) for nanoparticles-aided radiotherapy. <jats:italic>Materials and Methods.</jats:italic> AgNPs-Alg film was synthesized and characterized using standard techniques. The unlaminated Gafchromic EBT3 film was specially customized for dosimetric measurement. The dose enhancements due to AgNPs-Alg film was experimentally determined for ISO wide spectrum x-rays series (average energy ranging from 57–137 keV) and 6 and 10 MV x-rays using laminated and unlaminated Gafchromic EBT3 film. The radiation dose of 1 Gy was delivered to a combination of AgNPs-Alg films and EBT3 film. <jats:italic>Results.</jats:italic> Ultraviolet-Visible spectroscopy of silver nanoparticles shows a surface plasmon resonance peak at 400 nm. The average particle size of 13 ± 2 nm was measured using Atomic Force Microscopy. For unlaminated film, the dose enhancements of 29%, 23%, 14% and 2% was observed for ISO wide spectrum x-rays having average energy of 57, 79, 104 and 137 keV, respectively. The dose enhancement was negligible for 6 and 10 MV x-rays. In the case of laminated film, no significant dose enhancement was measured for all the x-ray energies. <jats:italic>Conclusion.</jats:italic> The unlaminated Gafchromic EBT3 film can be a suitable choice for the measurement of dose enhancement. Further, silver nanoparticles can be used during nanoparticle-aided radiotherapy when irradiated at low x-ray energy.</jats:p>