Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>Optical coherence tomography (OCT) is a well-established imaging technology for high-resolution, cross-sectional imaging of biological tissues. Imaging processing and light attenuation coefficient estimation allows to further improve the OCT diagnostic capability. In this paper we use a commercial OCT system, Telesto II-1325LR from Thorlabs, and demonstrate its ability to differentiate normal and tumor mammary mouse glands with the OCT attenuation coefficient. Using several OCT images of normal and tumor mammary mouse glands (n = 26), a statistical analysis was performed. The attenuation coefficient was calculated in depth, considering a slope of 0.5 mm. The normal glands present a median attenuation coefficient of 0.403 mm<jats:sup>−1</jats:sup>, comparatively to 0.561 mm<jats:sup>−1</jats:sup> obtained for tumor mammary glands. This translates in an attenuation coefficient approximately 39% higher for tumor mammary glands when compared to normal mammary glands. The OCT attenuation coefficient estimation eliminates the subjective analysis provided by direct visualization of the OCT images.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Online dose verification in proton therapy is a critical task for quality assurance. We further studied the feasibility of using a wavelet-based machine learning framework to accomplishing that goal in three dimensions, built upon our previous work in 1D. The wavelet decomposition was utilized to extract features of acoustic signals and a bidirectional long-short-term memory (Bi-LSTM) recurrent neural network (RNN) was used. The 3D dose distributions of mono-energetic proton beams (multiple beam energies) inside a 3D CT phantom, were generated using Monte-Carlo simulation. The 3D propagation of acoustic signal was modeled using the k-Wave toolbox. Three different beamlets (i.e. acoustic pathways) were tested, one with its own model. The performance was quantitatively evaluated in terms of mean relative error (MRE) of dose distribution and positioning error of Bragg peak (<jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{\Delta }}}_{BP}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">Δ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>B</mml:mi> <mml:mi>P</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac396dieqn1.gif" xlink:type="simple" /> </jats:inline-formula>), for two signal-to-noise ratios (SNRs). Due to the lack of experimental data for the time being, two SNR conditions were modeled (SNR = 1 and 5). The model is found to yield good accuracy and noise immunity for all three beamlets. The results exhibit an MRE below 0.6% (without noise) and 1.2% (SNR = 5), and <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{\Delta }}}_{BP}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">Δ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>B</mml:mi> <mml:mi>P</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac396dieqn2.gif" xlink:type="simple" /> </jats:inline-formula> below 1.2 mm (without noise) and 1.3 mm (SNR = 5). For the worst-case scenario (SNR = 1), the MRE and <jats:inline-formula> <jats:tex-math> <?CDATA ${{\rm{\Delta }}}_{BP}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">Δ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>B</mml:mi> <mml:mi>P</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexac396dieqn3.gif" xlink:type="simple" /> </jats:inline-formula> are below 2.3% and 1.9 mm, respectively. It is encouraging to find out that our model is able to identify the correlation between acoustic waveforms and dose distributions in 3D heterogeneous tissues, as in the 1D case. The work lays a good foundation for us to advance the study and fully validate the feasibility with experimental results.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>A 3-D dosimeter fills the need for treatment plan and delivery verification required by every modern radiation-therapy method used today. This report summarizes a proof-of-concept study to develop a water-equivalent solid 3-D dosimeter that is based on novel radiation-hard scintillating material. The active material of the prototype dosimeter is a blend of radiation-hard peroxide-cured polysiloxane plastic doped with scintillating agent P-Terphenyl and wavelength-shifter BisMSB. The prototype detector was tested with 6 MV and 10 MV x-ray beams at Ohio State University’s Comprehensive Cancer Center. A 3-D dose distribution was successfully reconstructed by a neural network specifically trained for this prototype. This report summarizes the material production procedure, the material’s water equivalency investigation, the design of the prototype dosimeter and its beam tests, as well as the details of the utilized machine learning approach and the reconstructed 3-D dose distributions.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Assessment of biocompatibility for the developed wound dressing plays a significant role in translational studies. In the present research work, a wound dressing has been developed using gelatin, hyaluronic acid and chondroitin sulfate using EDC as crosslinker in a specific manner. The characterized hydrogel wound dressing was evaluated for its biocompatibility studies by means of ISO-10993-11 medical device rules and standards. Various parameters like skin sensitization test, acute systemic toxic test, implantation study, intracutaneous reactivity test, <jats:italic>in vitro</jats:italic> cytotoxicity test and bacterial reverse mutation test, were evaluated and the results demonstrated its safety for the pre-clinical investigation.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The detailed physiological perspectives captured by medical imaging provides actionable insights to doctors to manage comprehensive care of patients. However, the quality of such diagnostic image modalities is often affected by mismanagement of the image capturing process by poorly trained technicians and older/poorly maintained imaging equipment. Further, a patient is often subjected to scanning at different orientations to capture the frontal, lateral and sagittal views of the affected areas. Due to the large volume of diagnostic scans performed at a modern hospital, adequate documentation of such additional perspectives is mostly overlooked, which is also an essential key element of quality diagnostic systems and predictive analytics systems. Another crucial challenge affecting effective medical image data management is that the diagnostic scans are essentially stored as unstructured data, lacking a well-defined processing methodology for enabling intelligent image data management for supporting applications like similar patient retrieval , automated disease prediction etc. One solution is to incorporate automated diagnostic image descriptions of the observation/findings by leveraging computer vision and natural language processing. In this work, we present multi-task neural models capable of addressing these critical challenges. We propose ESRGAN, an image enhancement technique for improving the quality and visualization of medical chest x-ray images, thereby substantially improving the potential for accurate diagnosis, automatic detection and region-of-interest segmentation. We also propose a CNN-based model called ViewNet for predicting the view orientation of the x-ray image and generating a medical report using Xception net, thus facilitating a robust medical image management system for intelligent diagnosis applications. Experimental results are demonstrated using standard metrics like BRISQUE, PIQE and BLEU scores, indicating that the proposed models achieved excellent performance. Further, the proposed deep learning approaches enable diagnosis in a lesser time and their hybrid architecture shows significant potential for supporting many intelligent diagnosis applications.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The three-dimensional cardiac monodomain model with inhomogeneous and anisotropic conductivity characterizes a complicated system that contains spatial and temporal approximation coefficients along with a nonlinear ionic current term. These complexities make its numerical modeling computationally challenging, and therefore, the formation of an efficient computational approximation is important for studying cardiac propagation. In this paper, a reduced order modeling approach has been developed for the simplified cardiac monodomain model, which yields a significant reduction of the full order dynamics of the cardiac tissue, reducing the required computational resources. Additionally, the discrete empirical interpolation technique has been implemented to accurately estimate the nonlinearity of the ionic current of the cardiac monodomain scheme. The proper orthogonal decomposition technique has been utilized, which transforms a given dataset called ‘snapshots’ to a new coordinate system. The snapshots are computed first from the original system, and they encapsulate all the information observed over both time and parameter variations. Next, the proper orthogonal decomposition provides a reduced order basis for projecting the original solution onto a low-dimensional orthonormal subspace. Finally, a reduced set of unknowns of the forward problem is obtained for which the solution involves significant computational savings compared to that for the original system of unknowns. The efficiency of the model order reduction technique for finite difference solution of cardiac electrophysiology is examined concerning simulation time, error potential, activation time, maximum temporal derivative, and conduction velocity. Numerical results for the monodomain show that its solution time can be reduced by a significant factor, with only 0.474 mV RMS error between the full order and reduced dimensions solution.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>In this paper, we investigate endovascular delivery to get a step ahead of the pharmacological limitations it has due to the complexity of dealing with a patient-specific vessel through a mathematical model. We divide the domain of computation into four sub-domains: the lumen, the lumen-tissue interface, the upper tissue and the lower tissue which are extracted from an asymmetric atherosclerotic image derived by the intravascular ultrasound (IVUS) technique. The injected drug at the luminal inlet is transported with the streaming blood which is considered Newtonian. An irreversible uptake kinetics of the injected drug at the lumen-tissue interface from the luminal side to the tissue domains is assumed. Subsequently, the drug is dispersed within the tissue followed by its retention in the extracellular matrix (ECM) and by receptor-mediated binding. The Marker and Cell (MAC) method has been leveraged to get a quantitative insight into the model considered. The effect of the wall absorption parameter on the concentration of all drug forms (free as well as two-phase bound) has been thoroughly investigated, and some other important factors, such as the averaged concentration, the tissue content, the fractional effect, the concentration variance and the effectiveness of drug have been graphically analyzed to gain a clear understanding of endovascular delivery. The simulated results predict that with increasing values of the absorption parameter, the averaged concentrations of all drug forms do decrease. An early saturation of binding sites takes place for smaller values of the absorption parameter, and also rapid saturation of ECM binding sites occurs as compared to receptor binding sites. Results also predict the influence of surface roughness as well as asymmetry of the domain about the centerline on the distribution and retention of drug. A thorough sensitivity analysis has been carried out to determine the influence of some parameters involved.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The aim of this study is the development of an irradiation method for the treatment of superficial tumours using a hydrogel bolus to produce thermal neutrons in accelerator-based Boron Neutron Capture Therapy (BNCT).</jats:p> <jats:p>To evaluate the neutron moderating ability of a hydrogel bolus, a water phantom with a hydrogel bolus was irradiated with an epithermal neutron beam from a cyclotron-based epithermal neutron source. Phantom simulating irradiation to the plantar position was manufactured using three-dimensional printing technology to perform an irradiation test of a hydrogel bolus. Thermal neutron fluxes on the surface of a phantom were evaluated and the results were compared with the Monte Carlo-based Simulation Environment for Radiotherapy Applications (SERA) treatment planning software. It was confirmed that a hydrogel bolus had the same neutron moderating ability as water, and the calculation results from SERA aligned with the measured values within approximately 5%. Furthermore, it was confirmed that the thermal neutron flux decreased at the edge of the irradiation field. It was possible to uniformly irradiate thermal neutrons by increasing the bolus thickness at the edge of the irradiation field, thereby successfully determining uniform dose distribution. An irradiation method for superficial tumours using a hydrogel bolus in the accelerator-based BNCT was established.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Severe burn injures lead to millions of fatalities every year due to lack of skin replacements. While skin is a very limited and expensive entity, split thickness skin grafting, which involves the projection of a parallel incision pattern on a small section of healthy excised skin, is typically employed to increase the expansion and cover a larger burn site. To date, the real expansion capacity of such grafts are low (&lt;3 times) and insufficient for treatment of severe burn injuries. In this study, novel I-shaped auxetic incision patterns, which are known to exhibit high negative Poisson’s ratios, have been tested on the skin to investigate their expansion potential. Fourteen two-layer skin graft models with varying incision pattern parameters (i.e., length, spacing, and orientation) were developed using finite element modelling and tested under uniaxial and biaxial tensile loads. The Poisson’s ratio, meshing ratios, and induced stresses were quantified across all models. Graft models tested uniaxially along the orthogonal directions indicated opposite trends in generated Poisson’s ratios, as the length of the I-shape incisions were increased. Biaxially, with a symmetric and closely spaced I-shape pattern, graft meshing ratios up to 15.65 were achieved without overstressing the skin. Overall, the findings from the study indicated that expansion potentials much higher than that of traditional skin grafts can be achieved with novel I-shaped auxetic skin grafts, which would be indispensable for covering large wounds in severe burn injuries.</jats:p>