Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>The successful development of the image denoising techniques for low-dose computed tomography (LDCT) was largely owing to the public-domain availability of spatially-aligned high- and low-dose CT image pairs. Even though low-dose CT scans are also highly desired in dental imaging, public-domain databases of dental CT image pairs have not been established yet. In this paper, we propose a dental CT image denoising method based on the transfer learning of a generative adversarial network (GAN) from the public-domain CT images. We trained a generative adversarial network with the Wasserstein loss function (WGAN) using 5,100 high- and low-dose medical CT image pairs of human chest and abdomen. For the generative network of GAN, we used the U-net structure of five stages to exploit its high computational efficiency. After training the proposed network, named U-WGAN, we fine-tuned the network with 3,006 dental CT image pairs of two different human skull phantoms. For the high- and low-dose scans of the phantoms, we set the tube current of the dental CT to 10 mA and 4 mA, respectively, with the tube voltage set to 90 kVp in both scans. We applied the trained network to denoising of low-dose dental CT images of dental phantoms and adult humans. The U-net processed images showed over-smoothing effects even though U-net had a good performance in the quantitative metrics. U-WGAN showed similar denoising performance to WGAN, but it reduced the computation time of WGAN by a factor of 10. The fine-tuning procedure in the transfer learning scheme enhanced the network performance in terms of the quantitative metrics, and it also improved visual appearance of the processed images. Even though the number of fine-tuning images was very limited in this study, we think the transfer learning scheme can be a good option for developing deep learning networks for dental CT image denoising.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Neurofilaments (NFs) are the most abundant cytoskeletal filaments undergoing ‘slow axonal transport’ in axons, and the population of NFs determines the axonal morphology. Both <jats:italic>in vitro</jats:italic> and <jats:italic>ex-vivo</jats:italic> experimental evidences show that the caliber of node is much thinner and the number of NFs in the node is much lower than the internode. Based on the Continuity equation, lower population of NFs indicates faster transport velocity. We propose that the local acceleration of NFs transport at node may result from the higher on-track rate <jats:inline-formula> <jats:tex-math> <?CDATA ${\gamma }_{on}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>γ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>o</mml:mi> <mml:mi>n</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexabb067ieqn1.gif" xlink:type="simple" /> </jats:inline-formula> or higher transition rate <jats:inline-formula> <jats:tex-math> <?CDATA ${\gamma }_{01}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>γ</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>01</mml:mn> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexabb067ieqn2.gif" xlink:type="simple" /> </jats:inline-formula> from pausing to running. We construct a segment of axon including both node and internode, and inject NFs by a fixed flux into it continuously. By upregulating transition rate of either <jats:inline-formula> <jats:tex-math> <?CDATA ${\gamma }_{on}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>γ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>o</mml:mi> <mml:mi>n</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexabb067ieqn3.gif" xlink:type="simple" /> </jats:inline-formula> or <jats:inline-formula> <jats:tex-math> <?CDATA ${\gamma }_{01}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>γ</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>01</mml:mn> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexabb067ieqn4.gif" xlink:type="simple" /> </jats:inline-formula> locally at the Node of Ranvier in the ‘6-state’model, we successfully accelerate NFs velocity and reproduce constriction of nodes. Our work demonstrates that local modulation of NF kinetics can change NFs distribution and shape the morphology of Node of Ranvier.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The human body contains approximately 20 billion blood vessels, which transport nutrients, oxygen, immune cells, and signals throughout the body. The brain's vasculature includes up to 9 billion of these vessels to support cognition, motor processes, and myriad other vital functions. To model blood flowing through a vasculature, a geometric description of the vessels is required. Previously reported attempts to model vascular geometries have produced highly-detailed models. These models, however, are limited to a small fraction of the human brain, and little was known about the feasibility of computationally modeling whole-organ-sized networks. We implemented a fractal-based algorithm to construct a vasculature the size of the human brain and evaluated the algorithm's speed and memory requirements. Using high-performance computing systems, the algorithm constructed a vasculature comprising 17 billion vessels in 1960 core-hours, or 49 minutes of wall-clock time, and required less than 32 GB of memory per node. We demonstrated strong scalability that was limited mainly by input/output operations. The results of this study demonstrated, for the first time, that it is feasible to computationally model the vasculature of the whole human brain. These findings provide key insights into the computational aspects of modeling whole-organ vasculature.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>With ever growing interest in far-reaching solutions for pervasive healthcare and medicine, polymer optical fibers have been rendered into textile forms. Having both fiber-optic functionalities and traditional fabric-like comfort, textile-integrated polymer optical fibers have been advocated to remove the technical barriers for long-term uninterrupted health monitoring and treatment. In this context, this paper spotlights and reviews the recently developed textile-integrated polymer optical fibers in conjunction with fabrication techniques, applications in long-term continuous health monitoring and treatment, and future perspectives in the vision of mobile health (mHealth), as well as the introductory basics of polymer optical fibers. It is designed to serve as a topical guidepost for scientists and engineers on this highly interdisciplinary and rapidly growing topic.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The purpose of this study was to develop a computational phantom for validation of automatic noise calculations applied to all parts of the body, to investigate kernel size in determining noise, and to validate the accuracy of automatic noise calculation for several noise levels. The phantom consisted of objects with a very wide range of HU values, from −1000 to +950. The incremental value for each object was 10 HU. Each object had a size of 15 × 15 pixels separated by a distance of 5 pixels. There was no dominant homogeneous part in the phantom. The image of the phantom was then degraded to mimic the real image quality of CT by convolving it with a point spread function (PSF) and by addition of Gaussian noise. The magnitude of the Gaussian noises was varied (5, 10, 25, 50, 75 and 100 HUs), and they were considered as the ground truth noise (N<jats:sub>G</jats:sub>). We also used a computational phantom with added actual noise from a CT scanner. The phantom was used to validate the automated noise measurement based on the average of the ten smallest standard deviations (SD) from the standard deviation map (SDM). Kernel sizes from 3 × 3 up to 27 × 27 pixels were examined in this study. A computational phantom for automated noise calculations validation has been successfully developed. It was found that the measured noise (N<jats:sub>M</jats:sub>) was influenced by the kernel size. For kernels of 15 × 15 pixels or smaller, the N<jats:sub>M</jats:sub> value was much smaller than the N<jats:sub>G</jats:sub>. For kernel sizes from 17 × 17 to 21 × 21 pixels, the N<jats:sub>M</jats:sub> value was about 90% of N<jats:sub>G</jats:sub>. And for kernel sizes of 23 × 23 pixels and above, N<jats:sub>M</jats:sub> is greater than N<jats:sub>G</jats:sub>. It was also found that even with small kernel sizes the relationship between N<jats:sub>M</jats:sub> and N<jats:sub>G</jats:sub> is linear with R<jats:sup>2</jats:sup> more than 0.995. Thus accurate noise levels can be automatically obtained even with small kernel sizes without any concern regarding the inhomogeneity of the object.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Kilovoltage intrafraction monitoring (KIM) is a method allowing to precisely infer the tumour trajectory based on cone beam computed tomography (CBCT) 2D-projections. However, its accuracy is deteriorated in the case of highly mobile tumours involving hysteresis. A first adaptation of KIM consisting of a prior amplitude based binning step has been developed in order to minimize the errors of the original model (phase-KIM). In this work, we propose enhanced methods (KIM<jats:sub>sub-arc optim</jats:sub> and phase-KIM<jats:sub>sub-arc optim</jats:sub>) to improve the accuracy of KIM and phase-KIM which relies on the selection of the optimal starting CBCT gantry angle. Aiming at demonstrating the interest of our approach, we carried out a simulation study and an experimental study: we compared the accuracy of the conventional versus sub-arc optim methods on simulated realistic tumour motions with amplitudes ranging from 5 to 30 mm in 1 mm increments. The same approach was performed using a lung dynamic phantom generating a 30 mm amplitude sinusoidal motion. The results show that for in-silico simulated motions of 10, 20 and 30 mm amplitude, the three-dimensional root mean square error (3D-RMSE) can be reduced by 0.67 mm, 0.91 mm, 0.94 mm and 0.18 mm, 0.25 mm, 0.28 mm using KIM<jats:sub>sub-arc optim</jats:sub> and phase-KIM<jats:sub>sub-arc optim</jats:sub> respectively. Considering all in-silico simulated trajectories, the percentage of errors larger than 1 mm decreases from 21.9% down to 1.6% for KIM (p &lt; 0.001) and from 6.6% down to 1.2% for phase-KIM (p &lt; 0.001). Experimentally, the 3D-RMSE is lowered by 0.5732 mm for KIM and by 0.1 mm for phase-KIM. The percentage of errors larger than 1 mm falls from 39.7% down to 18.5% for KIM and from 23.2% down to 11.1% for phase-KIM. In conclusion, our method efficiently anticipates CBCT gantry angles associated with a significantly better accuracy by using KIM and phase-KIM.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for example, in early prognosis and personalized medical therapy. This work presents a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson’s subtypes based on disease trajectory. Modeling patient-variable interactions as a bipartite network, TC first extracts communities of co-expressing disease variables at different stages of progression. Then, it identifies Parkinson’s subtypes by clustering similar patient trajectories that are characterized by severity of disease variables through a multi-layer network. Determination of trajectory similarity accounts for direct overlaps between trajectories as well as second-order similarities, i.e., common overlap with a third set of trajectories. This work clusters trajectories across two types of layers: (a) temporal, and (b) ranges of independent outcome variable (representative of disease severity), both of which yield four distinct subtypes. The former subtypes exhibit differences in progression of disease domains (Cognitive, Mental Health etc.), whereas the latter subtypes exhibit different degrees of progression, i.e., some remain mild, whereas others show significant deterioration after 5 years. The TC approach is validated through statistical analyses and consistency of the identified subtypes with medical literature. This generalizable and robust method can easily be extended to other progressive multi-variate disease datasets, and can effectively assist in targeted subtype-specific treatment in the field of personalized medicine.</jats:p>