Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objectives</jats:italic>. In laser lithotripsy, a green aiming beam overlying the infrared (IR) treatment radiation gives rise to reflection and fluorescence signals that can be measured via the treatment fiber. While stone autofluorescence is used for target detection, the condition of the fiber can be assessed based on its Fresnel reflection. For good applicability, fluorescence detection of stones should work even when the stone and fiber are not in direct contact. Fiber breakage detection, on the other hand, can be falsified if surfaces located in front of the fiber reflect light from the aiming laser back into it. For both applications, therefore, a fundamental investigation of the dependence of the signal amplitude on the distance between fiber and surface is important. <jats:italic>Methods</jats:italic>. Calculations of the signal drop of fluorescence or diffuse and specular reflection with increasing fiber distance were performed using ray tracing based on a simple geometric model for different fiber core diameters. Reflection signals from a mirror, diffuse reflector, human calculi, and porcine renal tissue placed in water were measured at varying distances (0–5 mm). For human calculi, fluorescence signals were recorded simultaneously. <jats:italic>Results</jats:italic>. The calculations showed a linear signal decrease down to ∼60% of the maximum signal (fiber in contact). The distance <jats:italic>z</jats:italic> at which the signal drops to for example 50% depends linearly on the diameter of the fiber core. For fibers used in lithotripsy and positioned in water, <jats:italic>z</jats:italic> <jats:sub>50%</jats:sub> ranges from 0.55 mm (200 <jats:italic>μ</jats:italic>m core diameter) to 2.73 mm, (1 mm core diameter). The calculations were in good agreement with the experimental results. <jats:italic>Conclusions</jats:italic>. The autofluorescence signals of stones can be measured in non-contact mode. Evaluating the Fresnel signal of the end face of the fiber to detect breakage is possible unless the fiber is situated less than some millimeters to reflecting surfaces.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Deviceless four-dimensional (4D) computed tomography (CT) allows the acquisition of respiratory signals from six features without requiring an external device for cine CT processing. This method has been recently introduced in radiation treatment planning of lung tumors. To validate deviceless 4D CT, it must be compared with conventional 4D CT, which requires an external monitoring device. We compared the two methods using a multicell 4D phantom that simulates patient’s movement during respiration regarding the target volume (TV), target position (TP), and internal TV for lung tumor radiation therapy. We retrospectively obtained images of 10 patients who underwent radiation treatment planning of lung tumors and compared the two methods, as in the phantom study. For the phantom study, the mean TV, root mean square errors of the TP, and mean internal TV differences between the two methods ranged from −4.5% to 1.2%, 0.7 to 2.6 mm, and −1.1% to 3.4%, respectively. The corresponding results of the clinical study ranged from −1.5% to 14.9%, 0.1 to 5.9 mm, and −9.7% to 10.1%, respectively. The results of deviceless 4D CT for the clinical study were consistent with those of conventional 4D CT, except for target movements with high excursions. Therefore, deviceless 4D CT can be an alternative to conventional 4D CT for radiation treatment planning of lung tumors.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> New technologies, including robots comprising germ-killing UV lamps, are increasingly being used to decontaminate hospitals and prevent the spread of COVID-19 and other superbugs. Existing approaches for modelling the irradiance field surrounding mobile UV disinfection robots are limited by their inability to capture the physics of their bespoke geometrical configurations and do not account for reflections. The goal of this research was to extend current models to address these limitations and to subsequently verify these models using empirically collected data. <jats:italic>Approach.</jats:italic> Two distinct parametric models were developed to describe a multi-lamp robotic UV system and adapted to incorporate the effects of irradiance amplification from the device's reflectors. The first model was derived from electromagnetic wave theory while the second was derived from conservation of energy and diffusion methods. Both models were tuned using data from empirical testing of an existing UV robot, and then validated using an independent set of measurements from the same device. <jats:italic>Results.</jats:italic> For each parameter, predictions made using the conservation of energy method were found to closely approximate the empirical data, offering more accurate estimates of the 3D irradiance field than the electromagnetic wave theory model. <jats:italic>Significance.</jats:italic> The versatility of the proposed method ensures that it can be easily adapted to different embodiments, providing a systematic way for researchers to develop accurate numerical models of custom UV robots, which may be used to inform deployment and/or to improve the accuracy of virtual simulation.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Purpose</jats:italic>: Molecular breast imaging (MBI) is used clinically to visualize the uptake of <jats:sup>99m</jats:sup>Tc-sestamibi in breast cancers. Here, we use Monte Carlo simulations to develop a methodology to estimate tumor diameter in focal lesions and explore a semi-automatic implementation for clinical data. <jats:italic>Methods</jats:italic>: A validated Monte Carlo simulation of the GE Discovery NM 750b was used to simulate &gt;75,000 unique spherical/ellipsoidal tumor, normal breast, and image acquisition conditions. Subsets of this data were used to 1) characterize the dependence of the full-width at half-maximum (FWHM) of a tumor profile on tumor, normal breast, and acquisition conditions, 2) develop a methodology to estimate tumor diameters, and 3) quantify the diameter accuracy in a broad range of clinical conditions. Finally, the methodology was implemented in patient images and compared to diameter estimates from physician contours on MBI, mammography, and ultrasound imaging. <jats:italic>Results</jats:italic>: Tumor profile FWHM was determined be linearly dependent on tumor diameter but independent of other factors such as tumor shape, uptake, and distance from the detector. A linear regression was used to calculate tumor diameter from the FWHM estimated from a background-corrected profile across a tumor extracted from a median-filtered single-detector MBI image, i.e., diameter = 1.2 mm + 1.2 × FWHM, for FWHM ≥ 13 mm. Across a variety of simulated clinical conditions, the mean error of the methodology was 0.2 mm (accuracy), with &gt;50% of cases estimated within 1-pixel width of the truth (precision). In patient images, the semi-automatic methodology provided the longest diameter in 94% (60/64) of cases. The estimated true diameters, for oval lesions with homogeneous uptake, differed by ± 5 mm from physician measurements. <jats:italic>Conclusion</jats:italic>: This work demonstrates the feasibility of accurately quantifying tumor diameter in clinical MBI, and to our knowledge, is the first to explore its implementation and application in patient data.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Low Performing Pixel (LPP)/bad pixel in CT detectors cause ring and streaks artifacts, structured non-uniformities and deterioration of the image quality. These artifacts make the image unusable for diagnostic purposes. A missing/defective detector pixel translates to a channel missing across all views in sinogram domain and its effect gets spill over entire image in reconstruction domain as artifacts. Most of the existing ring and streak removal algorithms perform correction only in the reconstructed image domain. In this work, we propose a supervised deep learning algorithm that operates in sinogram domain to remove distortions cause by the LPP. This method leverages CT scan geometry, including conjugate ray information to learn the interpolation in sinogram domain. While the experiments are designed to cover the entire detector space, we emphasize on LPPs near detector iso-center as these have most adverse impact on image quality specially if the LPPs fall on the high frequency region (bone-tissue interface). We demonstrated efficacy of the proposed method using data acquired on GE RevACT multi-slice CT system with flat-panel detector. Experimental results on head scans show significant reduction in ring artifacts regardless of LPP location in the detector geometry. We have simulated isolated LPPs accounting for 5% and 10% of total channels. Detailed statistical analysis illustrates approximately 5dB improvement in SNR in both sinogram and reconstruction domain as compared to classical bicubic and Lagrange interpolation methods. Also, with reduction in ring and streak artifacts, the perceptual image quality is improved across all the test images.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Previous study using thoracic phantom for estimating fluid volume has been obtained which represents the case of pleural effusion based on the size of the x-ray radiograph. The models are obtained in the form of three equations, the pleural effusion volume as a function of height, length times the height, and area of the radiograph image. The three models of estimation have high linearity with ratio value more than 0.988, higher than the modelling measurement using ultrasonography modality. The modelling is expected to give a contribution on developing method for helping clinicians estimate the pleural effusion volume as a basic for performing fluid aspiration and to monitor the therapy. However, because modelling is developed using phantoms, then to be applied clinically, further research is needed for its application to patients. The height function model yields correlation value of 0.966 and paired T-test value of 0.892. The height times length function model yields correlation value of 0.982 and paired T-test value of 0.611. The area function model yields correlation value of 0.997 and paired T-test value of 0.647. From the three equations, measurement of estimated pleural effusion volume using area function on chest x-ray lateral decubitus position is the most appropriate equation. Corresponding to the results of the measurement of gold standard using a CT scan. Height measurement is the measurement that is the fastest and easiest in the application. Limitation of the study is it only can be done in right lateral decubitus position of the patient, and also cannot be applied to patients with condition such as post lung surgery, massive subpulmonic/ supradiaphragmatic pleural effusion, empyema, an atypical pleural effusion such as septated, encapsulated, loculated pleural effusion and anatomical deformity, scoliosis, or abnormalities of thoracic cavity.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>One of the main challenges in fMRI processing is filtering the task BOLD signals from the noise. Independent component analysis with automatic removal of motion artifacts (ICA-AROMA) reduces motion artifacts by identifying ICA noise components based on their location at the brain edges and cerebrospinal fluid (CSF), high frequency content and correlation with motion regressors. In anatomical component correction (aCompCor), physiological noise regressors extracted from CSF were regressed out from the fMRI time series. In this study, we compared three methods to combine aCompCor and ICA-AROMA denoising in one denoising step. In the first analysis, we regressed the temporal signals of the ICA components identified as noise by ICA-AROMA together with the noise signals determined by aCompCor from the fMRI signals. For the second and third analyses, the correlation between the temporal signals of the ICA components and the aCompCor noise signals was used as an additional criterion to identify the noise components. In the second analysis, the temporal signals of the ICA components classified as noise were regressed from the fMRI signals. In the third analysis, the noise components were removed. To compare the denoising strategies, we examined the fractional amplitude of low-frequency fluctuations (fALFF) and the overlap between the contrast maps. Our results revealed that including the aCompCor noise signals as regressors in ICA-AROMA resulted in more correctly identified noise components, higher fALFF values, and larger activation maps. Moreover, combining the temporal signals of the noise components identified by ICA-AROMA with the aCompCor signals in a noise regression matrix resulted in deactivations. These results suggest that using the correlation between the ICA component temporal signals and the aCompCor signals as noise identification criteria in ICA-AROMA is the best approach for combining both denoising methods.</jats:p>