Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>Online monitoring of electroencephalogram (EEG) signals is challenging due to the high volume of data and power requirements. Compressed sensing (CS) may be employed to address these issues. Compressed sensing using a sparse binary matrix, owing to its low power features, and reconstruction/decompression using spatiotemporal sparse Bayesian learning have been shown to constitute a robust framework for fast, energy efficient and accurate multichannel bio-signal monitoring. EEG signal, however, does not show a strong temporal correlation. Therefore, the use of sparsifying dictionaries has been proposed to exploit the sparsity in a transformed domain instead. Assuming sparsification adds values, a challenge, therefore, in employing this CS framework for the EEG signal, is to identify the suitable dictionary. Using real multichannel EEG data from 15 subjects, in this paper, we systematically evaluate the performance of the framework when using various wavelet bases while considering their key attributes namely number of vanishing moments and coherence with sensing matrix. We identified Beylkin as the wavelet dictionary leading to the best performance. Using the same dataset, we then compared the performance of Beylkin with the discrete cosine basis, often used in the literature, and the alternative of not using a sparsifying dictionary. We further demonstrate that using dictionaries (Beylkin and Discrete Cosine Transform (DCT)) may improve performance tangibly only for a high compression ratio (CR) of 80% and with smaller block sizes, as compared to using no dictionaries.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Under physiological conditions biomarker concentrations tend to rise and fall over time e.g. for inflammation. <jats:italic>Ex vivo</jats:italic> measurements provide a snapshot in time of biomarker concentrations, which is useful, but limited. Approaching real time monitoring of biomarker concentration(s) using a wearable, implantable or injectable <jats:italic>in vivo</jats:italic> sensor is therefore an appealing target. As an early step towards developing an <jats:italic>in vivo</jats:italic> biomarker sensor, antibody (AB) tagged magnetic nanoparticles (NPs) are used here to demonstrate the <jats:italic>in vitro</jats:italic> measurement of ∼ 5 distinct biomarkers with high specificity and sensitivity. In previous work, aptamers were used to target a given biomarker <jats:italic>in vitro</jats:italic> and generate magnetic clusters that exhibit a characteristic rotational signature quite different from free NPs. Here the method is expanded to detect a much wider range of biomarkers using polyclonal ABs attached to the surface of the NPs. Commercial ABs exist for a wide range of targets allowing accurate and specific concentration measurements for most significant biomarkers. We show sufficient detection sensitivity, using an in-house spectrometer to measure the rotational signatures of the NPs, to assess physiological concentrations of hormones, cytokines and other signaling molecules. Detection limits for biomarkers drawn mainly from pain and inflammation targets were: 10 pM for mouse Granzyme B (mGZM-B), 40 pM for mouse interferon-gamma (mIFN-<jats:italic>γ</jats:italic>), 7 pM for mouse interleukin-6 (mIL-6), 40 pM for rat interleukin-6 (rIL-6), 40 pM for mouse vascular endothelial growth factor (mVEGF) and 250 pM for rat calcitonin gene related peptide (rCGRP). Much lower detection limits are certainly possible using improved spectrometers and nanoparticles.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>To preserve the fertility of young female cancer patients, ovarian tissue cryopreservation and transplantation have been investigated as next-generation reproductive medical technologies. Non-invasive visualization of follicles in ovarian tissue and cryopreservation of higher density tissue is essential for effective transplantation. We proposed the use of optical coherence tomography (OCT) that can noninvasively visualize the internal structure of the ovarian tissue. However, a method for quantifying cell density has not yet been established because of the lack of available techniques to visualize follicles noninvasively. We proposed the use of a convolutional neural network (CNN) to extract small features from medical images as an image analysis method to automatically detect follicles from the obtained OCT images. First, we collected a total of 13 ovarian tissues from four-day-old mice and acquired OCT images using a full-field-type OCT. Then, the acquired images were analyzed using three detection methods: filter processing, filter processing combined with the CNN, and only CNN. Finally, to verify the detection accuracy of each method, the detection rate and precision were calculated by taking the doctor’s detection as the correct result. The results showed that the detection method only using CNN achieved a detection rate of 0.81 and precision of 0.67; this indicated that follicles could be effectively detected using our proposed method. Furthermore, it is quantitatively evident that the density of follicles from the surface layer to the deep region differs depending on the tissue. In the future, these results could be used to detect follicles in tissues of different maturation stages and quantify follicles three-dimensionally, further accelerating next-generation reproductive medicine.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) usually regard lesion detection, segmentation and characterization as separate tasks, and typically require users to manually select 2D MRI slices or regions of interest as the input. In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention. The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification. Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS). The 3D MMS, which uses linear structuring elements to extract lesion-like patterns, can segment lesions from breast images accurately and efficiently. Analytical features are then extracted from all available 4D multimodal breast MRI sequences, including T1-, T2-weighted and DCE sequences, to represent the signal intensity, texture, morphological and enhancement kinetic characteristics of the region candidates. The region candidates are lastly classified as lesion or normal tissue by the random under-sampling boost (RUSboost), and as malignant or benign lesion by the random forest. Evaluated on a breast MRI dataset which contains a total of 117 cases with 141 biopsy-proven lesions (95 malignant and 46 benign lesions), the proposed system achieves a true positive rate (TPR) of 0.90 at 3.19 false positives per patient (FPP) for lesion detection and a TPR of 0.91 at a FPP of 2.95 for identifying malignant lesions without any user intervention. The average dice similarity index (DSI) is <jats:inline-formula> <jats:tex-math> <?CDATA $0.72\pm 0.15$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0.72</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.15</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bpexabc45cieqn1.gif" xlink:type="simple" /> </jats:inline-formula> for lesion segmentation. Compared with previously proposed lesion detection, detection-segmentation and detection-characterization systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Silicon photomultipliers (SiPMs) are now widely used for positron emission tomography (PET) applications because of their high gain and low noise characteristics. The PET image quality has been improved with the advancement of time-of-flight (TOF) and depth-of-interaction (DOI) measurement techniques. For brain-dedicated PET systems, both TOF and DOI information are beneficial for enhancing the reconstructed PET image quality. In a previous study, we proposed SiPM-based dual-ended readout PET detectors that used a mean time method to achieve coincidence timing resolution (CTR) of 349 ps and DOI resolution of 2.9 mm. However, the coincidence timing resolution (CTR) was worse than 300 ps since the crystal surface and the reflector type were not optimized. This study aimed at investigating the optimal crystal surface treatment and the reflector material to achieve a sub-200 ps CTR and sub-3 mm DOI resolution with a dual-ended readout PET detector using an LYSO crystal (2.9 × 2.9 × 20 mm<jats:sup>3</jats:sup>). The scintillation light inside the LYSO crystal was read out by two SiPMs using the dual-ended readout method. The CTR and DOI resolution were measured with two different crystal surfaces (polished and saw-cut) and three different reflector material scenarios of ESR without grease (i.e., air coupling), ESR with optical grease and Teflon. We digitized the timing and energy signals by using a V775N TDC module (35 ps bit<jats:sup>−1</jats:sup>) and V965 QDC module, respectively. The combination of the saw-cut LYSO crystal and the ESR with air coupling resulted in the best CTR (188 ± 32 ps) and DOI resolution (2.9 ± 0.2 mm) with the dual-ended readout configuration. We concluded the dual-ended readout method in combination with the saw-cut crystal and the ESR reflector with air coupling can provide a sub-200 ps CTR and sub-3.0 mm DOI resolution simultaneously.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>This work investigates the suitability of locally fabricated 6 mol% Ge-doped optical fibres as dosimeters for small-field output ratio measurements. Two fabrications of fibre, cylindrical (CF) and flat (FF) fibres, were used to measure doses in small photon fields, from 4 to 15 mm. The findings were compared to those of commercial Ge-doped fibre (COMM), EBT3 film and an IBA CC01 ionization chamber. Irradiations were carried out using a 6 MV SRS photon beam operating at a dose rate of 1000 cGy min<jats:sup>−1</jats:sup>, delivering a dose of 16 Gy. To minimise the possibility of the fibres failing to be exposed to the intended dose in small fields, the fibres were accommodated in a custom-made Perspex phantom. For the 4 mm cone the CF and FF measured output ratios were found to be smaller than obtained with EBT3 film by 32% and 13% respectively. Conversely, while for the 6 to 15 mm cone fields the FF output ratios were consistently greater than those obtained using EBT3 film, the CF output ratios differed from those of EBT3 film by at most 3.2%, at 6 mm, otherwise essentially agreeing with EBT3 values at the other field sizes. For the 4 to 7.5 mm cones, all output ratios obtained from Ge-doped optical fibre measurements were greater than those of IBA CC01 ionization chamber. The measured FF and CF output ratios for the 7.5 to 15 mm cones agreed with published MC estimates to within 15% and 13%, respectively. Down to 6 mm cone field, present measurements point to the potential of CF as a small-field dosimeter, its use recommended to be complemented by the use of EBT3 film for small-field dosimetry.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Chemotherapy drugs are generally cytotoxic and can cause major side effects, including vomiting/nausea, fatigue, hair loss and pain. The use of targeted nanostructures to deliver drugs directly to tumours has the potential to reduce the side effects by decreasing the exposure of healthy cells and reducing the amount of drug needed. DNA can be used as a structural material to build drug-delivering nanorobots, but questions remain over the practicality of this approach. Here we show that it is potentially feasible for DNA nanostructure drug delivery to be more cost-effective than the drug-only approach. Our result suggests that the barriers to the development of DNA nanostructure-based drug delivery are likely to be primarily technical, regulatory and ethical rather than financial, as the potential exists for this to be a profitable therapeutic approach.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective</jats:italic>: To implement OFM-recirculation and OFM-suction capable of direct and absolute <jats:italic>in-vivo</jats:italic> quantification of albumin in the ISF of pigs. <jats:italic>Approach</jats:italic>: OFM-recirculation and OFM-suction were used to collect ISF <jats:italic>in-vivo</jats:italic> in pigs and lymph was collected from the same pigs after OFM sampling. Blood was collected before and after OFM sampling, plasma was isolated and mean albumin plasma concentrations per pig were used to yield albumin ISF-to-plasma ratios. We characterized the quality of the collected undiluted ISF via (1) stable albumin ISF-to-plasma ratio in OFM-recirculation and in OFM-suction samples, (2) comparison of albumin ISF-to-plasma ratios from OFM-recirculation and OFM-suction and (3) comparison of normalized albumin concentrations in the ISF and lymph. <jats:italic>Main results</jats:italic>: Both advanced OFM methods were successfully implemented and albumin was quantified from the collected ISF samples. OFM-recirculation reached stable albumin ISF-to-plasma ratios after 20 recirculation cycles. Absolute ISF albumin concentrations were 11.2 mg ml<jats:sup>−1</jats:sup> (OFM-recirculation) and 14.2 mg ml<jats:sup>−1</jats:sup> (OFM-suction). Albumin ISF-to-plasma ratios were 0.39 ± 0.04 (OFM -recirculation) and 0.47 ± 0.1 (OFM-suction). <jats:italic>Significance</jats:italic>: Knowledge of the ISF protein content is of major importance when assessing PK/PD effects, especially of highly protein bound drugs. Up to now, only blood albumin values have been available to determine the degree of protein binding in several tissues. OFM-recirculation and OFM-suction allow direct, absolute quantification of albumin in ISF for the first time and enable investigation of the degree of protein binding of a drug directly in its target tissue.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The purpose of this study is to develop a method for characterisation of time-of-flight (ToF) imaging system for application in deep inspiration breath-hold radiotherapy (DIBH-RT). The performance of an Argos 3D P330 ToF camera (Bluetechnix, Austria) was studied for patient surface monitoring during DIBH-RT using a phantom to simulate the intra-patient and inter-patient stability of the camera. Patient setup error was also simulated by positioning the phantom at predefined shift positions (2, 5 and 10 mm) from the isocentre. The localisation accuracy of the phantom was measured using ToF imaging system and repeated using CBCT imaging alone (CBCT) and simultaneously using ToF imaging during CBCT imaging (ToF-CBCT). The mean and SD of the setup errors obtained from each of the imaging methods were calculated. Student t-test was used to compare the mean setup errors. Correlation and Bland-Altman analysis were also performed. The intra-and inter-patient stability of the camera were within 0.31 mm and 0.74 mm, respectively. The localisation accuracy in terms of the mean ±SD of the measured setup errors were 0.34 ± 0.98 mm, 0.12 ± 0.34 mm, and −0.24 ± 1.42 mm for ToF, CBCT and ToF-CBCT imaging, respectively. A strong correlation was seen between the phantom position and the measured position using ToF (r = 0.96), CBCT (r = 0.99) as well as ToF-CBCT (r = 0.92) imaging. The limits of agreement from Bland Altman analysis between the phantom position and ToF, CBCT and ToF-CBCT measured positions were −1.52, 2.31 mm, −0.55, 0.78 mm; and −3.03, 2.55 mm, respectively. The sensor shows good stability and high accuracy comparable to similar sensors in the market. The method developed is useful for characterisation of an optical surface imaging system for application in monitoring DIBH-RT.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Hypertension is the condition where the normal blood pressure is high. This situation is manifested by the high pressure of the blood in the vein towards the vessel wall. Hypertension mostly affects the brain, kidneys, eyes, arteries and heart. Therefore, the diagnosis of this common disease is important. It may take days, weeks or even months for diagnosis. Often a device, called a blood pressure holter, is connected to the person for 24 or 48 h and the person’s blood pressure is recorded at certain intervals. Diagnosis can be made by the specialist physician considering these results. In recent years, various physiological measurement techniques have been used to accelerate this time-consuming diagnostic phase and intelligent models have been proposed. One of these techniques is photopletesmography (PPG). In this study, a model for the detection of hypertension disease in individuals was proposed using chirp z-transform and statistical features (total band power, autoregressive model parameters, standard deviation of signal’s derivative and zero crossing rate) of optimal band-pass filtered short-time PPG signals. The proposed method was successfully applied to 657 PPG trials, which each of them had only 2.1 s signal length and achieved a classification accuracy rate of 77.52% on the test data. The results showed that the diagnosis of hypertension can be performed effectively by chirp z-transform and statistical features and support vector machine classifier using optimal frequency range of 1.4–6 Hz.</jats:p>