64 research outputs found
A tool to automatically analyze electromagnetic tracking data from high dose rate brachytherapy of breast cancer patients
During High Dose Rate Brachytherapy (HDR-BT) the spatial position of the radiation source inside catheters implanted into a female breast is determined via electromagnetic tracking (EMT). Dwell positions and dwell times of the radiation source are established, relative to the patient's anatomy, from an initial X-ray-CT-image. During the irradiation treatment, catheter displacements can occur due to patient movements. The current study develops an automatic analysis tool of EMT data sets recorded with a solenoid sensor to assure concordance of the source movement with the treatment plan. The tool combines machine learning techniques such as multi-dimensional scaling (MDS), ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA) and particle filter (PF) to precisely detect and quantify any mismatch between the treatment plan and actual EMT measurements. We demonstrate that movement artifacts as well as technical signal distortions can be removed automatically and reliably, resulting in artifact-free reconstructed signals. This is a prerequisite for a highly accurate determination of any deviations of dwell positions from the treatment plan
microRNA input into a neural ultradian oscillator controls emergence and timing of alternative cell states.
© 2014 Macmillan Publishers LimitedThis is an open access article that is freely available in ORE or from the publisher's web site. Please cite the published version.Progenitor maintenance, timed differentiation and the potential to enter quiescence are three fundamental processes that underlie the development of any organ system. In the nervous system, progenitor cells show short-period oscillations in the expression of the transcriptional repressor Hes1, while neurons and quiescent progenitors show stable low and high levels of Hes1, respectively. Here we use experimental data to develop a mathematical model of the double-negative interaction between Hes1 and a microRNA, miR-9, with the aim of understanding how cells transition from one state to another. We show that the input of miR-9 into the Hes1 oscillator tunes its oscillatory dynamics, and endows the system with bistability and the ability to measure time to differentiation. Our results suggest that a relatively simple and widespread network of cross-repressive interactions provides a unifying framework for progenitor maintenance, the timing of differentiation and the emergence of alternative cell states.Wellcome Trus
Who’s Superconnected and Who’s Not? Investment in the UK’s Information and Communication Technologies (ICT) Infrastructure
Exploration of the high-redshift universe enabled by THESEUS
At peak, long-duration gamma-ray bursts are the
most luminous sources of electromagnetic radiation known.
Since their progenitors are massive stars, they provide a tracer
of star formation and star-forming galaxies over the whole
of cosmic history. Their bright power-law afterglows provide ideal backlights for absorption studies of the interstellar and intergalactic medium back to the reionization era.
The proposed THESEUS mission is designed to detect large
samples of GRBs at z > 6 in the 2030s, at a time when
supporting observations with major next generation facilities will be possible, thus enabling a range of transformative science. THESEUS will allow us to explore the faint
end of the luminosity function of galaxies and the star formation rate density to high redshifts; constrain the progress
of re-ionisation beyond z & 6; study in detail early chemical enrichment from stellar explosions, including signatures
of Population III stars; and potentially characterize the dark
energy equation of state at the highest redshifts.peer-reviewe
Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases
The production of peroxide and superoxide is an inevitable consequence of
aerobic metabolism, and while these particular "reactive oxygen species" (ROSs)
can exhibit a number of biological effects, they are not of themselves
excessively reactive and thus they are not especially damaging at physiological
concentrations. However, their reactions with poorly liganded iron species can
lead to the catalytic production of the very reactive and dangerous hydroxyl
radical, which is exceptionally damaging, and a major cause of chronic
inflammation. We review the considerable and wide-ranging evidence for the
involvement of this combination of (su)peroxide and poorly liganded iron in a
large number of physiological and indeed pathological processes and
inflammatory disorders, especially those involving the progressive degradation
of cellular and organismal performance. These diseases share a great many
similarities and thus might be considered to have a common cause (i.e.
iron-catalysed free radical and especially hydroxyl radical generation). The
studies reviewed include those focused on a series of cardiovascular, metabolic
and neurological diseases, where iron can be found at the sites of plaques and
lesions, as well as studies showing the significance of iron to aging and
longevity. The effective chelation of iron by natural or synthetic ligands is
thus of major physiological (and potentially therapeutic) importance. As
systems properties, we need to recognise that physiological observables have
multiple molecular causes, and studying them in isolation leads to inconsistent
patterns of apparent causality when it is the simultaneous combination of
multiple factors that is responsible. This explains, for instance, the
decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference
MDSLAB: A toolbox for the analysis of point sets using multi-dimensional scaling, hartigan dip test and α
Factors affecting accuracy of S values and determination of time-integrated activity in clinical Lu-177 dosimetry
IntroductionIn any radiotherapy, the absorbed dose needs to be estimated based on two factors, the time-integrated activity of the administered radiopharmaceutical and the patient-specific dose kernel. In this study, we consider the uncertainty with which such absorbed dose estimation can be achieved in a clinical environment.MethodsTo calculate the total error of dose estimation we considered the following aspects: The error resulting from computing the time-integrated activity, the difference between the S-value and the patient specific full Monte Carlo simulation, the error from segmenting the volume-of-interest (kidney) and the intrinsic error of the activimeter.ResultsThe total relative error in dose estimation can amount to 25.0% and is composed of the error of the time-integrated activity 17.1%, the error of the S-value 16.7%, the segmentation error 5.4% and the activimeter accuracy 5.0%.ConclusionErrors from estimating the time-integrated activity and approximations applied to dose kernel computations contribute about equally and represent the dominant contributions far exceeding the contributions from VOI segmentation and activimeter accuracy
A deep learning approach to radiation dose estimation
Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather crude in daily clinical practice. Most importantly, individual tissue density distributions as well as local variations of the concentration of the radiopharmaceutical are commonly neglected. The current study proposes machine learning techniques like Green's function-based empirical mode decomposition and deep learning methods on U-net architectures in conjunction with soft tissue kernel Monte Carlo (MC) simulations to overcome current limitations in precision and reliability of dose estimations for clinical dosimetric applications. We present a hybrid method (DNN-EMD) based on deep neural networks (DNN) in combination with empirical mode decomposition (EMD) techniques. The algorithm receives x-ray computed tomography (CT) tissue density maps and dose maps, estimated according to the MIRD protocol, i.e. employing whole organ S-values and related time-integrated activities (TIAs), and from measured SPECT distributions of Lu-177 radionuclei, and learns to predict individual absorbed dose distributions. In a second step, density maps are replaced by their intrinsic modes as deduced from an EMD analysis. The system is trained using individual full MC simulation results as reference. Data from a patient cohort of 26 subjects are reported in this study. The proposed methods were validated employing a leave-one-out cross-validation technique. Deviations of estimated dose from corresponding MC results corroborate a superior performance of the newly proposed hybrid DNN-EMD method compared to its related MIRD DVK dose calculation. Not only are the mean deviations much smaller with the new method, but also the related variances are much reduced. If intrinsic modes of the tissue density maps are input to the algorithm, variances become even further reduced though the mean deviations are less affected. The newly proposed hybrid DNN-EMD method for individualized radiation dose prediction outperforms the MIRD DVK dose calculation method. It is fast enough to be of use in daily clinical practice
On the use of particle filters for electromagnetic tracking in high dose rate brachytherapy
Modern radiotherapy of female breast cancers often employs high dose rate brachytherapy, where a radioactive source is moved inside catheters, implanted in the female breast, according to a prescribed treatment plan. Source localization relative to the patient's anatomy is determined with solenoid sensors whose spatial positions are measured with an electromagnetic tracking system. Precise sensor dwell position determination is of utmost importance to assure irradiation of the cancerous tissue according to the treatment plan. We present a hybrid data analysis system which combines multi-dimensional scaling with particle filters to precisely determine sensor dwell positions in the catheters during subsequent radiation treatment sessions. Both techniques are complemented with empirical mode decomposition for the removal of superimposed breathing artifacts. We show that the hybrid model robustly and reliably determines the spatial positions of all catheters used during the treatment and precisely determines any deviations of actual sensor dwell positions from the treatment plan. The hybrid system only relies on sensor positions measured with an EMT system and relates them to the spatial positions of the implanted catheters as initially determined with a computed x-ray tomography
- …