Doppler radar monitoring of lava dome processes at Merapi Volcano, Indonesia

Merapi volcano in Central Java, Indonesia, is considered one of the most dangerous volcanoes worldwide. Due to the high viscosity of its magma, the lava emerging at the top the volcano cannot flow silently down the flanks of the volcano but builds a lava dome. An indicator for the stability of the lava dome are rockfalls and block and ash flows, which are caused by local instabilities at the dome. When the lava dome reaches a critical size, it collapses. This results in dangerous block and ash flows, which can reach several kilometers into the proximity of the volcano. In the past rockfall and block and ash flow activity has been observed visually or by seismic networks. However, visual observations are often impossible due to bad visibility conditions and until now seismic measurements allow only few insights into the dynamic processes that are involved in instability events, i.e. events of material breaks off the lava dome. In order to enhance monitoring of lava dome activity, a first prototype Doppler radar system has been installed at the western of the Merapi in October 2001. This system consists of a frequency modulated continuous wave (FMCW) 24GHz Doppler radar. The Doppler spectra recorded by the system give a relative measure of the amount of material moving through the beam as well as information about its velocities. Because the radar system is insensitive for clouds, the system provides first continuous "quasi-visual" observations of dome instabilities...thesi

Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management

Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV 1H MRS: Evaluation as an additional information procedure for novice radiologists

The results of a randomized pilot study and qualitative evaluation of the clinical decision support system Curiam BT are reported. We evaluated the system's feasibility and potential value as a radiological information procedure complementary to magnetic resonance (MR) imaging to assist novice radiologists in diagnosing brain tumours using MR spectroscopy (1.5 and 3.0T). Fifty-five cases were analysed at three hospitals according to four non-exclusive diagnostic questions. Our results show that Curiam BT improved the diagnostic accuracy in all the four questions. Additionally, we discuss the findings of the users' feedback about the system, and the further work to optimize it for real environments and to conduct a large clinical trial. & 2013 Elsevier Ltd. All rights reserved.This work has been supported by the Spanish Ministry of Science and Innovation - Institut de Salud Carlos III - FIS Contract PI09/90177; and Universitat Politecnica de Valencia - INNOVA UPV 2008 2 1834. We specially thank Miguel Angel Edo, Maria Vano, Carmen Barber, Ana Catala-Gregori, Enrique Molla and Cecilio Poyatos, for their collaboration in the development of this study.Sáez Silvestre, C.; Martí-Bonmatí, L.; Alberich Bayarri, Á.; Robles Viejo, M.; García Gómez, JM. (2014). Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV 1H MRS: Evaluation as an additional information procedure for novice radiologists. Computers in Biology and Medicine. 45:26-33. https://doi.org/10.1016/j.compbiomed.2013.11.009S26334

Improved Standardization of Type II-P Supernovae: Application to an Expanded Sample

In the epoch of precise and accurate cosmology, cross-confirmation using a variety of cosmographic methods is paramount to circumvent systematic uncertainties. Owing to progenitor histories and explosion physics differing from those of Type Ia SNe (SNe Ia), Type II-plateau supernovae (SNe II-P) are unlikely to be affected by evolution in the same way. Based on a new analysis of 17 SNe II-P, and on an improved methodology, we find that SNe II-P are good standardizable candles, almost comparable to SNe Ia. We derive a tight Hubble diagram with a dispersion of 10% in distance, using the simple correlation between luminosity and photospheric velocity introduced by Hamuy & Pinto 2002. We show that the descendent method of Nugent et al. 2006 can be further simplified and that the correction for dust extinction has low statistical impact. We find that our SN sample favors, on average, a very steep dust law with total to selective extinction R_V<2. Such an extinction law has been recently inferred for many SNe Ia. Our results indicate that a distance measurement can be obtained with a single spectrum of a SN II-P during the plateau phase combined with sparse photometric measurements.Comment: ApJ accepted version. Minor change