X-RAY SPECTRAL ANALYSIS IN X-RAY FLUORESCENCE IMAGING FOR BREAST CANCER DETECTION

The knowledge of X-ray spectrum plays a major role in exploiting and optimizing the X-ray utilizations, especially in biomedical application fields. Over the past decades, extensive research efforts have been made in better characterizing the X-ray spectral features in experimental and simulation studies. The objectives of this dissertation are to investigate the applications of X-ray spectral measurement and analysis in X-ray fluorescence (XRF) and micro-computed tomography (micro-CT) imaging modalities, to facilitate the development of new imaging modalities or to optimize the imaging performance of currently available imaging systems. The structure and primary discoveries of this dissertation are as follows: after a brief introduction of the objectives of this dissertation in Chapter 1, Chapter 2 gives a comprehensive background including electromagnetic properties, various applications, and different generation mechanisms of X-rays and their interactions with matter, X-ray spectral measurement and analysis methods, XRF principles and applications for cancer detection, and micro-CT system. Considering relatively high fluorescence production probability and sufficient penetrability of gold Kα fluorescence signals, Chapter 3 establishes a theoretical model of a gold nanoparticle (GNP) K-shell XRF imaging prototype consisting of a pencil-beam X-ray for excitation and a single collimated spectrometer for XRF detection. Then, the optimal energy windows of 66.99±0.56keV and 68.80±0.56keV for two gold Kα fluorescence peaks are determined in Chapter 4. Also, the linear interpolation method for background estimation under the Kα fluorescence peaks is suggested in this chapter. Chapters 5 and 6 propose a novel XRF based imaging modality, X-ray fluorescence mapping (XFM) for the purpose of breast cancer detection, especially emphasizing on the detection of breast tumor located posteriorly, close to the chest wall musculature. The mapping results in these two chapters match well with the known spatial distributions and different GNP concentrations in 2D/3D reconstructions. Chapter 7 presents a method for determining the modulation transfer function (MTF) in XRF imaging modality, evaluating and improving the imaging performance of XFM. Moreover, this dissertation also investigates the importance of X-ray spectral measurement and analysis in a rotating gantry based micro-CT system. A practical alignment method for X-ray spectral measurement is first proposed using 3D printing technology in Chapter 8. With the measured results and corresponding spectral analysis, Chapter 9 further evaluates the impact of spectral filtrations on image quality indicators such as CT number uniformity, noise, and contrast to noise ratio (CNR) in the micro-CT system using a mouse phantom comprising 11 rods for modeling lung, muscle, adipose, and bones (various densities). With a baseline of identical entrance exposure to the imaged mouse phantom, the CNRs are degraded with improved beam quality for bone with high density and soft tissue, while are enhanced for bone with low density, lung, and muscle. Finally, Chapter 10 summarizes the whole dissertation and prospects the future research directions

Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey

COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have adopted a number of extraordinary steps to restrict the spreading virus, where early COVID-19 diagnosis is essential. Medical images such as X-ray images and Computed Tomography scans are becoming one of the main diagnostic tools to combat COVID-19 with the aid of deep learning-based systems. In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis. We also provide a summary of the X-ray and CT image datasets used in various studies for COVID-19 detection. The key difficulties and potential applications of deep learning in fighting against COVID-19 are finally discussed. This work summarizes the latest methods of deep learning using medical images to diagnose COVID-19, highlighting the challenges and inspiring more studies to keep utilizing the advantages of deep learning to combat COVID-19

Conservation and reintroduction of the rare and endangered orchid Paphiopedilum armeniacum

Paphiopedilum armeniacum is a rare and endangered lady’s slipper orchid in China. It is distributed around the mid-elevations of the Nu Mountains in southwest China. Due to over-harvest, habitat loss, and degradation, wild populations of P. armeniacum has declined drastically. A combination of approaches involving biotechnology, habitat restoration, and interspecific relationship reconstruction was used to carry out the reintroduction of the species. Integrated conservation program for this species included in-situ protection, ex-situ conservation, and reintroduction, which helped to rebuild a harmonious relationship between local farmers and P. armeniacum. The sustainable utilization of native plant resources in poor areas can promote regional sustainable development which is compatible with species protection

2004 Roster

2004 Women\u27s Softball Roster, George Fox College

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe