Imaging and Radiation Research

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Computational Methods in Imaging and Radiation Research

Submission deadline: 2025-01-04

Section Editors

DR. SHUBHAM MAHAJAN

AJEENKYA D Y PATIL UNIVERSITY, PUNE

Interests:image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication

Section Collection Information

Dear colleagues,

The Section Collection Project on "Computational Methods in Imaging and Radiation Research" aims to bring together cutting-edge research and advancements in the interdisciplinary field of computational methods applied to imaging and radiation studies. This collection serves as a comprehensive resource for researchers, practitioners, and students seeking to explore the intersection of computational techniques and imaging technologies in the context of radiation research.

The collection delves into various aspects of computational methods, including image processing, machine learning, numerical simulations, and data analysis, as applied to medical imaging, industrial radiography, and radiation therapy. It provides insights into how computational tools enhance the quality and efficiency of imaging processes,

contribute to the development of innovative radiation technologies, and impact the diagnosis and treatment of diseases.

Contributions within this collection cover a spectrum of topics, including but not limited to image reconstruction algorithms, Monte Carlo simulations for dose calculations, artificial intelligence applications in medical imaging, optimization techniques for radiation treatment planning, and novel approaches in image-guided interventions. The

synergy of computational methods with imaging and radiation research opens new avenues for breakthroughs in diagnostic accuracy, treatment efficacy, and overall advancements in the field.

By consolidating diverse perspectives and methodologies, this collection fosters collaboration, encourages the exchange of ideas, and accelerates the translation of computational innovations into practical solutions for real-world challenges in imaging and radiation research.

Keywords

Computational Imaging , Radiation Research , Image Processing , Machine Learning in Medical Imaging , Monte Carlo Simulations , Radiation Therapy Optimization , Image Reconstruction Algorithms, Medical Image Analysis , Image-Guided Interventions, Numerical Simulations

Published Paper

Extreme low apparent temperature forecasting for early warning of mortality by data-driven deep learning

Lei Xu;Hongchu Yu;Xihao Zhang;Yuan Gan;

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low appar-ent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecast-ing methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.

Overview of large scale map production with UAV based photogrammetric technique: A case study in Izmir-Cesme territory of Turkey

Yalçın Yılmaz;Barış Süleymanoğlu;Mert Gürtürk;Arzu Soycan;Metin Soycan;

UAVs, also known as unmanned aerial vehicles, have emerged as an efficient and flexible system for offering a rapid and cost-effective solution. In recent years, large-scale mapping using UAV photogrammetry has gained significant popularity and has been widely adopted in academia as well as the private sector. This study aims to investigate the technical aspects of this field, provide insights into the procedural steps involved, and present a case study conducted in Cesme, Izmir. The findings derived from the case study are thoroughly discussed, and the potential applications of UAV photogrammetry in large-scale mapping are examined. The study area is divided into 12 blocks. The flight plans and the distrubition of ground control point (GCP) locations were determined based on these blocks. As a result of the data processing procedure, average GCP positional errors ranging from 1 to 18 cm have been obtained for the blocks.

On the elemental contents of aspen (Populus tremula L.) leaves grown in the mineralization area

Alaaddin Vural;

The aim of this study is to investigate the behavior of major and trace elements in aspen leaves grown in an intensely altered region from a geochemical point of view. In this context, major and trace element contents of aspen leaves (Pop-ulus tremula L.) grown on Gümüştuğ (Gümüşhane/Türkiye) antimony mineralization were investigated. For this purpose, 56 leaf samples were collected from the field by grid method and at intervals of approximately 50–60 m. After routine sample preparation processes, analyzes were carried out to determine the major and trace element concentrations. Subse-quently, elemental concentrations of aspen leaves were evaluated by different statistical methods. As a result, major ele-ments Ca, Mg, Na, K, Al and S concentrations were determined in the range of 2%–3%, 0.23%–0.85%, up to 0.02%, 0.79%–2.01%, 0.01%–0.03% and 0.13%–0.43%, respectively. The trace element concentrations were determined in the range of 127–339 ppm, 86.7–265.30 ppm, 0.1–1.21 ppm, 13.5–453.20 ppm, 6.0–12.0 ppm, 14.0–82.0 ppm and 0.1–0.60 ppm for Mn, Sr, La, Ba, Ti, B and Se, respectively. Considering that the samples were taken from aspen leaves grown on the antimony mineralization, the concentrations of the antimonite element were determined above the detection limit in only a few sampling points in the study. But the concentrations of B, Mn, Sr, La of aspen (Populus Tremula L.) leaves are remarkable.

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