scholarly journals Visualization of 4D multimodal imaging data and its applications in radiotherapy planning

2017 ◽  
Vol 18 (6) ◽  
pp. 183-193 ◽  
Author(s):  
Matthias Schlachter ◽  
Tobias Fechter ◽  
Sonja Adebahr ◽  
Tanja Schimek-Jasch ◽  
Ursula Nestle ◽  
...  
Keyword(s):  
Multimodal Imaging ◽  
Radiotherapy Planning ◽  
Imaging Data

scholarly journals Radiomics in radiation oncology—basics, methods, and limitations

2020 ◽  
Vol 196 (10) ◽  
pp. 848-855
Author(s):  
Philipp Lohmann ◽  
Khaled Bousabarah ◽  
Mauritius Hoevels ◽  
Harald Treuer
Keyword(s):  
Mathematical Models ◽  
Quantitative Imaging ◽  
Human Perception ◽  
Image Features ◽  
Radiotherapy Planning ◽  
Large Field ◽  
Imaging Features ◽  
Imaging Data ◽  
Complete Evaluation ◽  
Vast Number

Abstract Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

scholarly journals COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 149808-149824 ◽  
Author(s):  
Michael J. Horry ◽  
Subrata Chakraborty ◽  
Manoranjan Paul ◽  
Anwaar Ulhaq ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Multimodal Imaging ◽  
Imaging Data

scholarly journals Raman2imzML converts Raman imaging data into the standard mass spectrometry imaging format

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Stefania Alexandra Iakab ◽  
Lluc Sementé ◽  
María García-Altares ◽  
Xavier Correig ◽  
Pere Ràfols
Keyword(s):  
Mass Spectrometry ◽  
Open Source ◽  
Open Source Software ◽  
Multimodal Imaging ◽  
Mass Spectrometry Imaging ◽  
Raman Imaging ◽  
Imaging Analysis ◽  
Imaging Data ◽  
Data Format ◽  
Computational Tools

Abstract Background Multimodal imaging that combines mass spectrometry imaging (MSI) with Raman imaging is a rapidly developing multidisciplinary analytical method used by a growing number of research groups. Computational tools that can visualize and aid the analysis of datasets by both techniques are in demand. Results Raman2imzML was developed as an open-source converter that transforms Raman imaging data into imzML, a standardized common data format created and adopted by the mass spectrometry community. We successfully converted Raman datasets to imzML and visualized Raman images using open-source software designed for MSI applications. Conclusion Raman2imzML enables both MSI and Raman images to be visualized using the same file format and the same software for a straightforward exploratory imaging analysis.

Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition

2017 ◽  
Vol 1865 (7) ◽  
pp. 946-956 ◽  
Author(s):  
Judith M. Lotz ◽  
Franziska Hoffmann ◽  
Johannes Lotz ◽  
Stefan Heldmann ◽  
Dennis Trede ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Multimodal Imaging ◽  
Feature Recognition ◽  
Imaging Data

C-ICT for Discovery of Multiple Associations in Multimodal Imaging Data: Application to Fusion of fMRI and DTI Data

2019 ◽  
Author(s):  
Chunying Jia ◽  
Mohammad A. B. S. Akhonda ◽  
Qunfang Long ◽  
Vince D. Calhoun ◽  
Shari Waldstein ◽  
...  
Keyword(s):  
Multimodal Imaging ◽  
Imaging Data ◽  
Data Application
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