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The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI

Ilyass Moummad, Cyril Jaudet, Alexis Lechervy, Samuel Valable, Charlotte Raboutet, Zamila Soilihi, Juliette Thariat, Nadia Falzone, Joëlle Lacroix, Alain Batalla, Aurélien Corroyer-Dulmont
2021 Cancers  
The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI.  ...  Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images.  ...  of these DL models on radiomic feature reproducibility.  ... 
doi:10.3390/cancers14010036 pmid:35008198 pmcid:PMC8750741 fatcat:nlcxinmpxrff7ln3qzehnm7tpy

Radiomics and radiogenomics of primary liver cancers

Woo Kyoung Jeong, Neema Jamshidi, Ely Richard Felker, Steven Satish Raman, David Shinkuo Lu
2019 Clinical and Molecular Hepatology  
In order to develop such imaging surrogates radiomics and radiogenomics/imaging genomics will be necessary; there has been consistent progress in these fields for primary liver cancers.  ...  Concurrent advancements in imaging and genomic biomarkers have created opportunities to identify non-invasive imaging surrogates of molecular phenotypes.  ...  in reproducibility of the analysis.  ... 
doi:10.3350/cmh.2018.1007 pmid:30441889 pmcid:PMC6435966 fatcat:rizogcp2cbawfeb7lhqy6bdduq

Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology

Ian R. Duffy, Amanda J. Boyle, Neil Vasdev
2019 Molecular Imaging  
Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging  ...  and oncology.  ...  an increase in studies involving ML in oncology  ... 
doi:10.1177/1536012119869070 pmid:31429375 pmcid:PMC6702769 fatcat:r3pz2vrfm5emrdk7evx5s34j5m

Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges

Qian Chen, Tianyi Xia, Mingyue Zhang, Nengzhi Xia, Jinjin Liu, Yunjun Yang
2021 Aging and Disease  
Radiomics is mainly used in the field of oncology, which remains an area of active research.  ...  In this article, we elaborate the contributions of radiomics to stroke, as well as the subprocesses and techniques involved in radiomics studies.  ...  , accelerating the development of precision diagnosis and treatment in oncology [18] .  ... 
doi:10.14336/ad.2020.0421 pmid:33532134 pmcid:PMC7801280 fatcat:dut4soloz5abnp45ewougkvv4m

The Future of Nuclear Medicine, Molecular Imaging, and Theranostics

Wolfgang A. Weber, Johannes Czernin, Carolyn J. Anderson, Ramsey D. Badawi, Henryk Barthel, Frank Bengel, Lisa Bodei, Irène Buvat, Marcelo DiCarli, Michael M. Graham, Jan Grimm, Ken Herrmann (+8 others)
2020 Journal of Nuclear Medicine  
ACKNOWLEDGMENTS Carolyn Anderson, Michael Graham, and Jan Grimm thank Lydia Perkins for the graphics in Figure 1 . Henryk Barthel thanks John Seibyl and Victor Villemagne for fruitful discussions.  ...  increasing role of molecular imaging in biologic research and precision medicine.  ...  Nuclear medicine now provides diagnostic, prognostic, predictive, and intermediate endpoint biomarkers in oncology, cardiology, neurology, and infectious and inflammatory disorders.  ... 
doi:10.2967/jnumed.120.254532 pmid:33293447 fatcat:hfbrv3awhzfbvjog7yh62bdwze

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Riemer H J A Slart, Michelle C Williams, Luis Eduardo Juarez-Orozco, Christoph Rischpler, Marc R Dweck, Andor W J M Glaudemans, Alessia Gimelli, Panagiotis Georgoulias, Olivier Gheysens, Oliver Gaemperli, Gilbert Habib, Roland Hustinx (+10 others)
2021 European Journal of Nuclear Medicine and Molecular Imaging  
in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.  ...  of these imaging techniques in daily clinical practice.  ...  Knol from the Northwest Clinics in Alkmaar, The Netherlands for providing us with material for Fig. 3 . Author contribution All authors read and approved the final manuscript.  ... 
doi:10.1007/s00259-021-05341-z pmid:33864509 pmcid:PMC8113178 fatcat:q2eg5vwpcvhuxnwfnrxz6juj5u

From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities [article]

Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Anastasia Oikonomou, Habib Benali
2019 arXiv   pre-print
Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through  ...  Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic  ...  Higher Order Radiomics Features: Higher order features such as Wavelet and Fourier features capture imaging biomarkers in various frequencies [25] .  ... 
arXiv:1808.07954v3 fatcat:huc23wcklfey5aetnlbe6o4h34

Quantitative imaging for radiotherapy purposes

Oliver J. Gurney-Champion, Faisal Mahmood, Marcel van Schie, Robert Julian, Ben George, Marielle E.P. Philippens, Uulke A. van der Heide, Daniela Thorwarth, Kathrine R. Redalen
2020 Radiotherapy and Oncology  
Quantitative imaging biomarkers show great potential for use in radiotherapy.  ...  In this review, published standards, guidelines and recommendations on quantitative imaging assessment using CT, PET and MRI are discussed.  ...  The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.  ... 
doi:10.1016/j.radonc.2020.01.026 pmid:32114268 pmcid:PMC7294225 fatcat:wzdpemkqg5fodggick57vbn3uy

What scans we will read: imaging instrumentation trends in clinical oncology

Thomas Beyer, Luc Bidaut, John Dickson, Marc Kachelriess, Fabian Kiessling, Rainer Leitgeb, Jingfei Ma, Lalith Kumar Shiyam Sundar, Benjamin Theek, Osama Mawlawi
2020 Cancer Imaging  
of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time.  ...  and engineering, believe imaging methods will be in a few years from now.Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation  ...  Radiomics analysis of ultrasound data will further increase the accuracy and diagnostic impact [176] .  ... 
doi:10.1186/s40644-020-00312-3 pmid:32517801 fatcat:jbfxzyffg5cqhkpkshnhpuoabu

Deep learning in medical imaging and radiation therapy

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies  ...  We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods  ...  Currently, many imaging biomarkers of cancerous tumors include only size and simple enhancement measures (if dynamic imaging is employed), and thus, there is interest in expanding, through radiomic features  ... 
doi:10.1002/mp.13264 pmid:30367497 fatcat:bottst5mvrbkfedbuocbrstcnm

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis.  ...  We analyse and discuss 163 papers that apply adversarial training techniques in the context of cancer imaging and elaborate their methodologies, advantages and limitations.  ...  Acknowledgments This project has received funding from the European Union's Horizon 2020 research and innovation pro-  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis.  ...  Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry.  ...  Our work was financially supported by the Bergen Research Foundation through the project "Computational medical imaging and machine learning -methods, infrastructure and applications".  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm

Proceedings of the World Molecular Imaging Congress 2021, October 5-8, 2021: General Abstracts

2022 Molecular Imaging and Biology  
The uncertainty caused in the system was modeled as an iterative deconvolution with resolution subsets to denoise and enhance image resolution.  ...  Siemens AG, (2012). [2] "Syngo MR E11, increase your efficiency, expand your MRI services " ,, (2015) [3] Robson MD, Gore JC, Constable RT, "  ...  an excellent in vivo stability and radiopharmacology for imaging GPA33-expressing CRC liver metastasis, and appears to be an excellent radiohapten precursor for theranostic Zr-89/Lu-177 therapy.  ... 
doi:10.1007/s11307-021-01693-y pmid:34982365 pmcid:PMC8725635 fatcat:4sfb3isoyfdhfbiwxfr55gvqym

CARS 2016—Computer Assisted Radiology and Surgery Proceedings of the 30th International Congress and Exhibition Heidelberg, Germany, June 21–25, 2016

2016 International Journal of Computer Assisted Radiology and Surgery  
'', and Amazon Inc., for providing valuable computing resources through an ''AWS in Education Research'' grant.  ...  Acknowledgements This study was supported in part by grants from the Ohio Department of Development (TVSF 15-  ...  and reproducible.  ... 
doi:10.1007/s11548-016-1412-5 pmid:27206418 fatcat:uk5r46n2xvhedkfjzmeiweyneq

Computer-Assisted Analysis of Biomedical Images [article]

Leonardo Rundo
2021 arXiv   pre-print
In particular, quantitative imaging methods convey scientifically and clinically relevant information in prediction, prognosis or treatment response assessment, by also considering radiomics approaches  ...  In reference to biomedical image analysis, the advances in image acquisition modalities and high-throughput imaging experiments are creating new challenges.  ...  To this end, efficient and reproducible feature computation is a hot-topic in radiomics research [23] .  ... 
arXiv:2106.04381v1 fatcat:osqiyd3sbja3zgrby7bf4eljfm
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