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The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI
2021
Cancers
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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
2019
Clinical and Molecular Hepatology
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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
2019
Molecular Imaging
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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
2021
Aging and Disease
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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
2020
Journal of Nuclear Medicine
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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
2021
European Journal of Nuclear Medicine and Molecular Imaging
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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]
2019
arXiv
pre-print
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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
2020
Radiotherapy and Oncology
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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
2020
Cancer Imaging
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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
2018
Medical Physics (Lancaster)
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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]
2021
arXiv
pre-print
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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
2018
Zeitschrift für Medizinische Physik
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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
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The uncertainty caused in the system was modeled as an iterative deconvolution with resolution subsets to denoise and enhance image resolution. ...
Siemens AG, www.siemens.com/aera (2012). [2] "Syngo MR E11, increase your efficiency, expand your MRI services " , www.usa.siemens.com/E11, (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
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'', 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]
2021
arXiv
pre-print
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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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