Literature DB >> 31240329

EJNMMI supplement: bringing AI and radiomics to nuclear medicine.

Patrick Veit-Haibach1, Irène Buvat2, Ken Herrmann3.   

Abstract

Year:  2019        PMID： 31240329     DOI： 10.1007/s00259-019-04395-4

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN： 1619-7070            Impact factor:   9.236


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  10 in total

Review 1.  Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician?

Authors:  Roland Hustinx
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-07       Impact factor: 9.236

2.  Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications.

Authors:  Dimitris Visvikis; Catherine Cheze Le Rest; Vincent Jaouen; Mathieu Hatt
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-06       Impact factor: 9.236

3.  Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.

Authors:  Alex Zwanenburg
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-25       Impact factor: 9.236

4.  AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.

Authors:  Isabella Castiglioni; Francesca Gallivanone; Paolo Soda; Michele Avanzo; Joseph Stancanello; Marco Aiello; Matteo Interlenghi; Marco Salvatore
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-11       Impact factor: 9.236

5.  Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data.

Authors:  Andreas Holzinger; Benjamin Haibe-Kains; Igor Jurisica
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-15       Impact factor: 9.236

6.  Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning.

Authors:  Greg Zaharchuk
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-29       Impact factor: 9.236

Review 7.  EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies.

Authors:  Nicolas Aide; Charline Lasnon; Patrick Veit-Haibach; Terez Sera; Bernhard Sattler; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-06-16       Impact factor: 9.236

Review 8.  What can artificial intelligence teach us about the molecular mechanisms underlying disease?

Authors:  Gary J R Cook; Vicky Goh
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-12       Impact factor: 9.236

9.  Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.

Authors:  Martina Sollini; Lidija Antunovic; Arturo Chiti; Margarita Kirienko
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-18       Impact factor: 9.236

10.  EANM/EARL FDG-PET/CT accreditation - summary results from the first 200 accredited imaging systems.

Authors:  Andres Kaalep; Terez Sera; Wim Oyen; Bernd J Krause; Arturo Chiti; Yan Liu; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-12-01       Impact factor: 9.236
  10 in total
  2 in total

1.  A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions.

Authors:  Haowei Xiang; Hongki Lim; Jeffrey A Fessler; Yuni K Dewaraja
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-05-15       Impact factor: 9.236

2.  Tumor response prediction in 90Y radioembolization with PET-based radiomics features and absorbed dose metrics.

Authors:  Lise Wei; Can Cui; Jiarui Xu; Ravi Kaza; Issam El Naqa; Yuni K Dewaraja
Journal:  EJNMMI Phys       Date:  2020-12-09
  2 in total

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