Literature DB >> 30733322

Machine Learning in Nuclear Medicine: Part 1-Introduction.

Carlos F Uribe1, Sulantha Mathotaarachchi2, Vincent Gaudet3, Kenneth C Smith4, Pedro Rosa-Neto2, François Bénard1,5, Sandra E Black6, Katherine Zukotynski7,8.   

Abstract

This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when they can be helpful in nuclear medicine. Part 2 focuses on current contributions of ML to our field, addresses future expectations and limitations, and provides a critical appraisal of what ML can and cannot do.
© 2019 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  algorithms; artificial intelligence; machine learning; nuclear medicine

Mesh：

Year:  2019        PMID： 30733322     DOI： 10.2967/jnumed.118.223495

Source DB:  PubMed          Journal:  J Nucl Med        ISSN： 0161-5505            Impact factor:   10.057


  13 in total

1.  Artificial intelligence and radiomics in nuclear medicine: potentials and challenges.

Authors:  Cumali Aktolun
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12       Impact factor: 9.236

Review 2.  Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.

Authors:  Dimitris Visvikis; Philippe Lambin; Kim Beuschau Mauridsen; Roland Hustinx; Michael Lassmann; Christoph Rischpler; Kuangyu Shi; Jan Pruim
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-07-09       Impact factor: 9.236

3.  An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET.

Authors:  Ruiyao Ma; Jiaxi Hu; Hasan Sari; Song Xue; Clemens Mingels; Marco Viscione; Venkata Sai Sundar Kandarpa; Wei Bo Li; Dimitris Visvikis; Rui Qiu; Axel Rominger; Junli Li; Kuangyu Shi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-07-11       Impact factor: 10.057

4.  Research on E-Commerce Database Marketing Based on Machine Learning Algorithm.

Authors:  Nie Chen
Journal:  Comput Intell Neurosci       Date:  2022-06-29

5.  Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients.

Authors:  Masatoyo Nakajo; Megumi Jinguji; Atsushi Tani; Erina Yano; Chin Khang Hoo; Daisuke Hirahara; Shinichi Togami; Hiroaki Kobayashi; Takashi Yoshiura
Journal:  Abdom Radiol (NY)       Date:  2021-11-25

6.  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

Review 7.  Artificial intelligence for molecular neuroimaging.

Authors:  Amanda J Boyle; Vincent C Gaudet; Sandra E Black; Neil Vasdev; Pedro Rosa-Neto; Katherine A Zukotynski
Journal:  Ann Transl Med       Date:  2021-05

Review 8.  Introduction to Radiomics.

Authors:  Marius E Mayerhoefer; Andrzej Materka; Georg Langs; Ida Häggström; Piotr Szczypiński; Peter Gibbs; Gary Cook
Journal:  J Nucl Med       Date:  2020-02-14       Impact factor: 11.082

Review 9.  Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology.

Authors:  Ian R Duffy; Amanda J Boyle; Neil Vasdev
Journal:  Mol Imaging       Date:  2019 Jan-Dec       Impact factor: 4.488

10.  A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours.

Authors:  Alessandro Bevilacqua; Diletta Calabrò; Silvia Malavasi; Claudio Ricci; Riccardo Casadei; Davide Campana; Serena Baiocco; Stefano Fanti; Valentina Ambrosini
Journal:  Diagnostics (Basel)       Date:  2021-05-12
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