Literature DB >> 34191161

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

Hossein Arabi1, Habib Zaidi2,3,4,5.   

Abstract

This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and radiation therapy research. To this end, the applications of artificial intelligence in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis, and outcome prediction are discussed. This review sets out to cover briefly the fundamental concepts of AI and deep learning followed by a presentation of seminal achievements and the challenges facing their adoption in clinical setting.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Molecular imaging; Quantitative imaging; Radiation therapy

Year:  2020        PMID: 34191161     DOI: 10.1186/s41824-020-00086-8

Source DB:  PubMed          Journal:  Eur J Hybrid Imaging        ISSN: 2510-3636


  66 in total

1.  Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.

Authors:  Hossein Arabi; Guodong Zeng; Guoyan Zheng; Habib Zaidi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-01       Impact factor: 9.236

2.  Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis.

Authors:  Manon Ansart; Stéphane Epelbaum; Geoffroy Gagliardi; Olivier Colliot; Didier Dormont; Bruno Dubois; Harald Hampel; Stanley Durrleman
Journal:  Stat Methods Med Res       Date:  2019-01-30       Impact factor: 3.021

3.  Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning.

Authors:  Hossein Arabi; Nikolaos Koutsouvelis; Michel Rouzaud; Raymond Miralbell; Habib Zaidi
Journal:  Phys Med Biol       Date:  2016-08-15       Impact factor: 3.609

4.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network.

Authors:  Marios Anthimopoulos; Stergios Christodoulidis; Lukas Ebner; Andreas Christe; Stavroula Mougiakakou
Journal:  IEEE Trans Med Imaging       Date:  2016-02-29       Impact factor: 10.048

5.  Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data.

Authors:  Hossein Arabi; Habib Zaidi
Journal:  Med Image Anal       Date:  2020-05-19       Impact factor: 8.545

6.  Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans.

Authors:  T G W Boers; Y Hu; E Gibson; D C Barratt; E Bonmati; J Krdzalic; F van der Heijden; J J Hermans; H J Huisman
Journal:  Phys Med Biol       Date:  2020-03-11       Impact factor: 3.609

7.  A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets.

Authors:  Natalia Antropova; Benjamin Q Huynh; Maryellen L Giger
Journal:  Med Phys       Date:  2017-08-12       Impact factor: 4.071

8.  Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.

Authors:  Julian Betancur; Frederic Commandeur; Mahsaw Motlagh; Tali Sharir; Andrew J Einstein; Sabahat Bokhari; Mathews B Fish; Terrence D Ruddy; Philipp Kaufmann; Albert J Sinusas; Edward J Miller; Timothy M Bateman; Sharmila Dorbala; Marcelo Di Carli; Guido Germano; Yuka Otaki; Balaji K Tamarappoo; Damini Dey; Daniel S Berman; Piotr J Slomka
Journal:  JACC Cardiovasc Imaging       Date:  2018-03-14

9.  Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction.

Authors:  Paul Blanc-Durand; Maya Khalife; Brian Sgard; Sandeep Kaushik; Marine Soret; Amal Tiss; Georges El Fakhri; Marie-Odile Habert; Florian Wiesinger; Aurélie Kas
Journal:  PLoS One       Date:  2019-10-07       Impact factor: 3.240

10.  Deep segmentation networks predict survival of non-small cell lung cancer.

Authors:  Stephen Baek; Yusen He; Bryan G Allen; John M Buatti; Brian J Smith; Ling Tong; Zhiyu Sun; Jia Wu; Maximilian Diehn; Billy W Loo; Kristin A Plichta; Steven N Seyedin; Maggie Gannon; Katherine R Cabel; Yusung Kim; Xiaodong Wu
Journal:  Sci Rep       Date:  2019-11-21       Impact factor: 4.379

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  5 in total

1.  Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT).

Authors:  Rahul Pemmaraju; Robert Minahan; Elise Wang; Kornel Schadl; Heike Daldrup-Link; Frezghi Habte
Journal:  Tomography       Date:  2022-05-30

2.  MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.

Authors:  Hossein Arabi; Habib Zaidi
Journal:  Magn Reson Med       Date:  2021-09-04       Impact factor: 3.737

3.  Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review.

Authors:  Amir Masoud Rahmani; Elham Azhir; Morteza Naserbakht; Mokhtar Mohammadi; Adil Hussein Mohammed Aldalwie; Mohammed Kamal Majeed; Sarkhel H Taher Karim; Mehdi Hosseinzadeh
Journal:  Multimed Tools Appl       Date:  2022-03-31       Impact factor: 2.577

4.  Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients.

Authors:  Faeze Gholamiankhah; Samaneh Mostafapour; Nouraddin Abdi Goushbolagh; Seyedjafar Shojaerazavi; Parvaneh Layegh; Seyyed Mohammad Tabatabaei; Hossein Arabi
Journal:  Iran J Med Sci       Date:  2022-09

Review 5.  Nuclear-medicine probes: Where we are and where we are going.

Authors:  Andrea Gonzalez-Montoro; Cesar David Vera-Donoso; Georgios Konstantinou; Pablo Sopena; Manolo Martinez; Juan Bautista Ortiz; Montserrat Carles; Jose Maria Benlloch; Antonio Javier Gonzalez
Journal:  Med Phys       Date:  2022-05-20       Impact factor: 4.506

  5 in total

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