Literature DB >> 34079744

Applications of artificial intelligence in nuclear medicine image generation.

Zhibiao Cheng1, Junhai Wen1, Gang Huang2, Jianhua Yan2.   

Abstract

Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging have focused on the diagnosis, treatment monitoring, and correlation analyses with pathology or specific gene mutation. It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality. This work provides an overview of the application of AI in image generation for single-photon emission computed tomography (SPECT) and positron emission tomography (PET) either without or with anatomical information [CT or magnetic resonance imaging (MRI)]. This review focused on four aspects, including imaging physics, image reconstruction, image postprocessing, and internal dosimetry. AI application in generating attenuation map, estimating scatter events, boosting image quality, and predicting internal dose map is summarized and discussed. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Keywords:  Nuclear medicine imaging; artificial intelligence (AI); image postprocessing; image reconstruction; imaging physics; internal dosimetry

Year:  2021        PMID: 34079744      PMCID: PMC8107336          DOI: 10.21037/qims-20-1078

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  142 in total

1.  3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

Authors:  Yan Wang; Luping Zhou; Biting Yu; Lei Wang; Chen Zu; David S Lalush; Weili Lin; Xi Wu; Jiliu Zhou; Dinggang Shen
Journal:  IEEE Trans Med Imaging       Date:  2018-11-29       Impact factor: 10.048

2.  Proposed helmet PET geometries with add-on detectors for high sensitivity brain imaging.

Authors:  Hideaki Tashima; Taiga Yamaya
Journal:  Phys Med Biol       Date:  2016-09-20       Impact factor: 3.609

3.  Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning.

Authors:  Donghwi Hwang; Kyeong Yun Kim; Seung Kwan Kang; Seongho Seo; Jin Chul Paeng; Dong Soo Lee; Jae Sung Lee
Journal:  J Nucl Med       Date:  2018-02-15       Impact factor: 10.057

4.  DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

Authors:  Ida Häggström; C Ross Schmidtlein; Gabriele Campanella; Thomas J Fuchs
Journal:  Med Image Anal       Date:  2019-03-30       Impact factor: 8.545

Review 5.  Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.

Authors:  Tonghe Wang; Yang Lei; Yabo Fu; Walter J Curran; Tian Liu; Jonathon A Nye; Xiaofeng Yang
Journal:  Phys Med       Date:  2020-07-29       Impact factor: 2.685

6.  Recovery of missing data in partial geometry PET scanners: Compensation in projection space vs image space.

Authors:  Seyedehsamaneh Shojaeilangari; C Ross Schmidtlein; Arman Rahmim; Mohammad Reza Ay
Journal:  Med Phys       Date:  2018-10-25       Impact factor: 4.071

7.  Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging.

Authors:  Hyungseok Jang; Fang Liu; Gengyan Zhao; Tyler Bradshaw; Alan B McMillan
Journal:  Med Phys       Date:  2018-05-15       Impact factor: 4.071

8.  Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy.

Authors:  Matteo Maspero; Mark H F Savenije; Anna M Dinkla; Peter R Seevinck; Martijn P W Intven; Ina M Jurgenliemk-Schulz; Linda G W Kerkmeijer; Cornelis A T van den Berg
Journal:  Phys Med Biol       Date:  2018-09-10       Impact factor: 3.609

9.  Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy.

Authors:  Wen Li; Yafen Li; Wenjian Qin; Xiaokun Liang; Jianyang Xu; Jing Xiong; Yaoqin Xie
Journal:  Quant Imaging Med Surg       Date:  2020-06
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  5 in total

1.  Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function.

Authors:  Zhaotong Li; Xinrui Huang; Zeru Zhang; Liangyou Liu; Fei Wang; Sha Li; Song Gao; Jun Xia
Journal:  Quant Imaging Med Surg       Date:  2022-06

Review 2.  Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.

Authors:  Ioannis D Apostolopoulos; Nikolaos D Papathanasiou; Dimitris J Apostolopoulos; George S Panayiotakis
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-04-22       Impact factor: 10.057

Review 3.  Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.

Authors:  Virginia Liberini; Riccardo Laudicella; Michele Balma; Daniele G Nicolotti; Ambra Buschiazzo; Serena Grimaldi; Leda Lorenzon; Andrea Bianchi; Simona Peano; Tommaso Vincenzo Bartolotta; Mohsen Farsad; Sergio Baldari; Irene A Burger; Martin W Huellner; Alberto Papaleo; Désirée Deandreis
Journal:  Eur Radiol Exp       Date:  2022-06-15

4.  Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study.

Authors:  Zhibiao Cheng; Junhai Wen; Jun Zhang; Jianhua Yan
Journal:  Ann Transl Med       Date:  2022-04

5.  Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks.

Authors:  Neda Zaker; Kamal Haddad; Reza Faghihi; Hossein Arabi; Habib Zaidi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-06-18       Impact factor: 10.057

  5 in total

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