Applications of Artificial Intelligence and Deep Learning in Molecular Imaging and Radiotherapy

Overview

Journal Eur J Hybrid Imaging

Date 2021 Jun 30

PMID 34191161

Citations 24

Authors

Hossein Arabi

Habib Zaidi

Affiliations

Soon will be listed here.

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.

Citing Articles

Advancements in Artificial Intelligence in Noninvasive Cardiac Imaging: A Comprehensive Review.

Tolu-Akinnawo O, Ezekwueme F, Omolayo O, Batheja S, Awoyemi T Clin Cardiol. 2025; 48(1):e70087.

PMID: 39871619 PMC: 11772728. DOI: 10.1002/clc.70087.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Hasanabadi S, Aghamiri S, Abin A, Abdollahi H, Arabi H, Zaidi H Cancers (Basel). 2024; 16(20).

PMID: 39456604 PMC: 11505665. DOI: 10.3390/cancers16203511.

Joint Brain Tumor Segmentation from Multi-magnetic Resonance Sequences through a Deep Convolutional Neural Network.

Dehghani F, Karimian A, Arabi H J Med Signals Sens. 2024; 14:9.

PMID: 38993203 PMC: 11111160. DOI: 10.4103/jmss.jmss_13_23.

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging.

Azimi M, Kamali-Asl A, Ay M, Zeraatkar N, Hosseini M, Sanaat A Quant Imaging Med Surg. 2024; 14(3):2146-2164.

PMID: 38545051 PMC: 10963814. DOI: 10.21037/qims-23-871.

Automated segmentation of lesions and organs at risk on [Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR.

Yazdani E, Karamzadeh-Ziarati N, Cheshmi S, Sadeghi M, Geramifar P, Vosoughi H Cancer Imaging. 2024; 24(1):30.

PMID: 38424612 PMC: 10903052. DOI: 10.1186/s40644-024-00675-x.

References

Gong K, Catana C, Qi J, Li Q . PET Image Reconstruction Using Deep Image Prior. IEEE Trans Med Imaging. 2018; 38(7):1655-1665. PMC: 6584077. DOI: 10.1109/TMI.2018.2888491. View

Gao M, Bagci U, Lu L, Wu A, Buty M, Shin H . Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis. 2018; 6(1):1-6. PMC: 5881940. DOI: 10.1080/21681163.2015.1124249. View

Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D . Self-supervised learning for medical image analysis using image context restoration. Med Image Anal. 2019; 58:101539. PMC: 7613987. DOI: 10.1016/j.media.2019.101539. View

Maspero M, Savenije M, Dinkla A, Seevinck P, Intven M, Jurgenliemk-Schulz I . Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys Med Biol. 2018; 63(18):185001. DOI: 10.1088/1361-6560/aada6d. View

Wang Y, Zhou L, Yu B, Wang L, Zu C, Lalush D . 3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis. IEEE Trans Med Imaging. 2018; 38(6):1328-1339. PMC: 6541547. DOI: 10.1109/TMI.2018.2884053. View

Blanc-Durand P, Khalife M, Sgard B, Kaushik S, Soret M, Tiss A . Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction. PLoS One. 2019; 14(10):e0223141. PMC: 6779234. DOI: 10.1371/journal.pone.0223141. View

Nguyen D, Long T, Jia X, Lu W, Gu X, Iqbal Z . A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning. Sci Rep. 2019; 9(1):1076. PMC: 6355802. DOI: 10.1038/s41598-018-37741-x. View

Haggstrom I, Schmidtlein C, Campanella G, Fuchs T . DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal. 2019; 54:253-262. PMC: 6537887. DOI: 10.1016/j.media.2019.03.013. View

Antropova N, Huynh B, Giger M . A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys. 2017; 44(10):5162-5171. PMC: 5646225. DOI: 10.1002/mp.12453. View

10.

Arabi H, Dowling J, Burgos N, Han X, Greer P, Koutsouvelis N . Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region. Med Phys. 2018; 45(11):5218-5233. DOI: 10.1002/mp.13187. View

11.

Shi L, Onofrey J, Liu H, Liu Y, Liu C . Deep learning-based attenuation map generation for myocardial perfusion SPECT. Eur J Nucl Med Mol Imaging. 2020; 47(10):2383-2395. DOI: 10.1007/s00259-020-04746-6. View

12.

Boers T, Hu Y, Gibson E, Barratt D, Bonmati E, Krdzalic J . Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans. Phys Med Biol. 2020; 65(6):065002. DOI: 10.1088/1361-6560/ab6f99. View

13.

Ellmann S, Seyler L, Evers J, Heinen H, Bozec A, Prante O . Prediction of early metastatic disease in experimental breast cancer bone metastasis by combining PET/CT and MRI parameters to a Model-Averaged Neural Network. Bone. 2018; 120:254-261. DOI: 10.1016/j.bone.2018.11.008. View

14.

Gong K, Eric Berg , Cherry S, Qi J . Machine Learning in PET: from Photon Detection to Quantitative Image Reconstruction. Proc IEEE Inst Electr Electron Eng. 2023; 108(1):51-68. PMC: 10691821. DOI: 10.1109/JPROC.2019.2936809. View

15.

Chen K, Gong E, Carvalho Macruz F, Xu J, Boumis A, Khalighi M . Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs. Radiology. 2018; 290(3):649-656. PMC: 6394782. DOI: 10.1148/radiol.2018180940. View

16.

Hong X, Zan Y, Weng F, Tao W, Peng Q, Huang Q . Enhancing the Image Quality via Transferred Deep Residual Learning of Coarse PET Sinograms. IEEE Trans Med Imaging. 2018; 37(10):2322-2332. DOI: 10.1109/TMI.2018.2830381. View

17.

Zhao L, Lu Z, Jiang J, Zhou Y, Wu Y, Feng Q . Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images. J Digit Imaging. 2019; 32(3):462-470. PMC: 6499852. DOI: 10.1007/s10278-018-00173-0. View

18.

Largent A, Barateau A, Nunes J, Mylona E, Castelli J, Lafond C . Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning. Int J Radiat Oncol Biol Phys. 2019; 105(5):1137-1150. DOI: 10.1016/j.ijrobp.2019.08.049. View

19.

Mehranian A, Zaidi H, Reader A . MR-guided joint reconstruction of activity and attenuation in brain PET-MR. Neuroimage. 2017; 162:276-288. DOI: 10.1016/j.neuroimage.2017.09.006. View

20.

Xiang L, Qiao Y, Nie D, An L, Wang Q, Shen D . Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI. Neurocomputing (Amst). 2017; 267:406-416. PMC: 5714510. DOI: 10.1016/j.neucom.2017.06.048. View