Artificial Intelligence in PET: an Industry Perspective

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.

[1]  David Atkinson,et al.  NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis , 2017, Neuroinformatics.

[2]  Wei Lu,et al.  Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network , 2018, Physics in medicine and biology.

[3]  Alexander D'Amour,et al.  Underspecification Presents Challenges for Credibility in Modern Machine Learning , 2020, J. Mach. Learn. Res..

[4]  Simon R. Cherry,et al.  Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction , 2020, Proceedings of the IEEE.

[5]  Arkadiusz Sitek.  Data analysis in emission tomography using emission-count posteriors. , 2012, Physics in medicine and biology.

[6]  V. Goh,et al.  Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks , 2015, PloS one.

[7]  Bibb Allen,et al.  The Artificial Intelligence Ecosystem for the Radiological Sciences: Ideas to Clinical Practice. , 2018, Journal of the American College of Radiology : JACR.

[8]  Berkeley J. Dietvorst,et al.  Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err , 2014, Journal of experimental psychology. General.

[9]  Richard D. White,et al.  A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence. , 2019, Radiology. Artificial intelligence.

[10]  Spyridon Bakas,et al.  Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data , 2020, Scientific Reports.

[11]  Nan Wu,et al.  Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening , 2019, IEEE Transactions on Medical Imaging.

[12]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[13]  James Bailey,et al.  Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems , 2019, Pattern Recognit..

[14]  T. Leiner,et al.  Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure , 2021, Insights into Imaging.

[15]  Kris Thielemans,et al.  PET Image Reconstruction Using Physical and Mathematical Modelling For Time of Flight PET-MR Scanners in the STIR Library. , 2020, Methods.

[16]  Eric J. W. Visser,et al.  FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0 , 2014, European Journal of Nuclear Medicine and Molecular Imaging.

[17]  Charles Blundell,et al.  Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.

[18]  Kris Thielemans,et al.  SIRF: Synergistic Image Reconstruction Framework , 2020, Comput. Phys. Commun..

[19]  Richard D. White,et al.  Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use , 2020, Journal of Digital Imaging.

[20]  E. Siegel,et al.  Radiology's Information Architecture Could Migrate to One Emulating That of Smartphones. , 2020, Journal of the American College of Radiology : JACR.

[21]  Jeremy M Wolfe,et al.  Assessing Cancer Risk from Mammograms: Deep Learning Is Superior to Conventional Risk Models. , 2019, Radiology.

[22]  Tanmoy Bhattacharya,et al.  The need for uncertainty quantification in machine-assisted medical decision making , 2019, Nat. Mach. Intell..

[23]  Abbes Amira,et al.  Artificial Neural Network-Based System for PET Volume Segmentation , 2010, Int. J. Biomed. Imaging.

[24]  Andrew Gordon Wilson,et al.  The Case for Bayesian Deep Learning , 2020, ArXiv.

[25]  Francesco Renna,et al.  On instabilities of deep learning in image reconstruction and the potential costs of AI , 2019, Proceedings of the National Academy of Sciences.

[26]  Francisco Herrera,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2020, Inf. Fusion.

[27]  Joel S. Karp,et al.  Efficient 3-D TOF PET Reconstruction Using View-Grouped Histo-Images: DIRECT—Direct Image Reconstruction for TOF , 2009, IEEE Transactions on Medical Imaging.

[28]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[29]  Erich P Huang,et al.  Metrology Standards for Quantitative Imaging Biomarkers. , 2015, Radiology.

[30]  Jasper Snoek,et al.  Second opinion needed: communicating uncertainty in medical machine learning , 2021, npj Digital Medicine.

[31]  David Atkinson,et al.  ISMRM Raw data format: A proposed standard for MRI raw datasets , 2017, Magnetic resonance in medicine.

[32]  S. Cherry,et al.  Using convolutional neural networks to estimate time-of-flight from PET detector waveforms , 2018, Physics in medicine and biology.

[33]  H. Zaidi,et al.  Depth of Interaction Estimation in a Preclinical PET Scanner Equipped with Monolithic Crystals Coupled to SiPMs Using a Deep Neural Network , 2020, Applied Sciences.

[34]  A Todd-Pokropek,et al.  A file format for the exchange of nuclear medicine image data: a specification of Interfile version 3.3. , 1992, Nuclear medicine communications.