Pet Clinics
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Artificial intelligence (AI) has been rapidly adopted in various health care domains. Molecular imaging, accordingly, has demonstrated growing academic and commercial interest in AI. Unprepared and inequitable implementation and scale-up of AI in health care may pose challenges. Implementation of AI, as a complex intervention, may face various barriers, at individual, interindividual, organizational, health system, and community levels. To address these barriers, recommendations have been develo...
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#1Fanny Orlhac (Curie Institute)H-Index: 4
#2Christophe Nioche (Curie Institute)H-Index: 1
Last. Irene Buvat (Curie Institute)H-Index: 2
view all 5 authors...
Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization. Tips are provided for critica...
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#1Juan Liu (Yale University)H-Index: 2
#2Masoud Malekzadeh (University of Massachusetts Lowell)H-Index: 1
Last. Joyita Dutta (University of Massachusetts Lowell)H-Index: 14
view all 6 authors...
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the postreconstruction enhancement of PET images. We present a detailed review of recent efforts for artificial intelligence-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highli...
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#2Abhinav K. Jha (WashU: Washington University in St. Louis)H-Index: 14
Last. Arman Rahmim (UBC: University of British Columbia)H-Index: 38
view all 5 authors...
Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in recent years. Convolutional neural networks have shown impressive results and potential toward fully automated segmentation in medical imaging, and particularly PET imaging. To cope with the limited access to annotated data needed in supervised AI methods, given tedious and prone-to-error manual delineations, semi-supervised and unsupervised AI techniques have also been explored for segmentation ...
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#1E. William Webb (UM: University of Michigan)H-Index: 1
#2Peter J. H. Scott (UM: University of Michigan)H-Index: 24
Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic. In this perspective, the authors offer insights into how the technology could be applied to improve the radiosynthesis of new radiopharmaceuticals for PET imaging, including identification of an optimal labeling approach as well as strategies for radiolabeling reaction optimization.
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#1Babak Saboury (HUP: Hospital of the University of Pennsylvania)H-Index: 27
#2Arman Rahmim (UBC: University of British Columbia)H-Index: 38
Last. Eliot L. Siegel (UMB: University of Maryland, Baltimore)H-Index: 38
view all 3 authors...
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#1Julia Brosch-LenzH-Index: 2
Last. Carlos Uribe (UBC: University of British Columbia)H-Index: 4
view all 8 authors...
We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward person...
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#1Amirhosein ToosiH-Index: 3
#2Andrea Bottino (Polytechnic University of Turin)H-Index: 17
Last. Arman Rahmim (UBC: University of British Columbia)H-Index: 38
view all 5 authors...
Artificial intelligence has witnessed exponential growth in the past decade. Advances in computing power and the design of sophisticated artificial intelligence algorithms have enabled computers to outperform humans in a variety of tasks. Yet, artificial intelligence's path has never been smooth, having essentially fallen apart twice in its lifetime after periods of popular success. We provide a brief rundown of artificial intelligence's evolution, highlighting its crucial moments and major turn...
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#1Kuang Gong (Harvard University)H-Index: 15
#2Kyungsang Kim (Harvard University)H-Index: 17
Last. Quanzheng Li (Harvard University)H-Index: 33
view all 5 authors...
PET can provide functional images revealing physiologic processes in vivo. Although PET has many applications, there are still some limitations that compromise its precision: the absorption of photons in the body causes signal attenuation; the dead-time limit of system components leads to the loss of the count rate; the scattered and random events received by the detector introduce additional noise; the characteristics of the detector limit the spatial resolution; and the low signal-to-noise rat...
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