@misc{10481/90326,
year = {2023},
url = {https://hdl.handle.net/10481/90326},
abstract = {The integration of positron emission tomography (PET) and single-photon emission computed tomography
(SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a
promising approach. This integration enhances the precision and efficiency of current diagnostic and treatment
strategies while offering invaluable insights into disease mechanisms. In this comprehensive review, we delve
into the transformative impact of ML and DL in this domain. Firstly, a brief analysis is provided of how these
algorithms have evolved and which are the most widely applied in this domain. Their different potential applications
in nuclear imaging are then discussed, such as optimization of image adquisition or reconstruction,
biomarkers identification, multimodal fusion and the development of diagnostic, prognostic, and disease progression
evaluation systems. This is because they are able to analyse complex patterns and relationships within
imaging data, as well as extracting quantitative and objective measures. Furthermore, we discuss the challenges
in implementation, such as data standardization and limited sample sizes, and explore the clinical opportunities
and future horizons, including data augmentation and explainable AI. Together, these factors are propelling the
continuous advancement of more robust, transparent, and reliable systems.},
organization = {Grant RYC2021-030875-I},
organization = {MCIN/AEI/10.13039/501100011033},
organization = {European Union
NextGenerationEU/PRTR},
organization = {Grant
PID2022-137629OA-I00},
organization = {Grant PID2022-137451OB-I00},
organization = {MCIN/AEI/10.13039/501100011033},
organization = {FSE+},
organization = {Spanish Ministry of Universities
under FPU 18/04902},
organization = {Spanish Ministry of Universities
under Margarita Salas},
publisher = {Elsevier},
keywords = {Deep Learning},
keywords = {Diagnosis},
keywords = {Machine Learning},
title = {Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects},
doi = {10.1016/j.phrs.2023.106984},
author = {Jiménez Mesa, Carmen and Arco Martín, Juan Eloy and Martínez Murcia, Francisco Jesús and Suckling, John and Ramírez Pérez De Inestrosa, Javier and Gorriz Sáez, Juan Manuel},
}