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Personalized computational human phantoms via a hybrid model-based deep learning method
, Article 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020, 1 June 2020 through 3 June 2020 ; July , 2020 ; Bitarafan, A ; Dylov, D. V ; Soleymani Baghshah, M ; Hosseini, S. A ; IEEE; IEEE Instrumentation and Measurement Society; IEEE Sensors Council Italy Chapter; Politecnica di Bari; Politecnico di Torino; Societa Italiana di Analisi del Movimento in Clinica ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Computed tomography (CT) simulators are versatile tools for scanning protocol evaluation, optimization of geometrical design parameters, assessment of image reconstruction algorithms, and evaluation of the impact of future innovations attempting to improve the performance of CT scanners. Computational human phantoms (CHPs) play a key role in simulators for the radiation dosimetry and assessment of image quality tasks in the medical x-ray systems. Since the construction of patient-specific CHPs can be both difficult and time-consuming, nominal standard/reference CHPs have been established, yielding significant discrepancies in the special design and optimization demands of patient dose and...