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Title: Transfer Learning-based Attenuation Map Generation for Brain SPECT Using Simulation and Clinical Data
Authors: IONG, CHI HONG(容志匡)
Department: Department of Electrical and Computer Engineering
Faculty: Faculty of Science and Technology
Issue Date: May-2023
Citation: Iong, C. H. (2023). Transfer Learning-based Attenuation Map Generation for Brain SPECT Using Simulation and Clinical Data (Outstanding Academic Papers by Students (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository.
Abstract: Current attenuation correction (AC) of brain SPECT (Single-photon emission computed tomography) remains challenging in routine clinical practice due to the potential mismatch between SPECT and CT and the increase of radiation absorbed dose from CT. CT-less AC methods have been reported in SPECT based on deep learning (DL). However, DL-based AC methods usually require a large amount of training data while the collection of clinical data is challenging. Transfer learning (TL) has been introduced to develop robust target models via fine-tuning (FT) strategies, using a small set of target training data. This study aims to demonstrate the feasibility of estimating attenuation maps for SPECT based on a conditional generative adversarial network (cGAN) using a small amount of clinical data and a large amount of simulated data. The network was firstly trained by paired simulated none-attenuation-corrected (NAC) SPECT and attenuation map. Then the pre-trained network was fine-tuned by paired clinical NAC SPECT and attenuation map (TLAC). Finally, the challenges, opportunities, and barriers of TLAC were evaluated and discussed.
Instructor: Seng-Peng Mok
Programme: Bachelor of Science in Electrical and Computer Engineering
Appears in Collections:FST OAPS 2023

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