2022/07/06 by Mohammad-Saber Azimi, Azimi, Mohammad-Saber, Alireza Kamali‐Asl +7
Medicine · Physics and Astronomy · #FOS: Physical sciences #Medical Imaging Techniques and Applications #Medical Physics (physics.med-ph) #Radiation Detection and Scintillator Technologies #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.48550/arxiv.2207.02553
openalex publication_date 2022/07/06 · openalex created_date 2023/02/15 · openalex updated_date 2026/07/28
A standard dose of radioactive tracer must be delivered into the patients body to obtain high-quality Positron Emission Tomography (PET) images for diagnostic purposes, which raises the risk of radiation harm. A reduced tracer dose, on the other hand, results in poor image quality and a noise-induced quantitative bias in PET imaging. The partial volume effect (PVE), which is the result of PET intrinsic limited spatial resolution, is another source of quality and quantity degradation in PET imaging. The utilization of anatomical information for PVE correction (PVC) is not straightforward due to the internal organ motions, patient involuntary motions, and discrepancies in the appearance and size of the structures in anatomical and functional images. Furthermore, an additional MR imaging session is necessary for anatomical information, which may not be available. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD-PVC) pictures from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for PVC and denoise low-dose PET images.