Publications by authors named "A Kamali-Asl"
Purpose: We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (K) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy.
Methods: We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.
Article Synopsis
- The study addresses the issue of partial volume effects in PET imaging, which significantly impact image quality and accuracy due to the technology's limited resolution.
- Researchers developed a deep learning framework using a modified U-Net model to predict partial volume corrected full-dose images from standard or low-dose PET images, without needing anatomical data.
- Evaluation of their method showed varying error levels among different correction techniques, with the proposed framework successfully improving the denoising and correction processes for both low and full-dose PET images.
Article Synopsis
- The study introduces an attention-based deep neural network called ATB-Net, designed to predict partial volume corrected (PVC) images from brain PET data without relying on anatomical information.
- The performance of ATB-Net was assessed against two methods, iterative Yang (IY) and reblurred Van-Cittert (RVC), revealing it significantly outperformed the standard U-Net model, especially with the IY method, showing considerable improvements in metrics such as PSNR and SSIM.
- The findings indicate that the attention-based model can effectively handle PVC in PET images without needing anatomical data, suggesting its potential for practical applications in PET imaging.
Background: Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging.
Purpose: In this study, different input settings were considered in the model training to investigate deep learning-based AC in the image space.
This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules.
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