0% Complete
صفحه اصلی
/
سی و سومین کنفرانس بین المللی مهندسی برق
Unveiling Enhanced Image Quality in Sparse-View CT with OSEM- ANLM Algorithm
نویسندگان :
Sayna Jamaati
1
Seyed Abolfazl Hosseini
2
Mohammad Ghorbanzadeh
3
Hossein Arabi
4
1- Sharif university of technology
2- Sharif university of technology
3- Sharif university of technology
4- Geneva University Hospital
کلمات کلیدی :
CT،sparse-view،image reconstruction،OSEM،Asymptotic Non-Local Means
چکیده :
This research presents the Ordered Subset Expectation Maximization-Asymptotic Non-local Means (OSEM-ANLM) algorithm, a novel imaging reconstruction method aimed at improving Computed Tomography (CT) image quality from sparsely sampled data. The algorithm’s performance is evaluated using a patient’s chest CT scan and a brain-skull image from the Rando phantom, with projection views reduced to 60, 45, and 30 to simulate varying data sparsity levels. Comparisons are made against conventional methods, including the Algebraic Reconstruction Technique (ART), OSEM, and OSEM-Non-Local Means(NLM). Qualitative assessments demonstrate the OSEM-ANLM’s superior ability to preserve anatomical structures while minimizing noise and artifacts, even with fewer projection views. Quantitative metrics, including Peak Signal-to-Noise Ratio (PSNR), Normalized Root Mean Square Error (NRMSE), and Structural Similarity Index (SSIM), further validate its effectiveness. For the chest CT image with 30 views (the lowest number of views with the highest level of artifacts), OSEM-ANLM achieves the highest PSNR (38.2693) and SSIM (0.9797), outperforming ART (24.6231, 0.9466), OSEM (25.1310, 0.9512), and OSEM-NLM (36.4061, 0.9669). Similarly, it yields the lowest NRMSE (0.0128), compared to ART (0.0523), OSEM (0.0484), and OSEM-NLM (0.0170). For the brain-skull image, OSEM-ANLM achieves the highest PSNR (37.6986) and SSIM (0.9898), surpassing ART (21.7716, 0.9443), OSEM (23.2124, 0.9521), and OSEM-NLM (35.9652, 0.9833). It also records the lowest NRMSE (0.0160) compared to ART (0.0599), OSEM (0.0526), and OSEM-NLM (0.0279). These results highlight the proposed method’s superior reconstruction accuracy and image fidelity under sparse sampling conditions.
لیست مقالات
لیست مقالات بایگانی شده
Radio frequency energy harvesting with multi band rectenna in GSM 1800, UMTS2100 and WiFi
Sahar Bayat - Zahra Bahrami - Asghar Keshtkar
Using Convolutional Neural Networks for Sudden Cardiac Death prediction
Sara Tavazo - Farideh Ebrahimi
Evaluating the effect of electric vehicle charging station locations on line flows:An analytical approach
Mohammad Hasan Nikkhah - Mahdi Samadi
Stability Analysis for the Non-linear Model Predictive Control of a Flexible Joint Manipulator with Dynamics Uncertainties
Mohamadreza Satvati - Hossein Karimpour - Keivan Torabi - Mohammad Motaharifar
VM Placement in Accelerator-Equipped Data Centers Using Variable-Length Modified Genetic Algorithm
Aryo Yarahmadi - Mahmoud Momtazpour
Experimental Study of Pick and Place Operation for Packaging Using Delta Parallel Robot with Two-Fingered Gripper
Mona Mohades Mojtahedi - Arvin Mohammadi - Mehdi Tale Masouleh
Three Improved Boost Topologies with Continuous Input/Output Currents Suitable for High-Voltage Applications
Hossein Gholizadeh - Hesam Ehsan - Alireza Poursalan - Mohammad Hamed Samimi
Comparison of Channel Selection Methods for EEG Signal Classification
Soraya Charkas - MohammadBagher Shamsollahi
Design and Simulation of Long Slot SIW Leaky Wave Antenna for Automotive Radar Application
Jamal Kazazi - Alireza Rahmani - Mahmoud Kamarei
Design of Dual Frequency Conformal Leaky-wave Holographic Antenna
Mohammad Amin Chaychi zadeh - Nader Komjani
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 40.4.2