کنفرانس مهندسی برق ایران

صفحه اصلی / سی و سومین کنفرانس بین المللی مهندسی برق

CT Super-Resolution Using Arbitrary Scale Diffusion Model

نویسندگان :

Mahsa Nadafi Ghahnavieh¹ Saeed Masoudnia² Hamid Soltanian-Zadeh³

1- دانشگاه تهران، دانشکده مهندسی برق و کامپیوتر 2- دانشگاه تهران، دانشکده مهندسی برق و کامپیوتر 3- دانشگاه تهران، دانشکده مهندسی برق و کامپیوتر

کلمات کلیدی :

Super-Resolution،Computed Tomography،Diffusion Models،Low-Resolution،Image Reconstruction

چکیده :

Computed Tomography (CT) is a critical imaging modality widely used in medical diagnostics, offering detailed visualization of anatomical structures. However, low-resolution (LR) CT images, often necessitated by reduced radiation doses, suffer from loss of information, blurred edges, and increased noise. This study introduces a novel super-resolution (SR) model based on an enhanced Implicit Diffusion Model (IDM) architecture. It utilizes a dynamic scale factor adjustment mechanism and a multi-scale LR guidance network to address arbitrary-scale SR tasks. The model achieves high-fidelity CT image reconstruction with increased flexibility and scalability by incorporating neural implicit representations and modifications to the U-Net architecture. Experimental evaluations demonstrate the superiority of our proposed network over state-of-the-art methods such as SRGAN and multi-scale attention networks, achieving Peak Signal-to-Noise Ratio (PSNR) values of 40.38, 33.07, and 29.81 at ×2, ×4, and ×8 magnifications, respectively. Structural Similarity Index Measure (SSIM) scores confirm the model's preservation of critical details and textures. The proposed method offers significant improvements in CT image quality, paving the way for its integration into clinical workflows to enhance diagnostic accuracy while maintaining patient safety.

لیست مقالات

لیست مقالات بایگانی شده

Security and Privacy Smart Contract Architecture for Energy Trading based on Blockchains

Masoumeh Nazari - Siavash Khorsandi - Jaber Babaki

LPV Controller Design for Trajectory Tracking of Nonholonomic Wheeled Mobile Robots in the Presence of Slip

Mohammad Sabouri - Mohammad Hassan Asemani

A Hybrid Computer-aided Diagnosis System For Central Obesity Screening In A Large Sample Of Iranian Children and Adolescents

Amirhossein Koochekian - Morteza Farahi - Hamid Reza Sadr manouchehri Naeini - Mohammad Reza Mohebian - Hamid Reza Marateb - Marjan Mansourian - Roya Kelishadi

Deep Learning Meets Explainable AI: A Robust Framework for X-Ray Fracture Detection

Ali Tamizifar - Shakiba Berenjkoub - Mina Amiri

A Multi-Branch Deep Learning Architecture with Hierarchical Fusion for Real-Time Short-Term Multi-Horizon PV Energy Forecasting

Sepehr Shakibi - Melika Vafaee Taj Khatooni - Mohsen Hamzeh

Lane Change Decision Making Using Deep Reinforcement Learning

Pedram Lamei - Mohammad Haeri

Application of Statistical Techniques and Machine Learning in Forecasting Distribution Network Load: A Real Case Study on the Iranian Power System

Hossein Jafari - Mohammad Sadegh Sepasian - Fatemeh Teimori

Stability Analysis of Singular 2-D Positive systems

Mahmoud Zamani - Masoud Shafiee - Iman Zamani

Optimal Placement of Unified Power Flow Controller in Power System Considering Transient Stability and Voltage Stability Criteria

Esmail Zahmatkeshan - Mohsen Bandekhoda

Design and Implementation of a Data-Driven Controller for a Two-Wheeled Self-Balancing Robot

Mohammad Akhavan - Haniye Parvahan - Mojtaba Nouri Manzar

بیشتر