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Related Experiment Video

Updated: Oct 5, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

DIOR: Deep Iterative Optimization-Based Residual-Learning for Limited-Angle CT Reconstruction.

Dianlin Hu, Yikun Zhang, Jin Liu

IEEE Transactions on Medical Imaging

|January 31, 2022

Summary

This summary is machine-generated.

Limited-angle CT reconstruction faces artifacts due to incomplete data. Our Deep Iterative Optimization-based Residual-learning (DIOR) framework significantly improves image quality by combining iterative optimization and deep learning.

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Published on: April 13, 2013

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Computer Vision
Image Reconstruction

Background:

Limited-angle computed tomography (CT) is crucial but challenging due to incomplete projection data.
Incomplete data causes severe artifacts and distortions in reconstructed CT images, hindering clinical applications.

Purpose of the Study:

To introduce a novel reconstruction framework, Deep Iterative Optimization-based Residual-learning (DIOR), for limited-angle CT.
To address the limitations of existing methods by improving convergence and generalization abilities.

Main Methods:

DIOR combines iterative optimization with deep learning in the residual domain, avoiding direct regularization in image space.
Asymmetric convolutional modules enhance feature extraction in smooth regions for deep priors.
Perceptual loss evaluates low- and high-frequency components for improved tissue preservation.

Main Results:

DIOR demonstrated significant improvements in artifact removal, detail restoration, and edge preservation.
Quantitative and qualitative analyses on simulated and clinical datasets validated DIOR's effectiveness.
The proposed method outperformed existing competitive algorithms in limited-angle CT reconstruction.

Conclusions:

DIOR offers a promising solution for limited-angle CT reconstruction, enhancing image quality and diagnostic accuracy.
The residual domain approach and deep prior integration contribute to superior performance.
This framework advances the field of medical image reconstruction for challenging acquisition scenarios.