Intro
Preface
Organization
Contents - Part X
Image Reconstruction
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?
1 Introduction
2 Methodology
2.1 Model Components and Training
2.2 K-Space Conditioning Reverse Process
3 Experimental Results
3.1 Implementation Details and Evaluation Methods
3.2 Comparison and Ablation Studies
4 Discussion and Conclusion
References
Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction
1 Introduction
2 Method
2.1 Intensity Field

2.2 DIF-Net: Deep Intensity Field Network
2.3 Network Training
2.4 Volume Reconstruction
3 Experiments
3.1 Experimental Settings
3.2 Results
4 Conclusion
References
Revealing Anatomical Structures in PET to Generate CT for Attenuation Correction
1 Introduction
2 Method
3 Experiments
3.1 Materials
3.2 Comparison with Other Methods
4 Conclusion
References
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
1 Introduction
2 Methodology
2.1 Preliminaries
2.2 Proposed Methodology

3 Experiments
3.1 Dataset
3.2 Implementation Details
3.3 Results
4 Conclusion
References
An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis
1 Introduction
2 Methods
2.1 Multi-sequence Fusion
2.2 Task-Specific Enhanced Map
3 Experiments
3.1 Dataset and Evaluation Metrics
3.2 Implementation Details
3.3 Quantitative Results
3.4 Ablation Study
3.5 Interpretability Visualization
4 Conclusion
References
Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation
1 Introduction

2 Proposed Method
2.1 MaskGAN Architecture
2.2 CycleGAN Supervision
2.3 Mask and Cycle Shape Consistency Supervision
3 Experimental Results
3.1 Experimental Settings
3.2 Results and Discussions
4 Conclusion
References
Alias-Free Co-modulated Network for Cross-Modality Synthesis and Super-Resolution of MR Images
1 Introduction
2 Methodology
2.1 Co-modulated Network
2.2 Alias-Free Generator
2.3 Optimization
3 Experiments
3.1 Experimental Settings
3.2 Comparative Experiments
4 Conclusion
References

Multi-perspective Adaptive Iteration Network for Metal Artifact Reduction
1 Introduction
2 Method
3 Experiments
4 Results
5 Discussion and Conclusion
References
Noise Conditioned Weight Modulation for Robust and Generalizable Low Dose CT Denoising
1 Introduction
2 Method
3 Experimental Setting
4 Result and Discussion
5 Conclusion
References
Low-Dose CT Image Super-Resolution Network with Dual-Guidance Feature Distillation and Dual-Path Content Communication
1 Introduction
2 Method
2.1 Overall Architecture
2.2 Target Function
3 Experiments