Medical Image Computing and Computer Assisted Intervention - MICCAI 2024

Name: Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 | 27th International Conference, Marrakesh, Morocco, October 6-10, 2024, Proceedings, Part VII
Brand: Springer
Price: 213.99 EUR
Availability: OnlineOnly

27th International Conference, Marrakesh, Morocco, October 6-10, 2024, Proceedings, Part VII

Marius George Linguraru Qi Dou Aasa Feragen Stamatia Giannarou Ben Glocker Karim Lekadir Julia A. Schnabel(Editor)

Springer (Publisher)

Published on 2. October 2024

XLVII, 791 pages

E-Book

PDF with digital watermarking

System requirements

978-3-031-72104-5 (ISBN)

€213.99incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

More details

Other editions

Content

Intro
Preface
MICCAI 2024 Organization
Contents - Part VII
Image Reconstruction
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation
1 Introduction
2 Method
2.1 Depth Maps Estimation
2.2 Surface Reconstruction and 3D Shape Completion
3 Experiment and Result
3.1 3D Shape Results Without Shape Completion
3.2 3D Shape Results with Shape Completion
3.3 Depth Map Results
4 Conclusion and Summary
References
3DGR-CAR: Coronary Artery Reconstruction from Ultra-sparse 2D X-Ray Views with a 3D Gaussians Representation
1 Introduction
2 Methods
2.1 3D Gaussians Representation Reconstruction
2.2 Gaussian Center Predictor Training
3 Experiments
3.1 Setup
3.2 Comparison with Existing Methods
3.3 Ablation Study
4 Conclusion
References
3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks
1 Introduction
2 Method
2.1 Progressive Guided Reconstruction
2.2 3DPX with Hybrid MLP-CNN Block
2.3 Evaluation Metric
3 Experiments and Results
3.1 Dataset and Augmentation
3.2 Experiments
4 Conclusion
References
7T MRI Synthesization from 3T Acquisitions
1 Introduction
2 Method
3 Experimental Design
4 Results
4.1 Our V-Net Based Model for 7T MRI Synthesization Archives State-of-the-Art Performance
4.2 Qualitative Evaluation of Pathological Tissue Enhancement via Synthetic 7T Generation
4.3 Model Generalizability to Low-resolution MRI Input
5 Conclusion and Future Work
References
A Graph-Embedded Latent Space Learning and Clustering Framework for Incomplete Multimodal Multiclass Alzheimer's Disease Diagnosis
1 Introduction
2 Method
2.1 Multimodal Reconstruction
2.2 Subject-Similarity Graph Embedding
2.3 AD-Oriented Latent Clustering
3 Experiments
3.1 Materials and Experimental Setup
3.2 Performance of Proposed Method
3.3 Comparison with Other Methods
3.4 Ablation Study
4 Conclusions
References
Accelerated Multi-contrast MRI Reconstruction via Frequency and Spatial Mutual Learning
1 Introduction
2 Methodology
2.1 Frequency-Spatial Feature Extraction
2.2 Cross-Modal Frequency-Spatial Feature Fusion
2.3 Loss Function
3 Experiments
3.1 Datasets and Implementation Details
3.2 Experimental Results
4 Conclusion
References
All-In-One Medical Image Restoration via Task-Adaptive Routing
1 Introduction
2 Method
2.1 Network Architecture
2.2 Task-Adaptive Routing
2.3 Loss Function
3 Experiments and Results
3.1 Dataset
3.2 Implementation
3.3 Comparative Experiment
3.4 Ablation Study
4 Conclusion
References
An Evaluation of State-of-the-Art Projectors in the Presence of Noise and Nonlinearity in the Beer-Lambert Law
1 Introduction
2 Sensitivity Analysis of the Forward Model
3 Fast Projectors
4 Experiment and Results
4.1 Sensitivity to Perturbations of the Forward Model
4.2 Reconstruction Error
4.3 Reconstruction Error in Presence of Poisson Noise
4.4 Reconstruction Result
5 Conclusion
References
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
1 Introduction
2 Method
2.1 A Brief Review of Diffusion Models
2.2 Adding Segmentation Guidance to Diffusion Models
2.3 Mask-Ablated Training and Sampling
3 Datasets
4 Experiments
4.1 Comparison to Existing Image Generation Models
4.2 Advantages of Mask-Ablated-Training
5 Conclusion
References
APS-USCT: Ultrasound Computed Tomography on Sparse Data via AI-Physic Synergy
1 Introduction
2 Method
3 Experiment
3.1 Dataset, Implementation, and Evaluation Protocol
3.2 Main Results
3.3 Ablation Studies
4 Conclusion
References
AutoSkull: Learning-Based Skull Estimation for Automated Pipelines
1 Introduction
2 Method
2.1 Preprocessing for Training
2.2 MLP-Based Learning
2.3 Skull Inference from Face Scan
2.4 Adding a Teeth Prior
3 Evaluation and Results
3.1 Evaluation Setup
3.2 Comparisons
3.3 Ablation Study
3.4 Application Example
4 Discussion and Conclusion
References
Baikal: Unpaired Denoising of Fluorescence Microscopy Images Using Diffusion Models
1 Introduction
2 Methods
2.1 Generative Backbone on Clean Images
2.2 Conditional Samplers as Denoisers
3 Experiments
3.1 Dataset and Training
3.2 Quantitative Results
3.3 Ablations
3.4 Qualitative Results
4 Conclusion
References
Blind Proximal Diffusion Model for Joint Image and Sensitivity Estimation in Parallel MRI
1 Introduction
2 Methodology
2.1 Model Optimization
2.2 BPDM-PMRI
3 Experiments
4 Conclusion
References
Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution
1 Introduction
2 Related Works
2.1 Mesh Convolution
2.2 Predicting Gyro-Sulcus From fMRI
3 Method
3.1 Channel Attention Block
3.2 Mesh CNN with U-Net Architecture and Loss Function
4 Experiments
4.1 Dataset, Preprocessing and Implementation
4.2 Results and Methods Comparison
4.3 Ablation Study
5 Discussion
6 Conclusion
References
CAPTURE-GAN: Conditional Attribute Preservation Through Unveiling Realistic GAN for Artifact Removal in Dual-Energy CT Imaging
1 Introduction
2 Method
2.1 CAPTURE-GAN
2.2 Pre-trained Classifier and Mask Creator
2.3 Loss Function
2.4 Model Training
3 Experimental Results
3.1 Dataset
3.2 Evaluation
3.3 Qualitative and Quantitative Comparison Results
3.4 Ablation Study Results
4 Conclusion
References
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis
1 Introduction
2 Methodology
2.1 Problem Formulation and Method Overview
2.2 Model Architecture
2.3 Model Training
3 Experiments and Results
3.1 Experimental Setups
3.2 Performance Evaluation
3.3 Ablation Study
4 Conclusion
References
Center-to-Edge Denoising Diffusion Probabilistic Models with Cross-domain Attention for Undersampled MRI Reconstruction
1 Introduction
2 Method
2.1 Conditional DDPM by Undersampled Measurements
2.2 C2E Denoising Diffusion Strategy
2.3 Ag-Cd-J for Dual Domain Integration
3 Experiment and Results
4 Conclusion
References
Contrast Representation Learning from Imaging Parameters for Magnetic Resonance Image Synthesis
1 Introduction
2 Method
2.1 Problem Definition
2.2 Contrast Representation Learning
3 Experiments
3.1 Data Acquisition
3.2 Implementation Details
3.3 Comparison with State-of-the-Art Methods
3.4 Modulating Contrast Across All Sequences
4 Conclusion
References
Convolutional Implicit Neural Representation of Pathology Whole-Slide Images
1 Introduction
2 Method
2.1 Position Encoding
2.2 Convolutional Implicit Neural Representation
3 Results
3.1 Experiments
3.2 Reconstruction Results
4 Conclusion
References
Cross-conditioned Diffusion Model for Medical Image to Image Translation
1 Introduction
2 Method
2.1 Representation Learning for Target Modalities
2.2 Cross-conditioned UNet (C-UNet)
3 Experiments
3.1 Datasets and Implementation
3.2 Comparison with SOTA Methods
3.3 Ablation Study
4 Conclusion
References
Cycle-Consistent Learning for Fetal Cortical Surface Reconstruction
1 Introduction
2 Method
3 Experiments
4 Conclusion
References
DCDiff: Dual-Domain Conditional Diffusion for CT Metal Artifact Reduction
1 Introduction
2 Revisiting Diffusion Models
3 Methodology
3.1 Model Architecture and Training
3.2 Model Inference with Diffusion Interpolation
4 Experiments and Results
4.1 Datasets and Experiment Settings
4.2 Performance Evaluation
4.3 Ablation Study
5 Conclusion
References
Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions
1 Introduction
2 Methods
2.1 Simulation Setup
2.2 Unrolled Variational Network
2.3 Training and Evaluation
3 Results
4 Discussion
5 Conclusion
References
Deform-Mamba Network for MRI Super-Resolution
1 Introduction
2 Method
2.1 Preliminaries
2.2 Proposed Architecture
3 Experiments
3.1 Datasets and Evaluation Metrics
3.2 Experimental Details
3.3 Ablation Study
3.4 Comparison with State-of-the-Art Methods
4 Conclusion and Future Work
References
Differentiable Score-Based Likelihoods: Learning CT Motion Compensation from Clean Images
1 Introduction
2 Methods
2.1 Score-Based Diffusion Models
2.2 Likelihood Computation
2.3 Neural ODEs and Likelihood Target Function
2.4 Motion Compensation
3 Experiments and Results
4 Discussion
5 Conclusion
References
Dynamic Hybrid Unrolled Multi-scale Network for Accelerated MRI Reconstruction
1 Introduction
2 Problem Formulation
3 Method
4 Experiments
4.1 Datasets and Experimental Setting
4.2 Ablation Study
4.3 Comparison Study
5 Conclusions
References
Dynamic Single-Pixel Imaging on an Extended Field of View Without Warping the Patterns
1 Introduction
2 Single-Pixel Imaging
2.1 Forward Model
2.2 Image Reconstruction
3 Method
3.1 Dynamic Forward Model with Extended FOV
3.2 Dynamic System Matrix Without Warping the Patterns
4 Results
5 Conclusion
References
EchoNet-Synthetic: Privacy-Preserving Video Generation for Safe Medical Data Sharing
1 Introduction
2 Method
3 Experiments
4 Conclusion
References
EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
1 Introduction
2 Methodology
2.1 Preliminaries
2.2 Proposed Method: EndoUIC
3 Experiments
3.1 Dataset
3.2 Implementation Details
3.3 Results
4 Conclusion
References
Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans
1 Introduction
2 Related Work
3 Method
3.1 Background and Notation
3.2 Grid-Hash-Encoding of Harmonic Coefficient Fields
4 Experimental Setup
5 Results
5.1 Comparison with Current Methods
5.2 Ablation Studies
6 Discussion and Conclusion
References
Explanation-Driven Cyclic Learning for High-Quality Brain MRI Reconstruction from Unknown Degradation
1 Introduction
2 Methods
2.1 Front-End Image Classification
2.2 Back-End Image Reconstruction
2.3 Cross-Attention-Gradient Interpreting
3 Experiments and Results
3.1 Dataset Description and Data Partition
3.2 Experimental Settings
3.3 Experimental Results on Simulated Data
3.4 Experimental Results on Real Motion Data
3.5 Ablation Study
4 Conclusion
References
Fetal MRI Reconstruction by Global Diffusion and Consistent Implicit Representation
1 Introduction
2 Methods
2.1 Consistency Implicit Neural Network
2.2 Global Diffusion Discriminative Generation
2.3 Loss Functions
3 Experiments and Results
3.1 Datasets
3.2 Compared with Results of Different Methods
3.3 Simulated Fetal Brain Data
3.4 Reconstructing Volumes at Different Gestational Ages
3.5 Ablation Study
3.6 Downstream Task Analysis
4 Conclusion
References
Fine-Grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction
1 Introduction
2 Methods
2.1 ReMamba with Multi-directional State Space Model
2.2 Adaptive Fusion Strategy
2.3 Online Alignment Strategy
3 Experiments
4 Conclusion
References
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene Reconstruction
1 Introduction
2 Methodology
2.1 Preliminary: 3D Gaussian Splatting
2.2 Initialization from Monocular Depth
2.3 Flow-Induced Pose Estimation
2.4 3D Gaussians Optimization
3 Experiments
3.1 Implementation Details
3.2 Quantitative and Qualitative Results
4 Conclusion
References
HeartBeat: Towards Controllable Echocardiography Video Synthesis with Multimodal Conditions-Guided Diffusion Models
1 Introduction
2 Methodology
2.1 Preliminaries of T2V DDPMs
2.2 HeartBeat
3 Experimental Results
4 Conclusion
References
HF-ResDiff: High-Frequency-Guided Residual Diffusion for Multi-dose PET Reconstruction
1 Introduction
2 Methodology
2.1 Pre-train CNN for Predicting Low-Frequency Information
2.2 HF-ResDiff Model
2.3 Multi-dose PET Reconstruction
2.4 Implementation Details
3 Experiments
3.1 Dataset
3.2 Comparative Experiments
3.3 Ablation Study
4 Conclusion
References
IM-MoCo: Self-supervised MRI Motion Correction Using Motion-Guided Implicit Neural Representations
1 Introduction
2 Materials and Methods
2.1 Physical Model and Motion Simulation
2.2 IM-MoCo
3 Experimental Results
3.1 Datasets and Motion Simulation
3.2 Experimental Setup
3.3 Results
4 Discussion and Outlook
5 Conclusion
References
Inject Backdoor in Measured Data to Jeopardize Full-Stack Medical Image Analysis System
1 Introduction
2 Methodology
2.1 Problem Statement
2.2 Learnable Trigger Generation Method
3 Experiment
3.1 Experiment Settings
3.2 Experimental Results
4 Conclusion
References
Joint EM Image Denoising and Segmentation with Instance-Aware Interaction
1 Introduction
2 Instance-Aware Interaction Framework
2.1 Overview
2.2 Instance-Aware Embedding Module
2.3 Joint Training Mechanism
3 Experiments
3.1 Experimental Settings
3.2 Quantitative and Qualitative Evaluations
3.3 Generalization Evaluation
3.4 Ablation Study
4 Conclusion
References
k-t Self-consistency Diffusion: A Physics-Informed Model for Dynamic MR Imaging
1 Introduction
2 Method
2.1 TSPIRiT
2.2 k-t Self-consistency Diffusion
3 Experiments and Results
3.1 Dataset
3.2 Implementation Details
3.3 Comparison with State-of-the-Art Methods
3.4 Ablation Study
3.5 Performance on Temporally-Frame-Shuffled Data
4 Conclusion and Discussion
References
Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction
1 Introduction
2 Methods
2.1 Problem Formulation
2.2 DIF-Gaussian: Learning 3D Gaussians
2.3 Test-Time Optimization (TTO)
3 Experiments
3.1 Experimental Settings
3.2 Results
4 Conclusion
References
LiverUSRecon: Automatic 3D Reconstruction and Volumetry of the Liver with a Few Partial Ultrasound Scans
1 Introduction
2 Methodology
3 Experiments and Results
4 Conclusion
References
LSSNet: A Method for Colon Polyp Segmentation Based on Local Feature Supplementation and Shallow Feature Supplementation
1 Introduction
2 Method
2.1 Convolutional Branch
2.2 Multiscale Feature Extraction Module
2.3 Semantic Gap Reduction Module
2.4 Interlayer Attention Fusion Module
2.5 Shallow Feature Supplementation Module
3 Experiments
3.1 Datasets and Evaluation Metrics
3.2 Implementation Details
3.3 Results
3.4 Ablation Experiments
4 Conclusion
References
Material Decomposition in Photon-Counting CT: A Deep Learning Approach Driven by Detector Physics and ASIC Modeling
1 Introduction
2 Method
2.1 Physics Simulation Model and Dataset Construction
2.2 Detector Net and ASIC Net
2.3 Calibration
2.4 Material Decomposition
3 Experiment
3.1 Compared Methods
3.2 Calibration Results
3.3 Material Decomposition Results
4 Conclusion
References
MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
1 Introduction
2 Methodology
2.1 Adversarial Diffusive Network
2.2 Multi-modal Conditional Encoder (Mc-Encoder)
2.3 Multi-modal Masked Text Reconstruction (M3TRec)
2.4 Sampling
2.5 Objective Function
3 Experiments
3.1 Experimental Settings
3.2 Comparison with State-of-the-Art Methods
3.3 Ablation Studies
4 Conclusion
References
Memory-Efficient High-Resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Models
1 Introduction
2 Methodology
2.1 Non-Holistic Autoencoders
2.2 Cascaded Diffusion Processes
3 Experiments and Results
4 Conclusion
References
MiHATP:A Multi-hybrid Attention Super-Resolution Network for Pathological Image Based on Transformation Pool Contrastive Learning
1 Introduction
2 Method
2.1 Reversible and Irreversible Transformation Pool
2.2 Multi-hybrid Attention
2.3 Loss Function
3 Experiment and Result Discussion
3.1 Dataset and Implementation
3.2 Experimental Results
4 Conclusion
References
Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI-4pt
1 Introduction
2 Methodology
3 Experiments
4 Conclusion
References
On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction
1 Introduction
2 Related Works
2.1 DL-Based End-to-End Solution
2.2 Bayesian Image Reconstruction
2.3 Reconstructing MRI Using Diffusion Prior and Posterior Sampling
3 Worst-Case Instabilities in MRI Reconstruction
4 Experiments and Results
4.1 Data
4.2 Supervised Baselines
4.3 Denoising Diffusion Reconstruction
4.4 Experimental Design
4.5 Quantitative and Visual Evaluation
5 Conclusion
References
Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction
1 Introduction
2 Methodology
2.1 PEFT for PET Scan-Time Reduction
2.2 PETITE: Optimal Effectiveness of Mix-PEFT
3 Experiments and Results
3.1 Evaluation Results
4 Conclusions
References
PASTA: Pathology-Aware MRI to PET CroSs-modal TrAnslation with Diffusion Models
1 Introduction
2 Proposed Method
3 Experiments
4 Results
5 Conclusion
References
PET Image Denoising Based on 3D Denoising Diffusion Probabilistic Model: Evaluations on Total-Body Datasets
1 Introduction
2 Methodology
2.1 Diffusion Models
2.2 Conditional PET Image Denoising Based on 3D DDPM
3 Experiments and Results
3.1 Dataset and Implementation Details
3.2 Evaluation Metrics
3.3 Results
4 Conclusion
References
Physical-Priors-Guided Aortic Dissection Detection Using Non-Contrast-Enhanced CT Images
1 Introduction
2 Method
2.1 3D Aorta Segmentation
2.2 Blood Flow Parameter Calculation
2.3 3D Physical-Guided Model
3 Experiments
3.1 Datasets and Experiment Details
3.2 Results
3.3 Ablation Studies
4 Conclusion and Discussion
References
Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI*-12pt
1 Introduction
2 Background
2.1 Motion During MR Image Acquisition
2.2 T2* Quantification from Multi-echo GRE MRI
3 Methods
3.1 PHIMO
3.2 Data
3.3 Evaluation
4 Experiments and Results
5 Discussion and Conclusion
References
Pixel2Mechanics: Automated Biomechanical Simulations of High-Resolution Intervertebral Discs from Anisotropic MRIs
1 Introduction
2 Methods
2.1 MRI2Mesh
2.2 Differentiable Rendering Optimization
2.3 Objective Function
2.4 Biomechanical Simulation
3 Experiments
4 Results
4.1 IVD Reconstruction
4.2 Biomechanical Simulation
5 Discussion and Conclusion
References
Privacy Protection in MRI Scans Using 3D Masked Autoencoders
1 Introduction
2 Related Work
3 Method
4 Experiments and Conclusion
References
Reference-Free Axial Super-Resolution of 3D Microscopy Images Using Implicit Neural Representation with a 2D Diffusion Prior
1 Introduction
2 Method
2.1 Implicit Neural Representations for Isotropic Volume Reconstruction
2.2 Diffusion Models and Score Distillation Sampling
3 Experiments
3.1 Datasets and Implementation Details
3.2 Simulation Studies on FIB25
3.3 Real World Anisotropic Volumes
4 Conclusion and Future Work
References
Region Attention Transformer for Medical Image Restoration
1 Introduction
2 Method
2.1 Region Attention Transformer Block
2.2 Focal Region Loss
3 Experiments and Results
3.1 Dataset
3.2 Implementation
3.3 Comparison Experiment
3.4 Ablation Study
3.5 Conclusion
References
Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations
1 Introduction
2 Methods
2.1 k-Space Interpolation Using GRAPPA
2.2 From GRAPPA to PISCO
2.3 Dynamic MRI Reconstruction Using NIK with PISCO
3 Experimental Setup
3.1 Data
3.2 Training and Inference
3.3 Evaluation
4 Results
5 Discussion and Conclusion
References
Simulation Based Inference for PET Iterative Reconstruction
1 Introduction
2 Continuous Surrogate Projector
2.1 Conditional Density Estimation
3 Stochastic Simulator
4 Experiments Setup
4.1 Reconstruction Settings
4.2 Projector Binning (System Matrix)
4.3 Iterative Reconstruction (EM)
4.4 Evaluation Methodology
5 Results and Discussion
5.1 Discussion
References
Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution Using Conditional Diffusion Model
1 Introduction
2 Method
2.1 Overall Architecture
2.2 Tri-Modal Fusion Attention (TMFA) Block
2.3 Fusion Super-Resolution Joint Loss Function
3 Experiments
3.1 Experimental Detail
3.2 Objective Evaluation Metric
3.3 Comparison with SOTA Methods
3.4 Ablation Study
4 Conclusion
References
SinoSynth: A Physics-Based Domain Randomization Approach for Generalizable CBCT Image Enhancement
1 Introduction
2 Method
2.1 Preliminaries
2.2 Domain-Randomized CBCT Simulation
2.3 Structural Guidance for Generative Networks
3 Experiments
3.1 Datasets and Pre-processing
3.2 Comparisons with Actual CBCT-CT Data
3.3 Comparisons with Other Augmentation Methods
3.4 Ablation Studies
4 Conclusion
References
Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model
1 Introduction
2 Methods
2.1 Preliminaries: 2D Brownian Bridge Diffusion Model
2.2 Training Multi-slice 2D BBDM with Style Key Conditioning
2.3 Target Volume Sampling with Inter-Slice Trajectory Alignment
3 Experiments
3.1 Evaluations
4 Conclusion and Discussion
References
Spatial-Division Augmented Occupancy Field for Bone Shape Reconstruction from Biplanar X-Rays
1 Introduction
2 Methodology
2.1 Occupancy Field Network
2.2 Spatial-Division Augmentation
2.3 Teacher Occupancy Field Network
2.4 Spatial-Division Augmented Occupancy Field Network
3 Experiments
3.1 Experimental Settings
3.2 Results
4 Conclusion
References
SRE-CNN: A Spatiotemporal Rotation-Equivariant CNN for Cardiac Cine MR Imaging
1 Introduction
2 Method
2.1 Accelerated MRI Reconstruction Model
2.2 Spatiotemporal Rotation-Equivariant CNN Design
2.3 Filter Parametrization
3 Data and Experiments
4 Results and Discussion
5 Conclusion
References
Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis
1 Introduction
2 Method
3 Experimental Results
4 Conclusion
References
Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer
1 Introduction
2 Methodology
2.1 Light Spatial-Temporal Transformer (LiST2)
2.2 Recurrent Sliding Fine-Tuning (ReST)
3 Experiments and Results
4 Conclusions
References
TeethDreamer: 3D Teeth Reconstruction from Five Intra-Oral Photographs
1 Introduction
2 Method
2.1 Multiview Cross-Domain Diffusion Model
2.2 3D-Aware Feature Attention
2.3 Geometry-Aware Teeth Reconstruction
3 Experiments
3.1 Experimental Settings
3.2 Experimental Results
3.3 Ablation Study
4 Conclusion
References
Two Projections Suffice for Cerebral Vascular Reconstruction
1 Introduction
2 Method
2.1 Disambiguating Reconstructor
2.2 Refinement of 3D Vasculature with MAP Estimate
3 Experiments and Results
3.1 Dataset and Preprocessing
3.2 Implementation Details
3.3 Results and Discussion
4 Conclusion and Future Work
References
UnWave-Net: Unrolled Wavelet Network for Compton Tomography Image Reconstruction
1 Introduction and Related Works
2 Modelling of the Non-collimated Circular CST
3 Methodology
3.1 Related Theories
3.2 The Proposed UnWave-Net
4 Experiments
4.1 Experimental Setup
4.2 Comparison with State-of-the-Art Methods
4.3 Ablation Study
5 Conclusion
References
VolumeNeRF: CT Volume Reconstruction from a Single Projection View
1 Introduction
2 Related Works
3 Method
3.1 Prior Anatomical Knowledge Incorporation
3.2 3D Representation Generation
3.3 Volume Rendering
3.4 Overall Objective
4 Experiments and Results
4.1 Data and Settings
4.2 Results
5 Conclusion
References
Volumetric Conditional Score-Based Residual Diffusion Model for PET/MR Denoising
1 Introduction
2 Method
2.1 Score-Based Diffusion Model for Residuals
2.2 Patch-Wise Training of Volumetric Conditional Score-Based Diffusion Model
3 Experiments
3.1 Dataset Description
3.2 Implementation Details
4 Results
5 Conclusion and Discussion
References
WIA-LD2ND: Wavelet-Based Image Alignment for Self-supervised Low-Dose CT Denoising
1 Introduction
2 Method
2.1 Analysis of LDCT Denoising From Frequency Perspective
2.2 Wavelet-Based Image Alignment
2.3 Frequency-Aware Multi-scale Loss
3 Experiments
3.1 Dataset and Training Details
3.2 Experiments Results
4 Discussion and Conclusion
References
Zero-Shot Low-Field MRI Enhancement via Denoising Diffusion Driven Neural Representation
1 Introduction
2 Method
2.1 Problem Formulation
2.2 Data-Prior Sub-problem Solved by Diffusion Model
2.3 Data-Fidelity Sub-problem Solved by INR
3 Experiments
3.1 Setup
3.2 Results
4 Conclusion
References
Correction to: Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI
Author Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Medical Image Computing and Computer Assisted Intervention - MICCAI 2024

Description

More details

Other editions

Additional editions

Content

System requirements