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Found 32 papers, 6 papers with code
SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image Enhancement
To address this, we present SinoSynth, a physics-based degradation model that simulates various CBCT-specific artifacts to generate a diverse set of synthetic CBCT images from high-quality CT images without requiring pre-aligned data.
Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion
It comprises a generalizable 3D autoencoder that encodes and decodes MRIs through a 4D latent space, and a conditional latent diffusion model that learns the latent distribution and generates harmonized MRIs with anatomical information from source MRIs while conditioned on target image style.
Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model
The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown.
Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework
In this paper, we design a novel disentangled latent energy-based style translation (DLEST) framework for unpaired image-level MRI harmonization, consisting of (a) site-invariant image generation (SIG), (b) site-specific style translation (SST), and (c) site-specific MRI synthesis (SMS).
Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization
Their success is widely attributed to implicit regularization due to the spectral bias of suitable network architectures.
Reconstruction of Cortical Surfaces with Spherical Topology from Infant Brain MRI via Recurrent Deformation Learning
Moreover, this mapping is possible only if the topology of the surface mesh is homotopic to a sphere.
Federated Learning for Medical Image Analysis: A Survey
In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis.
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior
Existing methods mostly handcraft or search for the architecture from a large design space, due to the lack of understanding on how the architectural choice corresponds to the image.
Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning
In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model.
Source-Free Unsupervised Domain Adaptation: A Survey
Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden.
Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network
In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies.
Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.
Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline with Structural MRI
Meanwhile, the brain disease related regions can be highlighted by the attention mechanism.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Deformable Registration of Brain MR Images via a Hybrid Loss
Unsupervised learning strategy is widely adopted by the deformable registration models due to the lack of ground truth of deformation fields.
Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images
To deal with the large shape variation and unclear boundary of OARs in CT images, we represent the organ shape using an organ-specific unilateral inverse-distance map (UIDM) and guide the segmentation task from two different perspectives: direct shape guidance by following the segmentation prediction and across shape guidance by sharing the segmentation feature.
Learning MRI Artifact Removal With Unpaired Data
Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability.
SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection
We propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models.
Learning Multi-Site Harmonization of Magnetic Resonance Images Without Traveling Human Phantoms
Harmonization improves data consistency and is central to effective integration of diverse imaging data acquired across multiple sites.
A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning
In the training stage, the simulator maps jaw deformities of a patient bone to a normal bone to generate a simulated deformed bone.
Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting
Magnetic resonance Fingerprinting (MRF) is a relatively new multi-parametric quantitative imaging method that involves a two-step process: (i) reconstructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e. g., T1 and T2 relaxation times).
Navigator-Free Submillimeter Diffusion Imaging using Multishot-encoded Simultaneous Multi-slice (MUSIUM)
In this work, we developed a navigator-free multishot-encoded simultaneous multi-slice (MUSIUM) imaging approach on a 3T MR scanner, achieving enhanced SNR, low RF power and peak amplitude, and being free from slab boundary artifacts.
Medical Physics Image and Video Processing
Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces
Charting cortical growth trajectories is of paramount importance for understanding brain development.
An Auto-Context Deformable Registration Network for Infant Brain MRI
Deformable image registration is fundamental to longitudinal and population analysis.
Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)
Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways.
Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context
Next, a leaf cluster is used to generate one of the multiple kernels, and two corresponding predecessor clusters are used to fine-tune the adopted kernel.
Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors
Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.
DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks
GCNNs are capable of extracting the geometric features of each fiber tract and harnessing the resulting features for accurate fiber parcellation and ultimately avoiding the use of atlases and any registration method.
Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks
In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images.
Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning Based Registration
The registration network is trained with feedback from the discrimination network, which is designed to judge whether a pair of registered images are sufficiently similar.
BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks
In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance.
Regularized Spherical Polar Fourier Diffusion MRI with Optimal Dictionary Learning
Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., espectively, our work offers the following advantages.
