Search Results for author:
Found 15 papers, 5 papers with code
Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures
It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data. The synthetic images, especially for TI values between 400-800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei.
ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement
Super-resolution (SR) methods aim to address this problem, but previous methods do not address all of the following: slice profile shape estimation, slice gap, domain shift, and non-integer or arbitrary upsampling factors.
RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs
In this work, we introduce RATNUS, which uses synthetic T1-weighted images with many inversion times along with diffusion-derived features to enhance the visibility of nuclei within the thalamus.
Beyond MR Image Harmonization: Resolution Matters Too
In this paper, we evaluate the impact of image resolution on harmonization using a pretrained harmonization algorithm.
Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement
These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution
Our findings contribute valuable insights to the application of DDPMs for SR of anisotropic MR images.
Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation.
A latent space for unsupervised MR image quality control via artifact assessment
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.
HACA3: A Unified Approach for Multi-site MR Image Harmonization
Furthermore, HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
Information-based Disentangled Representation Learning for Unsupervised MR Harmonization
Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis.
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images.
Self domain adapted network
However, training a model for each target domain is time consuming and computationally expensive, even infeasible when target domain data are scarce or source data are unavailable due to data privacy.
Evaluating the Impact of Intensity Normalization on MR Image Synthesis
Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image.
Self Super-Resolution for Magnetic Resonance Images using Deep Networks
This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image.
