Self-Supervised Adversarial Diffusion Model for Accelerated MRI Reconstruction (SSAD-MRI)
SSAD-MRI, a novel self-supervised deep learning model, accelerates MRI acquisition without compromising image quality by using an adversarial mapper and diffusion model, eliminating the need for fully sampled training datasets.
자가 지도 노이즈 제거를 통한 딥러닝 기반 다중 코일 MRI 재구성 향상
잡음이 있는 MRI 데이터에서 딥러닝 모델을 학습시키기 전 자가 지도 노이즈 제거를 전처리 단계로 통합하면 다양한 조건에서 재구성된 이미지의 품질과 효율성이 향상됩니다.
自己教師ありノイズ除去を用いた深層学習駆動型マルチコイルMRI再構成の強化
深層学習ベースのMRI再構成において、事前学習として自己教師ありノイズ除去を用いることで、特に低SNR条件下で再構成品質が向上する。
基於幅度圖像並利用相位增強技術訓練生成式先驗模型以重建 MRI 圖像
使用從幅度圖像中訓練的生成式先驗模型，並結合相位增強技術，可以顯著提高 MRI 圖像的重建質量，尤其是在高度欠採樣的情況下。
マグニチュード画像のみから学習したMRI再構成のための生成的な事前分布：位相拡張を用いたアプローチ
本稿では、位相情報を含まないマグニチュード画像のみを用いて、高精度かつロバストなMRI画像再構成を実現する生成的な事前分布の構築手法を提案する。
Improving MRI Reconstruction Quality with Generative Priors Trained on Magnitude-Only Images Using Phase Augmentation
Training generative priors on magnitude-only MRI images augmented with synthetic phase information significantly improves image reconstruction quality, especially in high-undersampling scenarios.
빠른 MRI 재구성을 위한 이미지 안내를 사용한 연속 K 공간 복구 네트워크
본 논문에서는 암묵적 신경 표현(INR)과 이미지 안내를 활용하여 저해상도 MRI 이미지에서 고품질의 연속적인 k-공간을 복구하는 새로운 네트워크인 IGKR-Net을 제안합니다.
Image-Guided Continuous K-Space Recovery Network for Fast MRI Reconstruction using Implicit Neural Representation
This paper proposes a novel deep learning method for fast MRI reconstruction by leveraging implicit neural representations (INR) to recover undersampled k-space data with guidance from the image domain, leading to improved image quality compared to traditional methods.
Benchmarking Non-Cartesian 3D Multi-Coil MRI Reconstruction Using NC-PDNet and Comparing Various Undersampling Patterns
Extending NC-PDNet, a deep learning model, to reconstruct 3D multi-coil MRI data acquired with non-Cartesian undersampling, particularly showcasing the superior performance of the GoLF-SPARKLING trajectory and the practicality of channel-agnostic training with coil compression.
サブスペース拡散モデルを用いたMRI再構成のための直交分解を用いたサブスペース拡散モデル
本稿では、従来の拡散モデルよりも高速かつ高精度なMRI再構成を実現する、直交分解を用いたサブスペース拡散モデル（Sub-DM）を提案する。
