A Novel Low-Rank Tensor Method for Undersampling Artifact Removal in Respiratory Motion-Resolved Multi-Echo 3D Cones MRI
We propose a novel low-rank tensor method for respiratory motion-resolved multi-echo image reconstruction. The key idea is to construct a 3-way image tensor (space $\times$ echo $\times$ motion state) from the conventional gridding reconstruction of highly undersampled multi-echo k-space raw data, and exploit low-rank tensor structure to separate it from undersampling artifacts. Healthy volunteers and patients with iron overload were recruited and imaged on a 3T clinical MRI system for this study. Results show that our proposed method Successfully reduced severe undersampling artifacts in respiratory motion-state resolved complex source images, as well as subsequent R2* and quantitative susceptibility mapping (QSM). Compared to conventional respiratory motion-resolved compressed sensing (CS) image reconstruction, the proposed method had a reconstruction time at least three times faster, accounting for signal evolution along the echo dimension in the multi-echo data.
Code (0)
등록된 구현이 없습니다.
Tasks
compressed sensingImage ReconstructionSimilar Papers 제목 키워드 기반
Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging
Cardiovascular diseases (CVDs) remain the leading cause of mortality and morbidity worldwide. Both diagnosis and prognosis of these diseases benefit from high-quality imaging, which cardiac magnetic resonance imaging pro…
Anatomycompressed sensingPrognosisRespiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training
Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis. However, abdominal MRI often suffers from respiratory motion a…
Deep LearningGenerative Adversarial NetworkImage Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors. Our prior is specified through a convolutional ne…
Image ReconstructionSpeech Artifact Removal from EEG Recordings of Spoken Word Production with Tensor Decomposition
Research about brain activities involving spoken word production is considerably underdeveloped because of the undiscovered characteristics of speech artifacts, which contaminate electroencephalogram (EEG) signals and pr…
blind source separationEEGElectroencephalogram (EEG)Tensor DecompositionDeep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI
Real-time (RT) dynamic MRI plays a vital role in capturing rapid physiological processes, offering unique insights into organ motion and function. Among these applications, RT cine MRI is particularly important for funct…
Diagnostic