Echo-planar imaging (EPI), which is the main workhorse of functional MRI, suffers from field inhomogeneity-induced geometric distortions. The amount of distortion is proportional to the readout duration, which restricts the maximum achievable spatial resolution. The spatially varying nature of the decay makes it challenging for EPI schemes with a single echo time to obtain good sensitivity to functional activations in different brain regions. Despite the use of parallel MRI and multislice acceleration, the number of different echo times that can be acquired in a reasonable TR is limited. The main focus of this work is to introduce a rosette-based acquisition scheme and a structured low-rank reconstruction algorithm to overcome the above challenges. The proposed scheme exploits the exponential structure of the time series to recover distortion-free images from several echoes simultaneously.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8526283 | PMC |
| http://dx.doi.org/10.1109/isbi45749.2020.9098418 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A noise-robust post-processing pipeline for accelerated phase-cycled Na Multi-Quantum Coherences MRI.
Z Med Phys
January 2025
Aix-Marseille Univ, CNRS, CRMBM, Marseille, France; APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Purpose: To develop an improved post-processing pipeline for noise-robust accelerated phase-cycled Cartesian Single (SQ) and Triple Quantum (TQ) sodium (Na) Magnetic Resonance Imaging (MRI) of in vivo human brain at 7 T.
Theory And Methods: Our pipeline aims to tackle the challenges of Na Multi-Quantum Coherences (MQC) MRI including low Signal-to-Noise Ratio (SNR) and time-consuming Radiofrequency (RF) phase-cycling. Our method combines low-rank k-space denoising for SNR enhancement with Dynamic Mode Decomposition (DMD) to robustly separate SQ and TQ signal components.
Enhancing photon-counting computed tomography reconstruction via subspace dictionary learning and spatial sparsity regularization.
Quant Imaging Med Surg
January 2025
Henan Key Laboratory of Imaging and Intelligent Processing, Information Engineering University, Zhengzhou, China.
Background: Photon-counting computed tomography (CT) is an advanced imaging technique that enables multi-energy imaging from a single scan. However, the limited photon count assigned to narrow energy bins leads to increased quantum noise in the reconstructed spectral images. To address this issue, leveraging the prior information in the spectral images is essential.
View Article and Find Full Text PDFThe Dyson equalizer: adaptive noise stabilization for low-rank signal detection and recovery.
Inf inference
March 2025
Program in Applied Mathematics, Yale University, New Haven, CT 06520, US.
Detecting and recovering a low-rank signal in a noisy data matrix is a fundamental task in data analysis. Typically, this task is addressed by inspecting and manipulating the spectrum of the observed data, e.g.
View Article and Find Full Text PDFDual-Grid and Mixed-Precision Methods for Accelerating Plane-Wave Hybrid Functional Electronic Structure Calculations.
J Chem Theory Comput
January 2025
Key Laboratory of Precision and Intelligent Chemistry, and Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Hybrid functionals that incorporate exact Hartree-Fock exchange (HFX) into density functional theory (DFT) are crucial for accurately predicting the electronic structures of extended systems in condensed-matter physics and materials science. Although the exact exchange contributes only a small fraction of the total energy, HFX calculations in hybrid functionals demand significant computational resources. Here, we introduce dual-grid and mixed-precision techniques, based on two low-rank approximations, adaptively compressed exchange (ACE) and interpolative separable density fitting (ISDF) methods, to significantly improve the computational efficiency of plane-wave hybrid functional calculations in the software package PWDFT (plane wave density functional theory).
View Article and Find Full Text PDFAttention incorporated network for sharing low-rank, image and k-space information during MR image reconstruction to achieve single breath-hold cardiac Cine imaging.
Comput Med Imaging Graph
December 2024
Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany.
Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accelerate imaging and enhance reconstruction quality. Existing networks exhibit some common limitations that constrain further acceleration possibilities, including single-domain learning, reliance on a single regularization term, and equal feature contribution.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!