Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/s1076-6332(03)80454-3 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Combining Hyperpolarized Xe MR Imaging and Spectroscopy to Non-Invasively Estimate Pulmonary Vascular Resistance.
J Appl Physiol (1985)
January 2025
Medical Physics Graduate Program, Duke University, Durham, North Carolina, United States.
Hyperpolarized Xe MRI/MRS enables quantitative mapping of function in lung airspaces, membrane tissue, and red blood cells (RBCs) within the pulmonary capillaries. The RBC signal also exhibits cardiogenic oscillations that are reduced in pre-capillary pulmonary hypertension (PH). This effect is obscured in patients with concomitant defects in transfer from airspaces to RBCs, which increase RBC oscillation amplitudes.
View Article and Find Full Text PDFRF Heating Effects in CEST NMR with Hyperpolarized 129Xe Considering Different Spin Exchange Kinetics and Saturation Schemes.
Chemphyschem
January 2025
Deutsches Krebsforschungszentrum, Translational Molecular Imaging, Im Neuenheimer Feld 223, 69120, Heidelberg, GERMANY.
Chemical exchange saturation transfer (CEST) improves the sensitivity of NMR but depending on the spin exchange kinetics, it can require substantial RF energy deposition to label magnetization. Potential side effects like RF-induced heating may occur and must be monitored. Here, we explore the parameter space considering not only undesired heating but efficient CEST build-up (depolarization rate), spectral resolution (line width), and subsequent effects like changes in chemical shifts of CEST responses must be considered, too.
View Article and Find Full Text PDFUltrasensitive Xe Magnetic Resonance Imaging: From Clinical Monitoring to Molecular Sensing.
Adv Sci (Weinh)
January 2025
Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430071, China.
Magnetic resonance imaging (MRI) is a cornerstone technology in clinical diagnostics and in vivo research, offering unparalleled visualization capabilities. Despite significant advancements in the past century, traditional H MRI still faces sensitivity limitations that hinder its further development. To overcome this challenge, hyperpolarization methods have been introduced, disrupting the thermal equilibrium of nuclear spins and leading to an increased proportion of hyperpolarized spins, thereby enhancing sensitivity by hundreds to tens of thousands of times.
View Article and Find Full Text PDFQuantifying spatial and dynamic lung abnormalities with 3D PREFUL FLORET UTE imaging: A feasibility study.
Magn Reson Med
January 2025
Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany.
Purpose: Pulmonary MRI faces challenges due to low proton density, rapid transverse magnetization decay, and cardiac and respiratory motion. The fermat-looped orthogonally encoded trajectories (FLORET) sequence addresses these issues with high sampling efficiency, strong signal, and motion robustness, but has not yet been applied to phase-resolved functional lung (PREFUL) MRI-a contrast-free method for assessing pulmonary ventilation during free breathing. This study aims to develop a reconstruction pipeline for FLORET UTE, enhancing spatial resolution for three-dimensional (3D) PREFUL ventilation analysis.
View Article and Find Full Text PDFMagnetic resonance image denoising for Rician noise using a novel hybrid transformer-CNN network (HTC-net) and self-supervised pretraining.
Med Phys
December 2024
Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
Background: Magnetic resonance imaging (MRI) is a crucial technique for both scientific research and clinical diagnosis. However, noise generated during MR data acquisition degrades image quality, particularly in hyperpolarized (HP) gas MRI. While deep learning (DL) methods have shown promise for MR image denoising, most of them fail to adequately utilize the long-range information which is important to improve denoising performance.
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!