Purpose: Golden ratio (GR) radial reordering allows for retrospective choice of temporal resolution by providing a near-uniform k-space sampling within any reconstruction window. However, when applying GR to electrocardiogram (ECG)-gated cardiac imaging, the k-space coverage may not be as uniform because a single reconstruction window is broken into several temporally isolated ones. The goal of this study was to investigate the image artifacts caused by applying GR to ECG-gated cardiac imaging and to propose a segmented GR method to address this issue.
Methods: Computer simulation and phantom experiments were used to evaluate the image artifacts resulting from three k-space sampling patterns (ie, uniform radial, conventional GR, and segmented GR). Two- and three-dimensional cardiac cine images were acquired in seven healthy subjects. Imaging artifacts due to k-space sampling nonuniformity were graded on a 5-point scale by an experienced cardiac imaging reader.
Results: Segmented GR provides more uniform k-space sampling that is independent of heart-rate variation than conventional GR. Cardiac cine images using segmented GR have significantly higher and more reliable image quality than conventional GR.
Conclusion: Segmented GR successfully addresses the nonuniform sampling that occurs with combining conventional GR with ECG gating. This technique can potentially be applied to any ECG-gated cardiac imaging application to allow for retrospective selection of a reconstruction window. Magn Reson Med 76:94-103, 2016. © 2015 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/mrm.25861 | DOI Listing |
Magn Reson Med
January 2025
Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
Purpose: To develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI.
Methods: A neural network was designed to correct motion artifacts directly from a Z-spectrum frequency (Ω) domain rather than an image spatial domain. Motion artifacts were simulated by modeling 3D rigid-body motion and readout-related motion during k-space sampling.
Magn Reson Med
January 2025
Department of Radiology, Stanford University, Stanford, California, USA.
Purpose: To provide a fast quantitative imaging approach for a 0.55T scanner, where signal-to-noise ratio is limited by the field strength and k-space sampling speed is limited by a lower specification gradient system.
Methods: We adapted the three-dimensional spiral projection imaging MR fingerprinting approach to 0.
PLoS One
January 2025
Medical Image Processing Research Group (MIPRG), Dept. of Elect. & Comp. Engineering, COMSATS University Islamabad, Islamabad, Pakistan.
Recovering diagnostic-quality cardiac MR images from highly under-sampled data is a current research focus, particularly in addressing cardiac and respiratory motion. Techniques such as Compressed Sensing (CS) and Parallel Imaging (pMRI) have been proposed to accelerate MRI data acquisition and improve image quality. However, these methods have limitations in high spatial-resolution applications, often resulting in blurring or residual artifacts.
View Article and Find Full Text PDFNMR Biomed
February 2025
Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Susceptibility-weighted imaging (SWI) has been widely used in clinical contexts, in which the speed of acquisition is frequently a critical issue. In this study, we aim to test the feasibility of a deep learning (DL)-based reconstruction method for accelerating SWI acquisition in clinical settings. A total of 61 subjects were consecutively enrolled.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Medical Biophysics, University of Toronto, Toronto, ON M4N 3M5, Canada.
Most existing methods for magnetic resonance imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a fully sampled dataset with a high signal-to-noise ratio (SNR) is available for training. In many circumstances, however, such a dataset is highly impractical or even technically infeasible to acquire. Recently, a number of self-supervised methods for MRI reconstruction have been proposed, which use sub-sampled data only.
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