Purpose: To compare three k-space sampling schemes in cine True-FISP cardiac magnetic resonance imaging and to evaluate changes in calculated quantitative functional cardiac parameters as a function of underlying k-space sampling techniques.
Material And Methods: Using a 1.5 T MR imaging system (Magnetom Sonata, Siemens Medical Solutions, Erlangen, Germany), three k-space data-sampling schemes: rectilinear (2.96 ms/1.58 ms/70 degrees /12 s TR/TE/FA/AcquisitionTime), and two radial k-space acquisitions, with filtered back-projection (RADIAL) (2.45 ms/1.25 ms/ 50 degrees /3.3 s TR/TE/FA/AT), and steady-state projection imaging with dynamic echotrain readout (SPIDER) (3.39 ms/1.62 ms/55 degrees /1.8 s TR/TE/FA/AT) of a True-FISP sequence were applied in 10 healthy volunteers. Long- and short-axis breath-hold series were acquired and signal-to-noise ratios (SNR) for blood and myocardium were determined, as was contrast-to-noise ratios (CNR). Quantitative cardiac functional analysis included: determination of end-systolic/end-diastolic volumes, ejection fraction, and left ventricular mass. Functional analysis was performed by two independent readers three times for each volunteer and k-space sampling strategy. Statistical analysis evaluated the accuracy of the measurements obtained from each of the three sampling techniques and the intra- and interobserver reliability.
Results: Intraobserver and interobserver reliability measures of functional data were homogeneous without statistically significant differences. Intraobserver correlation coefficients ranged from 0.94-0.99; interobserver correlation coefficients ranged from 0.97-0.99. Direct comparison of SPIDER- and RADIAL-sampled True-FISP sequences showed no statistically significant differences in measured functional parameters with interstudy correlation coefficients from 0.88-0.98. RADIAL and SPIDER images had better temporal resolution and were qualitatively judged to provide superior wall/blood border definition. Statistically significant differences were identified in each volumetric functional parameter when results from the rectilinear sampling acquisitions were compared with either radial or SPIDER sampling techniques. RADIAL and SPIDER results were consistently higher than volumetric measures obtained from the rectilinear data set.
Conclusion: Employing faster sampling schemes led to enhanced signal homogeneity while maintaining the necessary CNR for estimation of functional cardiac parameters. Enhanced signal homogeneity and maintained CNR will most likely improve the accuracy of the cardiac functional parameter determination.
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http://dx.doi.org/10.1081/jcmr-200036124 | 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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