Purpose: To investigate the relationship between the k-space sampling patterns used for compressed sensing MR spectroscopic imaging (CS-MRSI) and the modulation transfer function (MTF) of the metabolite maps. This relationship may allow the desired frequency content of the metabolite maps to be quantitatively tailored when designing an undersampling pattern.
Methods: Simulations of a phantom were used to calculate the MTF of Nyquist sampled (NS) 32 × 32 MRSI, and four-times undersampled CS-MRSI reconstructions. The dependence of the CS-MTF on the k-space sampling pattern was evaluated for three sets of k-space sampling patterns generated using different probability distribution functions (PDFs). CS-MTFs were also evaluated for three more sets of patterns generated using a modified algorithm where the sampling ratios are constrained to adhere to PDFs.
Results: Strong visual correlation as well as high R was found between the MTF of CS-MRSI and the product of the frequency-dependant sampling ratio and the NS 32 × 32 MTF. Also, PDF-constrained sampling patterns led to higher reproducibility of the CS-MTF, and stronger correlations to the above-mentioned product.
Conclusions: The relationship established in this work provides the user with a theoretical solution for the MTF of CS MRSI that is both predictable and customizable to the user's needs.
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http://dx.doi.org/10.1118/1.4962930 | DOI Listing |
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.
View Article and Find Full Text PDFNMR Biomed
February 2025
High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Deuterium metabolic imaging (DMI) is an emerging Magnetic Resonance technique providing valuable insight into the dynamics of cellular glucose (Glc) metabolism of the human brain in vivo using deuterium-labeled (H) glucose as non-invasive tracer. Reliable concentration estimation of H-Glc and downstream synthesized neurotransmitters glutamate + glutamine (Glx) requires accurate knowledge of relaxation times, but so far tissue-specific T and T relaxation times (e.g.
View Article and Find Full Text PDFQuant Imaging Med Surg
December 2024
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background: Segmented cine imaging using a balanced steady-state free precession sequence is the gold standard for accurately quantifying cardiac function and myocardial mass. However, this method suffers from inefficient K-space sampling, resulting in long scan times, and requires multiple breath holds that can be difficult for some patients. Real-time compressed sensing (CS) cine reduces image acquisition time through K-space undersampling and iterative reconstruction, enabling rapid magnetic resonance (MR) imaging.
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