The use of NMR imaging as a quantitative research tool requires insight into the relationship between various imaging techniques and their resultant images. Work was undertaken to elucidate this relationship by using the following procedure. First, a theoretical model of NMR imaging under various pulse sequences was elaborated. Subsequently, a series of inversion recovery and saturation recovery images of a particular object slice was generated by varying the sequence parameters. Finally, pure rho, T1 and T2 images of that slice were obtained by solving the corresponding model equations. This procedure was applied to a test phantom containing tubes with suitable reference substances, including aqueous solutions of agar, manganese chloride and deuterium, and water-fat mixtures. The concentration of various samples was chosen such as to yield rho, T1 and T2 values usually encountered in clinical NMR imaging. Experiments were carried out with a prototype resistive NMR imager with a static magnetic field of 0.14 T, corresponding to a hydrogen proton resonance frequency of 5.9 MHz. For most samples a weighted non-linear regression analysis showed the theoretical model to produce an adequate parametrisation of the data at the 5% significance level, given the number of data points and the experimental accuracy. The quantitative information extracted from the NMR imaging experiments, i.e. rho, T1 and T2, appeared to be in good agreement with the results of conventional methods, including NMR spectroscopy. The clinical efficacy of the proposed methods is currently being investigated.
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http://dx.doi.org/10.1088/0031-9155/29/12/004 | DOI Listing |
Acta Radiol
January 2025
Department of Radiology, Changi General Hospital, Singapore, Republic of Singapore.
Background: Computed tomography (CT) is the gold standard imaging modality for the assessment of 3D bony morphology but incurs the cost of ionizing radiation exposure. High-resolution 3D magnetic resonance imaging (MRI) with CT-like bone contrast (CLBC) may provide an alternative to CT in allowing complete evaluation of both bony and soft tissue structures with a single MRI examination.
Purpose: To review the technical aspects of an optimized stack-of-stars 3D gradient recalled echo pulse sequence method (3D-Bone) in generating 3D MR images with CLBC, and to present a pictorial review of the utility of 3D-Bone in the clinical assessment of common musculoskeletal conditions.
J Cereb Blood Flow Metab
January 2025
A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
Zero echo time (zero-TE) pulse sequences provide a quiet and artifact-free alternative to conventional functional magnetic resonance imaging (fMRI) pulse sequences. The fast readouts (<1 ms) utilized in zero-TE fMRI produce an image contrast with negligible contributions from blood oxygenation level-dependent (BOLD) mechanisms, yet the zero-TE contrast is highly sensitive to brain function. However, the precise relationship between the zero-TE contrast and neuronal activity has not been determined.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Kolling Institute, Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia.
Aims: An explainable advanced electrocardiography (A-ECG) Heart Age gap is the difference between A-ECG Heart Age and chronological age. This gap is an estimate of accelerated cardiovascular aging expressed in years of healthy human aging, and can intuitively communicate cardiovascular risk to the general population. However, existing A-ECG Heart Age requires sinus rhythm.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
School of Mathematics and Computing, University of Southern Queensland, 487-535 West Street, Toowoomba, QLD 4350 Australia.
Purpose: This paper aims to develop a three-dimensional (3D) Alzheimer's disease (AD) prediction method, thereby bettering current predictive methods, which struggle to fully harness the potential of structural magnetic resonance imaging (sMRI) data.
Methods: Traditional convolutional neural networks encounter pressing difficulties in accurately focusing on the AD lesion structure. To address this issue, a 3D decoupling, self-attention network for AD prediction is proposed.
Eur J Obstet Gynecol Reprod Biol X
March 2025
Mother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
This review examines the emerging applications of machine learning (ML) and radiomics in the diagnosis and prediction of placenta accreta spectrum (PAS) disorders, addressing a significant challenge in obstetric care. It highlights recent advancements in ML algorithms and radiomic techniques that utilize medical imaging modalities like magnetic resonance imaging (MRI) and ultrasound for effective classification and risk stratification of PAS. The review discusses the efficacy of various deep learning models, such as nnU-Net and DenseNet-PAS, which have demonstrated superior performance over traditional diagnostic methods through high AUC scores.
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