Unilateral NMR devices are used in various applications including non-destructive testing and well logging, but are not used routinely for imaging. This is mainly due to the inhomogeneous magnetic field (B0) in these scanners. This inhomogeneity results in low sensitivity and further forces the use of the slow single point imaging scan scheme. Improving the measurement sensitivity is therefore an important factor as it can improve image quality and reduce imaging times. Short imaging times can facilitate the use of this affordable and portable technology for various imaging applications. This work presents a statistical signal-processing method, designed to fit the unique characteristics of imaging with a unilateral device. The method improves the imaging capabilities by improving the extraction of image information from the noisy data. This is done by the use of redundancy in the acquired MR signal and by the use of the noise characteristics. Both types of data were incorporated into a Weighted Least Squares estimation approach. The method performance was evaluated with a series of imaging acquisitions applied on phantoms. Images were extracted from each measurement with the proposed method and were compared to the conventional image reconstruction. All measurements showed a significant improvement in image quality based on the MSE criterion - with respect to gold standard reference images. An integration of this method with further improvements may lead to a prominent reduction in imaging times aiding the use of such scanners in imaging application.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jmr.2013.09.016 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K-means clustering.
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Correlations between postmortem quantitative MRI parameters and demyelination, axonal loss and gliosis in multiple sclerosis: A systematic review and meta-analysis.
Brain Imaging Behav
January 2025
Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS.
View Article and Find Full Text PDFSystematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.
J Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFDiagnostic Accuracy of Artificial Intelligence for Detection of Rib Fracture on X-ray and Computed Tomography Imaging: A Systematic Review.
J Imaging Inform Med
January 2025
Department of Orthopedic Surgery, Arrowhead Regional Medical Center, Colton, CA, USA.
Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload and serve as a tool to improve accurate diagnosis. To date, no reviews have been performed synthesizing identification of rib fracture data on AI and its diagnostic performance on X-ray and CT scans of rib fractures and its comparison to physicians.
View Article and Find Full Text PDFDesign and Validation of a High-Fidelity Left Atrial Cardiac Simulator for the Study and Advancement of Left Atrial Appendage Occlusion.
Cardiovasc Eng Technol
January 2025
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, MA, Cambridge, USA.
Purpose: Atrial fibrillation (AF) is the most common chronic cardiac arrhythmia that increases the risk of stroke, primarily due to thrombus formation in the left atrial appendage (LAA). Left atrial appendage occlusion (LAAO) devices offer an alternative to oral anticoagulation for stroke prevention. However, the complex and variable anatomy of the LAA presents significant challenges to device design and deployment.
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!