AI Article Synopsis
- Shear modulus estimation can be complicated by the challenges of the inverse elasticity problem, affecting both statistical accuracy and image quality.
- Experiments on simulated and gelatin phantoms revealed that the spatial resolution of magnetic resonance elastography (MRE) depends on regularization and spatial filtering methods.
- Increased regularization and spatial filtering were found to enhance the elastographic contrast-to-noise ratio (CNR) and improve the statistical accuracy of the shear modulus estimation, while subdomain size and overlap did not significantly impact results.
Article Abstract
Shear modulus estimation can be confounded by the ill-posed nature of the inverse elasticity problem. In this paper, we report the results of experiments conducted on simulated and gelatin phantoms to investigate the effect of various parameters (i.e., regularization, spatial filtering and the subzone generation process) associated with shear modulus reconstruction on the statistical accuracy (mean squared error), and image quality (i.e., contrast and spatial resolution) of the recovered mechanical properties. The results indicate several interesting observations. Firstly, the intrinsic spatial resolution of magnetic resonance elastography (MRE) is dependent on both regularization and spatial filtering. Secondly, the elastographic contrast-to-noise ratio (CNR(e)) increases with increasing regularization and spatial filtering, but it was not affected by the zoning parameters (i.e., the subzones and the extent of the overlap). Thirdly, the statistical accuracy (MSE) of the recovered property improved with increasing regularization, and spatial filtering weight, but the size of the subdomains and their overlap had no significant effect.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1088/0031-9155/52/10/002 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data.
J Headache Pain
January 2025
Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
View Article and Find Full Text PDFJoint k-b space diffusion-weighted image reconstruction and apparent diffusion coefficient fitting for diffusion-weighted turbo-spin-echo imaging.
Med Phys
January 2025
Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Background: Diffusion-weighted (DW) turbo-spin-echo (TSE) imaging offers improved geometric fidelity compared to single-shot echo-planar-imaging (EPI). However, it suffers from low signal-to-noise ratio (SNR) and prolonged acquisition times, thereby restricting its applications in diagnosis and MRI-guided radiotherapy (MRgRT).
Purpose: To develop a joint k-b space reconstruction algorithm for concurrent reconstruction of DW-TSE images and the apparent diffusion coefficient (ADC) map with enhanced image quality and more accurate quantitative measurements.
Deep proximal gradient network for absorption coefficient recovery in photoacoustic tomography.
Phys Med Biol
January 2025
North China Electric Power University - Baoding Campus, North China Electric Power University, Baoding, Hebei Province, P.R.China, Baoding, Hebei, 071003, CHINA.
Objective: The optical absorption properties of biological tissues in photoacoustic tomography are typically quantified by inverting acoustic measurements. Conventional approaches to solving the inverse problem of forward optical models often involve iterative optimization. However, these methods are hindered by several challenges, including high computational demands, the need for regularization, and sensitivity to both the accuracy of the forward model and the completeness of the measurement data.
View Article and Find Full Text PDFMultivariate sleep health and cognitive functions in healthy older adults: results from partial least square correlation analysis.
Alzheimers Dement
December 2024
National University of Singapore, Singapore, Singapore, Singapore
Background: Past studies examining sleep‐cognition relationships mostly employed univariate approaches, which are subject to problems such as multicollinearity and multiple comparisons. Further, results from small sample univariate analyses are difficult to compare, precluding the identification of the aspects of sleep health associated with a particular cognitive domain(s). The current study used a multivariate approach to identify key sleep metrics and cognitive domains that contribute to the maximum sleep‐cognition covariance in healthy older adults.
View Article and Find Full Text PDFInterference from a salient distractor is typically reduced when the appearance of the distractor follows either spatial or feature-based regularities. Although there is a growing body of literature on distractor location learning, the understanding of distractor feature learning remains limited. In the current study, we investigated distractor feature learning by using EEG measures.
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!