Objectives: The objective was to perform ex vivo evaluation of non-Gaussian diffusion kurtosis imaging (DKI) for assessment of hepatocellular carcinoma (HCC), including presence of treatment-related necrosis, using fresh liver explants.
Methods: Twelve liver explants underwent 1.5-T magnetic resonance imaging using a DKI sequence with maximal b-value of 2000 s/mm(2). A standard monoexponential fit was used to calculate apparent diffusion coefficient (ADC), and a non-Gaussian kurtosis fit was used to calculate K, a measure of excess kurtosis of diffusion, and D, a corrected diffusion coefficient accounting for this non-Gaussian behavior. The mean value of these parameters was measured for 16 HCCs based upon histologic findings. For each metric, HCC-to-liver contrast was calculated, and coefficient of variation (CV) was computed for voxels within the lesion as an indicator of heterogeneity. A single hepatopathologist determined HCC necrosis and cellularity.
Results: The 16 HCCs demonstrated intermediate-to-substantial excess diffusional kurtosis, and mean corrected diffusion coefficient D was 23% greater than mean ADC (P=.002). HCC-to-liver contrast and CV of HCC were greater for K than ADC or D, although these differences were significant only for CV of HCCs (P≤.046). ADC, D and K all showed significant differences between non-, partially and completely necrotic HCCs (P≤.004). Among seven nonnecrotic HCCs, cellularity showed a strong inverse correlation with ADC (r=-0.80), a weaker inverse correlation with D (-0.24) and a direct correlation with K (r=0.48).
Conclusions: We observed non-Gaussian diffusion behavior for HCCs ex vivo; this DKI model may have added value in HCC characterization in comparison with a standard monoexponential model of diffusion-weighted imaging.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.mri.2012.04.020 | DOI Listing |
AJNR Am J Neuroradiol
January 2025
Department of Radiology (M.Z., N.W., S.H., X.L., H.Z., C.Y., Q.S.), The First Affiliated Hospital of Dalian Medical University, Dalian, China
Background And Purpose: DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke.
View Article and Find Full Text PDFFood Res Int
January 2025
Department of Life Sciences, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy; Interdepartmental Research Centre for the Improvement of Agro-Food Biological Resources (BIOGEST-SITEIA), University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy.
This study investigates the underexplored area of the release mechanism and kinetics of the antimicrobial Ethyl Lauroyl Arginate (LAE®) from an innovative active packaging system based on poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). We evaluated the impact of food simulants and temperatures on LAE® release, diffusion, and partition coefficients. Mathematical modeling was used to elucidate LAE® release kinetics, offering understanding of the release behaviour in food matrices.
View Article and Find Full Text PDFJ Mol Model
January 2025
School of Semiconductors and Physics, North University of China, Xueyuan Road #3, 030051, Taiyuan, China.
Context: Based on the transition state theory, a molecular diffusion model in the narrow channels of Brewsterite zeolite was established. In this model, the molecular interaction at the potential barrier was simplified to only consider the repulsive potential, so that the analytical relationship between the diffusion coefficient and the temperature and the Lennard-Jones interaction parameter was derived. We used the molecular dynamics method to simulate the diffusion of four molecules, CF, CH, Ar, and Ne, in Brewsterite zeolite and evaluated the rationality of the model.
View Article and Find Full Text PDFNeurooncol Adv
December 2024
Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
Purpose: To implement and evaluate deep learning-based methods for the classification of pediatric brain tumors (PBT) in magnetic resonance (MR) data.
Methods: A subset of the "Children's Brain Tumor Network" dataset was retrospectively used ( = 178 subjects, female = 72, male = 102, NA = 4, age range [0.01, 36.
Pediatr Nephrol
January 2025
Childhood Chronic Diseases Department, University Hospital of Nantes, 7 Quai Moncousu, 44093, Nantes, France.
Background: Severe respiratory complications following kidney transplantation have been reported, yet remain poorly understood in the pediatric population. This study aimed to document respiratory disease in this population.
Methods: At annual follow-ups, patients completed a respiratory symptoms questionnaire and underwent pulmonary function tests (PFTs).
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!