Background: Advanced diffusion-based MRI biomarkers may provide insight into microstructural and perfusion changes associated with neurodegeneration and cognitive decline.
Purpose: To assess longitudinal microstructural and perfusion changes using apparent diffusion coefficient (ADC) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in cognitively impaired (CI) and healthy control (HC) groups.
Study Type: Prospective/longitudinal.
Population: Twelve CI patients (75% female) and 13 HC subjects (69% female).
Field Strength/sequence: 3 T; Spin-Echo-IVIM-DWI.
Assessment: Two MRI scans were performed with a 12-month interval. ADC and IVIM-DWI metrics (diffusion coefficient [D] and perfusion fraction [f]) were generated from monoexponential and biexponential fits, respectively. Additionally, voxel-based correlations were evaluated between change in Montreal Cognitive Assessment (ΔMoCA) and baseline imaging parameters.
Statistical Tests: Analysis of covariance with sex and age as covariates was performed for main effects of group and time (false discovery rate [FDR] corrected) with post hoc comparisons using Bonferroni correction. Partial-η and Hedges' g were used for effect-size analysis. Spearman's correlations (FDR corrected) were used for the relationship between ΔMoCA score and imaging. P < 0.05 was considered statistically significant.
Results: Significant differences were found for the main effects of group (HC vs. CI) and time. For group effects, higher ADC, IVIM-D, and IVIM-f were observed in the CI group compared to HC (ADC: 1.23 ± 0.08 10 vs. 1.09 ± 0.07 10 mm /sec; IVIM-D: 0.82 ± 0.01 10 vs. 0.73 ± 0.01 10 mm /sec; and IVIM-f: 0.317 ± 0.008 vs. 0.253 ± 0.009). Significantly higher ADC, IVIM-D, and IVIM-f values were observed in the CI group after 12 months (ADC: 1.45 ± 0.05 10 vs. 1.50 ± 0.07 10 mm /sec; IVIM-D: 0.87 ± 0.01 10 vs. 0.94 ± 0.02 10 mm /sec; and IVIM-f: 0.303 ± 0.007 vs. 0.332 ± 0.008), but not in the HC group at large effect size. ADC, IVIM-D, and IVIM-f negatively correlated with ΔMoCA score (ρ = -0.49, -0.51, and -0.50, respectively).
Data Conclusion: These findings demonstrate that longitudinal differences between CI and HC cohorts can be measured using IVIM-based metrics.
Level Of Evidence: 2 TECHNICAL EFFICACY STAGE: 2.
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http://dx.doi.org/10.1002/jmri.28172 | DOI Listing |
AJNR Am J Neuroradiol
December 2024
From the Department of Diagnostic Medicine, Dell Medical School at The University of Texas at Austin, Austin, TX, USA (C.Y.H.), Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA (N.S., G.A., Q.W., P.C., M.A., J.G.P., B.R.G., P.R.T., G.D.H.), Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA (E.C., P.R.T., S.A.P.), Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA (P.R.T., S.A.P.), and the Department of Radiology at Texas Children's Hospital, Houston, TX, USA (S.F.K.).
Background And Purpose: There are multiple MRI perfusion techniques, with limited available literature comparing these techniques in the grading of pediatric brain tumors. For efficiency and limiting scan time, ideally only one MRI perfusion technique can be used in initial imaging. We compared DSC, DCE, and IVIM along with ADC from DWI for differentiating high versus low grade pediatric brain tumors.
View Article and Find Full Text PDFBMC Med Imaging
December 2024
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Background: Diffusion-weighted imaging (DWI) can be used for quantitative tumor assessment. DWI with different models may show different aspects of tissue characteristics.
Objective: To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, stretched exponential magnetic resonance diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant solitary pulmonary lesions (SPLs).
Med Phys
December 2024
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian, China.
Background: Due to the low signal-to-noise ratio (SNR) and the limited number of b-values, precise parameter estimation of intravoxel incoherent motion (IVIM) imaging remains an open issue to date, especially for brain imaging where the relatively small difference between D and D easily leads to outliers and obvious graininess in estimated results.
Purpose: To propose a synthetic data driven supervised learning method (SDD-IVIM) for improving precision and noise robustness in IVIM parameter estimation without relying on real-world data for neural network training.
Methods: On account of the absence of standard IVIM parametric maps from real-world data, a novel model-based method for generating synthetic human brain IVIM data was introduced.
Eur Radiol
December 2024
Department of Radiology, Qilu Hospital, Shandong University, Jinan, China.
Objectives: To analyze the performance of multiparametric magnetic resonance imaging (MRI) in quantification of pancreatic ductal adenocarcinoma (PDAC) fibrosis grading.
Method: This prospective study enrolled 79 patients with PDAC confirmed by pathology. Multiparametric MRI including native T1 mapping, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), diffusion kurtosis imaging diffusion-weighted imaging (DKI-DWI), and enhanced T1 mapping were performed before surgery.
Quant Imaging Med Surg
December 2024
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Traditional echo-planar imaging with intravoxel incoherent motion (EPI-IVIM) exhibits significant magnetic susceptibility artifacts and geometric distortions, which limits its application in nasopharyngeal carcinoma (NPC). This study aimed to compare the qualitative and quantitative indicators between turbo spin echo with intravoxel incoherent motion (TSE-IVIM) and EPI-IVIM in patients with NPC and to provide optimal scanning strategies based on the relationships among these indicators.
Methods: A cross-sectional study was conducted between March 2022 and August 2022.
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