Journal: Frontiers in Endocrinology

Article Title: The value of functional magnetic resonance imaging in the evaluation of diabetic kidney disease: a systematic review and meta-analysis

doi: 10.3389/fendo.2023.1226830

Figure Lengend Snippet: Characteristics of the included studies.

Article Snippet: Deng 2018 ( ) , 1.5T , Siemens , IVIM , 3 renal parenchymal sites of interest in each of the upper, middle and lower parts, avoiding the renal sinuses , 19 , 12 , 0 , T2DM , 52.3 , 50.2 , CKD-EPI.

Techniques:

Journal: Journal of Cerebral Blood Flow & Metabolism

Article Title: A two-pool model to describe the IVIM cerebral perfusion

doi: 10.1177/0271678X16681310

Figure Lengend Snippet: Examples of IVIM data fitted with the bi-, mono-exponential and Kennan models. Typical IVIM signal, for one animal, resulting from the subtraction of the diffusion component from the total MRI signal versus b-value for Δ = 24 ms. The black circles represent the experimental data. The black, blue, and red lines correspond to the three fitting models, the bi-, mono-exponential and Kennan models, respectively. The ROI masks are given in the upper right corners: (a, b) LC, (c, d) LT. The curves were fit to the data with b-values ranging from 0 to 500 s/mm2. Error bars represent SD. Scale bar: 5 mm.

Article Snippet: IVIM MRI signals coming from microvascular networks were numerically simulated using MATLAB.

Techniques: Diffusion-based Assay

Journal: Journal of Cerebral Blood Flow & Metabolism

Article Title: A two-pool model to describe the IVIM cerebral perfusion

doi: 10.1177/0271678X16681310

Figure Lengend Snippet: Changes in the difference in AIC c and the IVIM parameters with the different diffusion times and ROIs.

Article Snippet: IVIM MRI signals coming from microvascular networks were numerically simulated using MATLAB.

Techniques: Diffusion-based Assay, Comparison

Journal: Journal of Cerebral Blood Flow & Metabolism

Article Title: A two-pool model to describe the IVIM cerebral perfusion

doi: 10.1177/0271678X16681310

Figure Lengend Snippet: Simulated IVIM signals at b = 50 s/mm2 against the diffusion time for the slow and fast pools. Simulated IVIM signals for the slow and fast pools at b = 50 s/mm2 weighted by the blood volume fractions fslow = 23% and ffast = 77% against the diffusion time for vslow = 1 mm/s, Lslow = 40 µm, vfast = 5 mm/s and Lfast = 150 µm. The red and blue circles represent the slow and fast pools, respectively. The filled circles correspond to the diffusion times acquired in this study, for Δ = 14, 24 and 34 ms, and the empty circles to two extreme diffusion times, for Δ = 3 and 60 ms.

Article Snippet: IVIM MRI signals coming from microvascular networks were numerically simulated using MATLAB.

Techniques: Diffusion-based Assay

Journal: Journal of Cerebral Blood Flow & Metabolism

Article Title: A two-pool model to describe the IVIM cerebral perfusion

doi: 10.1177/0271678X16681310

Figure Lengend Snippet: Changes in the IVIM parameters with TR.

Article Snippet: IVIM MRI signals coming from microvascular networks were numerically simulated using MATLAB.

Techniques:

Journal: Journal of Cerebral Blood Flow & Metabolism

Article Title: A two-pool model to describe the IVIM cerebral perfusion

doi: 10.1177/0271678X16681310

Figure Lengend Snippet: Contour plots of the 10% errors isolines for different vessel lengths and blood flow velocities and examples of IVIM signals superimposed with simulated signals. (a) 10% ɛ isolines for different combinations of vessel lengths and blood flow velocities. The red curves correspond to the slow pool for vfast = 4 mm/s, Lfast = 50 µm and the blue curves to the fast pool for vslow = 1 mm/s, Lslow = 40 µm. (b, c) Examples of IVIM signal superimposed with simulated signals versus b-value for Δ = 24 ms in the LC. The black circles stand for the experimental data and the blue solid lines for the simulated signals with the same vslow = 1 mm/s and Lslow = 40 µm but with two different combinations of vfast and Lfast, vfast = 3 mm/s, Lfast = 70 µm, ɛ = 6.50% and vfast = 3 mm/s, Lfast = 150 µm, ɛ = 6.38%, respectively. Error bars represent SD from the averaging step.

Article Snippet: IVIM MRI signals coming from microvascular networks were numerically simulated using MATLAB.

Techniques:

Journal: World Journal of Gastroenterology

Article Title: Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score

doi: 10.3748/wjg.v30.i4.381

Figure Lengend Snippet: Characteristics of included studies

Article Snippet: Li et al [ ], 2018 , P , PPF , MVI , 41 (HCC) , IVIM-DWI , M; 3D , MATLAB , 10 (histogram) , Univariate analysis , ROC , No , AUC = 0.87 , Histogram analysis of IVIM based on whole tumor volume can be useful for predicting MVI. The 5 th percentile of D was most useful value to predict MVI of HCC.

Techniques: Software, Expressing, Imaging, Biomarker Discovery, Functional Assay, Construct, Clinical Proteomics, Derivative Assay, Mutagenesis, Mann-Whitney U-Test, Marker, Selection, Sequencing, Diffusion-based Assay, Diagnostic Assay, Transformation Assay

Journal: Acta Obstetricia et Gynecologica Scandinavica

Article Title: Effect of Mediterranean diet or mindfulness‐based stress reduction during pregnancy on placental volume and perfusion: A subanalysis of the IMPACT BCN randomized clinical trial

doi: 10.1111/aogs.14874

Figure Lengend Snippet: Placental volume and intravoxel incoherent motion (IVIM) results using magnetic resonance (MR) according to the intervention groups.

Article Snippet: The parameters used in the IVIM for each machine were as follows: (1) Philips: TR 5500 ms, TE 76 ms, FOV 343 × 343 mm, slice thickness 4 mm, voxel spacing 0.98 × 0.98 mm, with 4 b ‐values (0, 300, 450, 600 s/mm 2 ); Siemens: TR 4500 ms, TE 84 ms, FOV 350 × 350 mm, slice thickness 3.4 mm, voxel spacing 3.4 × 3.4 mm, with 12 b ‐values (0, 10, 20, 30, 40, 50, 80, 100, 200, 400, 800, and 1000 s/mm 2 ).

Techniques: Diffusion-based Assay