scholarly journals Living tissue intravoxel incoherent motion (IVIM) diffusion MR analysis without b=0 image: an example for liver fibrosis evaluation

2019 ◽  
Vol 9 (2) ◽  
pp. 127-133 ◽  
Author(s):  
Yì Xiáng J. Wáng
Keyword(s):  
Liver Fibrosis ◽  
Intravoxel Incoherent Motion ◽  
Living Tissue ◽  
Diffusion Mr

Liver fibrosis: in vivo evaluation using intravoxel incoherent motion-derived histogram metrics with histopathologic findings at 3.0 T

2017 ◽  
Vol 42 (12) ◽  
pp. 2855-2863 ◽  
Author(s):  
Fubi Hu ◽  
Ru Yang ◽  
Zixing Huang ◽  
Min Wang ◽  
Hanmei Zhang ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Intravoxel Incoherent Motion ◽  
In Vivo Evaluation ◽  
3.0 T ◽  
Histopathologic Findings

scholarly journals Value of intravoxel incoherent motion in detecting and staging liver fibrosis: A meta-analysis

2020 ◽  
Vol 26 (23) ◽  
pp. 3304-3317
Author(s):  
Zheng Ye ◽  
Yi Wei ◽  
Jie Chen ◽  
Shan Yao ◽  
Bin Song
Keyword(s):  
Liver Fibrosis ◽  
Meta Analysis ◽  
Intravoxel Incoherent Motion

scholarly journals High performance of intravoxel incoherent motion diffusion MRI in detecting viral hepatitis-b induced liver fibrosis

2019 ◽  
Vol 7 (3) ◽  
pp. 39-39 ◽  
Author(s):  
Hua Huang ◽  
Nazmi Che-Nordin ◽  
Li-Fei Wang ◽  
Ben-Heng Xiao ◽  
Olivier Chevallier ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Hepatitis B ◽  
Viral Hepatitis ◽  
Diffusion Mri ◽  
High Performance ◽  
Intravoxel Incoherent Motion ◽  
Viral Hepatitis B

scholarly journals Dependence of intravoxel incoherent motion diffusion MR threshold b-value selection for separating perfusion and diffusion compartments and liver fibrosis diagnostic performance

2018 ◽  
Vol 60 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Yì Xiáng J Wáng ◽  
Yáo T Li ◽  
Olivier Chevallier ◽  
Hua Huang ◽  
Jason Chi Shun Leung ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Area Under The Curve ◽  
Regression Tree ◽  
B Value ◽  
Classification And Regression Tree ◽  
Intravoxel Incoherent Motion ◽  
Fibrotic Liver ◽  
B Values ◽  
D Values ◽  
The Right

Background Intravoxel incoherent motion (IVIM) tissue parameters depend on the threshold b-value. Purpose To explore how threshold b-value impacts PF ( f), Dslow ( D), and Dfast ( D*) values and their performance for liver fibrosis detection. Material and Methods Fifteen healthy volunteers and 33 hepatitis B patients were included. With a 1.5-T magnetic resonance (MR) scanner and respiration gating, IVIM data were acquired with ten b-values of 10, 20, 40, 60, 80, 100, 150, 200, 400, and 800 s/mm2. Signal measurement was performed on the right liver. Segmented-unconstrained analysis was used to compute IVIM parameters and six threshold b-values in the range of 40–200 s/mm2 were compared. PF, Dslow, and Dfast values were placed along the x-axis, y-axis, and z-axis, and a plane was defined to separate volunteers from patients. Results Higher threshold b-values were associated with higher PF measurement; while lower threshold b-values led to higher Dslow and Dfast measurements. The dependence of PF, Dslow, and Dfast on threshold b-value differed between healthy livers and fibrotic livers; with the healthy livers showing a higher dependence. Threshold b-value = 60 s/mm2 showed the largest mean distance between healthy liver datapoints vs. fibrotic liver datapoints, and a classification and regression tree showed that a combination of PF (PF < 9.5%), Dslow (Dslow < 1.239 × 10–3 mm2/s), and Dfast (Dfast < 20.85 × 10–3 mm2/s) differentiated healthy individuals and all individual fibrotic livers with an area under the curve of logistic regression (AUC) of 1. Conclusion For segmented-unconstrained analysis, the selection of threshold b-value = 60 s/mm2 improves IVIM differentiation between healthy livers and fibrotic livers.

scholarly journals Fitting Intravoxel Incoherent Motion Model to Diffusion MR Signals of the Human Breast Tissue using Particle Swarm Optimization

2019 ◽  
Vol 9 (2) ◽  
pp. 105-112
Author(s):  
Gokhan Ertas
Keyword(s):  
Breast Tissue ◽  
Particle Swarm ◽  
Trust Region ◽  
Intravoxel Incoherent Motion ◽  
Human Breast ◽  
Human Breast Tissue ◽  
Diffusion Mr ◽  
Noisy Signals ◽  
Value Sets ◽  
Lm Algorithm

Intravoxel incoherent motion (IVIM) modeling offers the parameters f, D and D* as biomarkers for different lesion types and cancer stages from diffusion MR signals. Challenges with the available optimization algorithms in fitting the model to the signals motive new studies for improved parameter estimations. In this study, one thousand value sets of f, D, D* for human breast are assembled and used to generate five thousand diffusion MR signals considering noise-free and noisy situations exhibiting signal-to-noise ratios (SNR) of 20, 40, 60 and 80. The estimates of f, D, D* are obtained using Levenberg-Marquardt (LM), trust-region (TR) and particle swarm (PS) algorithms. On average, the algorithms provide the highest fitting performance for the noise-free signals (R2adj=1.000) and great fitting performances on the noisy signals with SNR>20 (R2adj>0.988). TR algorithm performs slightly better for SNR=20 (R2adj=0.947). TR and PS algorithms achieve the highest parameter estimation performance for all the parameters while LM algorithm reveals the highest performance for f and D only on the noise-free signals (r=1.00). For the noisy signals, performances increase while SNR increases. All algorithms accomplish poor performances for D* (r=0.01-0.20) while TR and PS algorithms perform same for f (r=0.48-0.97) and D (r=0.85-0.99) but remarkably better than LM algorithm for f (r=0.08-0.97) and D (r=0.53-0.99). Overall, TR and PS algorithms demonstrate better but indistinguishable performances. Without requiring any user-given initial value, PS algorithm may facilitate improved estimation of IVIM parameters of the human breast tissue. Further studies are needed to determine its benefit in clinical practice.

scholarly journals Evaluation of liver fibrosis with a monoexponential model of intravoxel incoherent motion magnetic resonance imaging

Oncotarget ◽  
2018 ◽  
Vol 9 (37) ◽  
pp. 24619-24626
Author(s):  
Cuiyun Chen ◽  
Fangfang Fu ◽  
Jing Zhang ◽  
Fangfang Guo ◽  
Meiyun Wang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Liver Fibrosis ◽  
Intravoxel Incoherent Motion ◽  
Resonance Imaging ◽  
Monoexponential Model

Intravoxel incoherent motion MRI for liver fibrosis assessment: a pilot study

2014 ◽  
Vol 56 (12) ◽  
pp. 1428-1436 ◽  
Author(s):  
Sae Rom Chung ◽  
Seung Soo Lee ◽  
Namkug Kim ◽  
Eun Sil Yu ◽  
Eunki Kim ◽  
...  
Keyword(s):  
Pilot Study ◽  
Liver Fibrosis ◽  
Intravoxel Incoherent Motion ◽  
Motion Mri

scholarly journals A Combined Use of Intravoxel Incoherent Motion MRI Parameters Can Differentiate Early-Stage Hepatitis-b Fibrotic Livers from Healthy Livers

2017 ◽  
Vol 23 (3) ◽  
pp. 259-268 ◽  
Author(s):  
Yì Xiáng J. Wáng ◽  
Min Deng ◽  
Yáo T. Li ◽  
Hua Huang ◽  
Jason Chi Shun Leung ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Hepatitis B ◽  
Healthy Volunteers ◽  
Early Stage ◽  
Three Dimensional ◽  
Classification And Regression Tree ◽  
Intravoxel Incoherent Motion ◽  
Single Shot ◽  
Planar Imaging ◽  
Combined Use

This study investigated a combined use of intravoxel incoherent motion (IVIM) parameters, Dslow ( D), PF ( f), and Dfast ( D*), for liver fibrosis evaluation. Sixteen healthy volunteers (F0) and 33 hepatitis-b patients (stage F1 = 15, stage F2–4 = 18) were included. With a 1.5 T MR scanner and respiration gating, IVIM diffusion-weighted imaging was acquired using a single-shot echo-planar imaging sequence with 10 b values of 10, 20, 40, 60, 80, 100, 150, 200, 400, and 800 s/mm2. Signal measurement was performed on right liver parenchyma. With a three-dimensional tool, Dslow, PF, and Dfast values were placed along the x axis, y axis, and z axis, and a plane was defined to separate healthy volunteers from patients. The three-dimensional tool demonstrated that healthy volunteers and all patients with liver fibrosis could be separated. Classification and regression tree showed that a combination of PF (PF < 12.55%), Dslow (Dslow < 1.152 × 10−3 mm2/s), and Dfast (Dfast < 13.36 × 10−3 mm2/s) could differentiate healthy subjects and all fibrotic livers (F1–4) with an area under the curve of logistic regression (AUC) of 0.986. The AUC for differentiation of healthy livers versus F2–4 livers was 1. PF offered the best diagnostic value, followed by Dslow; however, all three parameters of PF, Dslow, and Dfast contributed to liver fibrosis detection.

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