Noninvasive MR diffusion and perfusion analysis of liver fibrosis using comprehensive parameters
qing li1, shuangshuang xie1, yu zhang2, wenjing hou1, yue cheng1, and wen shen1

1Radiology, Tianjin First Center Hospital, Tianjin, China, People's Republic of, 2Philips healthcare, Beijing, China, People's Republic of

Synopsis

This study investigated the value of multi-parametric analysis using MR IVIM, BOLD MRI and DKI for the diagnosis of liver fibrosis. Eight patients with clinically diagnosed liver fibrosis and thirteen healthy control subjects were scanned with DKI, IVIM, BOLD. IVIM derived D*, D, f, DKI derived MD, K value and BOLD derived R2* were compared between the two groups. Our results showed D*, D, f, MD decreased, R2* and K value increased in patients with liver fibrosis, but only D* and D demonstrated significant difference (P<0.05). We therefore conclude D* and D could be useful in the diagnosis of liver fibrosis.

Purpose

Liver fibrosis is an abnormal continuation of connective tissue production and deposition1. Recent evidence suggests that liver fibrosis can be reversible2. Accurate diagnosis of liver fibrosis is crucial for making therapeutic decisions, predicting progression and determining prognosis3. Diffusion dependent intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and blood oxygenation level dependent (BOLD) MRI are useful in quantifying the diffusion and perfusion effects in tissue4-6. Previous studies have demonstrated the capability of IVIM and BOLD to evaluate liver cirrhosis or fibrosis4, 5. However, no reports have been published to evaluted liver fibrosis using DKI or multi-parametric MR imaging. The purpose of this study was to investigate the value of multi-parametric analysis using MR IVIM, BOLD and DKI for the diagnosis of liver fibrosis.

Materials and Methods

Eight patients with clinically diagnosed liver fibrosis (mean age=50.5±9.1 years; F/M=3/5) and thirteen healthy control subjects (mean age=38.4±13.2 years; F/M=5/8) were recruited. DKI, IVIM, BOLD scans were acquired using a 3.0 T scanner (Ingenia, Philips, Healthcare, Best, the Netherlands) and a 32 channel phased-array receiver coil with a whole liver. IVIM and DKI data were analyzed by using DWI post-processing software performed in a proprietary programming environment (PRIDE; Philips Medical Systems) and bold data were analyzed using the software ImageJ (available at http://rsb.info.nih.gov/ij/). Ten regions of interest (ROIs) were drawn on the upper, hilar and lower slices with attention to avoid the large blood vessels, bile ducts and artifacts. The location and size of each ROI was as same as possible in different sequences. Diffusion and perfusion related parameters (IVIM derived D*, D, f; DKI derived MD, K value; BOLD derived R2*) were compared by Student t-test using IBM SPSS Statistics 20.0 (Armonk, New York, USA). P<0.05 indicated a significant difference.

Results

MR parameters derived from IVIM, DKI and BOLD were compared between the two groups and summarized in table 1. Typical parametric images of the two groups are demonstrated in Figure1. Compared to control group, the perfusion related D* decreased and R2* increased, and the diffusion related D, f and MD decreased and K value increased in patient group with liver fibrosis. But only D* and D demonstrated significant difference between the two groups (P<0.05).

Discussion

In this study, D* decreased and R2* increased in patient group with liver fibrosis compared to control group, which might indicate the reduction of whole liver perfusion and arterial blood supply. The decrease of D, MD and increase of K value might result from the limitation of water diffusion, and this was consistent with the pathological changes of liver fibrosis with the excessive deposition of extracellular matrix. Therefore, the combination of multi-parametric MR imaging can provide more diagnostic information to give overall assessment of liver fibrosis.

Conclusion

Among all parameters derived from MR IVIM, DKI and BOLD, decreased D* and D could be useful in the diagnosis of liver fibrosis.

Acknowledgements

No acknowledgement found.

References

1. Schuppan D, Afdhal NH. Liver cirrhosis. Lancet. 2008; 371(9615): 838-851.

2. Chang TT, Liaw YF, Wu SS, et al. Long-term entecavir therapy results in the reversal of fibrosis/cirrhosis and continued histological improvement in patients with chronic hepatitis B. Hepatology. 2010; 52(3): 886-893.

3. Papastergiou V, Tsochatzis E, Burroughs AK. Non-invasive assessment of liver fibrosis. Ann Gastroenterol. 2012; 25(3): 218-231.

4. Luciani A, Viqnaud A, Cavet M, et al. Liver cirrhosis: introvoxel incoherent motion MR Imaging-Pilot study. Radiology. 2008; 249(3):891-899.

5. N. Jin, J. Deng, T. Chadashvili. Carbogen gas-challenge BOLD MR imaging in a rat mode of diethylnitrosamine-induced liver fibrosis. Radiology, 2010; 254(1): 129-137.

6. Goshima S, Kanematsu M, Noda Yoshifumi, et al. Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma. Am J Roentgenol. 2015; 204(5):W543-549.

Figures

Fig.1 D*, D, MD and R2* maps of a healthy volunteer (A) and patient with fibrosis (B)

Table.1 Comprehensive MR parameters comparison between control subjects and patients with liver fibrosis



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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