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Computer-Aided Diagnosis of Hepatic Fibrosis: Preliminary Evaluation of MRI Texture Analysis Using the Finite Difference Method and an Artificial Neural Network

Hiroki Kato1, Masayuki Kanematsu1,2, Xuejun Zhang3, Masanao Saio4, Hiroshi Kondo1, Satoshi Goshima1 and Hiroshi Fujita5

1 Department of Radiology, Gifu University School of Medicine, 1-1 Yanagido, Gifu 501-1194, Japan.
2 Department of Radiology Services, Gifu University Hospital, Gifu, Japan.
3 College of Computer Science and Information Engineering, Guangxi University, Nanning City, Guangxi, P. R. China.
4 Department of Immunopathology, Gifu University Graduate School of Medicine, Gifu, Japan.
5 Department of Information Science, Faculty of Engineering, Gifu University, Gifu, Japan.


Figure 1
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  		Fig. 1 —Scheme shows texture feature analysis performed by artificial neural network program with three-layer learning algorithm of back propagation comprising seven-unit input layer, six-unit hidden layer, and one-unit output layer. Seven numeric parameters by finite difference method in 10 regions of interest placed in liver parenchyma were inputted into artificial neural network program, and probability value for presence of hepatic fibrosis in region of interest was outputted as continuous number between 0 (absent) and 1 (present).

 

Figure 2
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  		Fig. 2A —73-year-old man without hepatitis (F0 on Desmet scale [1]) who underwent partial hepatectomy for solitary liver metastasis from ascending colon cancer. T1-weighted spoiled gradient-recalled echo axial image (TR/TE, 150/1.6) (A), T2-weighted fast spin-echo axial image (4,286/80) (B), and gadolinium-enhanced equilibrium phase axial image (150/1.6) (C) show homogeneous signal intensity in liver parenchyma.

 

Figure 3
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  		Fig. 2B —73-year-old man without hepatitis (F0 on Desmet scale [1]) who underwent partial hepatectomy for solitary liver metastasis from ascending colon cancer. T1-weighted spoiled gradient-recalled echo axial image (TR/TE, 150/1.6) (A), T2-weighted fast spin-echo axial image (4,286/80) (B), and gadolinium-enhanced equilibrium phase axial image (150/1.6) (C) show homogeneous signal intensity in liver parenchyma.

 

Figure 4
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  		Fig. 2C —73-year-old man without hepatitis (F0 on Desmet scale [1]) who underwent partial hepatectomy for solitary liver metastasis from ascending colon cancer. T1-weighted spoiled gradient-recalled echo axial image (TR/TE, 150/1.6) (A), T2-weighted fast spin-echo axial image (4,286/80) (B), and gadolinium-enhanced equilibrium phase axial image (150/1.6) (C) show homogeneous signal intensity in liver parenchyma.

 

Figure 5
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  		Fig. 3A —76-year-old man with chronic type-B hepatitis and cirrhosis (F4 on Desmet scale [1]) who underwent partial hepatectomy for solitary hepatocellular carcinoma. T1-weighted spoiled gradient-recalled echo axial image (TR/TE, 150/1.6) (A) and T2-weighted fast spin-echo axial image (4,286/80) (B) show tiny hypointense nodules, presumably corresponding to regenerative nodules in cirrhosis.

 

Figure 6
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  		Fig. 3B —76-year-old man with chronic type-B hepatitis and cirrhosis (F4 on Desmet scale [1]) who underwent partial hepatectomy for solitary hepatocellular carcinoma. T1-weighted spoiled gradient-recalled echo axial image (TR/TE, 150/1.6) (A) and T2-weighted fast spin-echo axial image (4,286/80) (B) show tiny hypointense nodules, presumably corresponding to regenerative nodules in cirrhosis.

 

Figure 7
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  		Fig. 3C —76-year-old man with chronic type-B hepatitis and cirrhosis (F4 on Desmet scale [1]) who underwent partial hepatectomy for solitary hepatocellular carcinoma. Gadolinium-enhanced equilibrium phase axial image (150/1.6) shows reticular pattern of enhancement in liver parenchyma that is presumably due to hepatic fibrosis.

 

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