Measurement of attenuation and scatterer spacing in human liver tissue: preliminary results.

Abstract

A study measuring two quantitative parameters of human liver in vivo was performed to assess the reliability of measurement of the two parameters, and to evaluate their potential for diagnosing and grading diffuse fibrotic liver disease. The parameters measured were attenuation and "mean scatterer spacing," a measure of tissue structure. Components of variance analysis demonstrated that variation in the measured parameters was a function of the subject being examined, with significant variation noted between data acquisition sessions performed the same day. There was no significant additional variation of the measurements from week to week over a one-month period. A good correlation of the parameters with the severity of liver disease indicates that the technique may be useful in the clinical evaluation of diffuse liver disease.

A Review of Ultrasound Tissue Characterization with Mean Scatterer Spacing

“…34 Sommer et al demonstrated the potential of MSS and ultrasound attenuation for diagnosing and grading diffuse fibrotic liver disease in vivo . 32 Fellingham and Sommer reported an MSS of 1.27 ± 0.03 mm for four cirrhosis patients, and an MSS of 1.55 ± 0.17 mm for cirrhotic human liver specimen in vitro ( n = 10). 31…”

“…Also, the peaks in the FT spectrum can be detected by using the autocorrelation of the frequency spectrum. 30-35 The peaks in the time domain and the cepstrum domain occur at…”

“…33 Sommer et al reported an MSS of 1.10 ± 0.07 mm for 16 Hodgkin’s disease patients without splenic involvement, an MSS of 1.51 ± 0.28 mm for five Hodgkin’s disease patients with splenic involvement, and MSS of 1.10 ± 0.04 mm for normal human spleen in vivo ( n = 10). 32…”

“…In this review, MSS estimation methods and applications are summarized. The MSS estimation techniques can be broadly classified into five major groups: time-domain autocorrelation based, 22-29 spectrum (frequency spectrum, 30-35 generalized spectrum, 21,36-39 and autoregressive model power spectrum 14,40 ) based, cepstrum (Fourier transform power cepstrum, 41 autoregressive model power cepstrum, 42 and complex cepstrum 43 ) based, phase statistics based, 44-47 and mathematical transformation (quadratic transformation, 48,49 Wold decomposition, 50 singular spectrum analysis, 51,52 and wavelet transformation 53-55 ) based methods. Future developments are also discussed.…”