Comparison of four attenuation-compensation methods for backscatter coefficient estimation and characterization of...

Objective.To compare four attenuation-compensation methods for backscatter coefficient (BSC) estimation, assessment of contrast, and classification of focal liver lesions (FLL).Approach. Ninety-seven patients with 100 FLL were scanned to collect radiofrequency ultrasound images. BSC methods relied on a reference phantom for system and operator-settings independent estimations. Method #1 employeda prioritissue layer segmentation and documented attenuation coefficients (AC) of each layer. Method #2 used a fixed total AC (0.85 dB/cm/MHz). Method #3 used local AC compensation. Method #4 jointly estimated total AC and BSC with a power law frequency model (bfη). BSC@3 MHz,b,η, total AC slope, and total AC were computed within segmented lesions. Lesion contrast was assessed with the contrast-to-noise ratio (CNR) and classification performances were evaluated with the area under the receiver operating characteristic curve (AUC). The composite clinical reference standard was a combination of MRI and histopathology.Main results.The study included 30 primary and 26 secondary cancers, and 44 benign nodules. Parameterbprovided the highest CNRs among BSC parameters (p< 0.0001) and gave higher CNRs than B-mode images (p< 0.0001). Method #3 was unsuitable with out-of-range values. Methods #1, #2, and #4 showed no significant differences forb,η, total AC slope, and total AC, whereas BSC@3 MHz showed overestimations with Method #4 compared with Methods #1 and #2 (p< 0.001 andp< 0.0001, respectively). For differentiating benign and malignant lesions,ηprovided the highest AUC of 0.73 (95% confidence interval (CI): 0.62-0.82). For differentiating primary and secondary cancers, BSC@3 MHz provided the highest AUC of 0.71 (95% CI: 0.55-0.83). Classification AUCs did not differ between Methods #1, #2 and #4.Significance. BSC imaging improved lesion contrast compared to B-mode and could classify FLL. Method #4 emerged as the most practical as it does not require anya prioriAC or segmentation.