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In conventional 2D DCE-US, motion correction algorithms take advantage ofaccompanying side-by-side anatomical Bmode images that contain time-stablefeatures. However, current commercial models of 3D DCE-US do not provideside-by-side Bmode images, which makes motion correction challenging. This workintroduces a novel motion correction (MC) algorithm for 3D DCE-US and assessesits efficacy when handling clinical data sets. In brief, the algorithm uses apyramidal approach whereby short temporal windows consisting of 3-6 consecutiveframes are created to perform local registrations, which are then registered toa master reference derived from a weighted average of all frames. We evaluatedthe algorithm in 8 patients with metastatic lesions in the liver using thePhilips X6-1 matrix transducer at a frame rate of 1-3 Hz. We assessedimprovements in original vs. motion corrected 3D DCE-US cine using: i)frame-to-frame volumetric overlap of segmented lesions, ii) normalizedcorrelation coefficient (NCC) between frames (similarity analysis), and iii)sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R2)quality-of-fit from fitted time-intensity curves (TIC) extracted from asegmented lesion. Overall, results demonstrate significant decreases in 3DDCE-US motion after applying the proposed algorithm. We noted significantimprovements in frame-to-frame lesion overlap across all patients, from 68%without correction to 83% with motion correction (p = 0.023). Frame-to-framesimilarity as assessed by NCC also significantly improved on two different setsof time points from 0.694 (original cine) to 0.862 (corresponding MC cine) and0.723 to 0.886. TIC analysis displayed a significant decrease in RMSE (p =0.018) and a significant increase in R2 goodness-of-fit (p = 0.029) for thepatient cohort.

Motion Correction of 3D Dynamic Contrast-Enhanced Ultrasound Imaging without Anatomical Bmode Images