Estimation of respiratory breathing signal from 2D US sequences and 4DCT of the liver

Abstract

International audienceOne challenge of High Intensity Focused Ultrasound (HIFU) for treating primary liver tumors is the management of the breathing motion during treatment. An extracorporeal HIFU transducer with an integrated ultrasound (US) imaging probe has been specifically designed for treating the liver. The imaging probe produces 2D images and the tumor itself may not always be visible in US images. A pre-operative 4D-Computed Tomography (CT) image is acquired to infer the tumor location. To retrieve spatio-temporal correspondence between the 4DCT and 2D+t US sequences to guide extracorporeal HIFU treatments temporal Principal Component Analysis and Hilbert transform was applied. The correspondence was visually verified by radiologist

Modeling of Respiratory Motion to Support the Minimally Invasive Destruction of Liver Tumors

“…To determine the correlation between pre-operative and intraoperative data, the following method was proposed [ 36 , 37 ]: Two-dimensional CT sequences were generated from the four-dimensional CT that corresponded spatially to the US sequence; The breathing signal from the 2D CT sequence was estimated (based on the same method as described for US images); Hilbert’s transformation angles were calculated to estimate the breathing signal from the corresponding CT sequence, and the phase values were normalized in scope (0–100%); For every US frame, the corresponding 4D CT phase was determined by comparing the phase values. …”