Subject-specific and respiration-corrected 4D liver model from real-time ultrasound image sequences. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Li, Cheng; Wu, Jiaze; Gogna, Apoorva; Tan, Bien Soo; Teo, Jin Yao; Ooi, London Lucien; Liu, Jimin; Yu, Haoyong
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 6:Issue 1(2018)
• Page Start: 7
• Page End: 16
• Publication Date: 2018-01-02
• Subjects: 4D ultrasound imaging -- robotic-assisted procedure -- respiratory signal -- liver motion analysis; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2016.1147985
• Languages: English
• ISSNs: 2168-1163

Abstract

Abstract: Respiratory liver motion is one of the major issues affecting the liver interventions, such as biopsy and ablation, in the clinical practice. Traditional 4D liver model methods using magnetic resonance imaging (MRI) or computed tomography (CT) data sets for motion compensation to solve this problem have proved time-consuming and costly. In addition, the widely used freehand 3D ultrasound techniques, which lack respiratory corrections, cannot effectively track breathing-induced liver motion. On the other hand, clinical solutions are straightforward but present restrictions for patients. Motivated by both the technical and clinical needs, we propose a novel method for creating a sequence of subject-specific and respiration-corrected 3D ultrasound (US) images, from multiple robotic-assisted-swept 2D US image sequences. Moreover, we also observed the motion difference between subjects on the generated 4D model. The results of quantitative evaluation on the accuracy of the models show that the overlap ratio of the liver boundary between the generated model and ground truth at end of exhale and end of inhale phases were 0.90 and 0.89, respectively. The overall distance error of pinpointed landmarks was 2.44 mm, which is within the acceptance range of clinical applications. Therefore, we conclude that the reconstructed 3D image sequence can capture the moving liver during a half respiratory cycle, and the proposed method is feasible to visualise the 3D liver motion. The clinicians who worked with us in this study also suggest that this preoperative subject-specific motion information could help them to diagnose the existence, or determine the possible position, of a tumour.