title: A manifold learning method to detect respiratory signal from liver ultrasound images.
abstract: Respiratory gating has been widely applied for respiratory correction or compensation in image acquisition and image-guided interventions. A novel image-based method is proposed to extract respiratory signal directly from 2D ultrasound liver images. The proposed method utilizes a typical manifold learning method, based on local tangent space alignment based technique, to detect principal respiratory motion from a sequence of ultrasound images. This technique assumes all the images lying on a low-dimensional manifold embedding into the high-dimensional image space, constructs an approximate tangent space of each point to represent its local geometry on the manifold, and then aligns the local tangent spaces to form the global coordinate system, where the respiratory signal is extracted. The experimental results show that the proposed method can detect relatively accurate respiratory signal with high correlation coefficient (0.9775) with respect to the ground-truth signal by tracking external markers, and achieve satisfactory computing performance (2.3s for an image sequence of 256 frames). The proposed method is also used to create breathing-corrected 3D ultrasound images to demonstrate its potential application values.
authors.affiliation: Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #08-01, Matrix, 30 Biopolis Street, 138671 Singapore. Electronic address: wu_jiaze@sbic.a-star.edu.sg.
authors.forename: Jiaze
authors.initials: J
authors.lastname: Wu
authors.affiliation: Department of Diagnostic Radiology, Singapore General Hospital, Singapore. Electronic address: apoorva.gogna@sgh.com.sg.
authors.forename: Apoorva
authors.initials: A
authors.lastname: Gogna
authors.affiliation: Department of Diagnostic Radiology, Singapore General Hospital, Singapore. Electronic address: tan.bien.soo@sgh.com.sg.
authors.forename: Bien Soo
authors.initials: BS
authors.lastname: Tan
authors.affiliation: Department of Surgery, Singapore General Hospital, Singapore. Electronic address: london.lucien.ooi.p.j@sgh.com.sg.
authors.forename: London Lucien
authors.initials: LL
authors.lastname: Ooi
authors.affiliation: Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #08-01, Matrix, 30 Biopolis Street, 138671 Singapore. Electronic address: tianqig@gmail.com.
authors.forename: Qi
authors.initials: Q
authors.lastname: Tian
authors.affiliation: Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #08-01, Matrix, 30 Biopolis Street, 138671 Singapore. Electronic address: liu_feng@sbic.a-star.edu.sg.
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.affiliation: Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #08-01, Matrix, 30 Biopolis Street, 138671 Singapore. Electronic address: liujm@sbic.a-star.edu.sg.
authors.forename: Jimin
authors.initials: J
authors.lastname: Liu
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D015656:Respiratory Mechanics; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*
keywords: Liver ultrasound images; Local tangent space alignment; Manifold learning; Respiratory gating; Respiratory signal