Modeling and incorporating cardiac‐induced lung tissue motion in a breathing motion model. Issue 4 (5th March 2014)

• Record Type: Journal Article
• Authors: White, Benjamin M.; Santhanam, Anand; Thomas, David; Min, Yugang; Lamb, James M.; Neylon, Jack; Jani, Shyam; Gaudio, Sergio; Srinivasan, Subashini; Ennis, Daniel; Low, Daniel A.
• Is Part Of: Medical physics. Volume 41:Issue 4(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-03-05
• Subjects: Pneumodyamics, respiration -- Spatial resolution -- Noise -- Artifacts and distortion -- Segmentation -- Anatomic imaging; biomedical MRI -- blood vessels -- cardiovascular system -- deformation -- image resolution -- image sampling -- image segmentation -- image sequences -- lung -- medical image processing -- motion compensation -- optimisation -- physiological models -- pneumodynamics -- polynomials; cardiac‐induced motion -- optical flow; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of motion; Lungs -- Heart -- Cardiac haemodynamics -- Cardiac dynamics -- Polynomials -- Medical imaging -- Anatomy -- Image registration -- Three dimensional image processing -- Optical flow; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4866888
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : The purpose of this work is to develop a cardiac‐induced lung motion model to be integrated into an existing breathing motion model. Methods: : The authors' proposed cardiac‐induced lung motion model represents the lung tissueˈs specific response to the subjectˈs cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase ( c ) and the maximum displacement γ ⇀ ( X ⇀ 0 ) of each voxel ( X ⇀ 0 ) among all the cardiac phases. The function h ( c ) was estimated from cardiac‐gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short‐axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12–15 s breath‐hold maneuvers. Each view contained 30 temporal frames of equal time‐duration beginning with the end‐diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three‐dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end‐diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood‐flow. Results: : The average cardiac‐induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac‐induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac‐induced lung tissue motion, the relative error decreased to < 5% for cardiac‐induced lung tissue motion that exceeded 10 mm in displacement. Conclusions: : The authors' studies implied that modeling and including cardiac‐induced lung motion would improve breathing motion model accuracy for tissues with cardiac‐induced motion greater than 0.3 mm.