Estimating nonrigid motion from inconsistent intensity with robust shape features

• Published in: Medical physics (Lancaster) Vol. 40; no. 12; pp. 121912 - n/a
• Main Authors: Liu, Wenyang, Ruan, Dan
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.12.2013
• Subjects: ACCURACY; Analysis of motion; BENCHMARKS; Biological material, e.g. blood, urine; Haemocytometers; biomedical MRI; Clinical applications; Contrast; DCE‐MRI; DEFORMATION; Digital computing or data processing equipment or methods, specially adapted for specific applications; ERRORS; Flow visualization; Image data processing or generation, in general; Image Processing, Computer-Assisted - methods; image registration; image segmentation; image sequences; Interpolation; curve fitting; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging; Kidneys; Magnetic Resonance Imaging - methods; Medical image contrast; medical image processing; Medical image quality; Medical imaging; Medical magnetic resonance imaging; METRICS; motion estimation; Movement; NMR IMAGING; nonrigid registration; Numerical optimization; Optical flow; optimisation; OPTIMIZATION; Radiation Imaging Physics; RADIOLOGY AND NUCLEAR MEDICINE; Registration; Segmentation; Sequence analysis; splines (mathematics); VALIDATION; motion estimation; DCE-MRI; nonrigid registration
• ISSN: 0094-2405; 2473-4209; 1522-8541; 2473-4209; 0094-2405
• DOI: 10.1118/1.4829507

Purpose: To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. Methods: Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, and regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. Results: To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. Conclusions: The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.