25 January 2017 Deformable image registration for tissues with large displacements
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Abstract
Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients’ movements such as respiration and repositioning. In our previous work, we proposed a fast registration method for deformable tissues with small rotations. We extend our method to deformable registration of soft tissues with large displacements. We analyzed the deformation field of the liver by decomposing the deformation into shift, rotation, and pure deformation components and concluded that in many clinical cases, the liver deformation contains large rotations and small deformations. This analysis justified the use of linear elastic theory in our image registration method. We also proposed a region-based neuro-fuzzy transformation model to seamlessly stitch together local affine and local rigid models in different regions. We have performed the experiments on a liver MRI image set and showed the effectiveness of the proposed registration method. We have also compared the performance of the proposed method with the previous method on tissues with large rotations and showed that the proposed method outperformed the previous method when dealing with the combination of pure deformation and large rotations. Validation results show that we can achieve a target registration error of 1.87 ± 0.87    mm and an average centerline distance error of 1.28 ± 0.78    mm . The proposed technique has the potential to significantly improve registration capabilities and the quality of intraoperative image guidance. To the best of our knowledge, this is the first time that the complex displacement of the liver is explicitly separated into local pure deformation and rigid motion.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Xishi Huang, Jing Ren, Anwar Abdalbari, Mark Green, "Deformable image registration for tissues with large displacements," Journal of Medical Imaging 4(1), 014001 (25 January 2017). https://doi.org/10.1117/1.JMI.4.1.014001 . Submission: Received: 13 July 2016; Accepted: 30 December 2016
Received: 13 July 2016; Accepted: 30 December 2016; Published: 25 January 2017
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