A general framework in single and multi-modality registration for lung imaging analysis using statistical prior shapes. (April 2020)

• Record Type: Journal Article
• Authors: Cui, Zheng; Mahmoodi, Sasan; Guy, Matthew; Lewis, Emma; Havelock, Tom; Bennett, Michael; Conway, Joy
• Is Part Of: Computer methods and programs in biomedicine. Volume 187(2020)
• Publication Date: 2020-04
• Subjects: Multi-modality image fusion -- Parameter-reduced method -- Statistical modeling -- Non-rigid registration; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105232
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A multi-channel registration method is proposed to improve the performance of multi-modality image fusion. A novel parameter-reduced cost function is proposed to avoid the unnecessary adjustment for a weighting parameter. A new statistical representation is presented to properly regularize the displacement in non-rigid registration. The synthetic data offered by 4D XCAT together with RMDP are employed for validation. Abstract: Background and Objective: A fusion of multi-slice computed tomography (MSCT) and single photon emission computed tomography (SPECT) represents a powerful tool for chronic obstructive pulmonary disease (COPD) analysis. In this paper, a novel and high-performance MSCT/SPECT non-rigid registration algorithm is proposed to accurately map the lung lobe information onto the functional imaging. Such a fusion can then be used to guide lung volume reduction surgery. Methods: The multi-modality fusion method proposed here is developed by a multi-channel technique which performs registration from MSCT scan to ventilation and perfusion SPECT scans simultaneously. Furthermore, a novel function with less parameters is also proposed to avoid the adjustment of the weighting parameter and to achieve a better performance in comparison with the exisitng methods in the literature. Results: A lung imaging dataset from a hospital and a synthetic dataset created by software are employed to validate single- and multi-modality registration results. Our method is demonstrated to achieve the improvements in terms of registration accuracy and stability by up to 23% and 54% respectively. Our multi-channel technique proposed here is also proved to obtain improved registration accuracy in comparison with single-channel method. Conclusions: The fusion of lung lobes onto SPECT imaging is achievable by accurate MSCT/SPECT alignment. It can also be used to perform lobar lung activity analysis for COPD diagnosis and treatment.