A nonrigid registration method for correcting brain deformation induced by tumor resection. Issue 10 (1st October 2014)

• Record Type: Journal Article
• Authors: Liu, Yixun; Yao, Chengjun; Drakopoulos, Fotis; Wu, Jinsong; Zhou, Liangfu; Chrisochoides, Nikos
• Is Part Of: Medical physics. Volume 41:Issue 10(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-10-01
• Subjects: biomechanics -- biomedical MRI -- brain -- deformation -- expectation‐maximisation algorithm -- image registration -- medical image processing -- minimisation -- neurophysiology -- tumours; Registration -- Magnetic resonance imaging -- Neuroscience -- Biomechanics -- Mechanical and electrical properties of tissues and organs -- Numerical approximation and analysis; 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; brain deformation -- non‐rigid registration -- heterogeneous biomechanical model -- tumor resection -- expectation and maximization; Brain -- Magnetic resonance imaging -- Biomechanics -- Medical imaging -- Cancer -- Finite element methods -- Edge detection -- Retraction -- Interpolation -- Tensor methods; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4893754
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point‐based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model. Methods: : The method formulates the registration as a three‐variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical model; and resection region is represented by a maximal simply connected tetrahedral mesh. A nested expectation and maximization framework is developed to simultaneously resolve these three variables. Results: : To evaluate this method, the authors conducted experiments on both synthetic data and clinical MRI data. The synthetic experiment confirmed their hypothesis that the removal of additional elements from the biomechanical model can improve the accuracy of the registration. The clinical MRI experiments on 25 patients showed that the proposed method outperforms the ITK implementation of a physics‐based nonrigid registration method. The proposed method improves the accuracy by 2.88 mm on average when the error is measured by a robust Hausdorff distance metric on Canny edge points, and improves the accuracy by 1.56 mm on average when the error is measured by six anatomical points. Conclusions: : The proposed method can effectively correct brain deformation induced by tumor resection.