A method to analyze the evolution of malignant gliomas using MRI

作     者：Iacono, Maria Ida Passera, Katia Magrassi, Lorenzo Mainardi, Luca Bastianello, Stefano Lago, Paolo 

作者机构：Politecn Milan Dept Biomed Engn I-20133 Milan Italy Univ Pavia IRCCS S Matteo Dept Surg Neurosurg Div I-27100 Pavia Italy IRCCS Mondino Dept Neuroradiol Pavia Italy IRCCS S Matteo Clin Engn Dept Pavia Italy 

出 版 物：《INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY》 (国际计算机辅助放射学与外科学杂志)

年 卷 期：2008年第3卷第6期

页      面：571-579页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 1006[医学-中西医结合] 1002[医学-临床医学] 1009[医学-特种医学] 10[医学] 100602[医学-中西医结合临床] 

主　　题：Computer-assisted image processing Deformation field Jacobian operator Follow-up studies 

摘      要：Objective To introduce a novel approach for the monitoring of glioma evolution by the extraction of mathematical parameters from follow-up MRI. Material and methods The method consists of the registration of follow-up MR images and the analysis of the deformation field. The registration was performed through an affine transformation followed by a non-rigid registration using free-form deformations (FFDs). A subsequent analysis of the transformation non-linear component is then performed by using the jacobian operator in order to extract information related to tumor evolution. In order to test the algorithm s performance two different validations were performed: (a) a numerical validation utilizing both physical and digital phantoms, (b) a clinical validation using neurosurgeon clinical judgements. Results Quantitative validation showed that the jacobian describes the volumetric variations of the physical phantom with an error of 5%. Furthermore, simulations with a digital phantom provided an estimation of the error introduced by registration (6.4%). Clinical validation provided good clinical scores: the score evaluating the correspondence between extracted variables and patient evolution was 4.37 +/- 0.89 for the deformation field and 4.43 +/- 0.82 for the jacobian image (top score: 5). Conclusion The novel approach leads to an objective and quantitative description of tumor evolution. Therefore, it could be valuable for planning interventions and/or treatments.