Estimation of trabecular bone parameters in children from multisequence MRI using texture‐based regression. Issue 6 (24th May 2016)

• Record Type: Journal Article
• Authors: Lekadir, Karim; Hoogendoorn, Corné; Armitage, Paul; Whitby, Elspeth; King, David; Dimitri, Paul; Frangi, Alejandro F.
• Is Part Of: Medical physics. Volume 43:Issue 6(2016)Part 1
• Page Start: 3071
• Page End: 3079
• Publication Date: 2016-05-24
• Subjects: biomedical MRI -- bone -- feature extraction -- image resolution -- image sequences -- image texture -- least squares approximations -- medical image processing -- parameter estimation -- regression analysis; Clinical applications -- Pulse sequences -- General statistical methods -- Probability theory, stochastic processes, and statistics -- Spatial resolution -- MRI: anatomic, functional, spectral, diffusion; 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 -- Analysis of texture; prediction of trabecular parameters -- HR‐pQCT -- skeletal MRI -- texture descriptors -- feature selection -- partial least squares regression; Medical magnetic resonance imaging -- Sequence analysis -- Computer modeling -- Image analysis -- Medical X‐ray imaging -- Databases -- Spin echoes -- Statistical model calculations; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4950713
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: This paper presents a statistical approach for the prediction of trabecular bone parameters from low‐resolution multisequence magnetic resonance imaging (MRI) in children, thus addressing the limitations of high‐resolution modalities such as HR‐pQCT, including the significant exposure of young patients to radiation and the limited applicability of such modalities to peripheral bones in vivo . Methods: A statistical predictive model is constructed from a database of MRI and HR‐pQCT datasets, to relate the low‐resolution MRI appearance in the cancellous bone to the trabecular parameters extracted from the high‐resolution images. The description of the MRI appearance is achieved between subjects by using a collection of feature descriptors, which describe the texture properties inside the cancellous bone, and which are invariant to the geometry and size of the trabecular areas. The predictive model is built by fitting to the training data a nonlinear partial least square regression between the input MRI features and the output trabecular parameters. Results: Detailed validation based on a sample of 96 datasets shows correlations >0.7 between the trabecular parameters predicted from low‐resolution multisequence MRI based on the proposed statistical model and the values extracted from high‐resolution HRp‐QCT. Conclusions: The obtained results indicate the promise of the proposed predictive technique for the estimation of trabecular parameters in children from multisequence MRI, thus reducing the need for high‐resolution radiation‐based scans for a fragile population that is under development and growth.