Estimation of trabecular bone parameters in children from multisequence MRI using texture-based regression.
OBJECTIVE
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.
Center for Computational Imaging and Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona 08018, Spain.
Karim
K
Lekadir
The Academic Unit of Radiology, The University of Sheffield, Sheffield S10 2JF, United Kingdom.
Corné
C
Hoogendoorn
The Academic Unit of Reproductive and Developmental Medicine, The University of Sheffield, Sheffield S10 2SF, United Kingdom.
Paul
P
Armitage
The Academic Unit of Child Health, The University of Sheffield, Sheffield S10 2TH, United Kingdom.
Elspeth
E
Whitby
The Mellanby Centre for Bone Research, The University of Sheffield, Sheffield S10 2RX, United Kingdom.
David
D
King
Center for Computational Imaging and Simulation Technologies in Biomedicine, The University of Sheffield, Sheffield S1 3JD, United Kingdom.
Paul
P
Dimitri
The Academic Unit of Radiology, The University of Sheffield, Sheffield S10 2JF, United Kingdom.
Alejandro F
AF
Frangi