Texture analysis using CT and chemical shift encoding-based water-fat MRI can improve differentiation between patients with and without osteoporotic vertebral fractures
Creators: name: Sollmann, Nico
name: Becherucci, Edoardo A.
name: Boehm, Christof
name: Husseini, Malek El
name: Ruschke, Stefan
name: Burian, Egon
name: Kirschke, Jan S.
name: Link, Thomas M.
name: Subburaj, Karupppasamy
name: Karampinos, Dimitrios C.
name: Krug, Roland
name: Baum, Thomas
name: Dieckmeyer, Michael
Publisher: name: Universität Ulm
Year: 2022
Subjects: subject: bone mineral density
subject: convolutional neural network
subject: opportunistic imaging
subject: proton density fat fraction
subject: texture analysis
subject: vertebral fracture
subject: DDC 610 / Medicine &amp; health
subject: Osteoporosis
subject: Spine
subject: Fractures, Bone
subject: Osteoporotic fractures
subject: Bone density
subject: Tomography, X-ray computed
subject: Magnetic resonance imaging
subject: Osteoporose
subject: Wirbelsäule
subject: Wirbelkörper
subject: Knochenbruch
subject: Knochendichte
subject: Kernspintomografie

description: Purpose Osteoporosis is a highly prevalent skeletal disease that frequently entails vertebral fractures. Areal bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the reference standard, but has well-known limitations. Texture analysis can provide surrogate markers of tissue microstructure based on computed tomography (CT) or magnetic resonance imaging (MRI) data of the spine, thus potentially improving fracture risk estimation beyond areal BMD. However, it is largely unknown whether MRI-derived texture analysis can predict volumetric BMD (vBMD), or whether a model incorporating texture analysis based on CT and MRI may be capable of differentiating between patients with and without osteoporotic vertebral fractures. Materials and Methods Twenty-six patients (15 females, median age: 73 years, 11 patients showing at least one osteoporotic vertebral fracture) who had CT and 3-Tesla chemical shift encoding-based water-fat MRI (CSE-MRI) available were analyzed. In total, 171 vertebral bodies of the thoracolumbar spine were segmented using an automatic convolutional neural network (CNN)-based framework, followed by extraction of integral and trabecular vBMD using CT data. For CSE-MRI, manual segmentation of vertebral bodies and consecutive extraction of the mean proton density fat fraction (PDFF) and T2* was performed. First-order, second-order, and higher-order texture features were derived from texture analysis using CT and CSE-MRI data. Stepwise multivariate linear regression models were computed using integral vBMD and fracture status as dependent variables. Results Patients with osteoporotic vertebral fractures showed significantly lower integral and trabecular vBMD when compared to patients without fractures (p&lt;0.001). For the model with integral vBMD as the dependent variable, T2* combined with three PDFF-based texture features explained 40% of the variance (adjusted R2 [ R a 2 ] = 0.40; p&lt;0.001). Furthermore, regarding the differentiation between patients with and without osteoporotic vertebral fractures, a model including texture features from CT and CSE-MRI data showed better performance than a model based on integral vBMD and PDFF only ( R a 2 = 0.47 vs. R a 2  = 0.81; included texture features in the final model: integral vBMD, CT_Short-run_emphasis, CT_Varianceglobal, and PDFF_Variance). Conclusion Using texture analysis for spine CT and CSE-MRI can facilitate the differentiation between patients with and without osteoporotic vertebral fractures, implicating that future fracture prediction in osteoporosis may be improved.