MRI-derived bone porosity index correlates to bone composition and mechanical stiffness

Abstract

The MRI-derived porosity index (PI) is a non-invasively obtained biomarker based on an ultrashort echo time sequence that images both bound and pore water protons in bone, corresponding to water bound to organic collagenous matrix and freely moving water, respectively. This measure is known to strongly correlate with the actual volumetric cortical bone porosity. However, it is unknown whether PI may also be able to directly quantify bone organic composition and/or mechanical properties. We investigated this in human cadaveric tibiae by comparing PI values to near infrared spectral imaging (NIRSI) compositional data and mechanical compression data. Data were obtained from a cohort of eighteen tibiae from male and female donors with a mean ± SD age of 70 ± 21 years. Biomechanical stiffness in compression and NIRSI-derived collagen and bound water content all had significant inverse correlations with PI ( r = −0.79, −0.73, and −0.95 and p = 0.002, 0.007, and <0.001, respectively). The MRI-derived bone PI alone was a moderate predictor of bone stiffness ( R 2 = 0.63, p = 0.002), and multivariate analyses showed that neither cortical bone cross-sectional area nor NIRSI values improved bone stiffness prediction compared to PI alone. However, NIRSI-obtained collagen and water data together were a moderate predictor of bone stiffness (R 2 = 0.52, p = 0.04). Our data validates the MRI-derived porosity index as a strong predictor of organic composition of bone and a moderate predictor of bone stiffness, and also provides preliminary evidence that NIRSI measures may be useful in future pre-clinical studies on bone pathology.

Automatic Classifying of Patients With Non-Traumatic Fractures Based on Ultrasonic Guided Wave Spectrum Image Using a Dynamic Support Vector Machine

“…Image processing techniques, like StrAx1.0 software for QCT, propose to obtain cortical porosity (Ct.Po) estimates from gray level of both central QCT [7] and HR-pQCT [10]. Similarly, bone porosity index derived from Magnetic resonance imaging (MRI) has been found correlated to mechanical stiffness [11].…”

Applications of Vibrational Spectroscopy for Analysis of Connective Tissues

“…Further yet, considering that NIR light is non-ionizing, NIR spectroscopy may be applied for the analysis of living tissues and organisms, including in humans [ 51 , 52 , 53 , 54 , 55 , 56 ]. Considerable advances have been made in these applications in the last few years, including in functional NIR imaging [ 57 , 58 ], and tissue imaging [ 42 , 46 , 49 , 59 , 60 ], which shows the potential to provide valuable data on compositional properties associated with tissue quality and health in situ and in vivo.…”

“…Two examples of major advances in vibrational spectroscopy are the coupling of spectrometers to imaging systems and fiber optic probes, which enables an important expansion of the biomedical application of these methods. When coupled to imaging systems [ 1 , 2 , 27 , 31 , 34 , 42 , 46 , 49 , 59 , 60 , 61 , 63 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ], it is possible to obtain hyperspectral images of tissue sections, in which each micron-sized pixel corresponds to a spatially-defined spectrum. Each hyperspectral image can be comprised of arrays of hundreds to thousands of spectra and allow the analysis of multiple individual components based on the selection of specific intensities or absorbances present in the spectra, presenting an excellent source of information on the amount and distribution of tissue components.…”

Quantitative Ultrashort Echo Time (UTE) Magnetic Resonance Imaging of Bone: An Update

“…Although this technique does not estimate the absolute PW content, it gives an estimation of bone porosity. PI in human cadaveric tibiae has shown significant correlations with μCT-based porosity, mechanical stiffness, donor age and collagen estimation from near infrared spectroscopy ( 55 , 56 ). This technique is much faster that the multi-component fitting analyses even though the obtained ration between PW to TW is likely more accurate when calculated with multi-component techniques.…”