Synopsis

Quantification of bone mineral density (BMD) and/or bone fraction (BF) has long been a goal of MRI research, but thus far no method has successfully translated into clinical practice. Methods including R2* mapping, susceptibility mapping and ultrashort echo time MRI have shown promise but remain difficult to implement. A simpler approach to BMD/BF quantification is to measure the signal loss occurring in bone-containing voxels compared to the signal occurring in a bone-free voxel. We propose a method called SyNthetic Auto-interpolated in-Phase (SNAP) imaging which uses this principle and accounts for spatial variations in B1/coil sensitivity, enabling practical estimation of BMD/BF.

Introduction

MRI-based quantification of bone mineral density (BMD) and/or bone fraction (BF) has potential clinical utility in diseases of bone marrow. Whilst a number of methods have shown promise, each has inherent pitfalls: R2* measurements correlate with BMD but are confounded by variations in fat fraction (FF)¹; quantitative susceptibility mapping (QSM)² is sensitive to both phase errors and variations in FF³; UTE/ZTE acquisitions⁴ remain difficult to implement and are not widely available.

Ho et al. proposed an alternative approach to BMD/BF quantification, whereby the reduction in signal due to bone occupying space within a voxel is normalised to an estimated ‘maximum’ signal that would occur in a voxel without any zero-signal minerals⁵. For application to the knee, Ho et al. assumed a fixed maximum (i.e. the highest signal intensity in the image). However, this is inaccurate if there is inhomogeneity in the B1 field or receive coil, for example at high field or in the abdomen.

Here, we propose a method called SyNthetic Auto-interpolated in-Phase (SNAP) imaging, in which a hypothetical background maximum signal estimate is derived from pixels in a local neighbourhood, rather than from the entire image. We demonstrate this approach in phantoms and in the sacrum of a volunteer.

Theory

The fat and water signals from a voxel are decomposed using a fat-water separation algorithm such as IDEAL⁶. T1-bias is minimised by using a low flip angle; T2* relaxation and the spectral complexity of fat are accounted for through incorporation into the signal model⁷. A ‘synthetic’ in-phase (sIP) image is generated by summing the resulting fat and water images. It is assumed that bone consists of only fat, water and zero-signal mineral. If the signal occurring in a voxel with no mineral (S_BMD=0) is known, BMD/BF can be estimated based on the reduction of signal in each voxel against this baseline. Our proposed approach to determining a spatially varying S_BMD=0 baseline is as follows (see also Figure 1).

It is assumed that voxels consisting of (almost) pure fat (FF>90%) or pure water (FF<10%) are not within bone and do not contain mineral (in the pelvis, voxels meeting these criteria predominantly correspond to adipose tissue and muscle respectively). These voxels are used as ‘seed voxels’ to derive a spatially varying estimate of S_BMD=0. The missing parts of the image are interpolated to give an estimate of S_BMD=0 for the entire image. The BF in each voxel is then calculated using:

$$BF=\frac{S_{BMD=0}-S_{sIP}}{S_{BMD=0}}$$

where S_sIP is the signal in the sIP image.

BF can, in turn, be used to calculate BMD using $$$BMD=BF\cdot ρ_{mineral}$$$.

Methods

We conducted a series of phantom experiments on a Philips Ingenia 3T system to validate this method:

1. Hydroxyapatite was added to tubes containing 3% agar (with BMD values varying from 0-0.5g/cm³), which were set in a matching agar surround (Figure 2b). The phantom was scanned using mDixon Quant (a vendor-supplied proton density fat-fraction package¹ - acquisition parameters in Figure 2a). The tubes containing mineral were excluded from the seed voxel image. The sIP image was calculated as in Figure 1; interpolation was performed using a smoothing spline line-by-line across the image (implemented using the MATLAB ‘smoothingspline’ function; Figure 3a).

2. A second phantom with tubes containing a fat-agar mixture (FF=50%), with identical BMD values to the first phantom, was scanned with the same acquisition parameters and post-processing.

For both phantoms, measured BMD/BF was compared with reference BMD/BF using linear regression.

To provide proof-of-principle in vivo, we generated BMD images from the sacrum of a single subject (Figure 5).

Results and discussion

Interpolation results for the two phantoms are shown in Figure 3 (a-d and e-h for experiments 1 and 2 respectively). In both phantoms, a smooth fitted surface (c,g) was produced from the masked images (b,f), enabling measurement of the signal reduction due to hydroxyapatite in each of the tubes; an example line profile is shown (d,h).

Results of the regression analysis between reference and measured BF/BMD are shown in Figures 4c and 4d. There was a significant linear relationship between measured BMD/BF and reference BMD/BF for both phantoms. The proposed method was sensitive to changes in BMD over the range expected in human bone marrow, suggesting potential clinical utility for disorders causing both new bone formation (increased BMD) and bone destruction (reduced BMD), such as spondyloarthritis³ and multiple myeloma.

Conclusions

Acknowledgements

References

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