Maximilian Nikolaus Diefenbach1, Anh T. Van2, Jakob Meineke3, Hendrik Kooijman4, Axel Haase2, Ernst J. Rummeny5, Jan S. Kirschke6, Thomas Baum1, and Dimitrios C. Karampinos5
1Department of Diagnostic and Interventional Radiology, Technische Univeristät München, Munich, Germany, 2Zentralinstitut für Medizintechnik, Technische Universität München, Munich, Germany, 3Philips Research Laboratory, Hamburg, Germany, 4Philips Healthcare, Hamburg, Germany, 5Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany, 6Section of Neuroradiology, Technische Universität München, Munich, Germany
Synopsis
Trabecular
bone imaging has a high clinical significance for predicting fracture risk in
patients with osteoporosis. Quantitative susceptibility mapping (QSM) has
been recently emerging for mapping diamagnetic and paramagnetic substances. Recent
reports attempted to use QSM combined with ultra-short echo time imaging for
mapping the susceptibility of cortical bone. However, it remains unknown
whether QSM is feasible for measuring bone volume density in trabecular bone, where
the bone density is much lower than cortical bone. The purpose of
the present work is to study the feasibility of QSM for trabecular bone density
mapping, using numerical simulations, specimen measurements and
preliminary in vivo measurements.Purpose
Trabecular
bone imaging has a high clinical significance for predicting fracture risk in
patients with osteoporosis [1-5]. Quantitative susceptibility mapping (QSM) has
been recently emerging for mapping diamagnetic and paramagnetic substances [6]. Recent
reports attempted to use QSM combined with ultra-short echo time imaging for
mapping the susceptibility of cortical bone [7,8]. However, it remains unknown
whether QSM is feasible for measuring bone volume density in trabecular bone, where
the bone density is much lower than cortical bone. The purpose of
the present work is to study the feasibility of QSM for trabecular bone density
mapping, using numerical simulations, specimen measurements and
preliminary in vivo measurements.
Methods & Results
Simulations
From a micro CT dataset of a
healthy vertebral body a bone mask was derived by cropping out a cube containing
only trabecular bone structure and applying a simple threshold for
segmentation (Figure 1). Assigning the susceptibility of bone and water [5] inside and
outside the mask respectively yielded an estimate on the susceptibility map of
the bone cube. To match the simulation to the in vitro scan the bone cube was
isotropically surrounded by water. Next an object-based fieldmap was computed
in a forward simulation comparable to [9,10]. The resulting fieldmap was used
to simulate a complex 6-echo signal at 3T with TE1=2ms and ΔTE=1ms.
Since this signal had the high resolution of the original micro CT image
(0.055mm isotropic) the data was down-sampled to a MR achievable resolution of
0.5mm isotropic. Afterwards a fieldmap was fitted to the phase of the down-sampled
signal, which was passed to the MEDI QSM algorithm [12-15] that computed a
susceptibility map for the MR-like signal. The averaged susceptibility in the
QSM image inside and outside the bone cube was then put into relation with the
bone volume to total volume ratio (BV/TV) of the original bone mask. The
processing chain was repeated multiple times, where, before simulating the
object-based fieldmap, the chimap was eroded with standard image processing
tools to simulate the degeneration of trabecular bone as it happens in
osteoporosis. In the first row of Figure 2 three object-based chimaps are
depicted that differ by approximately 10% in BV/TV. The bottom row shows the
resulting QSM chimaps with their average susceptibility differences plotted in
Figure 5.
In Vitro Measurements
A
cubic bone specimen from a human femur with roughly the same size and shape as
the numerical phantom was scanned in a 3 T scanner (Ingenia, Philips
Healthcare) with a 12-echo gradient-echo sequence at 0.5 mm isotropic
resolution using the wrist coil with TE1=3.37ms and ΔTE=1.74ms. The same QSM
method was applied to the fieldmap resulting from a water-fat separation separation [11].
No BV/TV values could be obtained, but the resulting averaged susceptibility differences
between the inside of the bone cube and the outside water environment was -0.33ppm
(Figure 3).
In Vivo Measurements
The
knee of a healthy volunteer was also scanned using a knee coil. A 12-echo
gradient-echo sequence with TE1=3.37ms, ΔTE=1.83ms was used
for QSM (voxel size=0.9x0.9x0.9 mm3) and a high-resolution balanced SSFP
sequence (with 2 phase cycles) was performed for high-resolution trabecular bone
imaging (voxel size=0.3x0.3x0.45mm3). The resulting data was passed
through a water-fat separation algorithm [7] that estimates the fieldmap, which
was processed again with the MEDI algorithm [12-15]. For one slice in the high
resolution scan (see Figure 4) two ROIs covering the trabecular bone in the
femur (red) and the patella (green) were defined. With a 50% threshold on the ROIs
in the high resolution image the BV/TV in the trabecular bone regions were
estimated resulting in a BV/TV of approximately 34% in the femur and 65% in the
patella. In the QSM result (Figure 4) of the low resolution scan the averaged
susceptibility in ROIs inside the femur (blue) and the patella (orange) are
referenced to a fat only region (pink). $$$\Delta \chi$$$ in the patella was around
-0.14ppm, whereas the femur showed a difference of 0.18ppm. Figure 5 also
summarizes the findings of the in vivo measurements.
Discussion & Conclusion
The
present simulations show that magnetic susceptibility in trabecular bone
regions lies in the range between 0 and 0.3ppm, and the in vitro and in vivo
measurements show that trabecular bone QSM was able to give reasonable results
for trabecular bone density mapping at 3T. Further work is needed in order
to overcome problems with background field removal and the presence of fat. However, the present
work demonstrates the feasibility of QSM for trabecular bone density mapping.
Acknowledgements
The present work was supported by Philips Healthcare.References
[1]
Link TM, Radiology 263:3, 2012
[2]
Wehrli, J Magn Reson Imaging 25:390, 2007
[3]
Krug, Radiol Clin North Am 48:601, 2010
[4]
Wehrli, NMR Biomed 19:731, 2006
[5]
Buch, Magn Reson Med 73:2185, 2015
[6]
Wang, Magn Reson Med 73:82, 2015
[7]
Dimov, Proc. ISMRM 2015, p.938
[8]
He, Proc. ISMRM 2015, p. 1725
[9]
Sharma, Magn Reson Med 73:597, 2015
[10]
Koch, Phys Med 51:6381, 2006
[11]
Hernando, Magn Res Med 63:79, 2010
[12] T. Liu et al. MRM 69:467, 2013
[13] J. Liu et al. Neuroimage 59:2560, 2012.
[14] T. Liu et al. MRM 66:777, 2011
[15] de Rochefort et al. MRM 63:194, 2010