  We propose a 3D neural network with specific loss functions for quantitative
computed tomography (QCT) noise reduction to compute micro-structural
parameters such as tissue mineral density (TMD) and bone volume ratio (BV/TV)
with significantly higher accuracy than using no or standard noise reduction
filters. The vertebra-phantom study contained high resolution peripheral and
clinical CT scans with simulated in vivo CT noise and nine repetitions of three
different tube currents (100, 250 and 360 mAs). Five-fold cross validation was
performed on 20466 purely spongy pairs of noisy and ground-truth patches.
Comparison of training and test errors revealed high robustness against
over-fitting. While not showing effects for the assessment of BMD and
voxel-wise densities, the filter improved thoroughly the computation of TMD and
BV/TV with respect to the unfiltered data. Root-mean-square and accuracy errors
of low resolution TMD and BV/TV decreased to less than 17% of the initial
values. Furthermore filtered low resolution scans revealed still more TMD- and
BV/TV-relevant information than high resolution CT scans, either unfiltered or
filtered with two state-of-the-art standard denoising methods. The proposed
architecture is threshold and rotational invariant, applicable on a wide range
of image resolutions at once, and likely serves for an accurate computation of
further micro-structural parameters. Furthermore, it is less prone for
over-fitting than neural networks that compute structural parameters directly.
In conclusion, the method is potentially important for the diagnosis of
osteoporosis and other bone diseases since it allows to assess relevant 3D
micro-structural information from standard low exposure CT protocols such as
100 mAs and 120 kVp.
