Computational atlases based on nonrigid registration have found much use in the medical imaging community.
To avoid bias to any single element of the training set, there are two main approaches: using a (random) subject
to serve as an initial reference and posteriorly removing bias, and a true groupwise registration with a constraint
of zero average transformation for direct computation of the atlas. Major drawbacks are the possible selection
of an outlier on one side, and an initialization with an invalid instance on the other. In both cases there is great
potential for affecting registration performance, and producing a final average image in which the structure of
interest deviates from the central anatomy of the population under study.
We propose an inexpensive means of reference selection based on a groupwise correspondence measure, which
avoids the selection of an outlier and is independent from the atlas construction approach that follows. Thus,
it improves tractability of reference selection and robustness of automated atlas construction. We illustrate the
method using a set of 20 cardiac multislice computed tomography volumes.
KEYWORDS: Dual energy x-ray absorptiometry, Bone, 3D image reconstruction, 3D modeling, 3D image processing, Data modeling, Statistical analysis, Image registration, Minerals, In vitro testing
Area Bone Mineral Density (aBMD) measured by Dual-energy X-ray Absorptiometry (DXA) is an established
criterion in the evaluation of hip fracture risk. The evaluation from these planar images, however, is limited
to 2D while it has been shown that proper 3D assessment of both the shape and the Bone Mineral Density
(BMD) distribution improves the fracture risk estimation. In this work we present a method to reconstruct both
the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image. A statistical
model of shape and a separate statistical model of the BMD distribution were automatically constructed from
a set of Quantitative Computed Tomography (QCT) scans. The reconstruction method incorporates a fully
automatic intensity based 3D-2D registration process, maximizing the similarity between the DXA and a digitally
reconstructed radiograph of the combined model. For the construction of the models, an in vitro dataset of
QCT scans of 60 anatomical specimens was used. To evaluate the reconstruction accuracy, experiments were
performed on simulated DXA images from the QCT scans of 30 anatomical specimens. Comparisons between
the reconstructions and the same subject QCT scans showed a mean shape accuracy of 1.2mm, and a mean
density error of 81mg/cm3. The results show that this method is capable of accurately reconstructing both the
3D shape and 3D BMD distribution of the proximal femur from DXA images used in clinical routine, potentially
improving the diagnosis of osteoporosis and fracture risk assessments at a low radiation dose and low cost.
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