Randomly Sparsified Synthesis for Model-Based Deformation Analysis

Reinhold, Stefan; Jordt, Andreas; Koch, Reinhard

doi:10.1007/978-3-319-45886-1_12

Cited by 1 publication

(1 citation statement)

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“…Since this full synthesis is very expensive computationally, we simplify the scanning process to a blur, implemented by a convolution with the point spread functions (PSF) of the system. To simplify the synthesis further, we make use of sparse synthesis [14,23] where not the whole image is synthesized, but only a sparse subset of it. Here, the result of the sparse synthesis is a set of one dimensional profiles orthogonal to the cortical surface, equivalent to a sparse sampling of a full synthesis 1 .…”

Section: Introductionmentioning

confidence: 99%

An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT

Reinhold

Damm

Huber

et al. 2019

Lecture Notes in Computer Science

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Quantitative computed tomography (QCT) is a widely used tool for osteoporosis diagnosis and monitoring. The assessment of cortical markers like cortical bone mineral density (BMD) and thickness is a demanding task, mainly because of the limited spatial resolution of QCT. We propose a direct model based method to automatically identify the surface through the center of the cortex of human vertebra. We develop a statistical bone model and analyze its probability distribution after the imaging process. Using an as-rigid-as-possible deformation we find the cortical surface that maximizes the likelihood of our model given the input volume. Using the European Spine Phantom (ESP) and a high resolution µCT scan of a cadaveric vertebra, we show that the proposed method is able to accurately identify the real center of cortex ex-vivo. To demonstrate the in-vivo applicability of our method we use manually obtained surfaces for comparison.

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Section: Introductionmentioning

confidence: 99%