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    • For a better three dimensional impression Fig provides

    2018-10-30

    For a better three-dimensional impression Fig. 4 provides exemplary views of volume renderings of the acquired datasets. Bones were segmented from the attenuation signal, whereas lung tissue was extracted from the DFCTs and represented by a semi-transparent hot color map to enhance inter- and intra-pulmonary structural variations. The 3D views confirm the earlier findings from the 2D slices: The control lung exhibits a strong homogeneous scattering pattern all over its volume, whereas the BLZ945 lung shows reduced scattering in the periphery and the fibrotic lung lacks scattering areas in the central, peribronchial regions. In the supplemental material, we provide an animation of the rendered multi-modal volumes (see Movie S2) to allow for a three-dimensional assessment from different viewing angles. The strong difference in the identically windowed scatter-based gray-values between the cases (Fig. 2 g–l) suggests that differentiation between diseased and healthy tissue merely based on DF signal strength is possible. For a closer analysis, the histograms of the three lung-tissue volumes (including a non-scattering soft-tissue margin) are shown in Fig. 5. In attenuation-based CT – and recently in phase-contrast CT (Donath et al., 2010) – , universal Hounsfield Units (HU) were established to facilitate comparability of data acquired at different scanners. As an analogue approach, we introduce scatter-based Hounsfield Units (HUs). The established HU scales are defined via their tomographically reconstructed signals for water (0HU) and for air (−1000HU), such that the HU number for tissue x is calculated via the linear attenuation coefficient in case of the attenuation HU scale: However, this definition cannot be transferred to DFCT, since both water and air do not scatter and thus produce identical signals. Therefore, we suggest the usage of a zero-scatter reference area to determine 0HUs. This can be either water or air, or – in our case – non-scattering solid soft-tissue, as it is included in the histogram-analyzed volume for each of the three cases. The non-scattering parts form the three overlapping peaks to the left of the histogram range. To span a range of 1000HUs we propose the peak position of the strongest scatterer in our measurements, the control lung, as reference point for 1000HUs. Using the linear diffusion coefficient as a measure for the scattering strength of a material x as suggested by Bech et al. (Bech et al., 2010), we obtain the definitionwhere is the linear diffusion coefficient of a non-scattering area, so ideally equal to 0, and εControl describes healthy lung tissue. Hereby, destruction of alveolar structures results in a shift of the main pulmonary peak towards the zero-scatter point, as it is the case for the emphysematous lung with a peak position at approximately 373HUs. Elimination of the air-filled voids is so advanced in some areas of the fibrotic lung, that the pulmonary peak merges into the zero-scatter peak in this case. The increased peak height of the zero-scatter peak reflects the substitution of those voids by non-scattering solid soft tissue.
    Discussion The presented results reflect a proof-of-principle demonstration of in vivo DFCT imaging and its possible benefits in non-invasively evaluating pulmonary pathophysiology. For a detailed analysis of sensitivity and specificity for diagnosing various lung diseases, studies with larger sample sizes and several disease stages are required. To this end, our results show that this is in general feasible as dose-compatibility for longitudinal studies on small animals is given. Moreover, a whole atlas of healthy lung measurements should be developed to serve for sound establishment of the suggested lung-specific HUs reference point, and is beyond the scope of this proof-of-principle study. Ultimately, a standardized lung phantom needs to be developed based on this atlas to guarantee reliable comparability of the HUs scale from different scanners, because the DF signal strength depends on various parameters such as feature (alveolar) size, x-ray energy or interferometer specifications. We also want to point out that the applicability of a lung-based HUs scale to other DFI applications, for example bones (Potdevin et al., 2012), remains to be verified, since alternate applications may demand setups of higher sensitivity.