With the emergence of energy-resolved x-ray photon counting detectors multi-material spectral x-ray imaging has been made possible. This form of imaging allows the discrimination and quantification of individual materials comprising an inspected anatomical area. However, the acquisition of quantitative material data puts strong requirements on the performance capabilities of a given x-ray system. Scattered radiation is one of the key sources of influencing the quality of material quantification accuracy. The aim of the present investigation was to assess the impact of x-ray scatter on quantitative spectral CT imaging using a pre-clinical photon counting scanner prototype. Acquisitions of a cylindrical phantom with and without scatter were performed. The phantom contained iodine and gadolinium inserts placed at various locations. The projection data was then decomposed onto a water-iodine-gadolinium material basis and reconstructed. An analysis of the resulting iodine and gadolinium material images with and without scatter was conducted. It was concluded that, at an SPR level of up to 3.5%, scatter does not compromise material quantification for all investigated gadolinium concentrations, but for iodine a substantial underestimation was observed. The findings in this study suggest that scatter has a lower impact on K-edge material imaging in comparison with material imaging not featuring a K-edge.
Using lasers with different wavelengths in diffuse optical tomography (spectral DOT) has the advantage that the concentrations of chromophores can be reconstructed quantitatively. In continuous wave spectral DOT, it is furthermore possible to distinguish between scattering and absorption. The choice of the laser wavelengths has a strong impact on how well the scattering parameter and chromophore concentrations can be determined. Current methods to optimize the set of wavelengths disregard the sensitivity of the reconstruction result to uncertainties in the absorption spectra of the chromophores. But since available absorption spectra show significant deviations, it seems to be necessary to take this into account. The wavelength optimization approach presented here is an extension to a method of Corlu et al. The original method optimizes the wavelength sets such that scattering parameters and chromophore concentrations can be separated optimally. We introduce an additional criterion that evaluates the dependence of reconstructed chromophore concentrations on deviations of the extinction coefficients. The wavelength sets found by the new approach are different from those determined with the original method. Reconstructions of simulated data show the effect of using various absorption spectra for reconstruction with different wavelength sets and illustrate the advantages of the new wavelength sets.
Advances in the development of semiconductor based, photon-counting
x-ray detectors stimulate research in
the domain of energy-resolving pre-clinical and clinical computed tomography (CT). For counting detectors
acquiring x-ray attenuation in at least three different energy windows, an extended basis component decomposition
can be performed in which in addition to the conventional approach of Alvarez and Macovski a
third basis component is introduced, e.g., a gadolinium based CT contrast material. After the decomposition
of the measured projection data into the basis component projections, conventional filtered-backprojection
reconstruction is performed to obtain the basis-component images. In recent work, this basis component decomposition
was obtained by maximizing the likelihood-function of the measurements. This procedure is time
consuming and often unstable for excessively noisy data or low intrinsic energy resolution of the detector.
Therefore, alternative procedures are of interest. Here, we introduce a generalization of the idea of empirical
dual-energy processing published by Stenner et al. to multi-energy, photon-counting CT raw data. Instead of
working in the image-domain, we use prior spectral knowledge about the acquisition system (tube spectra, bin
sensitivities) to parameterize the line-integrals of the basis component decomposition directly in the projection
domain. We compare this empirical approach with the
maximum-likelihood (ML) approach considering image
noise and image bias (artifacts) and see that only moderate noise increase is to be expected for small bias in the
empirical approach. Given the drastic reduction of pre-processing time, the empirical approach is considered
a viable alternative to the ML approach.
We present a method to enhance tumor detectability in breasts imaged with our optical fluorescence mammography
system. During a measurement, transmission data at 4 wavelengths and fluorescence data for excitation at 1 wavelength
are collected after injection of an optical contrast agent. The data are reconstructed into 3D images of the absorption and
fluorescence distributions. Combining those images enables the identification of various breast tissue compounds. Here,
we investigate the relevance of our method in phantom experiments.
Using multiple lasers in continuous wave diffuse optical tomography has the advantages that scattering and absorption
can be distinguished, and that physiological parameters (chromophore concentrations) can be reconstructed. The choice
of the laser wavelengths is crucial to ensure a good separability of scattering and chromophores. Current methods to
optimize the wavelengths do not consider the sensitivity of the reconstruction result to deviations of extinction
coefficients of the chromophores. But since the available absorption spectra for the individual chromophores show
significant deviations, it seems to be necessary to take this into account when optimizing the wavelengths. The
wavelength optimization approach presented here is an extension of a method of Corlu et al. An additional criterion is
introduced, which evaluates the dependence of reconstructed chromophore concentrations on deviations of the
absorption coefficients. The wavelengths found by the new approach are compared to those resulting from the original
method. Reconstructions of simulated data show the effect of using various spectra for reconstruction with different
wavelength sets and illustrate the advantages of the new wavelength sets, leading to less crosstalk between the
chromophore concentrations and lower artifacts.
Diffuse optical tomography is a non-invasive method aiming at the detection of breast cancer. The sensitivity and
specificity of the method can be increased if a fluorescent contrast agent is used that accumulates in malignant
lesions. Recently, Philips developed an optical scanner, where the patient is lying on a bed, with one breast
hanging freely in a cup containing an optical matching fluid. 507 optical fibers are mounted in the surface of
the measurement cup. The breast is illuminated sequentially by half of these fibers while the other half is used
to collect the light that is emanating from the breast. The system uses near-infrared light of continuous wave
solid-state lasers to illuminate the breast at four different wavelengths. A complete measurement takes less than
ten minutes and involves five breast scans: transmission data are collected for four wavelengths, and fluorescence
data for excitation at one wavelength. Here, we present the image reconstruction scheme and a novel method to
assess the system performance in terms of lesion detectability. This method uses a statistical significance test on
simulated data with and without a lesion. It allows the quantification of the detectability of lesions for different
size, position, or contrast of the lesion. It also allows to analyze the potential impact of system improvements
or to judge the performance of an image reconstruction algorithm.