X-ray scattering and diffraction enhanced imaging studies of in vitro breast tissues

This thesis is a study of the x-ray scattering properties of tissues and tumours of the breast. Clinical radiography is based on the absorption of the x-rays when passing right through the human body and gives information about the densities of the tissues. Besides being absorbed, x-rays may change their direction within the tissues due to elastic scattering or even to refraction. The phenomenon of scattering is a nuisance to radiography in general, and to mammography in particular, because it reduces the quality of the images. However, scattered x-rays bear very useful information about the structure of the tissues at the supra-molecular level. Some pathologies, like breast cancer, produce alterations to the structures of the tissues, being especially evident in collagen-rich tissues. On the other hand, the change of direction due to refraction of the x-rays on the tissue boundaries can be mapped. The diffraction enhanced imaging (DEI) technique uses a perfect crystal to convert the angular deviations of the x-rays into intensity variations, which can be recorded as images. This technique is of especial interest in the cases were the densities of the tissues are very similar (like in mammography) and the absorption images do not offer enough contrast. This thesis explores the structural differences existing in healthy and pathological collagen in breast tissue samples by the small-angle x-ray scattering (SAXS) technique and compares these differences with the morphological information found in the DEI images and the histo-pathology of the same samples. Several breast tissue samples were studied by SAXS technique in the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. Scattering patterns of the different tissues of the breast were acquired and compared with the histology of the samples. The scattering signals from adipose tissue (fat), connective tissue (collagen) and necrotic tissue were identified. Moreover, a clear distinction could be done between the scattering signals from healthy collagen and from collagen from an invasive tumour. Scattering from collagen is very characteristic. It includes several scattering peaks and scattering features that carry information about the size and the spacing of the collagen fibrils in the tissues. It was found that the collagen fibrils in invaded tumours were thinner and had a d-spacing length 0,7% longer that fibrils from healthy tumours. The scattering signals from the breast tissues were compared with the histology by building colour-coded maps across the samples. They were also imaged with the DEI technique. There was a total agreement between the scattering maps, the morphological features seen in the images and the information of the histo- pathological examination. The thesis demonstrates that the x-ray scattering signal can be used to characterize tissues and that it carries important information about the pathological state of the breast tissues, thus showing the potential of the SAXS technique as a possible diagnostic tool for breast cancer.

Improving GPS time series for geodynamic studies

Accurate and stable time series of geodetic parameters can be used to help in understanding the dynamic Earth and its response to global change. The Global Positioning System, GPS, has proven to be invaluable in modern geodynamic studies. In Fennoscandia the first GPS networks were set up in 1993. These networks form the basis of the national reference frames in the area, but they also provide long and important time series for crustal deformation studies. These time series can be used, for example, to better constrain the ice history of the last ice age and the Earth s structure, via existing glacial isostatic adjustment models. To improve the accuracy and stability of the GPS time series, the possible nuisance parameters and error sources need to be minimized. We have analysed GPS time series to study two phenomena. First, we study the refraction in the neutral atmosphere of the GPS signal, and, second, we study the surface loading of the crust by environmental factors, namely the non-tidal Baltic Sea, atmospheric load and varying continental water reservoirs. We studied the atmospheric effects on the GPS time series by comparing the standard method to slant delays derived from a regional numerical weather model. We have presented a method for correcting the atmospheric delays at the observational level. The results show that both standard atmosphere modelling and the atmospheric delays derived from a numerical weather model by ray-tracing provide a stable solution. The advantage of the latter is that the number of unknowns used in the computation decreases and thus, the computation may become faster and more robust. The computation can also be done with any processing software that allows the atmospheric correction to be turned off. The crustal deformation due to loading was computed by convolving Green s functions with surface load data, that is to say, global hydrology models, global numerical weather models and a local model for the Baltic Sea. The result was that the loading factors can be seen in the GPS coordinate time series. Reducing the computed deformation from the vertical time series of GPS coordinates reduces the scatter of the time series; however, the long term trends are not influenced. We show that global hydrology models and the local sea surface can explain up to 30% of the GPS time series variation. On the other hand atmospheric loading admittance in the GPS time series is low, and different hydrological surface load models could not be validated in the present study. In order to be used for GPS corrections in the future, both atmospheric loading and hydrological models need further analysis and improvements.

Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.