Due to the recent development of transmission X-ray tubes with very small focal spot sizes, laboratory-based CT imaging with sub-micron resolutions is nowadays possible. We recently developed a novel X-ray nanoCT setup featuring a prototype nanofocus X-ray source and a single-photon counting detector. The system is based on mere geometrical magnification and can reach resolutions of 200 nm. To demonstrate the potential of the nanoCT system for biomedical applications we show high resolution nanoCT data of a small piece of human tooth comprising coronal dentin. The reconstructed CT data clearly visualize the dentin tubules within the tooth piece.
Compared to conventional computed tomography (CT), dual energy CT allows for improved material decomposition by conducting measurements at two distinct energy spectra. Since radiation exposure is a major concern in clinical CT, there is a need for tools to reduce the noise level in images while preserving diagnostic information. One way to achieve this goal is the application of image-based denoising algorithms after an analytical reconstruction has been performed. We have developed a modified dictionary denoising algorithm for dual energy CT aimed at exploiting the high spatial correlation between between images obtained from different energy spectra. Both the low-and high energy image are partitioned into small patches which are subsequently normalized. Combined patches with improved signal-to-noise ratio are formed by a weighted addition of corresponding normalized patches from both images. Assuming that corresponding low-and high energy image patches are related by a linear transformation, the signal in both patches is added coherently while noise is neglected. Conventional dictionary denoising is then performed on the combined patches. Compared to conventional dictionary denoising and bilateral filtering, our algorithm achieved superior performance in terms of qualitative and quantitative image quality measures. We demonstrate, in simulation studies, that this approach can produce 2d-histograms of the high- and low-energy reconstruction which are characterized by significantly improved material features and separation. Moreover, in comparison to other approaches that attempt denoising without simultaneously using both energy signals, superior similarity to the ground truth can be found with our proposed algorithm.
Scanning times have always been an important issue in x-ray micro-tomography. To reach high-quality reconstructions the exposure times for each projection can be very long due to small detector pixel sizes and limited flux of x-ray sources. In addition, the required number of projections is a factor which limits a reduction of exposure beyond a certain level. This applies particularly to grating-based phase-contrast computed tomography (PCCT), as several images per projection have to be acquired in order to obtain absorption, phase and dark-field information. In this work we qualitatively compare statistical iterative reconstruction (SIR) and filtered back-projection (FBP) reconstruction from undersampled projection data based on a formalin-fixated mouse sample measured in a grating-based phase-contrast small-animal scanner. The results from our assessment illustrate that SIR offers not only significantly higher image quality, but also enables high-resolution imaging from severely undersampled data in comparison to the FBP algorithm. Therefore, the application of advanced iterative reconstruction methods in micro-tomography entails major advantages over state-of-the-art FBP reconstruction while offering the opportunity to shorten scan durations via a reduction of exposure time per projection and number of angular views.