X-ray fluorescence tomography (XFCT) has potential for high-resolution 3D molecular x-ray bio-imaging. In this
technique the fluorescence signal from targeted nanoparticles (NPs) is measured, providing information about the spatial
distribution and concentration of the NPs inside the object. However, present laboratory XFCT systems typically have
limited spatial resolution (>1 mm) and suffer from long scan times and high radiation dose even at high NP
concentrations, mainly due to low efficiency and poor signal-to-noise ratio.
We have developed a laboratory XFCT system with high spatial resolution (sub-100 μm), low NP concentration and
vastly decreased scan times and dose, opening up the possibilities for in-vivo small-animal imaging research. The system
consists of a high-brightness liquid-metal-jet microfocus x-ray source, x-ray focusing optics and an energy-resolving
photon-counting detector. By using the source’s characteristic 24 keV line-emission together with carefully matched
molybdenum nanoparticles the Compton background is greatly reduced, increasing the SNR. Each measurement
provides information about the spatial distribution and concentration of the Mo nanoparticles. A filtered back-projection
method is used to produce the final XFCT image.
The NanoXCT project aims at developing a laboratory nano-CT system for non-destructive testing applications in the
micro- and nano-technology sector. The system concept omits the use of X-ray optics, to be able to provide up to 1 mm
FOV (at 285 nm voxel size) and down to 50 nm voxel size (at 0.175 mm FOV) while preserving the flexibility of stateof-
the-art micro-CT systems. Within the project a suitable X-ray source, detector and manipulation system are being
developed. To cover the demand for elemental analysis, the project will additionally include X-ray spectroscopic
techniques. These will be reported elsewhere while this paper is focused on the imaging part of the project. We introduce
the system concept including design goals and constraints, and the individual components. We present the current state
of the prototype development including first results.
We use propagation-based phase-contrast X-ray imaging with gas as contrast agent to visualize the microvasculature in small animals like mice and rats. The radiation dose required for absorption X-ray imaging is proportional to the minus fourth power of the structure size to be detected. This makes small vessels impossible to image at reasonable radiation doses using the absorption of conventional iodinated contrast agents. Propagation-based phase contrast gives enhanced contrast for high spatial frequencies by moving the detector away from the sample to let phase variations in the transmitted X-rays develop into intensity variations at the detector. Blood vessels are normally difficult to observe in phase contrast even with iodinated contrast agents as the density difference between blood and most tissues is relatively small. By injecting gas into the blood stream this density difference can be greatly enhanced giving strong phase contrast. One possible gas to use is carbon dioxide, which is a clinically accepted X-ray contrast agent. The gas is injected into the blood stream of patients to temporarily displace the blood in a region and thereby reduce the X-ray absorption in the blood vessels. We have shown that this method can be used to image blood vessels down to 8 μm in diameter in mouse ears. The low dose requirements of this method indicate a potential for live small-animal imaging and longitudinal studies of angiogenesis.
X-ray phase-contrast imaging has been developed as an alternative to conventional absorption imaging, partly for its
dose advantage over absorption imaging at high resolution. Grating-based imaging (GBI) and propagation-based
imaging (PBI) are two phase-contrast techniques used with polychromatic laboratory sources. We compare the two
methods by experiments and simulations with respect to required dose. A simulation method based on the projection
approximation is designed and verified with experiments. A comparison based on simulations of the doses required for
detection of an object with respect to its diameter is presented, showing that for monochromatic radiation, there is a dose
advantage for PBI for small features but an advantage for GBI at larger features. However, GBI suffers more from the
introduction of polychromatic radiation, in this case so much that PBI gives lower dose for all investigated feature sizes.
Furthermore, we present and compare experimental images of biomedical samples. While those support the dose
advantage of PBI, they also highlight the GBI advantage of quantitative reconstruction of multimaterial samples. For all
experiments a liquid-metal-jet source was used. Liquid-metal-jet sources are a promising option for laboratory-based
phase-contrast imaging due to the relatively high brightness and small spot size.
X-ray tomography of small animals is an important tool for medical research. For high-resolution x-ray imaging of
few-cm-thick samples such as, e.g., mice, high-brightness x-ray sources with energies in the few-10-keV range are
required. In this paper we perform the first small-animal imaging and tomography experiments using
liquid-metal-jet-anode x-ray sources. This type of source shows promise to increase the brightness of microfocus x-ray
systems, but present sources are typically optimized for an energy of 9 keV. Here we describe the details of a
high-brightness 24-keV electron-impact laboratory microfocus x-ray source based on continuous operation of a heated
liquid-In/Ga-jet anode. The source normally operates with 40 W of electron-beam power focused onto the metal jet,
producing a 7×7 μm2 FWHM x-ray spot. The peak spectral brightness is 4 × 109 photons / ( s × mm2 × mrad2 × 0.1%BW) at the
24.2 keV In Kα line. We use the new In/Ga source and an existing Ga/In/Sn source for high-resolution imaging and
tomography of mice.
We investigate the possibility of using x-ray in-line phase-contrast imaging with gaseous carbon dioxide as contrast
agent to visualize small blood vessels. These are difficult to image at reasonable radiation doses using the absorption of
conventional iodinated contrast agents. In-line phase contrast is a method for retrieving information on the electron
density of the sample as well as the absorption, by moving the detector away from the sample to let phase variations in
the transmitted x-rays develop into intensity variations at the detector. Blood vessels are normally difficult to observe in
phase contrast even with iodinated contrast agents as the density difference compared to most tissues is small. Carbon
dioxide is a clinically accepted x-ray contrast agent. The gas is injected into the blood stream of patients to temporarily
displace the blood in a region and thereby reduce the x-ray absorption in the blood vessels. This gives a large density
difference which is ideal for phase-contrast imaging. We demonstrate the possibilities of the method by imaging the
arterial system of a rat kidney injected with carbon dioxide. Vessels down to 23 μm in diameter are shown. The method
shows potential for live small-animal imaging.
Phase contrast in X-ray imaging offers imaging of fine features at lower doses than absorption. Of the phasecontrast
methods in use in-line phase contrast is interesting due to its experimental simplicity, but to extract
information on absorption and phase distributions from the resulting images, phase retrieval is needed. Many
phase-retrieval methods suitable for different situations have been developed, but few comparisons of those
methods done. We consider a sub-group of phase-retrieval methods that are suitable for tomography, i.e., that
use only one exposure (for practical experimental reasons) and are non-iterative (for speed). In total we have
found seven suitable methods in the literature. All, though derived in different ways under different assumptions,
follow the same pattern and can be outlined as a single method where each specific version is marked by variations
in particular steps. We summarize this unified approach, and give the variations of the individual methods. In
addition, we outline approximations and assumptions of each method. Using this approach it is possible to
conclude which specific algorithms are most suitable in specific situations and to test this based on simulated
and experimental data. Ultimately, this leads to conclusions on which methods are the most suitable in different