X-ray phase contrast tomography from whole organ down to single cells

Abstract

We use propagation based hard x-ray phase contrast tomography to explore the three dimensional structure of neuronal tissues from the organ down to sub-cellular level, based on combinations of synchrotron radiation and laboratory sources. To this end a laboratory based microfocus tomography setup has been built in which the geometry was optimized for phase contrast imaging and tomography. By utilizing phase retrieval algorithms, quantitative reconstructions can be obtained that enable automatic renderings without edge artifacts. A high brightness liquid metal microfocus x-ray source in combination with a high resolution detector yielding a resolution down to 1.5 µm. To extend the method to nanoscale resolution we use a divergent x-ray waveguide beam geometry at the synchrotron. Thus, the magnification can be easily tuned by placing the sample at different defocus distances. Due to the small Fresnel numbers in this geometry the measured images are of holographic nature which poses a challenge in phase retrieval.

Probing Intracellular Element Concentration Changes during Neutrophil Extracellular Trap Formation Using Synchrotron Radiation Based X-Ray Fluorescence

“…Synchrotron radiation induced X-ray fluorescence distinguishes itself due to (ultra) trace level sensitivity (down to 100 ppb in imaging mode) with superior sub-micrometer resolution (currently at the 10 nm scale), deep penetrating nature, low susceptibility for contaminations and non-destructive character. Recently, also novel X-ray imaging techniques have become available such as X-ray phase contrast tomography providing morphological information on different length scales from organ to sub-cellular level [18, 19] and X-ray based scanning coherent diffraction imaging (CDI), also known as ptychography, capable of morphological imaging with a resolution of about 10 nm [20, 21] and currently being used to investigate the function of nanoscopic objects and materials within single cells [22, 23]. …”

Regularized Newton methods for x-ray phase contrast and general imaging problems

“…Assuming vanishing attenuation µ ≈ 0 or single-material objects µ ∝ φ , this formula can be directly inverted for image reconstruction. This approach yields satisfactory reconstructions especially if data from multiple defocus distances is available [33,48]. From the CTF solution (16), it can be seen that regularized Newton methods are well-suited for phase contrast imaging in two respects: firstly, the zeros of the oscillatory prefactors make the image reconstruction problem ill-posed even in the simplest case of a weak non-absorbing object so that regularization is required.…”

“…(c) Magnification of (b) in the framed region around the logo. For comparison, the dashed inset shows the corresponding part of the phase map reconstructed by direct inversion of the CTF (16) via the methods of[48]. Scale bars: 2 µm in the effective geometry.…”

Stability Estimates for Linearized Near-Field Phase Retrieval in X-ray Phase Contrast Imaging

“…for weakly interacting objects similarly as in a recent stability analysis of domain reconstructions in phaseless inverse scattering [1]. The linearization is known as contrast transfer function model [12,42] and frequently applied in X-ray phase contrast imaging [7,13,15,20,21]. In this work, we analyze the arising linear forward operator T under the assumption that h has compact support.…”

“…By axial translation of the object in Figure 1(a), holograms I 1 , I 2 may be recorded for different sampledetector distances and thus at different Fresnel numbers f 1 = f 2 . It is often stated [5,7,17,20] that the acquired additional data permits a more stable phase retrieval in this setting -in particular if phase shifts φ and attenuation µ are to be recovered as independent parameters. Within the weak object approximation (see §2.2), this setting amounts to reconstructing h = −µ − iφ from measurements (T 1 h, T 2 h), where T j denotes the linearized forward operator in (5) to the Fresnel number f = f j .…”