Information-based analysis of X-ray in-line phase tomography with application to the detection of iron oxide nanoparticles in the brain

Precise phase retrieval for propagation-based images using discrete mathematics

The ill-posed problem of phase retrieval in optics, using one or more intensity measurements, has a multitude of applications using electromagnetic or matter waves. Many phase retrieval algorithms are computed on pixel arrays using discrete Fourier transforms due to their high computational efficiency. However, the mathematics underpinning these algorithms is typically formulated using continuous mathematics, which can result in a loss of spatial resolution in the reconstructed images. Herein we investigate how phase retrieval algorithms for propagation-based phase-contrast X-ray imaging can be rederived using discrete mathematics and result in more precise retrieval for single- and multi-material objects and for spectral image decomposition. We validate this theory through experimental measurements of spatial resolution using computed tomography (CT) reconstructions of plastic phantoms and biological tissues, using detectors with a range of imaging system point spread functions (PSFs). We demonstrate that if the PSF substantially suppresses high spatial frequencies, the potential improvement from utilising the discrete derivation is limited. However, with detectors characterised by a single pixel PSF (e.g. direct, photon-counting X-ray detectors), a significant improvement in spatial resolution can be obtained, demonstrated here at up to 17%.

Brain virtual histology with X-ray phase-contrast tomography Part I: whole-brain myelin mapping in white-matter injury models

White-matter injury leads to severe functional loss in many neurological diseases. Myelin staining on histological samples is the most common technique to investigate white-matter fibers. However, tissue processing and sectioning may affect the reliability of 3D volumetric assessments. The purpose of this study was to propose an approach that enables myelin fibers to be mapped in the whole rodent brain with microscopic resolution and without the need for strenuous staining. With this aim, we coupled in-line (propagation-based) X-ray phase-contrast tomography (XPCT) to ethanol-induced brain sample dehydration. We here provide the proof-of-concept that this approach enhances myelinated axons in rodent and human brain tissue. In addition, we demonstrated that white-matter injuries could be detected and quantified with this approach, using three animal models: ischemic stroke, premature birth and multiple sclerosis. Furthermore, in analogy to diffusion tensor imaging (DTI), we retrieved fiber directions and DTI-like diffusion metrics from our XPCT data to quantitatively characterize white-matter microstructure. Finally, we showed that this non-destructive approach was compatible with subsequent complementary brain sample analysis by conventional histology. In-line XPCT might thus become a novel gold-standard for investigating white-matter injury in the intact brain. This is Part I of a series of two articles reporting the value of in-line XPCT for virtual histology of the brain; Part II shows how in-line XPCT enables the whole-brain 3D morphometric analysis of amyloid- β (A β ) plaques.

X-ray-Based 3D Virtual Histology-Adding the Next Dimension to Histological Analysis

Histology and immunohistochemistry of thin tissue sections have been the standard diagnostic procedure in many diseases for decades. This method is highly specific for particular tissue regions or cells, but mechanical sectioning of the specimens is required, which destroys the sample in the process and can lead to non-uniform tissue deformations. In addition, regions of interest cannot be located beforehand and the analysis is intrinsically two-dimensional. Micro X-ray computed tomography (μCT) on the other hand can provide 3D images at high resolution and allows for quantification of tissue structures, as well as the localization of small regions of interest. These advantages advocate the use of μCT for virtual histology tool with or without subsequent classical histology. This review summarizes the most recent examples of virtual histology and provides currently known possibilities of improving contrast and resolution of μCT. Following a background in μCT imaging, ex vivo staining procedures for contrast enhancement are presented as well as label-free virtual histology approaches and the technologies, which could rapidly advance it, such as phase-contrast CT. Novel approaches such as zoom tomography and nanoparticulate contrast agents will also be considered. The current evidence suggests that virtual histology may present a valuable addition to the workflow of histological analysis, potentially reducing the workload in pathology, refining tissue classification, and supporting the detection of small malignancies.

Computer vision tools to optimize reconstruction parameters in x-ray in-line phase tomography

In this article, a set of three computer vision tools, including scale invariant feature transform (SIFT), a measure of focus, and a measure based on tractography are demonstrated to be useful in replacing the eye of the expert in the optimization of the reconstruction parameters in x-ray in-line phase tomography. We demonstrate how these computer vision tools can be used to inject priors on the shape and scale of the object to be reconstructed. This is illustrated with the Paganin single intensity image phase retrieval algorithm in heterogeneous soft tissues of biomedical interest, where the selection of the reconstruction parameters was previously made from visual inspection or physical assumptions on the composition of the sample.