It is well known that properly designed image reconstruction methods can facilitate reductions in imaging doses and data-acquisition times in tomographic imaging. The ability to do so is particularly important for emerging modalities, such as differential x-ray phase-contrast tomography (D-XPCT), which are currently limited by these factors. An important application of D-XPCT is high-resolution imaging of biomedical samples. However, reconstructing high-resolution images from few-view tomographic measurements remains a challenging task due to the high-frequency information loss caused by data incompleteness. In this work, a subspace-based reconstruction strategy is proposed and investigated for use in few-view D-XPCT image reconstruction. By adopting a two-step approach, the proposed method can simultaneously recover high-frequency details within a certain region of interest while suppressing noise and/or artifacts globally. The proposed method is investigated by the use of few-view experimental data acquired by an edge-illumination D-XPCT scanner.
Edge illumination X-ray phase-contrast tomography (EIXPCT) is an imaging technique that estimates the spatially variant X-ray refractive index and absorption distribution within an object while seeking to circumvent the limitations of previous benchtop implementations of X-ray phase-contrast tomography. As with gratingor analyzer-based methods, conventional image reconstruction methods for EIXPCT require that two or more images be acquired at each tomographic view angle. This requirement leads to increased data acquisition times, hindering in vivo applications. To circumvent these limitations, a joint reconstruction (JR) approach is proposed that concurrently produces estimates of the refractive index and absorption distributions from a tomographic data set containing only a single image per tomographic view angle. The JR reconstruction method solves a nonlinear optimization problem by use of a novel iterative gradient-based algorithm. The JR method is demonstrated in both computer-simulated and experimental EIXPCT studies.
It is well-known that properly designed image reconstruction methods can facilitate reductions in imaging doses and data-acquisition times in tomographic imaging. The ability to do so is particularly important for emerging modalities such as differential X-ray phase-contrast tomography (D-XPCT), which are currently limited by these factors. An important application of D-XPCT is high-resolution imaging of biomedical samples. However, reconstructing high-resolution images from few-view tomographic measurements remains a challenging task. In this work, a two-step sub-space reconstruction strategy is proposed and investigated for use in few-view D-XPCT image reconstruction. It is demonstrated that the resulting iterative algorithm can mitigate the high-frequency information loss caused by data incompleteness and produce images that have better preserved high spatial frequency content than those produced by use of a conventional penalized least squares (PLS) estimator.
We present the results from a benchtop X-ray phase-contrast (XPC) method for lung imaging that represents a paradigm shift in the way small animal lung imaging is performed. In our method, information regarding airway microstructure that is encoded within speckle texture of a single XPC radiograph is decoded to spatially resolve changes in lung properties such as microstructure sizes, air volumes, and compliance, to name a few. Such functional information cannot be derived from conventional lung radiography or any other 2D imaging modality. By computing these images at different time points within a breathing cycle, dynamic functional imaging can be potentially achieved without the need for tomography.
X-ray phase-contrast (XPC) imaging methods are well-suited for lung imaging applications due to the weakly absorbing nature of lung tissue and the strong refractive effects associated with tissue-air interfaces. Until recently, XPC lung imaging had only been accomplished at synchrotron facilities. In this work, we investigate the manifestation of speckle in propagation-based XPC images of mouse lungs acquired in situ by use of a benchtop imager. The key contributions of the work are: a) the demonstration that lung speckle can be observed by use of a benchtop XPC imaging system employing a polychromatic tube-source; and b) a systematic experimental investigation of how the texture of the speckle pattern depends on the parameters of the imaging system. Our analyses consists of image texture characterization based on the statistical properties of pixel intensity distributions. Results show how image texture measures of lung regions are strongly dependent on imaging system parameters associated with XPC sensitivity.
In-line x-ray phase-contrast (XPC) tomosynthesis combines the concepts of tomosynthesis and in-line XPC imaging to utilize the advantages of both for biological imaging applications. Tomosynthesis permits reductions in acquisition times compared with conventional tomography scans while in-line XPC imaging provides high contrast and resolution in images of weakly absorbing materials. In this work, we develop an advanced iterative algorithm as an approach for dealing with the incomplete (and often noisy) data inherent to XPC tomosynthesis. We also investigate the depth resolution properties of XPC tomosynthesis and demonstrate that the z-resolution properties of XPC tomosynthesis is superior to that of conventional absorption-based tomosynthesis. More specifically, we find in-plane structures display strong boundary-enhancement while out-of-plane structures do not. This effect can facilitate the identification of in-plane structures.
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