Evaluation of phase retrieval approaches in magnified X-ray phase nano computerized tomography applied to bone tissue

Yu, Boliang; Weber, Loriane; Pacureanu, Alexandra; Langer, Max; Olivier, Cécile; Cloetens, Peter; Peyrin, Françoise

doi:10.1364/oe.26.011110

Cited by 31 publications

(34 citation statements)

References 37 publications

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“…Phase retrieval consisted in processing the four projections recorded at the different propagation distances for each rotation angle to obtain a phase map. Following a previous study 37 , this was performed based on an extended Paganin's algorithm, followed by an iterative optimization. Tomographic reconstruction was then achieved by using the Filtered Back-Projection (FBP) algorithm implemented at the ESRF within the PyHST2 software 38 .…”

Section: Methodsmentioning

confidence: 99%

Assessment of the human bone lacuno-canalicular network at the nanoscale and impact of spatial resolution

Pacureanu

Olivier

et al. 2020

Sci Rep

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cécile olivier 1,2 , peter cloetens 2 & françoise peyrin 1,2* Recently, increasing attention has been given to the study of osteocytes, the cells that are thought to play an important role in bone remodeling and in the mechanisms of bone fragility. the interconnected osteocyte system is deeply embedded inside the mineralized bone matrix and lies within a closely fitted porosity known as the lacuno-canalicular network. However, quantitative data on human samples remain scarce, mostly measured in 2D, and there are gaps to be filled in terms of spatial resolution. In this work, we present data on femoral samples from female donors imaged with isotropic 3D spatial resolution by magnified X-ray phase nano computerized-tomography. We report quantitative results on the 3D structure of canaliculi in human femoral bone imaged with a voxel size of 30 nm. We found that the lacuno-canalicular porosity occupies on average 1.45% of the total tissue volume, the ratio of the canalicular versus lacunar porosity is about 37.7%, and the primary number of canaliculi stemming from each lacuna is 79 on average. The examination of this number at different distances from the surface of the lacunae demonstrates branching in the canaliculi network. We analyzed the impact of spatial resolution on quantification by comparing parameters extracted from the same samples imaged with 120 nm and 30 nm voxel sizes. To avoid any bias related to the analysis region, the volumes at 120 nm and 30 nm were registered and cropped to the same field of view. Our results show that the measurements at 120 and 30 nm are strongly correlated in our data set but that the highest spatial resolution provides more accurate information on the canaliculi network and its branching properties.

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Section: Methodsmentioning

confidence: 99%

Assessment of the human bone lacuno-canalicular network at the nanoscale and impact of spatial resolution

Pacureanu

Olivier

et al. 2020

Sci Rep

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“…Each sample was scanned at four different focus‐to‐sample distances, which was shown to provide the best image quality for such samples (see Figure 1). 33 The beam energy was 17 and 33.6 keV during different data collection sessions. For each distance, 1800 or 2000 projections were recorded over the angular range of 180°.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the aim of this work is to present a method including image acquisition and data processing to extract 3D quantitative parameters of the LCN in human bone. Imaging was performed using magnified X‐ray phase nanocomputerised tomography (CT) at the beamline ID16A of the European Synchrotron Radiation Facility (ESRF), Grenoble, France 33 . The setup provides reconstructed volumes 2048 3 with a voxel size of 120 nm on a field of view of 246 μm in the three spatial directions.…”

Section: Introductionmentioning

confidence: 99%

Quantification of the bone lacunocanalicular network from 3D X‐ray phase nanotomography images

Pacureanu

Olivier

et al. 2020

Journal of Microscopy

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Summary There is a growing interest in developing 3D microscopy for the exploration of thick biological tissues. Recently, 3D X‐ray nanocomputerised tomography has proven to be a suitable technique for imaging the bone lacunocanalicular network. This interconnected structure is hosting the osteocytes which play a major role in maintaining bone quality through remodelling processes. 3D images have the potential to reveal the architecture of cellular networks, but their quantitative analysis remains a challenge due to the density and complexity of nanometre sized structures and the need to handle and process large datasets, for example, 20483 voxels corresponding to 32 GB per individual image in our case. In this work, we propose an efficient image processing approach for the segmentation of the network and the extraction of characteristic parameters describing the 3D structure. These parameters include the density of lacunae, the porosity of lacunae and canaliculi, and morphological features of lacunae (volume, surface area, lengths, anisotropy etc.). We also introduce additional parameters describing the local environment of each lacuna and its canaliculi. The method is applied to analyse eight human femoral cortical bone samples imaged by magnified X‐ray phase nanotomography with a voxel size of 120 nm, which was found to be a good compromise to resolve canaliculi while keeping a sufficiently large field of view of 246 μm in 3D. The analysis was performed on a total of 2077 lacunae showing an average length, width and depth of 17.1 μm × 9.2 μm × 4.4 μm, with an average number of 58.2 canaliculi per lacuna and a total lacuno‐canalicular porosity of 1.12%. The reported descriptive parameters provide information on the 3D organisation of the lacuno‐canalicular network in human bones.

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“…, µ is the gradient step size, and R is defined as in (1). According to the above procedures, x ij is recovered from Z ij , then we can get X i .…”

Section: A Recovery Of the Phases Of The Mixed Image Signalsxmentioning

confidence: 99%

“…T HE problem of recovering a one-dimensional signal from its Fourier transform magnitude, known as phase retrieval, is of paramount importance in various engineering and scientific applications, such as X-ray crystallography [1], [2], optics [3], [4], astronomy [5], [6], blind channel estimation [7], [8], and blind image deblurring [9], [10]. This problem has a long history and has been studied by many researchers [3], [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

Blind Multiple-Input Multiple-Output Image Phase Retrieval

Guo

Zhao

et al. 2020

IEEE Trans. Ind. Electron.

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In this paper, we consider the problem of recovering the phase information of the multiple images from the multiple mixed phaseless Short-Time Fourier Transform (STFT) image measurements, which is called the blind multiple input multiple output image phase retrieval (BMIPR) problem. It is an inherently ill-posed problem due to the lack of the phase and mixing information, and the existing phase retrieval algorithms are not explicitly designed for this case. To address the BMIPR phase retrieval problem, an integrated algorithm is presented, which combines a gradient descent (GD) algorithm by minimizing a nonconvex loss function with an independent component analysis (ICA) algorithm and a non-local means (NM) algorithm. Experimental evaluation has been conducted to show that under appropriate conditions the proposed algorithms can explicitly recover the images, the phases of the images and the mixing matrix. In addition, the algorithm is robust to noise. Index Terms-Blind multiple input multiple output image phase retrieval (BMIPR), short-time Fourier transform (STFT), non-convex optimization, independent component analysis (ICA), non-local means (NM).

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Evaluation of phase retrieval approaches in magnified X-ray phase nano computerized tomography applied to bone tissue

Cited by 31 publications

References 37 publications

Assessment of the human bone lacuno-canalicular network at the nanoscale and impact of spatial resolution

Assessment of the human bone lacuno-canalicular network at the nanoscale and impact of spatial resolution

Quantification of the bone lacunocanalicular network from 3D X‐ray phase nanotomography images

Blind Multiple-Input Multiple-Output Image Phase Retrieval

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