A comparison of quantitative reconstruction techniques for PIXE-tomography analysis applied to biological samples

Beasley, Daniel; Alves, L.C.; Barberet, Philippe; Bourret, Stéphane; Devès, Guillaume; Gordillo, N.; Michelet, Claire; Trequesser, Quentin Le; Marques, A.; Seznec, Hervé; Silva, R.C. da

doi:10.1016/j.nimb.2014.01.028

Cited by 12 publications

(9 citation statements)

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“…Despite these drawbacks, DISRA has remained the most complete reconstruction code designed for STIMT and PIXET and for this reason, has been used and/or modified by several research groups [20,21,22]. To overcome the first two difficulties, a major improvement of the code was proposed (JPIXET) by replacing FBP by a method widely used for SPECT imaging, MLEM (Maximum Likelihood Expectation Maximization) [23,24]. Moreover Graphics Processing Unit (GPU) parallel computing using CUDA (Compute Unified Device Architecture) programming was implemented to fasten the reconstruction process.…”

Section: First Attempts To Perform Stimt and Pixet Data Fusion Used Amentioning

confidence: 99%

An implementation of the NiftyRec medical imaging library for PIXE-tomography reconstruction

Michelet

Barberet

Desbarats

et al. 2017

Nuclear Instruments and Methods in Physics Research Section B:

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International audienceA new development of the TomoRebuild software package is presented, including “thick sample” correction for non linear X-ray production (NLXP) and X-ray absorption (XA). As in the previous versions, C++ programming with standard libraries was used for easier portability. Data reduction requires different steps which may be run either from a command line instruction or via a user friendly interface, developed as a portable Java plugin in ImageJ. All experimental and reconstruction parameters can be easily modified, either directly in the ASCII parameter files or via the ImageJ interface. A detailed user guide in English is provided. Sinograms and final reconstructed images are generated in usual binary formats that can be read by most public domain graphic softwares. New MLEM and OSEM methods are proposed, using optimized methods from the NiftyRec medical imaging library. An overview of the different medical imaging methods that have been used for ion beam microtomography applications is presented. In TomoRebuild, PIXET data reduction is performed for each chemical element independently and separately from STIMT, except for two steps where the fusion of STIMT and PIXET data is required: the calculation of the correction matrix and the normalization of PIXET data to obtain mass fraction distributions. Correction matrices for NLXP and XA are calculated using procedures extracted from the DISRA code, taking into account a large X-ray detection solid angle. For this, the 3D STIMT mass density distribution is used, considering a homogeneous global composition. A first example of PIXET experiment using two detectors is presented. Reconstruction results are compared and found in good agreement between different codes: FBP, NiftyRec MLEM and OSEM of the TomoRebuild software package, the original DISRA, its accelerated version provided in JPIXET and the accelerated MLEM version of JPIXET, with or without correction

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Section: First Attempts To Perform Stimt and Pixet Data Fusion Used Amentioning

confidence: 99%

An implementation of the NiftyRec medical imaging library for PIXE-tomography reconstruction

Michelet

Barberet

Desbarats

et al. 2017

Nuclear Instruments and Methods in Physics Research Section B:

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“…A first attempt to quantify the accuracy of the reconstructed images using Geant4 simulations on numerical phantoms, used as reference data, has been already carried out [15]. A very good agreement was obtained for thin samples (for example a 5 µm cubic phantom) using the two main reconstruction algorithms available in TomoRebuild: i) Filtered Back Projection (FBP), based on the exact analytical solution of the continuous description of the tomography problem; ii) Maximum Likelihood Expectation Maximization (MLEM), an iterative method based on a discrete formulation of the tomographic process, often used for PIXE tomography as a robust algorithm even with noisy and/or incomplete data [11,29,30]. A good agreement was obtained (< 3%) between PIXE reconstructed and reference density values in the regions of interest.…”

Section: Reconstruction Of Tomographic Imagesmentioning

confidence: 99%

“…precise determination of each element content, in terms of element density (g•cm -3 ) or concentration (g•g -1 ). A possible way to evaluate the precision of the reconstruction process is to compare the results obtained from the same experimental data sets, using different data reduction and tomographic reconstruction methods compared to one another [10,11]. In order to quantify in a more direct way the accuracy of the reconstruction, we proposed to use the Geant4 toolkit (http://geant4.org) [12][13][14] as a benchmark and we defined numerical phantoms for this purpose [15].…”

Section: Introductionmentioning

confidence: 99%

A Geant4 simulation of X-ray emission for three-dimensional proton imaging of microscopic samples

Michelet

Jalenques

et al. 2022

Physica Medica

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“…The trouble with PIXE-T is that it is very damaging, and only rather robust samples can survive the many slices required. 201 We should mention that although simple tomography theory calls for a very large number of slices there are various mathematical ways of dramatically reducing this number and consequently reducing the analysis time (together with sample damage! ), 202 including maximum likelihood methods.…”

Section: Analystmentioning

confidence: 99%

Thin film depth profiling by ion beam analysis

Jeynes

Colaux

2016

Analyst

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The analysis of thin films is of central importance for functional materials, including the very large and active field of nanomaterials. Quantitative elemental depth profiling is basic to analysis, and many techniques exist, but all have limitations and quantitation is always an issue. We here review recent significant advances in ion beam analysis (IBA) which now merit it a standard place in the analyst's toolbox. Rutherford backscattering spectrometry (RBS) has been in use for half a century to obtain elemental depth profiles non-destructively from the first fraction of a micron from the surface of materials: more generally, "IBA" refers to the cluster of methods including elastic scattering (RBS; elastic recoil detection, ERD; and non-Rutherford elastic backscattering, EBS), nuclear reaction analysis (NRA: including particle-induced gamma-ray emission, PIGE), and also particle-induced X-ray emission (PIXE). We have at last demonstrated what was long promised, that RBS can be used as a primary reference technique for the best traceable accuracy available for non-destructive model-free methods in thin films. Also, it has become clear over the last decade that we can effectively combine synergistically the quite different information available from the atomic (PIXE) and nuclear (RBS, EBS, ERD, NRA) methods. Although it is well known that RBS has severe limitations that curtail its usefulness for elemental depth profiling, these limitations are largely overcome when we make proper synergistic use of IBA methods. In this Tutorial Review we aim to briefly explain to analysts what IBA is and why it is now a general quantitative method of great power. Analysts have got used to the availability of the large synchrotron facilities for certain sorts of difficult problems, but there are many much more easily accessible mid-range IBA facilities also able to address (and often more quantitatively) a wide range of otherwise almost intractable thin film questions.

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A comparison of quantitative reconstruction techniques for PIXE-tomography analysis applied to biological samples

Cited by 12 publications

References 12 publications

An implementation of the NiftyRec medical imaging library for PIXE-tomography reconstruction

An implementation of the NiftyRec medical imaging library for PIXE-tomography reconstruction

A Geant4 simulation of X-ray emission for three-dimensional proton imaging of microscopic samples

Thin film depth profiling by ion beam analysis

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