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12 pages, 3735 KiB

Open AccessArticle

Micro-Computed Tomography Beamline of the Australian Synchrotron: Micron-Size Spatial Resolution X-ray Imaging

by Benedicta D. Arhatari, Andrew W. Stevenson, Darren Thompson, Adam Walsh, Tom Fiala, Gary Ruben, Nader Afshar, Sinem Ozbilgen, Tingting Feng, Stephen Mudie and Prithi Tissa

Appl. Sci. 2023, 13(3), 1317; https://doi.org/10.3390/app13031317 - 18 Jan 2023

Cited by 4 | Viewed by 2003

Abstract

The first new beamline of the BRIGHT project—involving the construction of eight new beamlines at the Australian Synchrotron—is the Micro-Computed Tomography (MCT) beamline. MCT will extend the facility’s capability for higher spatial resolution X-ray-computed tomographic imaging allowing for commensurately smaller samples in comparison [...] Read more.

The first new beamline of the BRIGHT project—involving the construction of eight new beamlines at the Australian Synchrotron—is the Micro-Computed Tomography (MCT) beamline. MCT will extend the facility’s capability for higher spatial resolution X-ray-computed tomographic imaging allowing for commensurately smaller samples in comparison with the existing Imaging and Medical Beamline (IMBL). The source is a bending-magnet and it is operating in the X-ray energy range from 8 to 40 keV. The beamline provides important new capability for a range of biological and material-science applications. Several imaging modes will be offered such as various X-ray phase-contrast modalities (propagation-based, grating-based, and speckle-based), in addition to conventional absorption contrast. The unique properties of synchrotron radiation sources (high coherence, energy tunability, and high brightness) are predominantly well-suited for producing phase contrast data. An update on the progress of the MCT project in delivering high-spatial-resolution imaging (in the order of micron size) of mm-scale objects will be presented in detail with some imaging results from the hot-commissioning stage. Full article

(This article belongs to the Special Issue X-ray Medical and Biological Imaging)

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Four different beam modes in MCT beamline: (a) white beam, (b) pink beam, (c) monochromatic beam, and (d) monochromatic beam without a third harmonic contribution. Full article ">Figure 2
MCT beamline layout. Full article ">Figure 3
(a). Image of the “first light” captured by a visible light camera connected to a computer. (b). Diagnostic screen inside vacuum tube (blue dot line square) with visible light camera system (green arrow direction) that captured the X-rays (red arrow direction). Full article ">Figure 4
(a). White-beam detector with three objective lenses. (b). Monochromatic-beam detector with three objective lenses. Full article ">Figure 5
Resolution pattern images in horizontal direction taken using 1× objective lens (for QRM MicroCT test pattern) and using 5× and 20× objective lenses (for Xradia X500-200-30 test pattern) at several sample-to-detector distances of 160 mm, 700 mm, and 1410 mm with corresponding theoretical resFWHM values horizontally. The numbers below the test pattern of 1× images indicate the corresponding linewidth in µm. The half period or linewidth in µm unit of the 5× and 20× objective lens images are visible on the pattern itself. Full article ">Figure 6
Resolution pattern images in vertical direction taken using 1× objective lens (for QRM MicroCT test pattern) and using 5× and 20× objective lenses (for Xradia X500-200-30 test pattern) at several sample-to-detector distances of 160 mm, 700 mm, and 1410 mm with corresponding theoretical resolution in resFWHM values vertically. Full article ">Figure 7
X-ray phase-contrast images (at 11 keV monochromatic beam collected using 5× objective lens): (a) an insect, (b) fibrous paper, and (c) a eucalyptus leaf. Full article ">Figure 8
Slice reconstruction of wood structure (toothpick) without TIE-Hom phase-retrieval in (a) and with TIE-Hom phase-retrieval in (b), with the 3D visualization of the data such as in (b) presented in (c). Data were collected using white beam, 20× objective lens, exposure time of 0.7 s, and sample-detector distance of 160 mm. Full article ">

13 pages, 1424 KiB

Open AccessArticle

Multi-Modal X-ray Imaging and Analysis for Characterization of Urinary Stones

by Somayeh Saghamanesh, Henning Richter, Antonia Neels and Robert Zboray

Appl. Sci. 2022, 12(8), 3798; https://doi.org/10.3390/app12083798 - 9 Apr 2022

Cited by 1 | Viewed by 2279

Abstract

Backgound: The composition of stones formed in the urinary tract plays an important role in their management over time. The most common imaging method for the non-invasive evaluation of urinary stones is radiography and computed tomography (CT). However, CT is not very sensitive, [...] Read more.

Backgound: The composition of stones formed in the urinary tract plays an important role in their management over time. The most common imaging method for the non-invasive evaluation of urinary stones is radiography and computed tomography (CT). However, CT is not very sensitive, and cannot differentiate between all critical stone types. In this study, we propose the application, and evaluate the potential, of a multi-modal (or multi-contrast) X-ray imaging technique called speckle-based imaging (SBI) to differentiate between various types of urinary stones. Methods: Three different stone samples were extracted from animal and human urinary tracts and examined in a laboratory-based speckle tracking setup. The results were discussed based on an X-ray diffraction analysis and a comparison with X-ray microtomography and grating-based interferometry. Results: The stones were classified through compositional analysis by X-ray diffraction. The multi-contrast images obtained using the SBI method provided detailed information about the composition of various urinary stone types, and could differentiate between them. X-ray SBI could provide highly sensitive and high-resolution characterizations of different urinary stones in the radiography mode, comparable to those by grating interferometry. Conclusions: This investigation demonstrated the capability of the SBI technique for the non-invasive classification of urinary stones through radiography in a simple and cost-effective laboratory setting. This opens the possibility for further studies concerning full-field in vivo SBI for the clinical imaging of urinary stones. Full article

(This article belongs to the Special Issue X-ray Medical and Biological Imaging)

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Depiction of the experimental setup for the X-ray speckle tracking imaging. Full article ">Figure 2
The 2D diffraction patterns of the stone samples from (a) dog, (b) cat, and (c) human urinary stone samples. (d) The 1D diffraction profiles of the three stones. Full article ">Figure 3
Reconstructed vertical slice of X-ray microtomography through three stone samples from (a) cat, (b) dog, and (c) human urinary stones. (d–f) Corresponding 3D rendering of the full stones in (a–c). In (e), the porosity size distribution is depicted by the side colorbar. The effective voxel sizes for the (a–c) microtomographs are 5.15, 12.86, and 39.9 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, respectively. Full article ">Figure 4
Multi-contrast radiographs extracted from dog, cat, and human urinary stones. (a) Horizontal refraction angle, (b) vertical refraction angle, (c) attenuation, and (d) dark-field contrast. Full article ">Figure 5
Retrieved (a) attenuation, (b) differential phase, and (c) dark-field images of three urinary stone samples taken by GI on the Talbot–Lau interferometer. Full article ">Figure A1
Detailed phase analysis of three urinary stones. XRD 1D profiles of stones from (a) a cat, (b) a human, and (c) a dog. (d) A 2D diffraction image with the high-intensity dots showing the single crystallinity. Full article ">

17 pages, 4069 KiB

Open AccessArticle

Materials Separation via the Matrix Method Employing Energy-Discriminating X-ray Detection

by Viona S. K. Yokhana, Benedicta D. Arhatari and Brian Abbey

Appl. Sci. 2022, 12(6), 3198; https://doi.org/10.3390/app12063198 - 21 Mar 2022

Cited by 6 | Viewed by 2037

Abstract

The majority of lab-based X-ray sources are polychromatic and are not easily tunable, which can make the 3D quantitative analysis of multi-component samples challenging. The lack of effective materials separation when using conventional X-ray tube sources has motivated the development of a number [...] Read more.

The majority of lab-based X-ray sources are polychromatic and are not easily tunable, which can make the 3D quantitative analysis of multi-component samples challenging. The lack of effective materials separation when using conventional X-ray tube sources has motivated the development of a number of potential solutions including the application of dual-energy X-ray computed tomography (CT) as well as the use of X-ray filters. Here, we demonstrate the simultaneous decomposition of two low-density materials via inversion of the linear attenuation matrices using data from the energy-discriminating PiXirad detector. A key application for this method is soft-tissue differentiation which is widely used in biological and medical imaging. We assess the effectiveness of this approach using both simulation and experiment noting that none of the materials investigated here incorporate any contrast enhancing agents. By exploiting the energy discriminating properties of the detector, narrow energy bands are created resulting in multiple quasi-monochromatic images being formed using a broadband polychromatic source. Optimization of the key parameters for materials separation is first demonstrated in simulation followed by experimental validation using a phantom test sample in 2D and a small-animal model in 3D. Full article

(This article belongs to the Special Issue X-ray Medical and Biological Imaging)

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11 pages, 6875 KiB

Open AccessArticle

An In-House Cone-Beam Tomographic Reconstruction Package for Laboratory X-ray Phase-Contrast Imaging

by Jürgen Hofmann and Robert Zboray

Appl. Sci. 2022, 12(3), 1430; https://doi.org/10.3390/app12031430 - 28 Jan 2022

Cited by 1 | Viewed by 2757

Abstract

Phase-contrast, and in general, multi-modal, X-ray micro-tomography is proven to be very useful for low-density, low-attention samples enabling much better contrast than its attenuation-based pendant. Therefore, it is increasingly applied in bio- and life sciences primarily dealing with such samples. Although there is [...] Read more.

Phase-contrast, and in general, multi-modal, X-ray micro-tomography is proven to be very useful for low-density, low-attention samples enabling much better contrast than its attenuation-based pendant. Therefore, it is increasingly applied in bio- and life sciences primarily dealing with such samples. Although there is a plethora of literature regarding phase-retrieval algorithms, access to implementations of those algorithms is relatively limited and very few packages combining phase-retrieval methods with the full tomographic reconstruction pipeline are available. This is especially the case for laboratory-based phase-contrast imaging typically featuring cone-beam geometry. We present here an in-house cone-beam tomographic reconstruction package for laboratory X-ray phase-contrast imaging. It covers different phase-contrast techniques and phase retrieval methods. The paper explains their implementation and integration in the filtered back projection chain. Their functionality and efficiency will be demonstrated through applications on a few dedicated samples. Full article

(This article belongs to the Special Issue X-ray Medical and Biological Imaging)

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Micro-CT slices of a horse fly based on attenuation (a,c), and based on Paganin phase retrieval (b,d). The upper images are showing a horizontal slice at the region of the flight muscles (orange framed in Figure 4a,b), whereas the lower images are taken at the level of the compound eye showing clearly the individual ommatidia (blue framed in Figure 4c,d). The insets show the gray value histograms for the different images (black-linear scale, gray-log scale is shown to be able to better discern the differences). It is clear from those that the Paganin method strongly increases the CNR of the images as the histograms are becoming much more structured, showing several peaks compared to the attenuation case. A <math display="inline"><semantics> <mi>ν</mi> </semantics></math> value of 10.0 was used in Equation (<a href="#FD6-applsci-12-01430" class="html-disp-formula">6</a>) to obtain the images. The CNR values for the different methods and samples are summarized in Table 1. Full article ">Figure 2
(a) Propagation based projection image of a coffee bean (b) Illustrates the result of the optimization of the <math display="inline"><semantics> <mi>α</mi> </semantics></math> and <math display="inline"><semantics> <mi>γ</mi> </semantics></math> parameters of the BAC method showing a comparison of the original and BAC corrected image gray scale profiles over the white vertical line shown in (a). Note that foamy looking material around the bean is florist’s foam in which the bean was embedded for the scan. Full article ">Figure 3
(a) Attenuation-based micro-CT image of a coffee bean. Propagation-based micro-CT images of the coffee bean reconstructed by (b) Paganin’s phase retrieval method, Equation (<a href="#FD6-applsci-12-01430" class="html-disp-formula">6</a>), and (c) by the BAC method Equations (<a href="#FD3-applsci-12-01430" class="html-disp-formula">3</a>) and (<a href="#FD4-applsci-12-01430" class="html-disp-formula">4</a>). Note the foamy looking material around the bean is florist’s foam in which the bean was embedded for the scan. The insets in the upper right corner of each image show the gray value histograms. It shows that the separation, i.e., CNR increases for the BAC and Paganin retrieval compared to the attenuation case. The regions of interest outlined in orange show with higher magnification how the different retrievals might blur the visibility of small-scale feature (see, e.g., red arrows). For the BAC method the optimized parameters from Figure 2b, whereas for the Paganin method <math display="inline"><semantics> <mi>ν</mi> </semantics></math> = 10.0 were used. The areas circumvented in red indicate the ROIs used for determining the CNR values between sample material and air (see the values in Table 1). Full article ">Figure 4
(a) Propagation based projection image of the horse fly. (b) Illustration of the result of the optimization of the <math display="inline"><semantics> <mi>α</mi> </semantics></math> and <math display="inline"><semantics> <mi>γ</mi> </semantics></math> parameters of the BAC method showing a comparison of the original and BAC corrected image gray scale profiles over the white horizontal line shown in (a). Full article ">Figure 5
Comparison of the attenuation-based micro-CT images of the horse fly (a,d), with the propagation-based images reconstructed by using Paganin’s phase retrieval method, (b,e), based on Equation (<a href="#FD6-applsci-12-01430" class="html-disp-formula">6</a>), and with the BAC method based on Equations (<a href="#FD3-applsci-12-01430" class="html-disp-formula">3</a>) and (<a href="#FD4-applsci-12-01430" class="html-disp-formula">4</a>), (c,f). The upper images show a horizontal slice at the region of the flight muscles (see orange line in <a href="#applsci-12-01430-f004" class="html-fig">Figure 4</a>a), whereas the lower images are taken at the level of the compound eye showing clearly the individual ommatidia (see the blue line in <a href="#applsci-12-01430-f004" class="html-fig">Figure 4</a>a). The areas circumvented in red indicate the ROIs used for determining the CNR values between sample material and air (see <a href="#applsci-12-01430-t001" class="html-table">Table 1</a>). Full article ">Figure 6
Grating based phase contrast tomogram slice of a cellulose foam. (a) shows the attenuation, (b) the phase contrast and (c) the dark field, small angle scattering, image. Full article ">

13 pages, 4214 KiB

Open AccessArticle

Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach

by Francesco Guzzi, George Kourousias, Alessandra Gianoncelli, Lorella Pascolo, Andrea Sorrentino, Fulvio Billè and Sergio Carrato

Appl. Sci. 2021, 11(16), 7598; https://doi.org/10.3390/app11167598 - 18 Aug 2021

Cited by 3 | Viewed by 2458

Abstract

The high resolution of synchrotron cryo-nano tomography can be easily undermined by setup instabilities and sample stage deficiencies such as runout or backlash. At the cost of limiting the sample visibility, especially in the case of bio-specimens, high contrast nano-beads are often added [...] Read more.

The high resolution of synchrotron cryo-nano tomography can be easily undermined by setup instabilities and sample stage deficiencies such as runout or backlash. At the cost of limiting the sample visibility, especially in the case of bio-specimens, high contrast nano-beads are often added to the solution to provide a set of landmarks for a manual alignment. However, the spatial distribution of these reference points within the sample is difficult to control, resulting in many datasets without a sufficient amount of such critical features for tracking. Fast automatic methods based on tomography consistency are thus desirable, especially for biological samples, where regular, high contrast features can be scarce. Current off-the-shelf implementations of such classes of algorithms are slow if used on a real-world high-resolution dataset. In this paper, we present a fast implementation of a consistency-based alignment algorithm especially tailored to a multi-GPU system. Our implementation is released as open-source. Full article

(This article belongs to the Special Issue X-ray Medical and Biological Imaging)

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21 pages, 12438 KiB

Open AccessArticle

Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic—Elettra

by Alessandra Gianoncelli, Valentina Bonanni, Gianluca Gariani, Francesco Guzzi, Lorella Pascolo, Roberto Borghes, Fulvio Billè and George Kourousias

Appl. Sci. 2021, 11(16), 7216; https://doi.org/10.3390/app11167216 - 5 Aug 2021

Cited by 25 | Viewed by 3387

Abstract

Progress in nanotechnology calls for material probing techniques of high sensitivity and resolution. Such techniques are also used for high-impact studies of nanoscale materials in medicine and biology. Soft X-ray microscopy has been successfully used for investigating complex biological processes occurring at micrometric [...] Read more.

Progress in nanotechnology calls for material probing techniques of high sensitivity and resolution. Such techniques are also used for high-impact studies of nanoscale materials in medicine and biology. Soft X-ray microscopy has been successfully used for investigating complex biological processes occurring at micrometric and sub-micrometric length scales and is one of the most powerful tools in medicine and the life sciences. Here, we present the capabilities of the TwinMic soft X-ray microscopy end-station at the Elettra synchrotron in the context of medical and biological imaging, while we also describe novel uses and developments. Full article

(This article belongs to the Special Issue X-ray Medical and Biological Imaging)

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Schematic view of the TwinMic STXM mode setup with the microprobe forming zone plate (ZP) on the specimen (S), diffraction order-selecting aperture (OSA), transmission detection system with fast-read-out CCD camera (FRCCD) and visible light converting system (VLCS), and low-energy X-ray fluorescence (LEXRF) detector system based on 8 silicon drift detectors (SDDs) in backscattered configuration. The VLCS consists of a phosphor screen (PS), a lens (L) and a 45°-tilted mirror (M). Full article ">Figure 2
Schematic view of the TwinMic current (A) and future (B) LEXRF system setup. Full article ">Figure 3
Schematic view of the TwinMic full field imaging mode setup, with a condenser ZP (CZP) illuminating the specimen (S), an order-selecting aperture (OSA), and a micro zone plate (MZP) that magnifies the image of the specimen into the detector with a directly illuminated CCD camera. Full article ">Figure 4
Schematic view of the TwinMic ptychography setup with the zone plate (ZP), the order-selecting aperture (OSA), the specimen (S), and the CCD camera. Full article ">Figure 5
Absorption (Abs) and differential phase contrast (DPC) images of mouse fibroblast cells cultured on Si3N4 membranes and exposed to a 500 µM concentration of CoFe2O4 NPs, depicted with the corresponding XRF maps of C, O, Na, Mg, Fe, Co, and Scattering (E0). The maps were collected at 1.5 keV over an area of 40 µm × 70 µm with a 400 nm step size, with a 4 s/pixel acquisition time for XRF. Scale bar is 10 µm. Full article ">Figure 6
Absorption (Abs) and differential phase contrast in X (X mom) and Y (Y mom) images of primary oocyte in an ovarian tissue, depicted with the corresponding XRF maps of O, Na, and Mg. The maps were collected at 1.5 keV over an area of 70 µm × 70 µm (STXM images) or 60 µm × 60 µm (XRF maps) with 300 nm and 1 µm step sizes, respectively, with a 7 s/pixel acquisition time for XRF. Scale bar is 10 µm. Full article ">Figure 7
Absorption (Abs) and differential phase contrast in X (X mom) and Y (Y mom) images of spermatozoa deposited on Si3N4 windows, depicted with the corresponding XRF maps of O, Na, and Mg. The maps were collected at 1.5 keV over an area of 80 µm × 80 µm with a 400 nm and 1 µm step size for STXM images and XRF, respectively, and a 10 s/pixel acquisition time for XRF. Scale bar is 10 µm. Full article ">Figure 8
Panel (A) shows the ptychographic phase reconstruction of chemically fixed mesothelial cells. This is an example of a large field of view (>60 × 60 µm2) examined with a high resolution (approx. 50 nm). Panel (B) and (C) show enlarged ROIs, as indicated in panel (A). Full article ">Figure 9
Absorption (Abs) and differential phase contrast (DPC) images of a section of a Rhodnius prolixus head [<a href="#B80-applsci-11-07216" class="html-bibr">80</a>] acquired at a 1.7 keV excitation energy over an area of 1200 µm × 720 µm with a 1.5 µm step size and probe size, and 50 ms acquisition time. Scale bar is 50 µm. Full article ">Figure 10
LEXRF maps of C, O, Na, Mg, Al, and Fe acquired at a 1.7 keV excitation energy with a 1.5 µm step size and probe size, and 2 s acquisition time. The STXM images were acquired over an area of 1200 µm × 720 µm with a 1.5 µm step size and probe size, while the XRF signal was collected only on selected areas, based on a preselected absorption threshold value in the absorption signal. Scale bar is 50 µm. Full article ">Figure 11
Absorption (Abs) and differential contrast image (DPC) of a section of coronary artery from a rat [<a href="#B81-applsci-11-07216" class="html-bibr">81</a>], acquired at a 1.5 keV excitation energy with a 2.5 µm step size, over an area of 2100 µm × 2000 µm. Scale bar is 100 µm. Full article ">Figure 12
XRF maps of C, Na, Mg, and scattering signal, collected on the coronary artery section shown in <a href="#applsci-11-07216-f011" class="html-fig">Figure 11</a>, at 1.7 keV, with a step size of 2.5 µm and a 1 s acquisition time per pixel, only on the areas with an absorption signal below a specific pre-selected threshold. The bottom row depicts a sub-region of the absorption (Abs) and differential phase contrast (DPC) images shown in <a href="#applsci-11-07216-f011" class="html-fig">Figure 11</a> together with the corresponding LXERF maps of Mg and Na (Panels Mg_K 2 and Na_K 2, respectively). Scale bar in map C is 100 µm, while in panel Abs, it is 50 µm. Full article ">

13 pages, 3685 KiB

Open AccessArticle

X-ray Phase-Contrast Computed Tomography for Soft Tissue Imaging at the Imaging and Medical Beamline (IMBL) of the Australian Synchrotron

by Benedicta D. Arhatari, Andrew W. Stevenson, Brian Abbey, Yakov I. Nesterets, Anton Maksimenko, Christopher J. Hall, Darren Thompson, Sheridan C. Mayo, Tom Fiala, Harry M. Quiney, Seyedamir T. Taba, Sarah J. Lewis, Patrick C. Brennan, Matthew Dimmock, Daniel Häusermann and Timur E. Gureyev

Appl. Sci. 2021, 11(9), 4120; https://doi.org/10.3390/app11094120 - 30 Apr 2021

Cited by 13 | Viewed by 3363

Abstract

The Imaging and Medical Beamline (IMBL) is a superconducting multipole wiggler-based beamline at the 3 GeV Australian Synchrotron operated by the Australian Nuclear Science and Technology Organisation (ANSTO). The beamline delivers hard X-rays in the 25–120 keV energy range and offers the potential [...] Read more.

The Imaging and Medical Beamline (IMBL) is a superconducting multipole wiggler-based beamline at the 3 GeV Australian Synchrotron operated by the Australian Nuclear Science and Technology Organisation (ANSTO). The beamline delivers hard X-rays in the 25–120 keV energy range and offers the potential for a range of biomedical X-ray applications, including radiotherapy and medical imaging experiments. One of the imaging modalities available at IMBL is propagation-based X-ray phase-contrast computed tomography (PCT). PCT produces superior results when imaging low-density materials such as soft tissue (e.g., breast mastectomies) and has the potential to be developed into a valuable medical imaging tool. We anticipate that PCT will be utilized for medical breast imaging in the near future with the advantage that it could provide better contrast than conventional X-ray absorption imaging. The unique properties of synchrotron X-ray sources such as high coherence, energy tunability, and high brightness are particularly well-suited for generating PCT data using very short exposure times on the order of less than 1 min. The coherence of synchrotron radiation allows for phase-contrast imaging with superior sensitivity to small differences in soft-tissue density. Here we also compare the results of PCT using two different detectors, as these unique source characteristics need to be complemented with a highly efficient detector. Moreover, the application of phase retrieval for PCT image reconstruction enables the use of noisier images, potentially significantly reducing the total dose received by patients during acquisition. This work is part of ongoing research into innovative tomographic methods aimed at the introduction of 3D X-ray medical imaging at the IMBL to improve the detection and diagnosis of breast cancer. Major progress in this area at the IMBL includes the characterization of a large number of mastectomy samples, both normal and cancerous, which have been scanned at clinically acceptable radiation dose levels and evaluated by expert radiologists with respect to both image quality and cancer diagnosis. Full article

(This article belongs to the Special Issue X-ray Medical and Biological Imaging)

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Schematic diagram of the experimental setup for absorption-contrast CT (detector at 0.19 m) and propagation-based PCT (detector at 6 m). Full article ">Figure 2
Comparison of absorption-contrast CT at 0.19 m sample-detector distance (top row) with PCT using TIE-Hom phase-retrieval images at a 6 m sample-detector distance (bottom row) of a breast tissue sample. The PCT images exhibit a better signal-to-noise ratio compared to the contact image. Full article ">Figure 3
Comparison of PCT images of a breast tissue sample from a sagittal slice obtained using four different X-ray energies: (a) 26, (b) 28, (c) 32, and (d) 34 keV. For the dense glandular component (white color), the result from 34 keV shows the best quality, while 32 keV produces the best quality for the adipose component (gray color). Full article ">Figure 4
Comparison of PCT images of a breast tissue sample from a sagittal slice obtained with different MGD values: (a) 2, (b) 4, and (c) 8 mGy. Full article ">Figure 5
Comparison between images produced by the Hamamatsu (left column) and the Xineos (right column) detectors at 32 keV (6 m distance, 4 mGy) of a breast tissue sample inside an 11 cm diameter plastic container (top row) and a resolution test phantom (bottom row). Full article ">Figure 6
Comparison of the line pattern visibility for PCT images obtained using data from the Hamamatsu and the Xineos detectors as a function of the spatial frequency of the resolution test phantom (expressed as line pairs/cm). Full article ">

Review

Jump to: Research

16 pages, 2634 KiB

Open AccessReview

Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy

by Agata Nowak-Stępniowska, Wiktoria Kasprzycka, Paulina Natalia Osuchowska, Elżbieta Anna Trafny, Andrzej Bartnik, Henryk Fiedorowicz and Przemysław Wachulak

Appl. Sci. 2022, 12(14), 7030; https://doi.org/10.3390/app12147030 - 12 Jul 2022

Cited by 1 | Viewed by 2886

Abstract

Soft X-ray microscopy is a powerful technique for imaging cells with nanometer resolution in their native state without chemical fixation, staining, or sectioning. The studies performed in several laboratories have demonstrated the potential of applying this technique for imaging the internal structures of [...] Read more.

Soft X-ray microscopy is a powerful technique for imaging cells with nanometer resolution in their native state without chemical fixation, staining, or sectioning. The studies performed in several laboratories have demonstrated the potential of applying this technique for imaging the internal structures of intact cells. However, it is currently used mainly on synchrotrons with restricted access. Moreover, the operation of these instruments and the associated sample-preparation protocols require interdisciplinary and highly specialized personnel, limiting their wide application in practice. This is why soft X-ray microscopy is not commonly used in biological laboratories as an imaging tool. Thus, a laboratory-based and user-friendly soft X-ray contact microscope would facilitate the work of biologists. A compact, desk-top laboratory setup for soft X-ray contact microscopy (SXCM) based on a laser-plasma soft X-ray source, which can be used in any biological laboratory, together with several applications for biological imaging, are described. Moreover, the perspectives of the correlation of SXCM with other super-resolution imaging techniques based on the current literature are discussed. Full article

(This article belongs to the Special Issue X-ray Medical and Biological Imaging)

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A flowchart of the contact X-ray microscopy imaging procedure: (a) exposure with radiation penetrating the biological sample and absorbed in the recording medium—photoresist, (b) chemical development of the photoresist converting spatial 2D modulation of the radiation density into the surface morphology, (c) digitization—conversion of the surface morphology into the digital image using high-resolution imaging methods, e.g., AFM or SEM. Full article ">Figure 2
Schematic of the desk-top laboratory system for soft X-ray contact microscopy (SXCM) based on a laser plasma soft X-ray source with a double-stream gas puff target. Full article ">Figure 3
AFM images of cellular imprints of fixed HCC38 breast cancer cells saved in PMMA photoresists by soft X-ray contact microscopy. Images were obtained with SXR exposures of (A) 400, (B) 600, and (C) 800 pulses. The nucleoli of the nuclei are indicated by arrows and the endoplasmic reticulum, vacuoles, and mitochondria are outlined with a frame. Imprints were recorded by AFM working in contact mode (Xe120, Park Systems Suwon, South Korea) with OTR4 cantilevers (0.02 N/m, Bruker, Camarillo, CA, USA). Scan size of 80 × 80 μm2. A deflection signal is presented. Reproduced with permission from Osuchowska et al. [<a href="#B44-applsci-12-07030" class="html-bibr">44</a>]. Full article ">Figure 4
Analysis of topography of the single FA imprints. (A) Scheme of the FA structure; (B) the AFM image of the FA; (C) the 3D reconstruction; (D) the height distribution of the FA imprints; (E) a side view of the FA reconstruction. AFM images were processed using Gwyddion 2.53 software. Reproduced with permission from Osuchowska et al. [<a href="#B9-applsci-12-07030" class="html-bibr">9</a>]. Full article ">Figure 5
The anticipated development of SXCM. The development of compact X-ray sources makes SXCM much cheaper, smaller, and easier to use in biological laboratories. The correlation of SXCM with other developing microscopic techniques, such as optical, fluorescence, and super-resolution microscopy, improves the identification of various biological structures and provides additional information on their distribution and functionality. Full article ">

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