Universidad Nacional de Chimborazo

NOVASINERGIA, 2019, Vol. 2, No. 2, junio-noviembre, (58-67)

ISSN: 2631-2654

https://doi.org/10.37135/unach.ns.001.04.06

Research Article

Graphic user interface for synchrotron beamline

Interfaz gr

aﬁca de usuario para una l

ınea de luz de sincrotr

Jose Brito del Pino

*, Felipe Brito del Pino

, Moshe Brito del Pino

, Diego Reina

Facultad de Ingenier

ıa, Universidad Nacional de Chimborazo, Riobamba, Ecuador, 060108; mybrito.ﬁs@unach.edu.ec;

dreina@unach.edu.ec

Transmissions Department, Coorporaci

on Nacional de Telecomunicaciones, Riobamba, Ecuador, 060102;

andres.brito@cnt.gob.ec

* Correspondence: jose.brito@unach.edu.ec

Recibido 15 abril 2019; Aceptado 05 noviembre 2019; Publicado 10 diciembre 2019

Abstract:

This research consisted of developing a graphical user interface in Python language

for the reconstruction of images based on the propagation technique (PBI).

The methodology used consisted of rewriting and ordering the existing code by

reformulating it in the form of classes and methods to link them to the graphical user

interface template using PyQt. The analysis requirements, design, and implementation

of user graphic interface were explained. The graphical user interface permitted

conducted two experiments using the PBI technique and analyzing their differences

and similarities, and demonstrating that the algorithm for reconstruction was testing

successfully.

Keywords:

Beamline, graphical user interface, image processing, python, tomography

Resumen:

Esta investigaci

on consisti

o en desarrollar una interfaz gr

aﬁca de usuario en lenguaje

Python para la reconstrucci

on de im

agenes basada en la t

ecnica de propagaci

basada en im

agenes (PBI). La metodolog

ıa utilizada consisti

o en volver a escribir

y ordenar el c

odigo existente reformul

andolo en forma de clases y m

etodos para

vincularlo a la plantilla de interfaz gr

aﬁca de usuario mediante PyQt. Se explicaron los

requisitos de an

alisis, el dise

no y la implementaci

on de la interfaz gr

aﬁca del usuario.

La prueba de la interfaz gr

aﬁca se realiz

o en dos experimentos utilizando la t

ecnica

PBI, analizando sus diferencias y similitudes, lo que demuestra que la interfaz gr

aﬁca

de usuario, su herramienta, y el algoritmo para la reconstrucci

on fueron probados con

exito.

Palabras clave:

Interfaz gr

aﬁca de usuario, l

ınea de luz, procesamiento de im

agenes, python,

tomograf

ıa

1 Introduction

Micro computed tomography (micro-CT)

is a non-destructive technique tool which

obtains tri-dimensional images of the sum of

bi-dimensional images or slices from a sample

(Boerckel et al., 2014). The principle of micro-CT

is based on the attenuation of x-rays passing

through the object or sample being imaged. As an

x-ray passes through the tissue, the intensity of the

incident X-ray beam is diminished according to the

equation, I

= I

−µx

, where I

is the intensity of

the incident beam, x is the distance from the source,

is the intensity of the beam at distance x from the

http://novasinergia.unach.edu.ec

source, and µ is the linear attenuation coefﬁcient

(Stauber & M

uller, 2008).

Conventionally computed tomography (CT) uses

the absorption contrast which does not permit

some details to be seen in some type of samples

due to weak absorption, but phase shifts in the

X-ray beam can be applied for obtaining the

best image (Bronnikov, 2006a). A signiﬁcant

improvement over conventional attenuation-based

X-ray imaging, which lacks contrast in small

objects and soft biological tissues, is obtained

by introducing phase-contrast imaging (Zhou

& Brahme, 2008). The inline phase-contrast

X-ray imaging method, sometimes also called

the image-based propagation technique (PBI)

or the in-line holography process, exploits the

Fresnel diffraction and is dubbed the phase-contrast

imaging method in microtomography, which

is made possible by using third generation

synchrotron radiation sources or microfocus X-ray

tubes (Bronnikov, 2006a) (Zhou & Brahme, 2008).

Absorption-contrast X-ray imaging serves to

visualize the variation in its attenuation within the

volume of a given sample, whereas phase contrast

allows one to visualize variations in the X-ray

refractive index (Gureyev et al., 2009).

In the principle of in-line phase-contrast imaging,

the projected images of the computer phantom of

the spheres are shown for different positions of the

detector along the z-axis. If the phase contrast

occurs in the Fresnel diffraction region, it means

that it is necessary to place the detector at a certain

distance from the source (Bronnikov, 2006a).

The image is edge-enhanced, and for soft tissues, it

is possible to retrieve the phase projection from a

single in-line image. The phase contrast technique

is used for reconstructing the phase coefﬁcient using

the retrieved phase projections (Cai, 2009). In the

PBI phase of contrast X-ray imaging, the phase

contrast effects are due to thick samples, where the

contrast effect increases linearly with the sample

according to detector distance (Lussani et al., 2015):

High-resolution X-ray produces detailed

three-dimensional images of soft tissue and

bone structure. In the case of soft tissue, the highest

resolutions are achieved with the help of a contrast

agent, which increases the X-ray attenuation of

the tissue of interest. For this technique, the

need for high X-ray doses to obtain the ﬁnest

resolution precludes scanning of live specimens

(Phenogenomics, 2019). The multi-contrast

X-ray images of a mouse can be obtained with a

conventional X-ray image based on attenuation, the

differential phase-contrast image based on X-ray

refraction, and dark-ﬁeld image based on X-ray

scattering (Bech et al., 2013).

The reconstruction process transforms the raw

acquisition data into a stack of 2D cross-sections

through the sample, resulting in a 3D data set. A

number of artifact and noise reduction algorithms

are integrated to reduce ring artifacts, beam

hardening artifacts, COR (center of rotation)

misalignment, detector or stage tilt, pixel

non-linearities, among others (Zhao et al., 2009).

Phase retrieval is a method permitted the

quantitative analysis of images, reconstructing

the image phase from the measured intensity of

projections, in-line phase-contrast X-ray imaging

provides characteristics of images in absorption and

refraction (Chen et al., 2013).

Phase retrieval enables us to obtain quantitative

information about the sample from phase-contrast

images (Mayo et al., 2003a). Microtomography

experiments can be performed in the so-called

Propagation Based Imaging (PBI) modality by

using a sufﬁciently coherent X-ray beam, such

as the one obtained at e.g. third generation

synchrotron then, both the amplitude and phase

transmitted through the sample can be retrieved

from the radiography a quantitative relationship

exists between the phase shift induced by the object

and the recorded intensity. The inversion of this

relationship is called phase retrieval and in PBI

this can be performed via digital image processing

(Bronnikov, 2006a). Propagation-based imaging

requires that the detector is positioned at a sufﬁcient

distance from the sample; optical elements are

unnecessary between the sample and the detector.

Python is a high-level general purpose

programming language because code is

automatically compiled to byte code and executed.

Python is suitable for use as a scripting language,

web application implementation language, etc.

Python can be extended in C and C++, and can

provide the speed needed for even computer

intensive tasks, because of its strong structuring

constructs (nested code blocks, functions, classes,

modules, and packages) and its consistent use

of objects and object-oriented programming

(Kuhlman, 2009).

Python has a huge number of Graphical User

Interface (GUI) frameworks (or toolkits) available

for it, the major cross-platform technologies upon

which Python frameworks are based include Gtk,

Qt, Tk and wxWidgets, although many other

technologies provide actively maintained Python

bindings (Simon, 2019). PyQt a Python graphic

http://novasinergia.unach.edu.ec 59

tool, brings together the Qt C++ cross-platform

application framework and the cross-platform

interpreted by the Python language. PyQt is a

set of Python v2 and v3 multiplatform bindings

(Riverbank, 2019).

Python is being widely used to create scripts

which cover different necessities in computational

scenarios. For example, the Brazilian Synchrotron

Light Laboratory successfully developed Python

scripts to control beamlines operations, including

a case of GUI creation using Tkinter (Beniz &

Espindola, 2016).

Python, Qt and some Python libraries: PyQt,

PyDM and Py4syn are powerful resources of

these modules and Python straightforward coding

guarantees ﬂexible user interfaces: it is possible

to combine graphical applications with intelligent

control procedures (Fedel et al., 2017).

Py4Syn, an open-source Python-based library for

data acquisition, device manipulation, scan routines

and other helper functions which is driven by

easy-to-use and scalability ideals, offers control

system agnostic solutions and high customization

levels for scans and data output, covering distinct

techniques and facilities for synchrotrons (Slepicka

et al., 2015).

2 Methodology

2.1 Biomedical Beamline for in vivo

and in-vitro experiments

The biomedical beamline, part of the European

Synchrotron Radiation Facility (ESRF), located in

Grenoble (France), is involved both in diagnostic

imaging and therapeutic irradiation for pre-clinical

and potentially clinical applications (ESRF,

2019a). In the scan process the object is moved

vertically through the beam while the detector type

Charge-Couple Device (CCD) camera constantly

acquires projection images (Donzelli et al., 2016).

The wiggler in biomedical beamline is a neat way

to increase the intensity of synchrotron radiation.

It is a periodic arrangement of dipole magnets

generating an alternating static magnetic ﬁeld which

deﬂects the electron beam sinusoidally. The ﬁlters

are also placed as beam attenuators. They are

located between the source and the sample to avoid

overheating of the optical elements in the optic

hutch (ESRF, 2019b).

2.2 Phanton

The routine preclinical CT calibration phanton

(Price et al., 1990) consists of known

tissue-mimicking material (King et al., 2011)

that are ideally suited for preclinical CT number

calibration or for further multiple energy

research when using a dedicated image-guided

microirradiators (micro-IR) or preclinical in vivo

cone-beam CT devices. This routine phantom

consists of a validation and calibration phantom

as an independent test to determine and assess the

accuracy and precision of dual energy computed

tomography (DE-CT) material decomposition

algorithms (Solutions, 2019a).

2.3 Microtomography Technique

Figure 1 shows the phantom placed on the CT stage

during the acquisition, the distance between the

detector and the stage was 2.5 m.

Figure 1: Phantom placed on the Computed Tomographic

stage during the acquisition.

After preparing and aligning the sample stage, two

wigglers were set: W 150:55 mm and W 125:65

mm, for each selected energy at 30 keV, two ﬁlters

(0.8 C + 0.5 Al) were manually introduced to keep

the same incoming ﬂux on the object and to reduce

the heat load on the optical elements.

2.4 Software Development

A Graphic User Interface was developed in Python

Language for beamline in ESRF (Grenoble −

France). It was designed for online image

reconstruction during experiments. Autonomous

handling of reconstruction software and the speciﬁc

routines were developed for the speciﬁc users.

The ﬁrst step was to structure the present code

into different clases and methods, the second

step involved the PyQt library for construction

http://novasinergia.unach.edu.ec 60

the GUI. The main goal of the GUI is to

provide a user-friendly multi-technique platform

that can be used to handle the data obtained from

Propagation-based imaging (PBI) technique.

The program template was linked to the functions

grouped into classes in the python code, according

to the python code made by scientists of beamline.

The template generates a text ﬁle, according to the

instruction manual.

The tab ”Range Parameters” contains the COR

RANGE parameter which refers to the center

of rotation, this parameter allows linking to the

script to reconstruct a single slice of the volume

concerned using different centers of rotation in the

range generated automatically from reconstruction

software. The CT ﬁles are saved in the COR

folder (see ﬁgure 7). The delta-beta range parameter

allows linking to the script to reconstruct a single

slice of the volume concerned using a different

Paganin number in the range (2480-2550). The CT

ﬁles are saved in the PAG folder. The folder is

created automatically (see ﬁgure 7).

The GUI allows the following operations: The

”Save Parameters” button writes the expert

parameters and basic parameters used for

reconstruction in a text ﬁle. The ”Load Parameters”

button updates the expert and basic parameters

reconstruction from a text ﬁle.

For the development of the GUI the Cascade

Model is used, the same one that is used

for the development of the application, the

development steps are seen downwards (one after

the other, in a linear and sequential way); this

model is used in the development of applications

whose approach considers development times of

applications, small or standard systems, cost is

a factor that is not considered, and backsliding

to the previous phases is not accepted. The

phases of the model are: Requirement Analysis,

Design, Development and Implementation, Testing

and Release (Maintenance) (Pressman, 2010).

2.4.1 Tool for reconstruction and Algorithms

PyHST2 is a tool for reconstruction based on

Python language which implements parallel

geometry for phasecontrast and absorption

tomography; the code implements besides a default

ﬁltered back projection reconstruction algorithm

(Bronnikov, 2006b), iterative reconstruction

techniques with a-priori knowledge to improve the

reconstruction quality or reduce the required data

volume and reach a given quality goal.

The acquired images were reconstructed using

the ﬁltered-back projection algorithm after the

application of the single defocused-image Paganin

algorithm (Mirone et al., 2013).

2.4.2 Requirements Analysis

The purpose of the needs analysis is to examine

the feasibility and importance of the functions.

The results of this phase are the speciﬁcations that

contain the requirements that must be developed;

thus, the following requirements were determined:

immplement a graphical interface that allows the

entry of parameters for image reconstruction,

upload data to the interface through an external ﬁle,

provide an easy-to-use interface,and multitechnical

platform that allows the management of the

instrumentation for the acquisition of images.

2.4.3 GUI Design

The components of this phase use the diagrams

that show the behavior of the system, the

instructions on the architecture of the GUI and the

applied technologies which are also adjusted, such

as: programming languages, use of libraries or

libraries, programming techniques and structures of

control. So, based on the requirements we have

designed some diagrams that describe its operation,

behavior, elements, actors, etc. (see ﬁgures 2 to 4).

a) System modeling

Figure 2: Use Case of Graphical User Interface.

b) Coding Structure

The image reconstruction code was structured into

classes and methods for its call from the GUI using

http://novasinergia.unach.edu.ec 61

Figure 3: Class Diagram of Graphical User Interface

Figure 4: Estate Diagram of Graphical User Interface

the PyQt tools for the construction of the GUI (see

table 1.).

2.4.4 Implementation

The ﬁgures 5 and 6 show a part of the structure of

programming code for the calibration of the Paganin

length.

Figure 5: Part 1 - Coding for Range of Paganin.

Figure 6: Part 2 - Coding for Range of Paganin

2.4.5 Parameters GUI

The basic parameters are explained according to

ﬁgure 4:

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Table 1: Code Structure of Graphical User Interface.

Classes / Methods / Libraries Description

Library: PyQt4 Set of Python bindings for Qt cross-platform.

Class 1: Calculation Calc center of rotation.

Method 1.1: CalcCenterOfRotation Calculate center of rotation.

Method 1.2: DoSino Create sinograms

Method 1.3: normalization Make the normalization of projections

Class 2: Parameters Read text ﬁle and store the values into a dictionary

Method: 2.1: addParameters Creation parameters for reconstruction

Method 2.2: DoParFile Create text ﬁle containing the info about the scan

Class 3: Utility Reconstruction send to the cluster,

Method 3.1: DoScript The reconstruction is submitted to the cluster.

Class 4: Ui RangeCor Create GUI for calibrate center of rotation

Method 4.1: setupUi Create template for RangeCor

Method 4.2: retranslateUi Calls for the RangeCor class occur.

Class 5: Ui RangePag Create the GUI for calibrate the Paganin legth

Method 5.1: setupUi Create template for RangePag

Method 5.2: retranslateUi Calls for the RangePag class occur.

Class 6: Ui Basic Expert Create the GUI for calibrate the Basic Expert

Method 6.1: setupUi Create template for Basic Expert

Method 6.2: retranslateUi Calls for the Basic Expert class occur.

- Reconstruction starts in line: indicates the starts

in line of reconstruction

- Reconstruction ends in line: indicates the end in

line of reconstruction

- Paganin Length: refers to Paganin dimension

which input previous reconstruction

- Center of rotation: deﬁnes rotation center of

reconstruction

- Sampling Reduction (SR), is a practical algorithm

based on Random Sampling Reduction (RSR)

(Buchmann & Ludwig, 2003).

- Ring Correction (yes/no): refers to PyHST ring

correction

- Apply Paganin (yes/no)?: refers to Paganin

algorithm

The expert parameters are explained according to

ﬁgure 5:

- Scan Name radix: corresponds to the radix name

of the ﬁle without extension.

- Number of projections: corresponds to the

effective number of projections acquired

- CT Range (180/360): refers to scan at 180 or 360

degrees

- y start: region of interest (ROI) start

- y end: region of interest (ROI) end

- Width: pixels image width

- Height: pixels image height

- Pixel Size: refers to effective pixel size

- Binning XY: binning in the reconstruction plane

(Mohammadi et al., 2014).

- Binning Z: binning along the vertical axis

- WF name: name of one white ﬁeld ﬁle

- Number of WF in the ﬁle: number of white ﬁelds

acquired

- Half Acquisition (yes/no)?: refers to half

acquisition

- Sample Positioning (left/right): left in the case of

half acquisition

- Rotation Axis (H/V): H= horizontal and V=

vertical rotation axes.

3 Results and Discussion

3.1 Software Test

The ﬁrst operation into the reconstruction process

was to determine the exact centre of rotation for

each dataset. For this purpose, the GUI was

used, and a series of reconstructions performed

automatically using different centres of rotation. As

a second step, the optimal ratio δ\β (Mayo et al.,

2003b) to perform the phase-retrieval was chosen

again using another automatic method present in the

reconstruction software (see ﬁgure 3).

Figure 7: Range Parameters. Rotation Center and δ\β of

slice.

Figure 8 shows the basic parameters of the previous

reconstruction, which would be ﬁxed after ﬁnding

the correct values in the range parameters. The

expert parameters were loaded automatically from

a text ﬁle generated after the micro-CT procedure

(see ﬁgure 9).

3.2 Scanning of Phantom

Figure 10 illustrates a reconstructed slice at a photon

energy equal to 30 keV where the regions of interest

(ROI) are numbered for each material according to

table 2.

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Figure 8: Basic Parameters of Slice.

Figure 9: Experts Parameters of Slice.

Table 2: Materials of Phantom.

ROI Material

1 Liver

2 Cortical bone (SB3)

3 Cortical bone CB2-50%

4 Bone (B200)

5 Air

6 Air

7 Breast

8 Inner bone

9 Brain

10 Cortical bone CB2-30%

11 Solid Water

12 Adipose

Figure 10: Slice reconstructed. Depiction of CT

calibration phantom µCT scan at 30 Kev.

3.3 Scanning of rat sample/specimen

The PlastiRat is an extremely useful tool for

conducting imaging quality assurance tests as

well as for irradiation planning studies, and for

educational purposes. The beneﬁt of PlastiRat is

that it is biologically inert, being a motionless rigid

object that represents the true anatomy of a living

rat including all the mineralized components of the

bones (see ﬁgure 11) (Solutions, 2019b).

Parameters of experiment:

- The distance between the sample stage and

the detector was 11 m.

- Wigglers: W150 - 55 mm, W125 - 45 mm.

- Filter: 2.5 mm Al + 0.8 mm C.

- Energy: 60 KeV.

- FReLoN CCD camera coupled with 1:3.6 optic.

- Effective pixel size 47 µm

Parameters of reconstruction:

Sampling reduction = 1

Width = 2048

COR = 180

CT Range = 360

Number of projections = 4000

Sample Positioning = 1

Rotation Axis = 1

End Range = 181.0

http://novasinergia.unach.edu.ec 64

Figure 11: Rat on the computed tomography stage.

Scan Name = plastiﬁed rat 001

Number of WF = 100

y end = 211

Only CT = 1

y start = 1

Paganin Length = 50

Slices Number = 105

Slices NumberP = 105

Number of Points = 2

Reconstruction starts = 1

Reconstruction ends = 211

Half Acquisition = 1

Do Paganin = 1

Number of PointsP = 20

AngleCT = 0.09

DEFINE = RETRIEVAL

BinningXY = 1

Start Range = 179.0

Pixel Size = 2.95

End RangeP = 65.0

WF name = ref0000 0000.edf

Make Sinos = 0

BinningZ = 1

Height = 211

Ring Correction = 1

Start RangeP = 35.0

3D Rendering and Segmentation:

ImageJ was used for 3D rendering and

segmentation. The segmentation assigns a label to

each pixel of the image describing which region or

material it belongs (Konrad-Zuse-Zentrum, 2019).

Figure 12 shows the external tissue, ﬁgures 13-14

depict the skeleton view, and ﬁgures 15-16 illustrate

some different views of the 3D brain of a rat using

the technique region growing segmentation (Jarek,

2019).

Figure 12: Rat tissue.

Figure 13: Rat Skeleton - top view.

3.4 Comparison of results

The reconstruction parameters entered in the GUI

were adequate in both experiments, allowing the 2D

reconstruction to be optimized in both experiments

free of image artifacts. The 3D rendering for the

reconstruction of the plasticized rat, permitted to

verify the accuracy of the parameters entered in

the GUI, for reconstruction of each slice based on

the PyHST2 tool.It was possible to segment the

http://novasinergia.unach.edu.ec 65

Figure 14: Rat Skeleton - bottom view.

Figure 15: Rat brain - top view.

Figure 16: Rat brain - side view

external tissue, the bone structure and an internal

organ (brain) of the plasticized rat. The scan of the

plasticized rat and its subsequent rendering in 3D

was the most complete test of the system, where the

accuracy of the parameters entered into the GUI was

evaluated, the use of PyHST2 as a reconstruction

tool for each slice. A single data set of 700

slices was used for phantom reconstruction; instead,

the plasticized rat, was used 54 data sets with

1400 slices each one for reconstruction. Another

important factor is the Paganin value which is

minor for the rat scan with respect to the phantom

parameter allowing us to obtain a better contrast in

the rat tissue and other small details. The binning

values were 16 for the phantom scan and 1 for

the rat scan, with the greater binning permitting an

improvement in the contrast sensitivity and signal

improvements.

4 Conclusions

It can be concluded that the methods and techniques

used that allowed the development of this graphic

interface were carried out successfully. The

reconstruction parameters entered in the graphic

interface using the image-based propagation

technique were successfully tested in two

experiments, which was demonstrated in the

2D and 3D reconstructions.

References

Bech, M., Tapfer, A., Velroyen, A., Yaroshenko, A.,

Pauwels, B., Hostens, J., & Pfeiffer, F. (2013). In-vivo

dark-ﬁeld and phase-contrast x-ray imaging. Scientiﬁc

reports,, 3(3209).

Beniz, D., & Espindola, A. . (2016). Using Tkinter of

Python to Create Graphical User Interface (GUI) for

Scripts in LNLS. WEPOPRPO25, 09, 25–28.

Boerckel, J. D., Mason, D. E., McDermott, A. M.,

& Alsberg, E. (2014). Microcomputed tomography:

approaches and applications in bioengineering. Stem

cell research and therapy, 5(6), 144.

Bronnikov, A. V. (2006a). Phase-contrast CT:

fundamental theorem and fast image reconstruction

algorithms.

Bronnikov, A. V. (2006b). Phase-contrast ct: fundamental

theorem and fast image reconstruction algorithms. in

developments in x-ray tomography. International

Society for Optics and Photonics., 6318, 63180Q.

Buchmann, J., & Ludwig, C. (2003). Practical lattice

basis sampling reduction. Lecture Notes in Computer

Science, 4076(19).

Cai, W. (2009). Feasibility study of phase-contrast cone

beam CT imaging systems. University of Rochester,

Rochester, USA, 1 edition. isbn = 9781109634686.

Chen, R. C., Rigon, L., & Longo, R. (2013). Comparison

of single distance phase retrieval algorithms by

considering different object composition and the effect

of statistical and structural noise. Optics express, 21(6),

7384–7399.

Donzelli, M., Brauer-Krisch, E., Nemoz, C., Brochard,

T., & Oelfke, U. (2016). Conformal image-guided

microbeam radiation therapy at the esrf biomedical

beamline id17. 46.

http://novasinergia.unach.edu.ec 66

ESRF (2019a). Id17 - biomedical beamline.

http://www.esrf.eu/UsersAndScience/

Experiments/CBS/ID17,accessed2019-04-10.

ESRF (2019b). Overview of the beamline optics.

http://www.esrf.eu/UsersAndScience/

Experiments/CRG/BM02/optic, accessed

2019-04-10.

Fedel, G., Piton, J., Do Carmo, L., & Beniz, D. (2017).

Python for User Interfaces at Sirius. Proceedings, 16th

International Conference on Accelerator and Large

Experimental Physics Control Systems (ICALEPCS

2017), 09, 8–13.

Gureyev, T. E., Mayo, S. C., Myers, D. E., Nesterets,

Y., Paganin, D. M., Pogany, A., & Wilkins, S. W.

(2009). Refracting r

ontgen’s rays: propagation-based

x-ray phase contrast for biomedical imaging. Journal

of Applied Physics, 105(10).

Jarek, S. (2019). Seeded region growing

(imagej plugin). http://ij-plugins.

sourceforge.net/plugins/segmentation/

Howto-Seeded-Region-Growing-Segmentation.

pdf, accessed 2019-05-24.

King, D. M., Moran, C. M., McNamara, J. D., Fagan,

A. J., & Browne, J. E. (2011). Development of

a vessel-mimicking material for use in anatomically

realistic doppler ﬂow phantoms. Ultrasound in

medicine and biology, 37(5), 813–826.

Konrad-Zuse-Zentrum (2019). Amira users guide.

http://www1.udel.edu/ctcr/sites/udel.

edu.ctcr/files/Amira%20Users%20Guide.pdf,

accessed 2019-05-24.

Kuhlman, D. (2009). A python book: Beginning

python, advanced python, and python exercises. Dave

Kuhlman Lutz.

Lussani, F. C., Vescovi, R. F. D. C., Souza, T. D. D.,

Leite, C. A., & Giles, C. (2015). A versatile x-ray

microtomography station for biomedical imaging and

materials research. Review of Scientiﬁc Instruments,

86(6).

Mayo, S. C., Davis, T. J., Gureyev, T. E., Miller,

P. R., Paganin, D., Pogany, A., & Wilkins, S. W.

(2003a). X-ray phase-contrast microscopy and

microtomography. Optics express, 11(19), 2289–2302.

Mayo, S. C., Davis, T. J., Gureyev, T. E., Miller,

P. R., Paganin, D., Pogany, A., & Wilkins,

S. W. (2003b). X-ray phase-contrast microscopy

and microtomography. Optics Express, 11(19),

2289–2302.

Mirone, A., Brun, E., Gouillart, E., Tafforeau, P., &

Kieffer, J. (2013). The pyhst2 hybrid distributed

code for high speed tomographic reconstruction

with iterative reconstruction and a priori knowledge

capabilities. Nuclear Instruments and Methods in

Physics Research Section B: Beam Interactions with

Materials and Atoms., 3(324), 41–48.

Mohammadi, S., Larsson, E., Alves, F., Dal Monego, S.,

Bifﬁ, S., Garrovo, C., & Dullin, C. (2014). Quantitative

evaluation of a single-distance phase-retrieval method

applied on in-line phase-contrast images of a mouse

lung. Journal of Synchrotron Radiation, 21(4),

784–789.

Phenogenomics (2019). Micro-computed tomography

(micro-ct). http://www.mouseimaging.ca/

technologies/microct.html, accessed 2019-05-23.

Pressman, R. (2010). Ingenier

ıa del Software (Un

Enfoque Pr

actico), volume 1. Mc-GrawHill., Espa

na,

1 edition. isbn = 978-607-15-0314-5.

Price, R. R., Axel, L., Morgan, T., Newman, R.,

Perman, W., Schneiders, N., & Thomas, S. R. (1990).

Quality assurance methods and phantoms for magnetic

resonance imaging: report of aapm nuclear magnetic

resonance task group no. 1. Medical physics, 17(2),

287–295.

Riverbank (2019). What is pyqt? https://

riverbankcomputing.com/software/pyqt/intro,

accessed 2019-04-10.

Simon, C. (2019). Gui programming in python. https:

//wiki.python.org/moin/GuiProgramming,

accessed 2019-05-23.

Slepicka, H. H., Canova, H. F., Beniz, D. B., & Piton, J. R.

(2015). Py4syn: Python for synchrotrons. Journal of

Synchrotron Radiation, 22(05), 1182–9.

Solutions, S. S. (2019a). Ct imaging calibration phantom

for quantitative imaging and dose calculations. http:

//smartscientificsolutions.com/wp-content/

uploads/2016/03/Promotional_Leaflet_DECT_

phantoms_2016_single-sided.pdf, accessed

2019-04-10.

Solutions, S. S. (2019b). Plastimouse and plastirat. http:

//smartscientificsolutions.com/wp-content/

uploads/2016/03/Promotional_Leaflet_

PlastiMouse-PlastiRat_2016_doublesided.pdf,

accessed 2019-05-23.

Stauber, M., & M

uller, R. (2008). Micro-computed

tomography: a method for the nondestructive

evaluation of the three-dimensional structure of

biological specimens. Osteoporosis: Methods and

Protocols, pp. 273–292.

Zhao, W., Chen, Y., Shen, L., & Allen, Y. Y. (2009).

Investigation of the refractive index distribution in

precision compression glass molding by use of 3d

tomography. Measurement Science and Technology,

20(5).

Zhou, S., & Brahme, A. (2008). Development of

phase-contrast x-ray imaging techniques and potential

medical applications. Physica Medica, 24(3),

129–148.

http://novasinergia.unach.edu.ec 67