File:RadiationEnvironment 06-11.pdf

2017-11-06T12:24:21Z

Jars: File uploaded with MsUpload

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Main Page

2017-10-18T12:46:39Z

Jars:

2017-05-10T09:54:43Z

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FLUKA installation

2017-05-09T22:47:19Z

Jars: Created page with "==Prerequisites== The required software and packages in order to use FLUKA in combination with flair (FLUKA Advanced Interface) and flair-geoviewer: Gfortran/gcc Gnuplot Py..."

Software for design optimization

2017-04-03T09:38:15Z

Jars: /* Creating the full simulations files for a range-energy look-up-table */

== Required software ==
In order to run the software to generate the geomtry, Monte Carlo data and do full simulations, you need the following software:
* [[ROOT installation | ROOT]]
* [[Geant 4 installation | Geant4]]
* [[Gate installation | Gate]]
* [[DTC toolkit]] (Helge's code)

== GATE Simulations ==
=== Generating the geometry ===
To generate the geometry files to run in Gate, a Python script is supplied.
It is located within the ''gate/python'' subfolder.
[gate/python] $ python gate/python/makeGeometryDTC.py
[[File:GATE geometry builder.PNG||500px]]

Choose the wanted characteristics of the detector, and use ''write files'' in order to create the geometry file Module.mac, which is automatically included in Main.mac.
Note that the option "Use water degrader phantom" should be checked (as is the default behavior)!

=== Creating the full simulations files for a range-energy look-up-table ===
First, enable the full scoring of all volumes by commenting / uncommenting in ''Main.mac'':
#/control/execute readout.mac
/control/execute readout_full.mac
Then, in the same file, save to the correct ROOT file:
/gate/output/root/setFileName ../../DTCToolkit/Data/MonteCarlo/DTC_Full_Aluminium_Absorber{absorberthickness}mm_Degrader{degraderthickness}mm_{energy}MeV
#/gate/output/root/setFileName ../../DTCToolkit/Data/MonteCarlo/DTC_Aluminium_Absorber{absorberthickness}mm_{energy}MeV
It is not critical to have a high number of primaries in this part. 10000 is enough for this step. Change this in the ''Main.mac'' file!
To loop through different energy degrader thicknesses, run the script ''runDegrader.sh'':
[gate/python] $ sh runDegraderFull.sh <absorber thickness> <degraderthickness from> <degraderthickness stepsize> <degraderthickness to>
The brackets indicate the folder in the Github repository to run the code from.

For example, with a 3 mm degrader, and simulating a 250 MeV beam passing through a phantom of 50, 55, 60, 65 and 70 mm water:
[gate/python] $ sh runDegraderFull.sh 3 50 5 70
Please note that there is a variable NCORES in this script, which ensures that NCORES versions of the Gate executable are run in parallel, and then waits for the last background process to complete before a new set of NCORES executables are run. So if you set NCORES=8, and run <code>sh runDegraderFull.sh 3 50 1 70</code>, first 50-57 will run in parallel, and when they're done, 58-65 will start, etc. The default value is NCORES=4.

=== Creating the chip-readout simulations files for resolution calculation ===
First, undo the changes to Main.mac:
/control/execute readout.mac
#/control/execute readout_full.mac
Then, in the same file, save to the correct ROOT file:
#/gate/output/root/setFileName ../../DTCToolkit/Data/MonteCarlo/DTC_Full_Aluminium_Absorber{absorberthickness}mm_Degrader{degraderthickness}mm_{energy}MeV
/gate/output/root/setFileName ../../DTCToolkit/Data/MonteCarlo/DTC_Aluminium_Absorber{absorberthickness}mm_{energy}MeV
In this step a higher number of particles is desired. I usually use 25000 since we need O(100) simulations. A sub 1-mm step size will really tell us if we manage to detect such small changes in a beam energy.

And loop through the different absorber thicknesses:
[gate/python] $ sh runDegrader.sh <absorber thickness> <degraderthickness from> <degraderthickness stepsize> <degraderthickness to>
The same parallel-in-sequential run mode has been configured here.

== Creating the basis for range-energy calculations ==
=== The range-energy look-up-table ===
Now we have ROOT output files from Gate, all degraded differently through a varying water phantom and therefore stopping at different places in the DTC.
We want to follow all the tracks to see where they end, and make a histogram over their stopping positions. This is of course performed from a looped script, but to give a small recipe:
# Retrieve the first interaction of the first particle. Note its event ID (history number) and edep (energy loss for that particular interaction)
# Repeat until the particle is outside the phantom. This can be found from the volume ID or the z position (the first interaction with {math|z>0}). Sum all the found edep values, and this is the energy loss inside the phantom. Now we have the "initial" energy of the proton before it hits the DTC
# Follow the particle, noting its z position. When the event ID changes, the next particle is followed, and save the last z position of where the proton stopped in a histogram
# Do a Gaussian fit of the histogram after all the particles have been followed. The mean value is the range of the beam with that particular "initial" energy. The spread is the range straggling. Note that the range straggling is more or less constant, but the contributions to the range straggling from the phantom and DTC, respectively, are varying linearly.

This recipe has been implemented in <code>DTCToolkit/Scripts/findRange.C</code>. Test run the code on a few of the cases (smallest and biggest phantom size ++) to see that
# The correct start- and end points of the histogram looks sane. If not, this can be corrected for by looking how <code>xfrom</code> and <code>xto</code> is calculated and playing with the calculation.
# The mean value and straggling is calculated correctly
# The energy loss is calculated correctly
You can run <code>findRange.C</code> in root by compiling and giving it three arguments; Energy of the protons, absorber thickness, and the degrader thickness you wish to inspect.
[DTCToolkit/Scripts] $ root
ROOT [1] .L findRange.C+
// void findRange(Int_t energy, Int_t absorberThickness, Int_t degraderThickness)
ROOT [2] findRange f(250, 3, 50); f.Run();

The output should look like this: Correctly places Gaussian fits is a good sign.

[[File:findRanges.JPG|600px]]

If you're happy with this, then a new script will run <code>findRange.C</code> on all the different ROOT files generated earlier.
[DTCToolkit/Scripts] $ root
ROOT [1] .L findManyRangesDegrader.C
// void findManyRanges(Int_t degraderFrom, Int_t degraderIncrement, Int_t degraderTo, Int_t absorberThicknessMmFrom, Int_t absorberThicknessMmIncrement, Int_t absorberThicknessMmTo)
ROOT [2] findManyRanges(50, 5, 70, 3, 1, 3)

This is a serial process, so don't worry about your CPU.
The output is stored in <code>DTCToolkit/Output/findManyRangesDegrader.csv</code>.
It is a good idea to look through this file, to check that the values are not very jumpy (Gaussian fits gone wrong).

We need the initial energy and range in ascending order. The findManyRangesDegrader.csv files contains more rows such as initial energy straggling and range straggling for other calcualations. This is sadly a bit tricky, but do (assuming a 3 mm absorber geometry):

[DTCToolkit] $ cat OutputFiles/findManyRangesDegrader.csv | awk '{print ($6 " " $3)}' | sort -n > Data/Ranges/3mm_Al.csv

NB: If there are many different absorber geometries in findManyRangesDegrader, either copy the interesting ones or use <code>| grep " X " |</code> to only keep X mm geometry

When this is performed, the range-energy table for that particular geometry has been created, and is ready to use in the analysis. Note that since the calculation is based on cubic spline interpolations, it cannot extrapolate -- so have a larger span in the full Monte Carlo simulation data than with the chip readout. For more information about that process, see this document: [[:File:Comparison of different calculation methods of proton ranges.pdf]]

=== Range straggling parameterization and <math>R_0 = \alpha E^p</math> ===
It is important to know the amount of range straggling in the detector, and the amount of energy straggling after the degrader. In addition, to calculate the parameters <math>\alpha, p</math> from the somewhat inaccurate Bragg-Kleeman equation <math>R_0 = \alpha E ^ p</math>, in order to correctly model the "depth-dose curve" <math>dE / dz = p^{-1} \alpha^{-1/p} (R_0 - z)^{1/p-1}</math>. This is done by fitting the Bragg-Kleeman equation to the range-energy look up tables found by using <code>DTCToolkit/Scripts/findManyRangesDegrader.C</code>.

To find all this, run the script <code>DTCToolkit/Scripts/findAPAndStraggling.C</code>. This script will loop through all available data lines in the <code>DTCToolkit/OutputFiles/findManyRangesDegrader.csv</code> file that has the correct absorber thickness, so you need to clean the file first (or just delete it before running <code>findManyRangesDegrader.C</code>).

[DTCToolkit/Scripts] $ root
ROOT [0] .L findAPAndStraggling.C+
// void findAPAndStraggling(int absorberthickness)
ROOT [1] findAPAndStraggling(3)

The output from this function should be something like this:

[[File:findAPAndStraggling.JPG|700px]]

In addition, the following parameters should be extracted:

Bragg-Kleeman parameters: R = 0.011626 E ^ 1.743151
Straggling = 1.8568 + 0.000856 R

== Running the DTC Toolkit ==

=== Configuring the DTC Toolkit to run with correct geometry ===
The values from <code>findManyRanges.C</code> should already be in <code>DTCToolkit/Data/Ranges/3mm_Al.csv</code> (or the corresponding material / thickness). Check that the file is correctly loaded in the file <code>DTCToolkit/GlobalConstants/MaterialConstants.C</code>. The values from <code>findAPAndStraggling.C</code> are put into the same file <code>DTCToolkit/GlobalConstants/MaterialConstants.C</code>:
81 void createSplines() {
...
107 else if (kAbsorbatorThickness = 3) {
108 in.open("Data/Ranges/3mm_Al.csv");
109 }
...
192 else if (kAbsorbatorThickness = 3) {
193 alpha_aluminum = 0.011626;
194 p_aluminum = 1.743151;
195 straggling_a = 1.8568;
196 straggling_b = 0.000856;
197 }

Or in the corresponding material (alpha_pmma, alpha_carbon, etc.) and absorbatorthickness lines.

And in the file <code>DTCToolkit/Scripts/makePlots.C</code>, put the \alpha, p parameters.

144 else if (absorberThickness == 3) {
145 a_dtc = 0.011626;
146 p_dtc = 1.743151;
147 }

Then, look in the file <code>DTCToolkit/GlobalConstants/Constants.h</code> and check that the correct absorber thickness values etc. are set:
...
39 Bool_t useDegrader = true;
...
52 const Float_t kAbsorberThickness = 3;
...
59 Int_t kEventsPerRun = 100000;
...
66 const Int_t kMaterial = kAluminum;

Since we don't use tracking but only MC truth in the optimization, the number kEventsPerRun (<math>n_p</math> in the NIMA article) should be higher than the number of primaries per energy.

=== How does the DTC Toolkit calculate resolution? ===
The resolution in this case is defined as the width of the final range histogram for all protons.
The goal is to match the range straggling which manifests itself in the Gaussian distribution of the range of all protons in the DTC, from the full Monte Carlo simulations:

[[File:findRanges_onlyrange.JPG|300px]]

To characterize the resolution, a realistic analysis is performed. Instead of scoring the complete detector volume, including the massive energy absorbers, only the sensor chips placed at intervals (<math>\Delta z = 0.375\ \textrm{mm} + d_{\textrm{absorber}}</math>) are scored. Tracks are compiled by using the eventID tag from GATE, so that the track reconstruction efficiency is 100%. Each track is then put in a depth / edep graph, and a Bragg curve is fitted on the data:

[[File:BK fit.JPG|300px]]

The distribution of all fitted ranges (simple to calculate from fitted energy) should match the distribution above - with a perfect system. All degradations during analysis, sampling error, sparse sampling, mis-fitting etc. will ensure that the peak is broadened.

[[File:distribution_after_analysis.JPG|300px]]

PS: Please forgive me the fact that the first figure is given in projected range, the second figure is given in initial energy and the third figure is given in projected water equivalent range...... They are converted losslessly since LUTs are used.

=== Finding the resolution ===
To find this resolution, or degradation in the straggling width, for a single energy, run the DTC toolkit analysis.
[DTCToolkit] $ root Load.C
// drawBraggPeakGraphFit(Int_t Runs, Int_t dataType = kMC, Bool_t recreate = 0, Float_t energy = 188, Float_t degraderThickness = 0)
ROOT [0] drawBraggPeakGraphFit(1, 0, 1, 250, 34)
This is a serial process, so don't worry about your CPU when analysing all ROOT files in one go.
With the result

[[File:distribution_after_analysis2.JPG|600px]]

The following parameters are then stored in <code>DTCToolkit/OutputFiles/results_makebraggpeakfit.csv</code>:

{| class="wikitable"
|-
| Absorber thickness || Degrader thickness || Nominal WEPL range || Calculated WEPL range || Nominal WEPL straggling || Calculated WEPL straggling
|-
| 3 (mm) || 34 (mm) || 345 (mm WEPL) || 345.382 (mm WEPL) || 2.9 (mm WEPL) || 6.78 (mm WEPL)
|}

To perform the analysis on all different degrader thicknesses, use the script <code>DTCToolkit/makeFitResultPlotsDegrader.sh</code> (arguments: degrader from, degrader step and degrader to):
[DTCToolkit] $ sh makeFitResultsPlotsDegrader.sh 1 1 380
This may take a few minutes...
When it's finished, it's important to look through the file results_makebraggpeakfit.csv to identify all problem energies, as this is a more complicated analysis than the range finder above.
If any is identified, run the drawBraggPeakGraphFit at that specific degrader thickness to see where the problems are.

=== Displaying the results ===
If there are no problems, use the script <code>DTCToolkit/Scripts/makePlots.C</code> to plot the contents of the file <code>DTCToolkit/OutputFiles/results_makebraggpeakfit.csv</code>:
[DTCToolkit] $ root Scripts/makePlots.C
The output is a map of the accuracy of the range determination, and a comparison between the range resolution (#sigma of the range determination) and its lower limit, the range straggling.

[[File:makePlots_accuracy.JPG|800px]]

[[File:makePlots_resolution.JPG|800px]]

Meetings

2017-03-30T19:45:35Z

Jars:

== Common meetings ==
Time, place, Vidyo link

for each: date, notes and slides

=== 2016-12-22 ===
Agenda:
* Status update from the WPs

* [[Media:Mrichter_wp7-status.pdf | WP7 slides]]

* [[Media:pCT_meeting_22_12_2016_Ilker_Meric.pdf | WP1 slides]]

=== 2017-01-12 ===
Agenda:
* Gitlab tutorial and best-practice discussion
* slides: [[Media:2017-01-12_Gitlab-bestpractice.pdf | Gitlab best-practice]]

=== 2017-01-26 ===
Agenda:
* status reports
* [[Media:JRSølie_WP1_Optimization.pdf | WP1 slides]]
* [[Media:status-20170126.pdf | WP4 slides]]
* [[Media:IMeric_WP5_ALPIDE_staves.pdf | WP5 slides]]

* AOB
** Announcement: RTK course in Lyon on April 10 2017 http://training.kitware.fr/browse/152

=== 2017-03-09 ===
Agenda:

* status reports
* [[Media:WP1_09-mar-2017.pdf | WP1 slides_1]]
* [[Media:WP1_09_03_2017.pdf | WP1 slides_2]]

=== 2017-03-30 ===
Agenda:
*
*
* [[Media: GroupMeeting30-Mar-2017(ITCCIR).pdf | Impressions of an external course (International Training Course on Carbon-ion Radiotherapy)]]

== Workpackage meetings ==

=== Workpackage 1 (Physics simulations, verification and design optimization) meetings ===

==== 03/03/2017 ====
Agenda:
* status reports
* [[Media: Optimizing_the_DTC.pdf | Slides from the meeting]]

* Announcement: IRRMA X -- 10th International Topical Meeting on Industrial Radiation and Radioisotope Measurement Applications http://conferences.illinois.edu/irrma2017/

==== 17/03/2017 ====
Agenda:
* status reports
* [[Media: WP1_17_03_2017.pdf | Slides from the meeting]]

* Announcement: Easy-to-use task manager: https://trello.com/

File:GroupMeeting30-Mar-2017(ITCCIR).pdf

2017-03-30T19:42:56Z

Jars:

Meetings

2017-03-30T19:42:31Z

Jars:

== Common meetings ==
Time, place, Vidyo link

for each: date, notes and slides

=== 2016-12-22 ===
Agenda:
* Status update from the WPs

* [[Media:Mrichter_wp7-status.pdf | WP7 slides]]

* [[Media:pCT_meeting_22_12_2016_Ilker_Meric.pdf | WP1 slides]]

=== 2017-01-12 ===
Agenda:
* Gitlab tutorial and best-practice discussion
* slides: [[Media:2017-01-12_Gitlab-bestpractice.pdf | Gitlab best-practice]]

=== 2017-01-26 ===
Agenda:
* status reports
* [[Media:JRSølie_WP1_Optimization.pdf | WP1 slides]]
* [[Media:status-20170126.pdf | WP4 slides]]
* [[Media:IMeric_WP5_ALPIDE_staves.pdf | WP5 slides]]

* AOB
** Announcement: RTK course in Lyon on April 10 2017 http://training.kitware.fr/browse/152

=== 2017-03-09 ===
Agenda:

* status reports
* [[Media:WP1_09-mar-2017.pdf | WP1 slides_1]]
* [[Media:WP1_09_03_2017.pdf | WP1 slides_2]]

=== 2017-03-30 ===
Agenda:
*
*
* [[Media:GroupMeeting30-Mar-2017(ITCCIR).pdf | Impressions of an external course (International Training Course on Carbon-ion Radiotherapy)]]

== Workpackage meetings ==

=== Workpackage 1 (Physics simulations, verification and design optimization) meetings ===

==== 03/03/2017 ====
Agenda:
* status reports
* [[Media: Optimizing_the_DTC.pdf | Slides from the meeting]]

* Announcement: IRRMA X -- 10th International Topical Meeting on Industrial Radiation and Radioisotope Measurement Applications http://conferences.illinois.edu/irrma2017/

==== 17/03/2017 ====
Agenda:
* status reports
* [[Media: WP1_17_03_2017.pdf | Slides from the meeting]]

* Announcement: Easy-to-use task manager: https://trello.com/

Software for design optimization

2017-03-03T11:27:15Z

Jars: Added findRange.C usage

== Required software ==
In order to run the software to generate the geomtry, Monte Carlo data and do full simulations, you need the following software:
* [[ROOT installation | ROOT]]
* [[Geant 4 installation | Geant4]]
* [[Gate installation | Gate]]
* [[DTC toolkit]] (Helge's code)

== GATE Simulations ==
=== Generating the geometry ===
To generate the geometry files to run in Gate, a Python script is supplied.
It is located within the ''gate/python'' subfolder.
[gate/python] $ python gate/python/makeGeometryDTC.py
[[File:GATE geometry builder.PNG||500px]]

Choose the wanted characteristics of the detector, and use ''write files'' in order to create the geometry file Module.mac, which is automatically included in Main.mac.
Note that the option "Use water degrader phantom" should be checked (as is the default behavior)!

=== Creating the full simulations files for a range-energy look-up-table ===
First, enable the full scoring of all volumes by commenting / uncommenting in ''Main.mac'':
#/control/execute readout.mac
/control/execute readout_full.mac
Then, in the same file, save to the correct ROOT file:
/gate/output/root/setFileName ../../DTCToolkit/Data/MonteCarlo/DTC_Full_Aluminium_Absorber{absorberthickness}mm_Degrader{degraderthickness}mm_{energy}MeV
#/gate/output/root/setFileName ../../DTCToolkit/Data/MonteCarlo/DTC_Aluminium_Absorber{absorberthickness}mm_{energy}MeV
It is not critical to have a high number of primaries in this part. 10000 is enough for this step. Change this in the ''Main.mac'' file!
To loop through different energy degrader thicknesses, run the script ''runDegrader.sh'':
[gate/python] $ sh runDegrader.sh <absorber thickness> <degraderthickness from> <degraderthickness stepsize> <degraderthickness to>
The brackets indicate the folder in the Github repository to run the code from.

For example, with a 3 mm degrader, and simulating a 250 MeV beam passing through a phantom of 50, 55, 60, 65 and 70 mm water:
[gate/python] $ sh runDegrader.sh 3 50 5 70
This is a parallel process, so don't do too much together. I've found that on my 4 core i5, 100 parallel simulations are OK (of course they only get a few % CPU each), but with >200 the virtual machine stops working... So turn on overnight, but know your limits!

=== Creating the chip-readout simulations files for resolution calculation ===
First, undo the changes to Main.mac:
/control/execute readout.mac
#/control/execute readout_full.mac
Then, in the same file, save to the correct ROOT file:
#/gate/output/root/setFileName ../../DTCToolkit/Data/MonteCarlo/DTC_Full_Aluminium_Absorber{absorberthickness}mm_Degrader{degraderthickness}mm_{energy}MeV
/gate/output/root/setFileName ../../DTCToolkit/Data/MonteCarlo/DTC_Aluminium_Absorber{absorberthickness}mm_{energy}MeV
In this step a higher number of particles is desired. I usually use 25000 since we need O(100) simulations. A sub 1-mm step size will really tell us if we manage to detect such small changes in a beam energy.

And loop through the different absorber thicknesses:
[gate/python] $ sh runDegrader.sh <absorber thickness> <degraderthickness from> <degraderthickness stepsize> <degraderthickness to>

== Creating the basis for range-energy calculations ==
=== The range-energy look-up-table ===
Now we have ROOT output files from Gate, all degraded differently through a varying water phantom and therefore stopping at different places in the DTC.
We want to follow all the tracks to see where they end, and make a histogram over their stopping positions. This is of course performed from a looped script, but to give a small recipe:
# Retrieve the first interaction of the first particle. Note its event ID (history number) and edep (energy loss for that particular interaction)
# Repeat until the particle is outside the phantom. This can be found from the volume ID or the z position (the first interaction with {math|z>0}). Sum all the found edep values, and this is the energy loss inside the phantom. Now we have the "initial" energy of the proton before it hits the DTC
# Follow the particle, noting its z position. When the event ID changes, the next particle is followed, and save the last z position of where the proton stopped in a histogram
# Do a Gaussian fit of the histogram after all the particles have been followed. The mean value is the range of the beam with that particular "initial" energy. The spread is the range straggling. Note that the range straggling is more or less constant, but the contributions to the range straggling from the phantom and DTC, respectively, are varying linearly.

This recipe has been implemented in <code>DTCToolkit/Scripts/findRange.C</code>. Test run the code on a few of the cases (smallest and biggest phantom size ++) to see that
# The correct start- and end points of the histogram looks sane. If not, this can be corrected for by looking how <code>xfrom</code> and <code>xto</code> is calculated and playing with the calculation.
# The mean value and straggling is calculated correctly
# The energy loss is calculated correctly
You can run <code>findRange.C</code> in root by compiling and giving it three arguments; Energy of the protons, absorber thickness, and the degrader thickness you wish to inspect.
[DTCToolkit/Scripts] $ root
ROOT [1] .L findRange.C+
// void findRange(Int_t energy, Int_t absorberThickness, Int_t degraderThickness)
ROOT [2] findRange f(250, 3, 50); f.Run();

The output should look like this: Correctly places Gaussian fits is a good sign.

[[File:findRanges.JPG|600px]]

If you're happy with this, then a new script will run <code>findRange.C</code> on all the different ROOT files generated earlier.
[DTCToolkit/Scripts] $ root
ROOT [1] .L findManyRangesDegrader.C
// void findManyRanges(Int_t degraderFrom, Int_t degraderIncrement, Int_t degraderTo, Int_t absorberThicknessMmFrom, Int_t absorberThicknessMmIncrement, Int_t absorberThicknessMmTo)
ROOT [2] findManyRanges(50, 5, 70, 3, 1, 3)

This is a serial process, so don't worry about your CPU.
The output is stored in <code>DTCToolkit/Output/findManyRangesDegrader.csv</code>.
It is a good idea to look through this file, to check that the values are not very jumpy (Gaussian fits gone wrong).

We need the initial energy and range in ascending order. The findManyRangesDegrader.csv files contains more rows such as initial energy straggling and range straggling for other calcualations. This is sadly a bit tricky, but do (assuming a 3 mm absorber geometry):

[DTCToolkit] $ cat OutputFiles/findManyRangesDegrader.csv | awk '{print ($6 " " $3)}' | sort -n > Data/Ranges/3mm_Al.csv

NB: If there are many different absorber geometries in findManyRangesDegrader, either copy the interesting ones or use <code>| grep " X " |</code> to only keep X mm geometry

When this is performed, the range-energy table for that particular geometry has been created, and is ready to use in the analysis. Note that since the calculation is based on cubic spline interpolations, it cannot extrapolate -- so have a larger span in the full Monte Carlo simulation data than with the chip readout. For more information about that process, see this document: [[:File:Comparison of different calculation methods of proton ranges.pdf]]

=== Range straggling parameterization and <math>R_0 = \alpha E^p</math> ===
It is important to know the amount of range straggling in the detector, and the amount of energy straggling after the degrader. In addition, to calculate the parameters <math>\alpha, p</math> from the somewhat inaccurate Bragg-Kleeman equation <math>R_0 = \alpha E ^ p</math>, in order to correctly model the "depth-dose curve" <math>dE / dz = p^{-1} \alpha^{-1/p} (R_0 - z)^{1/p-1}</math>. This is done by fitting the Bragg-Kleeman equation to the range-energy look up tables found by using <code>DTCToolkit/Scripts/findManyRangesDegrader.C</code>.

To find all this, run the script <code>DTCToolkit/Scripts/findAPAndStraggling.C</code>. This script will loop through all available data lines in the <code>DTCToolkit/OutputFiles/findManyRangesDegrader.csv</code> file that has the correct absorber thickness, so you need to clean the file first (or just delete it before running <code>findManyRangesDegrader.C</code>).

[DTCToolkit/Scripts] $ root
ROOT [0] .L findAPAndStraggling.C+
// void findAPAndStraggling(int absorberthickness)
ROOT [1] findAPAndStraggling(3)

The output from this function should be something like this:

[[File:findAPAndStraggling.JPG|700px]]

In addition, the following parameters should be extracted:

Bragg-Kleeman parameters: R = 0.011626 E ^ 1.743151
Straggling = 1.8568 + 0.000856 R

== Running the DTC Toolkit ==

=== Configuring the DTC Toolkit to run with correct geometry ===
The values from <code>findManyRanges.C</code> should already be in <code>DTCToolkit/Data/Ranges/3mm_Al.csv</code> (or the corresponding material / thickness). Check that the file is correctly loaded in the file <code>DTCToolkit/GlobalConstants/MaterialConstants.C</code>. The values from <code>findAPAndStraggling.C</code> are put into the same file <code>DTCToolkit/GlobalConstants/MaterialConstants.C</code>:
81 void createSplines() {
...
107 else if (kAbsorbatorThickness = 3) {
108 in.open("Data/Ranges/3mm_Al.csv");
109 }
...
192 else if (kAbsorbatorThickness = 3) {
193 alpha_aluminum = 0.011626;
194 p_aluminum = 1.743151;
195 straggling_a = 1.8568;
196 straggling_b = 0.000856;
197 }

Or in the corresponding material (alpha_pmma, alpha_carbon, etc.) and absorbatorthickness lines.

And in the file <code>DTCToolkit/Scripts/makePlots.C</code>, put the \alpha, p parameters.

144 else if (absorberThickness == 3) {
145 a_dtc = 0.011626;
146 p_dtc = 1.743151;
147 }

Then, look in the file <code>DTCToolkit/GlobalConstants/Constants.h</code> and check that the correct absorber thickness values etc. are set:
...
39 Bool_t useDegrader = true;
...
52 const Float_t kAbsorberThickness = 3;
...
59 Int_t kEventsPerRun = 100000;
...
66 const Int_t kMaterial = kAluminum;

Since we don't use tracking but only MC truth in the optimization, the number kEventsPerRun (<math>n_p</math> in the NIMA article) should be higher than the number of primaries per energy.

=== How does the DTC Toolkit calculate resolution? ===
The resolution in this case is defined as the width of the final range histogram for all protons.
The goal is to match the range straggling which manifests itself in the Gaussian distribution of the range of all protons in the DTC, from the full Monte Carlo simulations:

[[File:findRanges_onlyrange.JPG|300px]]

To characterize the resolution, a realistic analysis is performed. Instead of scoring the complete detector volume, including the massive energy absorbers, only the sensor chips placed at intervals (<math>\Delta z = 0.375\ \textrm{mm} + d_{\textrm{absorber}}</math>) are scored. Tracks are compiled by using the eventID tag from GATE, so that the track reconstruction efficiency is 100%. Each track is then put in a depth / edep graph, and a Bragg curve is fitted on the data:

[[File:BK fit.JPG|300px]]

The distribution of all fitted ranges (simple to calculate from fitted energy) should match the distribution above - with a perfect system. All degradations during analysis, sampling error, sparse sampling, mis-fitting etc. will ensure that the peak is broadened.

[[File:distribution_after_analysis.JPG|300px]]

PS: Please forgive me the fact that the first figure is given in projected range, the second figure is given in initial energy and the third figure is given in projected water equivalent range...... They are converted losslessly since LUTs are used.

=== Finding the resolution ===
To find this resolution, or degradation in the straggling width, for a single energy, run the DTC toolkit analysis.
[DTCToolkit] $ root Load.C
// drawBraggPeakGraphFit(Int_t Runs, Int_t dataType = kMC, Bool_t recreate = 0, Float_t energy = 188, Float_t degraderThickness = 0)
ROOT [0] drawBraggPeakGraphFit(1, 0, 1, 250, 34)
This is a serial process, so don't worry about your CPU when analysing all ROOT files in one go.
With the result

[[File:distribution_after_analysis2.JPG|600px]]

The following parameters are then stored in <code>DTCToolkit/OutputFiles/results_makebraggpeakfit.csv</code>:

{| class="wikitable"
|-
| Absorber thickness || Degrader thickness || Nominal WEPL range || Calculated WEPL range || Nominal WEPL straggling || Calculated WEPL straggling
|-
| 3 (mm) || 34 (mm) || 345 (mm WEPL) || 345.382 (mm WEPL) || 2.9 (mm WEPL) || 6.78 (mm WEPL)
|}

To perform the analysis on all different degrader thicknesses, use the script <code>DTCToolkit/makeFitResultPlotsDegrader.sh</code> (arguments: degrader from, degrader step and degrader to):
[DTCToolkit] $ sh makeFitResultsPlotsDegrader.sh 1 1 380
This may take a few minutes...
When it's finished, it's important to look through the file results_makebraggpeakfit.csv to identify all problem energies, as this is a more complicated analysis than the range finder above.
If any is identified, run the drawBraggPeakGraphFit at that specific degrader thickness to see where the problems are.

=== Displaying the results ===
If there are no problems, use the script <code>DTCToolkit/Scripts/makePlots.C</code> to plot the contents of the file <code>DTCToolkit/OutputFiles/results_makebraggpeakfit.csv</code>:
[DTCToolkit] $ root Scripts/makePlots.C
The output is a map of the accuracy of the range determination, and a comparison between the range resolution (#sigma of the range determination) and its lower limit, the range straggling.

[[File:makePlots_accuracy.JPG|800px]]

[[File:makePlots_resolution.JPG|800px]]

Publications

2017-02-23T22:19:20Z

Jars:

== From Bergen Proton CT group ==
* Pettersen, H.E.S., J. Alme, A. van den Brink, M. Chaar, D. Fehlker, I. Meric, O.H. Odland, et al. n.d. '''“Proton Tracking in a High-Granularity Digital Tracking Calorimeter for Proton CT Purposes.”''' Nuclear Instruments and Methods in Physics Research Section A: Accelerators, 157 Spectrometers, Detectors and Associated Equipment. doi:10.1016/j.nima.2017.02.007. [[https://arxiv.org/abs/1611.02031]]
* Under construction, by H. Pettersen: [[:File:Comparison of different calculation methods of proton ranges.pdf|'''Comparison of different calculation methods of proton ranges''']].
* Under construction, by J. Sølie, I. Meric and H. Pettersen: [[:File:NACP2017ActaDraft_(1).pdf|'''A comparison of longitudinal and lateral range for protons traversing complex media using GATE, MCNP6 and FLUKA Monte Carlo simulations''']]

== Review articles on proton CT ==
* G. Poludniowski, N.M. Allinson, P.M. Evans, '''Proton radiography and tomography with application to proton therapy''', The British Journal of Radiology. 88 (2015) 20150134. doi:10.1259/bjr.20150134.
* C. Civinini, '''Tracking for pCT - global status update''', (2013).

== Historical ==
* K.M. Hanson, C.A. Taylor, M.A. Paciotti, R.J. Macek, D.B. Laubacher, R.L. Hutson, T.M. Cannon, J.N. Bradbury, '''Computed tomography using proton energy loss''', Physics in Medicine and Biology. 26 (1981) 965.
* M. Urie, M. Goitein, W.R. Holley, G.T. Chen, '''Degradation of the Bragg peak due to inhomogeneities''', Physics in Medicine and Biology. 31 (1986) 1.
* K.M. Hanson, J.N. Bradbury, R.A. Koeppe, R.J. Macek, D.R. Machen, R. Morgado, M.A. Paciotti, S.A. Sandford, V.W. Steward, '''Proton computed tomography of human specimens''', Physics in Medicine and Biology. 27 (1982) 25.
* A.M. Koehler, '''Proton Radiography''', Science. 160 (1968) 303–304.
* M. Gotein, '''The measurement of tissue heterodensity to guide charged particle radiotherapy''', International Journal of Radiation Oncology Biological Physics. 3 (1977) 27–33.
* B. Schaffner, E. Pedroni, '''The precision of proton range calculations in proton radiotherapy treatment planning: experimental verification of the relation between CT-HU and proton stopping power''', Physics in Medicine and Biology. 43 (1998) 1579.
* H.F.-W. Sadrozinski, V. Bashkirov, B. Keeney, L.R. Johnson, S.G. Peggs, G. Ross, T. Satogata, R.W.M. Schulte, A. Seiden, K. Shanazi, D.C. Williams, '''Toward Proton Computed Tomography''', IEEE Transactions on Nuclear Science. 51 (2004) 3–9. doi:10.1109/TNS.2003.823044.

== On proton CT reconstruction with some novel approaches ==
* R. Rescigno, C. Bopp, M. Rousseau, D. Brasse, '''A pencil beam approach to proton computed tomography''', Medical Physics. 42 (2015) 6610–6624. doi:10.1118/1.4933422.
* C.T. Quiñones, J.M. Létang, S. Rit, '''Filtered back-projection reconstruction for attenuation proton CT along most likely paths, Physics in Medicine and Biology.''' 61 (2016) 3258–3278. doi:10.1088/0031-9155/61/9/3258.
* C. Bopp, J. Colin, D. Cussol, C. Finck, M. Labalme, M. Rousseau, D. Brasse, '''Proton computed tomography from multiple physics processes''', Physics in Medicine and Biology. 58 (2013) 7261–7276. doi:10.1088/0031-9155/58/20/7261.
* M. Bruzzi, N. Blumenkrantz, J. Feldt, J. Heimann, H.F.-W. Sadrozinski, A. Seiden, D.C. Williams, V. Bashkirov, R. Schulte, D. Menichelli, M. Scaringella, G.A.P. Cirrone, G. Cuttone, N. Randazzo, V. Sipala, D. Lo Presti, '''Prototype Tracking Studies for Proton CT''', IEEE Transactions on Nuclear Science. 54 (2007) 140–145. doi:10.1109/TNS.2006.889642.
* C. Bopp, R. Rescigno, M. Rousseau, D. Brasse, '''Quantitative proton imaging from multiple physics processes: a proof of concept''', Physics in Medicine and Biology. 60 (2015) 5325–5341. doi:10.1088/0031-9155/60/13/5325.
* C. Bopp, R. Rescigno, M. Rousseau, D. Brasse, '''The impact of tracking system properties on the most likely path estimation in proton CT''', Physics in Medicine and Biology. 59 (2014) N197–N210. doi:10.1088/0031-9155/59/23/N197.

== Publications on proton CT sorted by group ==

=== From PRaVDA ===
* M. Esposito, T. Anaxagoras, P.M. Evans, S. Green, S. Manolopoulos, J. Nieto-Camero, D.J. Parker, G. Poludniowski, T. Price, C. Waltham, N.M. Allinson, '''CMOS Active Pixel Sensors as energy-range detectors for proton Computed Tomography''', Journal of Instrumentation. 10 (2015) C06001–C06001. doi:10.1088/1748-0221/10/06/C06001.
* M. Esposito, T. Anaxagoras, A. Fant, K. Wells, A. Konstantinidis, J.P.F. Osmond, P.M. Evans, R.D. Speller, N.M. Allinson, '''DynAMITe: a wafer scale sensor for biomedical applications''', Journal of Instrumentation. 6 (2011) C12064–C12064. doi:10.1088/1748-0221/6/12/C12064.
* T. Price, M. Esposito, G. Poludniowski, J. Taylor, C. Waltham, D.J. Parker, S. Green, S. Manolopoulos, N.M. Allinson, T. Anaxagoras, P. Evans, J. Nieto-Camero, '''Expected proton signal sizes in the PRaVDA Range Telescope for proton Computed Tomography''', Journal of Instrumentation. 10 (2015) P05013–P05013. doi:10.1088/1748-0221/10/05/P05013.
* G. Poludniowski, N.M. Allinson, P.M. Evans, '''Proton computed tomography reconstruction using a backprojection-then-filtering approach''', Physics in Medicine and Biology. 59 (2014) 7905–7918. doi:10.1088/0031-9155/59/24/7905.
* G. Poludniowski, N.M. Allinson, T. Anaxagoras, M. Esposito, S. Green, S. Manolopoulos, J. Nieto-Camero, D.J. Parker, T. Price, P.M. Evans, '''Proton-counting radiography for proton therapy: a proof of principle using CMOS APS technology''', Physics in Medicine and Biology. 59 (2014) 2569–2581. doi:10.1088/0031-9155/59/11/2569.

=== From Lomda Linda ===
* T. Plautz, V. Bashkirov, V. Feng, F. Hurley, R.P. Johnson, C. Leary, S. Macafee, A. Plumb, V. Rykalin, H.F.-W. Sadrozinski, K. Schubert, R. Schulte, B. Schultze, D. Steinberg, M. Witt, A. Zatserklyaniy, '''200 MeV Proton Radiography Studies With a Hand Phantom Using a Prototype Proton CT Scanner''', IEEE Transactions on Medical Imaging. 33 (2014) 875–881. doi:10.1109/TMI.2013.2297278.
* R. P. Johnson, V. Bashkirov, L. DeWitt, V. Giacometti, R. F. Hurley, P. Piersimoni, T. E. Plautz, H. F. W. Sadrozinski, K. Schubert, R. Schulte, B. Schultze, A. Zatserklyaniy, '''A Fast Experimental Scanner for Proton CT: Technical Performance and First Experience With Phantom Scans''', IEEE Transactions on Nuclear Science. 63 (2016) 52–60. doi:10.1109/TNS.2015.2491918.
* R. Schulte, V. Bashkirov, T. Li, Z. Liang, K. Mueller, J. Heimann, L.R. Johnson, B. Keeney, H.-W. Sadrozinski, A. Seiden, others, '''Conceptual design of a proton computed tomography system for applications in proton radiation therapy''', Nuclear Science, IEEE Transactions on. 51 (2004) 866–872.
* H.-W. Sadrozinski, R.P. Johnson, S. Macafee, A. Plumb, D. Steinberg, A. Zatserklyaniy, V.A. Bashkirov, R.F. Hurley, R.W. Schulte, '''Development of a head scanner for proton CT''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 699 (2013) 205–210.
* H.-W. Sadrozinski, V. Bashkirov, M. Bruzzi, L.R. Johnson, B. Keeney, G. Ross, R.W. Schulte, A. Seiden, K. Shahnazi, D.C. Williams, others, '''Issues in proton computed tomography''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 511 (2003) 275–281.
* R.W. Schulte, A.J. Wroe, '''New developments in treatment planning and verification of particle beam therapy''', Translational Cancer Research. 1 (2012) 184–195.
* V.A. Bashkirov, R.W. Schulte, R.F. Hurley, R.P. Johnson, H.F.-W. Sadrozinski, A. Zatserklyaniy, T. Plautz, V. Giacometti, '''Novel scintillation detector design and performance for proton radiography and computed tomography''', Medical Physics. 43 (2016) 664–674. doi:10.1118/1.4939255.
* R.W. Schulte, V. Bashkirov, R. Johnson, H.-W. Sadrozinski, K.E. Schubert, '''Overview of the LLUMC/UCSC/CSUSB Phase 2 Proton CT Project''', Transactions of the American Nuclear Society. 106 (2012) 59.
* R. Johnson, V.A. Bashkirov, V. Giacometti, R.F. Hurley, P. Piersimoni, T. Plautz, '''Results from a pre-clinical head scanner for proton CT''', (2014).
* R.F. Hurley, R.W. Schulte, V.A. Bashkirov, G. Coutrakon, H.-W. Sadrozinski, B. Patyal, '''The Phase I Proton CT Scanner and Test Beam Results at LLUMC''', invited, Transactions of the American Nuclear Society. 106 (2012) 63.
* R.P. Johnson, J. DeWitt, C. Holcomb, S. Macafee, H.F.-W. Sadrozinski, D. Steinberg, '''Tracker Readout ASIC for Proton Computed Tomography Data Acquisition''', IEEE Transactions on Nuclear Science. 60 (2013) 3262–3269. doi:10.1109/TNS.2013.2274663.
* R.F. Hurley, R.W. Schulte, V.A. Bashkirov, A.J. Wroe, A. Ghebremedhin, H.F.-W. Sadrozinski, V. Rykalin, G. Coutrakon, P. Koss, B. Patyal, '''Water-equivalent path length calibration of a prototype proton CT scanner''', Medical Physics. 39 (2012) 2438. doi:10.1118/1.3700173.

=== From the NIU project (using scitillating fibres) ===
* M. Naimuddin, G. Coutrakon, G. Blazey, S. Boi, A. Dyshkant, B. Erdelyi, D. Hedin, E. Johnson, J. Krider, V. Rukalin, others, '''Development of a proton Computed Tomography detector system''', Journal of Instrumentation. 11 (2016) C02012.
* S.A. Uzunyan, G. Blazey, S. Boi, G. Coutrakon, A. Dyshkant, B. Erdelyi, A. Gearhart, D. Hedin, E. Johnson, J. Krider, others, '''Development of a proton Computed Tomography (pCT) scanner at NIU''', arXiv Preprint arXiv:1312.3977. (2013). http://arxiv.org/abs/1312.3977 (accessed January 15, 2015).

=== From the PRIMA project at INFN ===
* M. Scaringella, M. Bruzzi, M. Bucciolini, M. Carpinelli, G.A.P. Cirrone, C. Civinini, G. Cuttone, D.L. Presti, S. Pallotta, C. Pugliatti, N. Randazzo, F. Romano, V. Sipala, C. Stancampiano, C. Talamonti, E. Vanzi, M. Zani, '''A proton Computed Tomography based medical imaging system''', Journal of Instrumentation. 9 (2014) C12009–C12009. doi:10.1088/1748-0221/9/12/C12009.
* C. Civinini, M. Bruzzi, M. Bucciolini, M. Carpinelli, G.A.P. Cirrone, G. Cuttone, D. Lo Presti, S. Pallotta, C. Pugliatti, N. Randazzo, others, '''Recent results on the development of a proton computed tomography system''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 732 (2013) 573–576.
* E. Vanzi, M. Bruzzi, M. Bucciolini, G.A.P. Cirrone, C. Civinini, G. Cuttone, D. Lo Presti, S. Pallotta, C. Pugliatti, N. Randazzo, F. Romano, M. Scaringella, V. Sipala, C. Stancampiano, C. Talamonti, M. Zani, '''The PRIMA collaboration: Preliminary results in FBP reconstruction of pCT data''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 730 (2013) 184–190. doi:10.1016/j.nima.2013.05.193.
* M. Scaringella, M. Brianzi, M. Bruzzi, M. Bucciolini, M. Carpinelli, G.A.P. Cirrone, C. Civinini, G. Cuttone, D. Lo Presti, S. Pallotta, others, '''The PRIMA (Proton Imaging) collaboration: development of a proton Computed Tomography apparatus''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 730 (2013) 178–183.
* V. Sipala, N. Randazzo, D.L. Presti, C. Stancampiano, M. Bruzzi, M. Bucciolini, S. Pallotta, M. Scaringella, C. Talamonti, M. Tesi, others, '''Upgrade of the proton Computed Tomography system of the PRIMA project''', PoS (RD11). 13 (2011). http://www.researchgate.net/publication/230709286_Upgrade_of_the_proton_Computed_Tomography_system_of_the_PRIMA_project_Speaker/file/9fcfd5034f8b703175.pdf (accessed January 15, 2015).

=== From the proton radiography project at Heidelberg ===
* I. Rinaldi, S. Brons, O. Jäkel, B. Voss, K. Parodi, '''A method to increase the nominal range resolution of a stack of parallel-plate ionization chambers''', Physics in Medicine and Biology. 59 (2014) 5501–5515. doi:10.1088/0031-9155/59/18/5501.
* C. Kurz, A. Mairani, K. Parodi, '''First experimental-based characterization of oxygen ion beam depth dose distributions at the Heidelberg Ion-Beam Therapy Center''', Physics in Medicine and Biology. 57 (2012) 5017–5034. doi:10.1088/0031-9155/57/15/5017.

=== (Historical) proton radiography at PSI ===
* P. Pemler, J. Besserer, J. De Boer, M. Dellert, C. Gahn, M. Moosburger, U. Schneider, E. Pedroni, H. Stäuble, '''A detector system for proton radiography on the gantry of the Paul-Scherrer-Institute''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 432 (1999) 483–495.
* N.S.P. King, E. Ables, K. Adams, K.R. Alrick, J.F. Amann, S. Balzar, P.D. Barnes Jr, M.L. Crow, S.B. Cushing, J.C. Eddleman, others, '''An 800-MeV proton radiography facility for dynamic experiments''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 424 (1999) 84–91.
* S. Braccini, A. Ereditato, I. Kreslo, U. Moser, C. Pistillo, S. Studer, P. Scampoli, A. Coray, E. Pedroni, '''First results on proton radiography with nuclear emulsion detectors''', Journal of Instrumentation. 5 (2010) P09001.
* S. Braccini, A. Ereditato, I. Kreslo, U. Moser, C. Pistillo, P. Scampoli, S. Studer, '''Nuclear Emulsion Film Detectors for Proton Radiography: Design and Test of the First Prototype''', arXiv Preprint arXiv:1001.0857. (2010). http://arxiv.org/abs/1001.0857 (accessed January 15, 2015).

== Interesting theses on proton CT ==
* P.C. Tsopelas, '''Applying GridPix as 3D particle tracker for proton radiography''', MSc, University of Utrecht, 2011. https://wiki.nikhef.nl/detector/pub/Main/ArticlesAndTalks/Panagiotis_Tsopelas_Master_Thesis.pdf (accessed January 12, 2015).
* E. Hansen, '''Charge diffusion modelling for a monolithic active pixel sensor detector with application to proton CT''', BSc Project work, NTNU, 2016.
* M. Yang, '''Dual Energy computed tomography for proton therapy treatment planning''', PhD, University of Texas, 2011.
* S.N. Penfold, '''Image reconstruction and Monte Carlo simulations in the development of proton computed tomography for applications in proton radiation therpy''', PhD, University of Wollongong, 2010.
* D.C. Hansen, '''Improving Ion Computed Tomography''', PhD, Aarhus Universitet, 2014. http://pure.au.dk/portal/files/83515131/dissertation.pdf (accessed January 12, 2015).
* L. Maczewski, '''Measurements and simulations of MAPS (Monolithic Active Pixel Sensors) response to charged particles - a study towards a vertex detector at the ILC''', PhD, 2010. http://arxiv.org/abs/1005.3710 (accessed January 12, 2015).

== On the ALPIDE chip ==
* M. Mager, '''ALPIDE, the Monolithic Active Pixel Sensor for the ALICE ITS upgrade''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 824 (2016) 434–438. doi:10.1016/j.nima.2015.09.057.
* C. Cavicchioli, P.L. Chalmet, P. Giubilato, H. Hillemanns, A. Junique, T. Kugathasan, M. Mager, C.A. Marin Tobon, P. Martinengo, S. Mattiazzo, H. Mugnier, L. Musa, D. Pantano, J. Rousset, F. Reidt, P. Riedler, W. Snoeys, J.W. Van Hoorne, P. Yang, '''Design and characterization of novel monolithic pixel sensors for the ALICE ITS upgrade''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 765 (2014) 177–182. doi:10.1016/j.nima.2014.05.027.
* G. Aglieri Rinella, '''The ALPIDE pixel sensor chip for the upgrade of the ALICE Inner Tracking System''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. (2016). doi:10.1016/j.nima.2016.05.016.

Publications

2017-02-23T22:18:22Z

Jars:

== From Bergen Proton CT group ==
* Pettersen, H.E.S., J. Alme, A. van den Brink, M. Chaar, D. Fehlker, I. Meric, O.H. Odland, et al. n.d. '''“Proton Tracking in a High-Granularity Digital Tracking Calorimeter for Proton CT Purposes.”''' Nuclear Instruments and Methods in Physics Research Section A: Accelerators, 157 Spectrometers, Detectors and Associated Equipment. doi:10.1016/j.nima.2017.02.007. [[https://arxiv.org/abs/1611.02031]]
* Under construction, by H. Pettersen: [[:File:Comparison of different calculation methods of proton ranges.pdf|'''Comparison of different calculation methods of proton ranges''']].
* Under construction, by J. Sølie, I. Meric and H. Pettersen: [[:File:NACP2017ActaDraft_(1).pdf|'''A comparison of longitudinal and lateral range for protons traversing complex media using GATE, MCNP6 and FLUKA Monte Carlo simulations''']]

== Review articles on proton CT ==
* G. Poludniowski, N.M. Allinson, P.M. Evans, '''Proton radiography and tomography with application to proton therapy''', The British Journal of Radiology. 88 (2015) 20150134. doi:10.1259/bjr.20150134.
* C. Civinini, '''Tracking for pCT - global status update''', (2013).

== Historical ==
* K.M. Hanson, C.A. Taylor, M.A. Paciotti, R.J. Macek, D.B. Laubacher, R.L. Hutson, T.M. Cannon, J.N. Bradbury, '''Computed tomography using proton energy loss''', Physics in Medicine and Biology. 26 (1981) 965.
* M. Urie, M. Goitein, W.R. Holley, G.T. Chen, '''Degradation of the Bragg peak due to inhomogeneities''', Physics in Medicine and Biology. 31 (1986) 1.
* K.M. Hanson, J.N. Bradbury, R.A. Koeppe, R.J. Macek, D.R. Machen, R. Morgado, M.A. Paciotti, S.A. Sandford, V.W. Steward, '''Proton computed tomography of human specimens''', Physics in Medicine and Biology. 27 (1982) 25.
* A.M. Koehler, '''Proton Radiography''', Science. 160 (1968) 303–304.
* M. Gotein, '''The measurement of tissue heterodensity to guide charged particle radiotherapy''', International Journal of Radiation Oncology Biological Physics. 3 (1977) 27–33.
* B. Schaffner, E. Pedroni, '''The precision of proton range calculations in proton radiotherapy treatment planning: experimental verification of the relation between CT-HU and proton stopping power''', Physics in Medicine and Biology. 43 (1998) 1579.
* H.F.-W. Sadrozinski, V. Bashkirov, B. Keeney, L.R. Johnson, S.G. Peggs, G. Ross, T. Satogata, R.W.M. Schulte, A. Seiden, K. Shanazi, D.C. Williams, '''Toward Proton Computed Tomography''', IEEE Transactions on Nuclear Science. 51 (2004) 3–9. doi:10.1109/TNS.2003.823044.

== On proton CT reconstruction with some novel approaches ==
* R. Rescigno, C. Bopp, M. Rousseau, D. Brasse, '''A pencil beam approach to proton computed tomography''', Medical Physics. 42 (2015) 6610–6624. doi:10.1118/1.4933422.
* C.T. Quiñones, J.M. Létang, S. Rit, '''Filtered back-projection reconstruction for attenuation proton CT along most likely paths, Physics in Medicine and Biology.''' 61 (2016) 3258–3278. doi:10.1088/0031-9155/61/9/3258.
* C. Bopp, J. Colin, D. Cussol, C. Finck, M. Labalme, M. Rousseau, D. Brasse, '''Proton computed tomography from multiple physics processes, Physics in Medicine and Biology.''' 58 (2013) 7261–7276. doi:10.1088/0031-9155/58/20/7261.
* M. Bruzzi, N. Blumenkrantz, J. Feldt, J. Heimann, H.F.-W. Sadrozinski, A. Seiden, D.C. Williams, V. Bashkirov, R. Schulte, D. Menichelli, M. Scaringella, G.A.P. Cirrone, G. Cuttone, N. Randazzo, V. Sipala, D. Lo Presti, '''Prototype Tracking Studies for Proton CT''', IEEE Transactions on Nuclear Science. 54 (2007) 140–145. doi:10.1109/TNS.2006.889642.
* C. Bopp, R. Rescigno, M. Rousseau, D. Brasse, '''Quantitative proton imaging from multiple physics processes: a proof of concept''', Physics in Medicine and Biology. 60 (2015) 5325–5341. doi:10.1088/0031-9155/60/13/5325.
* C. Bopp, R. Rescigno, M. Rousseau, D. Brasse, '''The impact of tracking system properties on the most likely path estimation in proton CT''', Physics in Medicine and Biology. 59 (2014) N197–N210. doi:10.1088/0031-9155/59/23/N197.

== Publications on proton CT sorted by group ==

=== From PRaVDA ===
* M. Esposito, T. Anaxagoras, P.M. Evans, S. Green, S. Manolopoulos, J. Nieto-Camero, D.J. Parker, G. Poludniowski, T. Price, C. Waltham, N.M. Allinson, '''CMOS Active Pixel Sensors as energy-range detectors for proton Computed Tomography''', Journal of Instrumentation. 10 (2015) C06001–C06001. doi:10.1088/1748-0221/10/06/C06001.
* M. Esposito, T. Anaxagoras, A. Fant, K. Wells, A. Konstantinidis, J.P.F. Osmond, P.M. Evans, R.D. Speller, N.M. Allinson, '''DynAMITe: a wafer scale sensor for biomedical applications''', Journal of Instrumentation. 6 (2011) C12064–C12064. doi:10.1088/1748-0221/6/12/C12064.
* T. Price, M. Esposito, G. Poludniowski, J. Taylor, C. Waltham, D.J. Parker, S. Green, S. Manolopoulos, N.M. Allinson, T. Anaxagoras, P. Evans, J. Nieto-Camero, '''Expected proton signal sizes in the PRaVDA Range Telescope for proton Computed Tomography''', Journal of Instrumentation. 10 (2015) P05013–P05013. doi:10.1088/1748-0221/10/05/P05013.
* G. Poludniowski, N.M. Allinson, P.M. Evans, '''Proton computed tomography reconstruction using a backprojection-then-filtering approach''', Physics in Medicine and Biology. 59 (2014) 7905–7918. doi:10.1088/0031-9155/59/24/7905.
* G. Poludniowski, N.M. Allinson, T. Anaxagoras, M. Esposito, S. Green, S. Manolopoulos, J. Nieto-Camero, D.J. Parker, T. Price, P.M. Evans, '''Proton-counting radiography for proton therapy: a proof of principle using CMOS APS technology''', Physics in Medicine and Biology. 59 (2014) 2569–2581. doi:10.1088/0031-9155/59/11/2569.

=== From Lomda Linda ===
* T. Plautz, V. Bashkirov, V. Feng, F. Hurley, R.P. Johnson, C. Leary, S. Macafee, A. Plumb, V. Rykalin, H.F.-W. Sadrozinski, K. Schubert, R. Schulte, B. Schultze, D. Steinberg, M. Witt, A. Zatserklyaniy, '''200 MeV Proton Radiography Studies With a Hand Phantom Using a Prototype Proton CT Scanner''', IEEE Transactions on Medical Imaging. 33 (2014) 875–881. doi:10.1109/TMI.2013.2297278.
* R. P. Johnson, V. Bashkirov, L. DeWitt, V. Giacometti, R. F. Hurley, P. Piersimoni, T. E. Plautz, H. F. W. Sadrozinski, K. Schubert, R. Schulte, B. Schultze, A. Zatserklyaniy, '''A Fast Experimental Scanner for Proton CT: Technical Performance and First Experience With Phantom Scans''', IEEE Transactions on Nuclear Science. 63 (2016) 52–60. doi:10.1109/TNS.2015.2491918.
* R. Schulte, V. Bashkirov, T. Li, Z. Liang, K. Mueller, J. Heimann, L.R. Johnson, B. Keeney, H.-W. Sadrozinski, A. Seiden, others, '''Conceptual design of a proton computed tomography system for applications in proton radiation therapy''', Nuclear Science, IEEE Transactions on. 51 (2004) 866–872.
* H.-W. Sadrozinski, R.P. Johnson, S. Macafee, A. Plumb, D. Steinberg, A. Zatserklyaniy, V.A. Bashkirov, R.F. Hurley, R.W. Schulte, '''Development of a head scanner for proton CT''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 699 (2013) 205–210.
* H.-W. Sadrozinski, V. Bashkirov, M. Bruzzi, L.R. Johnson, B. Keeney, G. Ross, R.W. Schulte, A. Seiden, K. Shahnazi, D.C. Williams, others, '''Issues in proton computed tomography''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 511 (2003) 275–281.
* R.W. Schulte, A.J. Wroe, '''New developments in treatment planning and verification of particle beam therapy''', Translational Cancer Research. 1 (2012) 184–195.
* V.A. Bashkirov, R.W. Schulte, R.F. Hurley, R.P. Johnson, H.F.-W. Sadrozinski, A. Zatserklyaniy, T. Plautz, V. Giacometti, '''Novel scintillation detector design and performance for proton radiography and computed tomography''', Medical Physics. 43 (2016) 664–674. doi:10.1118/1.4939255.
* R.W. Schulte, V. Bashkirov, R. Johnson, H.-W. Sadrozinski, K.E. Schubert, '''Overview of the LLUMC/UCSC/CSUSB Phase 2 Proton CT Project''', Transactions of the American Nuclear Society. 106 (2012) 59.
* R. Johnson, V.A. Bashkirov, V. Giacometti, R.F. Hurley, P. Piersimoni, T. Plautz, '''Results from a pre-clinical head scanner for proton CT''', (2014).
* R.F. Hurley, R.W. Schulte, V.A. Bashkirov, G. Coutrakon, H.-W. Sadrozinski, B. Patyal, '''The Phase I Proton CT Scanner and Test Beam Results at LLUMC''', invited, Transactions of the American Nuclear Society. 106 (2012) 63.
* R.P. Johnson, J. DeWitt, C. Holcomb, S. Macafee, H.F.-W. Sadrozinski, D. Steinberg, '''Tracker Readout ASIC for Proton Computed Tomography Data Acquisition''', IEEE Transactions on Nuclear Science. 60 (2013) 3262–3269. doi:10.1109/TNS.2013.2274663.
* R.F. Hurley, R.W. Schulte, V.A. Bashkirov, A.J. Wroe, A. Ghebremedhin, H.F.-W. Sadrozinski, V. Rykalin, G. Coutrakon, P. Koss, B. Patyal, '''Water-equivalent path length calibration of a prototype proton CT scanner''', Medical Physics. 39 (2012) 2438. doi:10.1118/1.3700173.

=== From the NIU project (using scitillating fibres) ===
* M. Naimuddin, G. Coutrakon, G. Blazey, S. Boi, A. Dyshkant, B. Erdelyi, D. Hedin, E. Johnson, J. Krider, V. Rukalin, others, '''Development of a proton Computed Tomography detector system''', Journal of Instrumentation. 11 (2016) C02012.
* S.A. Uzunyan, G. Blazey, S. Boi, G. Coutrakon, A. Dyshkant, B. Erdelyi, A. Gearhart, D. Hedin, E. Johnson, J. Krider, others, '''Development of a proton Computed Tomography (pCT) scanner at NIU''', arXiv Preprint arXiv:1312.3977. (2013). http://arxiv.org/abs/1312.3977 (accessed January 15, 2015).

=== From the PRIMA project at INFN ===
* M. Scaringella, M. Bruzzi, M. Bucciolini, M. Carpinelli, G.A.P. Cirrone, C. Civinini, G. Cuttone, D.L. Presti, S. Pallotta, C. Pugliatti, N. Randazzo, F. Romano, V. Sipala, C. Stancampiano, C. Talamonti, E. Vanzi, M. Zani, '''A proton Computed Tomography based medical imaging system''', Journal of Instrumentation. 9 (2014) C12009–C12009. doi:10.1088/1748-0221/9/12/C12009.
* C. Civinini, M. Bruzzi, M. Bucciolini, M. Carpinelli, G.A.P. Cirrone, G. Cuttone, D. Lo Presti, S. Pallotta, C. Pugliatti, N. Randazzo, others, '''Recent results on the development of a proton computed tomography system''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 732 (2013) 573–576.
* E. Vanzi, M. Bruzzi, M. Bucciolini, G.A.P. Cirrone, C. Civinini, G. Cuttone, D. Lo Presti, S. Pallotta, C. Pugliatti, N. Randazzo, F. Romano, M. Scaringella, V. Sipala, C. Stancampiano, C. Talamonti, M. Zani, '''The PRIMA collaboration: Preliminary results in FBP reconstruction of pCT data''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 730 (2013) 184–190. doi:10.1016/j.nima.2013.05.193.
* M. Scaringella, M. Brianzi, M. Bruzzi, M. Bucciolini, M. Carpinelli, G.A.P. Cirrone, C. Civinini, G. Cuttone, D. Lo Presti, S. Pallotta, others, '''The PRIMA (Proton Imaging) collaboration: development of a proton Computed Tomography apparatus''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 730 (2013) 178–183.
* V. Sipala, N. Randazzo, D.L. Presti, C. Stancampiano, M. Bruzzi, M. Bucciolini, S. Pallotta, M. Scaringella, C. Talamonti, M. Tesi, others, '''Upgrade of the proton Computed Tomography system of the PRIMA project''', PoS (RD11). 13 (2011). http://www.researchgate.net/publication/230709286_Upgrade_of_the_proton_Computed_Tomography_system_of_the_PRIMA_project_Speaker/file/9fcfd5034f8b703175.pdf (accessed January 15, 2015).

=== From the proton radiography project at Heidelberg ===
* I. Rinaldi, S. Brons, O. Jäkel, B. Voss, K. Parodi, '''A method to increase the nominal range resolution of a stack of parallel-plate ionization chambers''', Physics in Medicine and Biology. 59 (2014) 5501–5515. doi:10.1088/0031-9155/59/18/5501.
* C. Kurz, A. Mairani, K. Parodi, '''First experimental-based characterization of oxygen ion beam depth dose distributions at the Heidelberg Ion-Beam Therapy Center''', Physics in Medicine and Biology. 57 (2012) 5017–5034. doi:10.1088/0031-9155/57/15/5017.

=== (Historical) proton radiography at PSI ===
* P. Pemler, J. Besserer, J. De Boer, M. Dellert, C. Gahn, M. Moosburger, U. Schneider, E. Pedroni, H. Stäuble, '''A detector system for proton radiography on the gantry of the Paul-Scherrer-Institute''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 432 (1999) 483–495.
* N.S.P. King, E. Ables, K. Adams, K.R. Alrick, J.F. Amann, S. Balzar, P.D. Barnes Jr, M.L. Crow, S.B. Cushing, J.C. Eddleman, others, '''An 800-MeV proton radiography facility for dynamic experiments''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 424 (1999) 84–91.
* S. Braccini, A. Ereditato, I. Kreslo, U. Moser, C. Pistillo, S. Studer, P. Scampoli, A. Coray, E. Pedroni, '''First results on proton radiography with nuclear emulsion detectors''', Journal of Instrumentation. 5 (2010) P09001.
* S. Braccini, A. Ereditato, I. Kreslo, U. Moser, C. Pistillo, P. Scampoli, S. Studer, '''Nuclear Emulsion Film Detectors for Proton Radiography: Design and Test of the First Prototype''', arXiv Preprint arXiv:1001.0857. (2010). http://arxiv.org/abs/1001.0857 (accessed January 15, 2015).

== Interesting theses on proton CT ==
* P.C. Tsopelas, '''Applying GridPix as 3D particle tracker for proton radiography''', MSc, University of Utrecht, 2011. https://wiki.nikhef.nl/detector/pub/Main/ArticlesAndTalks/Panagiotis_Tsopelas_Master_Thesis.pdf (accessed January 12, 2015).
* E. Hansen, '''Charge diffusion modelling for a monolithic active pixel sensor detector with application to proton CT''', BSc Project work, NTNU, 2016.
* M. Yang, '''Dual Energy computed tomography for proton therapy treatment planning''', PhD, University of Texas, 2011.
* S.N. Penfold, '''Image reconstruction and Monte Carlo simulations in the development of proton computed tomography for applications in proton radiation therpy''', PhD, University of Wollongong, 2010.
* D.C. Hansen, '''Improving Ion Computed Tomography''', PhD, Aarhus Universitet, 2014. http://pure.au.dk/portal/files/83515131/dissertation.pdf (accessed January 12, 2015).
* L. Maczewski, '''Measurements and simulations of MAPS (Monolithic Active Pixel Sensors) response to charged particles - a study towards a vertex detector at the ILC''', PhD, 2010. http://arxiv.org/abs/1005.3710 (accessed January 12, 2015).

== On the ALPIDE chip ==
* M. Mager, '''ALPIDE, the Monolithic Active Pixel Sensor for the ALICE ITS upgrade''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 824 (2016) 434–438. doi:10.1016/j.nima.2015.09.057.
* C. Cavicchioli, P.L. Chalmet, P. Giubilato, H. Hillemanns, A. Junique, T. Kugathasan, M. Mager, C.A. Marin Tobon, P. Martinengo, S. Mattiazzo, H. Mugnier, L. Musa, D. Pantano, J. Rousset, F. Reidt, P. Riedler, W. Snoeys, J.W. Van Hoorne, P. Yang, '''Design and characterization of novel monolithic pixel sensors for the ALICE ITS upgrade''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 765 (2014) 177–182. doi:10.1016/j.nima.2014.05.027.
* G. Aglieri Rinella, '''The ALPIDE pixel sensor chip for the upgrade of the ALICE Inner Tracking System''', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. (2016). doi:10.1016/j.nima.2016.05.016.