Introduction The Digital Tracking Calorimeter Beam

Proton tracking in a high-granularity Digital Tracking Calorimeter for proton CT purposes 1,2

H. E. S. Pettersen

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, J. Alme , A. van den Brink , M. Chaar , D. Fehlker , I. Meric , O. H. Odland ,

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T. Peitzmann , E. Rocco , J. R. Sølie , K. Ullaland , H. Wang , C. Zhang , D. Röhrich 1

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Department of oncology and medical physics, Haukeland University Hospital, Bergen, Norway 2

Institute of physics and technology, University of Bergen, Norway 3

Faculty of engineering, Bergen University College, Norway 4

Nikhef, Utrecht University, the Netherlands

Corresponding author:

[email protected]

Introduction

Monte Carlo setup

Proton CT is an imaging modality which calculates the patient's Proton Stopping Power, for use in proton therapy dose planning. A proton CT measures the energy loss and reconstructs the tracks of protons traversing the patient. Several protoypes have been constructed, applying dierent sensor and calorimeter technologies [1].

The MC code GATE 7.0 / Geant4 9.6.4 is used in this study, together with the ROOT analysis framework. The complete DTC geometry, including upstream scintillators for triggering purposes, has been simulated using the QGSP_BIC_EMY physics list.

Figure 1: The Digital Tracking Calorimeter is shown here as part of a proton CT setup.

Residual range estimation

Estimation of energy deposition

Figure 5: Distribution of tted ranges from the exp.

By using a 1-bit pixel readout, a proton must liberate sucient electron-hole pairs for the pixel to be activated. Clusters containing N pixels surrounding the proton track are activated due to charge diusion of the liberated charge carriers. By applying a charge diusion model, we calculate the Edep of a passing proton from N .

Due to the large sampling spacing of 32 mm WET, the tted ranges tend to group around the sensor layer depths. By identifying the sensor layers where enough protons stop (shown in red), the residual range can be calculated: sP P

We benchmark a high-granularity, high-speed Digital Tracking Calorimeter (DTC) for proton CT use. Methods for tracking and range determination has been developed through Monte Carlo (MC) simulations and beam tests.

The Digital Tracking Calorimeter The DTC is a sampling pixel detector consisting of 24 sensor layers interleaved with 3.3 mm W plates acting as absorbers layers. Each layer contains four MIMOSA23 Monolithic Ac2 tive Pixel Sensors, covering 15 cm . The pixel pitch is 30 µm, and the number of pixels in a layer is 1280 x 1280. 1-bit readout of the 41 megapixels enables a proton frequency of 2 kHz.

Figure 2: Left: A module, two of which constitute a layer. Right: The assembled 48 modules.

The high granularity allows for 500 protons to be reconstructed in a single readout frame, enabling an eective proton frequency of 1 MHz. The prototype is made available through the ALICEFoCal collaboration, where it was constructed as part of the ALICE ITS upgrade [2].

Beam measurements The beam measurements were performed at AGORFIRM at KVI Groningen, the Netherlands. Proton beams of energies 122  188 MeV with a beam frequency of 1.2 kHz were recorded with the detector and readout system. 500 readout frames are accumulated prior to each reconstruction in order to increase the eective readout frequency.

Proton radiography and tomography with application to proton therapy, British Journal of Radiology 88

(2015) 20150134. [2] Elena Rocco Highly

granular digital electromagnetic Calorimeter with MAPS, 37th Intl. conference on High Energy Physics (ICHEP 2016) 10901095.

∞ w i xi i=i0 ∞ wi i=i0

hR0 i = P

±

∞ i=i0

wi (xi − hR0 i)2

P∞

i=i0



wi − 1

(2)

Here, xi is the range, wi is the bin weight and xi0 is found 3σ below the rst identied sensor layer.

The Gaussian charge diusion model has been developed by comparing the expected Edep in the sensor regions (from MC simulations) to the cluster sizes in the exp. data.

Applying Eq. 2 on the data shown in the above gures, the resulting energy estimates are 188 ± 3 MeV for the 188 MeV beam, and 167 ± 9 MeV for the 170 MeV beam. The latter error is larger since the tted range distribution is centered between two sensor layers.

Proton tracking

Results

The clusters in each layer are connected through proton tracks. A track-following algorithm has been applied, identying track candidates by extrapolating vectors from the early-layer clusters and matching them to the closest clusters in subsequent layers. The reconstruction accuracy is 80% when reconstructing 500 concurrent proton tracks, by comparison with track ID from MC.

We nd hR0 i in all available datasets, both exp. data and MC simulations at 1 MeV intervals from 145 MeV to 200 MeV.

Figure 3: The Gaussian charge diusion model, applied on the exp. data in order to nd Edep .

Depth-dose curve tting The principle of the range estimation is to nd the most probable Bragg Peak depth for each reconstructed proton. With the Edep in each layer, we extract the BP depth from a t to the the dierentiated Bragg-Kleeman (BK) equation:

dE 1 − = , dz pα1/p (R0 − z)1−1/p

(1)

where z the depth and R0 is the range to nd. The parameters α, p are found through MC ts to the BK equation R0 = αE p .

Figure 6: hR0 i values calculated using several datasets. The expected PSTAR range is shown for each energy with its corresponding range straggling.

On average, the relative deviation from the expected PSTAR range is 4.1% in the experimental data, and 1.7% in MC. The average size of the error dened in Eq. 1 is 4.1% in the experimental data, and 4.6% in MC.

Conclusion The range resolution of DTC is 4% and the eective readout frequency is 1 MHz.

References [1] Gavin Poludniowski, Nigel Allinson and Phil Evans

data of 188 MeV (left) and 170 MeV (right).

Figure 4: Depth-dose curve ts to 188 Mev exp. data.

Note that missing data in non-sequential layers is allowed during reconstruction, as shown by missing data points in some layers.

This is comparable to similar prototypes [1]. Bergen Proton CT group is currently developing a new proton CT prototype with nextgeneration sensors and an optimized geometry for increased resolution.

Acknowledgements This work was supported by Helse Vest RHF [911933].