A High-Granularity Digital Tracking Calorimeter Optimized for Proton CT

Alme, J.; Barnaföldi, G. G.; Barthel, Rene; Borshchov, V. I.; Bodova, Tea; Brink, A. van den; Brons, Stephan; Chaar, Mamdouh; Eikeland, Viljar Nilsen; Феофилов, Г.; Genov, Georgi; Grimstad, Silje; Grøttvik, Ola Slettevoll; Helstrup, H.; Herland, Alf Kristoffer; Hilde, Annar Eivindplass; Igolkin, S. N.; Kobdaj, C.; Kolk, N. van der; Listratenko, O.M.; Malik, Qasim Waheed; Mehendale, Shruti; Meriç, Ilker; Nesbo, S. V.; Odland, O.H.; Papp, Gábor; Peitzmann, T.; Pettersen, Helge Egil Seime; Piersimoni, Pierluigi; Protsenko, Maksym; Rehman, A.; Richter, Matthias; Röhrich, D.; Samnøy, Andreas Tefre; Seco, Joao; Setterdahl, Lena; Shafiee, Hesam; Skjolddal, Øistein Jelmert; Solheim, I.; Songmoolnak, A.; Sudár, Ákos; Sølie, Jarle Rambo; Tambave, Ganesh Jagannath; Tymchuk, I.; Ullaland, K.; Underdal, Håkon Andreas; Varga-Kőfaragó, M.; Volz, Lennart; Wagner, B.; Widerøe, Fredrik; Xiao, RenZheng; Yokoyama, H.

doi:10.3389/fphy.2020.568243

Cited by 34 publications

(42 citation statements)

References 63 publications

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“…Subsequent design optimization and experimental measurements (Pettersen et al 2019a, Tambave et al 2019 showed promise in regards to proton imaging, as well as detection of heavier ions. The DTC is currently under development and prototyping (Alme et al 2020).…”

Section: Helium Imagingmentioning

confidence: 99%

Helium radiography with a digital tracking calorimeter—a Monte Carlo study for secondary track rejection

Pettersen¹,

Volz²,

Sølie³

et al. 2021

Phys. Med. Biol.

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Radiation therapy using protons and heavier ions is a fast-growing therapeutic option for cancer patients. A clinical system for particle imaging in particle therapy would enable online patient position verification, estimation of the dose deposition through range monitoring and a reduction of uncertainties in the calculation of the relative stopping power of the patient.Several prototype imaging modalities offer radiography and computed tomography using protons and heavy ions. A Digital Tracking Calorimeter (DTC), currently under development, has been proposed as one such detector. In the DTC 43 longitudinal layers of laterally stacked ALPIDE CMOS monolithic active pixel sensor chips are able to reconstruct a large number of simultaneously recorded proton tracks.In this study, we explored the capability of the DTC for helium imaging which offers favorable spatial resolution over proton imaging. Helium ions exhibit a larger cross section for inelastic nuclear interactions, increasing the number of produced secondaries in the imaged object and in the detector itself. To that end, a filtering process able to remove a large fraction of the secondaries was identified, and the track reconstruction process was adapted for helium ions.By filtering on the energy loss along the tracks, on the incoming angle and on the particle ranges, 97.5% of the secondaries were removed. After passing through 16 cm water, 50.0% of the primary helium ions survived; after the proposed filtering 42.4% of the primaries remained; finally after subsequent image reconstruction 31% of the primaries remained. Helium track reconstruction leads to more track matching errors compared to protons due to the increased available focus strength of the helium beam. In a head phantom radiograph, the Water Equivalent

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Section: Helium Imagingmentioning

confidence: 99%

Helium radiography with a digital tracking calorimeter—a Monte Carlo study for secondary track rejection

Pettersen¹,

Volz²,

Sølie³

et al. 2021

Phys. Med. Biol.

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“…Possible input variables of known uncertainty are the detector output or reconstructed proton paths. All steps in the pipeline from this input to the output must be differentiated in order to apply (6). Thus derivatives of GATE are not required in any case.…”

Section: Quantification Of Uncertaintymentioning

confidence: 99%

“…37 Figure 11a shows one column of its Jacobian matrix, i. e. the change of the reconstructed image if one sensor pixel changes. We used the Jacobian matrix according to (6) to propagate uncertainties in the sinogram x through the FBP algorithm to the reconstructed image y, and visualized it in figure 11b with the level-crossing probability colouring that indicates the likelihood of each voxel to be on the wrong side of the isocontour. 53 Note that all these results can be produced without AD because the FBP is a linear map.…”

Section: Derivatives Of the Filtered Back-projection Algorithmmentioning

confidence: 99%

“…1,2,3 Besides dual-energy X-ray CT, the direct measurement of a proton CT image using a higher-energy proton beam and a particle detector produces a direct RSP map and has the potential to be more accurate. 4,5 tracking layers calorimeter layers reconstruct the RSP Ŝ( x) at each voxel inside the phantom beam delivery system st ra ig ht st ra ig ht M L P To this end, the Bergen pCT collaboration 6 is designing and building a high-granularity digital tracking calorimeter (DTC) as a clinical prototype to add imaging capabilities to an existing treatment facility for proton therapy. Its sensitive hardware consists of two "tracking" and 41 "calorimeter" layers of 108 ALPIDE (ALICE pixel detector) chips each.…”

Section: Introductionmentioning

confidence: 99%

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Derivatives in Proton CT

Max¹,

Alme²,

Barnaföldi³

et al. 2022

Preprint

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Algorithmic derivatives can be useful to quantify uncertainties and optimize parameters using computer simulations. Whether they actually are, depends on how "well-linearizable" the program is.Proton computed tomography (pCT) is a medical imaging technology with the potential to increase the spatial accuracy of the dose delivered in protonbeam radiotherapy. The Bergen pCT collaboration is developing and constructing a digital tracking calorimeter (DTC) to measure the position, direction and energy of protons after they passed through a patient, and a software pipeline to process these data into a pCT image.We revisit the software pipeline from the perspective of algorithmic differentiation (AD). In the early subprocedures, several obstacles such as discrete variables or frequent discontinuities were identified, and are probably tackled best by using surrogate models. The model-based iterative reconstruction (MBIR) subprocedure in the end seems to be AD-ready, and we propose changes in the AD workflow that can reduce the memory consumption in reverse mode.

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“…Proton CT scanner prototypes proposed and developed so far rely either on a calorimeter, e.g. a scintillator, to measure the energy deposited by a proton therein (Civinini et al, 2013;Johnson et al, 2016;DeJongh et al, 2021) or a range telescope (Taylor et al, 2016;Alme et al, 2020;Pemler et al, 1999). An alternative measurement principle determines the proton's time of flight (TOF) between two or more sensor planes from which the velocity and thus the kinetic energy can be calculated.…”

Section: Introductionmentioning

confidence: 99%

Relative stopping power precision in time-of-flight proton CT

Krah,

Dauvergne,

Létang

et al. 2021

Preprint

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Objective Proton computed tomography (CT) is similar to x-ray CT but relies on protons rather than photons to form an image. In its most common operation mode, the measured quantity is the amount of energy that a proton has lost while traversing the imaged object from which a relative stopping power map can be obtained via tomographic reconstruction. To this end, a calorimeter which measures the energy deposited by protons downstream of the scanned object has been studied or implemented as energy detector in several proton CT prototypes. An alternative method is to measure the proton's residual velocity and thus its kinetic energy via the time of flight (TOF) between at least two sensor planes. In this work, we study the precision, i.e. image noise, which can be expected from TOF proton CT systems.Approach We rely on physics models on the one hand and statistical models of the relevant uncertainties on the other to derive closed form expressions for the noise in projection images. The TOF measurement error scales with the distance between the TOF sensor planes and is reported as velocity error in ps/m. We use variance reconstruction to obtain noise maps of a water cylinder phantom given the scanner characteristics and additionally reconstruct noise maps for a calorimeter-based proton CT system as reference. Main resultsWe find that TOF proton CT with 30 ps/m velocity error reaches similar image noise as a calorimeter-based proton CT system with 1% energy error (1 sigma error). A TOF proton CT system with a 50 ps/m velocity error produces slightly less noise than a 2% calorimeter system. Noise in a reconstructed TOF proton CT image is spatially inhomogeneous with a marked increase towards the object periphery.Significance This systematic study of image noise in TOF proton CT can serve as a guide for future developments of this alternative solution for estimating the residual energy of protons after the scanned object.

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A High-Granularity Digital Tracking Calorimeter Optimized for Proton CT

Cited by 34 publications

References 63 publications

Helium radiography with a digital tracking calorimeter—a Monte Carlo study for secondary track rejection

Helium radiography with a digital tracking calorimeter—a Monte Carlo study for secondary track rejection

Derivatives in Proton CT

Relative stopping power precision in time-of-flight proton CT

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