Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Development of a Head Scanner for Proton CT Hartmut F.-W. Sadrozinski R. P. Johnson, S. Macafee, A. Plumb, H. F.-W. Sadrozinski, D. Steinberg, A. Zatserklanyi SCIPP, UC Santa Cruz, CA USA V. Bashkirov, F. Hurley, R. Schulte Loma Linda University Medical Center, CA USA K. Schubert, M. Witt CSU San Bernardino, San Bernardino, CA 92407, USA S. Penfold CMR, Univ. of Wollongong NSW 2522, Australia

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Large-scale Imaging with Silicon Sensors Attenuation of Photons, Z N(x) = N o e -  x Energy Loss of Protons,  NIST Data Measure statistical process of X-ray removal Measure energy loss on individual protons Bethe-Bloch

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Alderson Head Phantom Range Uncertainties (measured with PTR) > 5 mm > 10 mm > 15 mm Schneider U. (1994), “Proton radiography as a tool for quality control in proton therapy,” Med Phys. 22, 353. RSP H Proton CT Basics Proton therapy and treatment planning requires the knowledge of the stopping power in the patient, so that the Bragg peak can be located within the tumor. X-ray CT has been shown to give insufficiently accurate stopping power (S.P.) maps in complicated phantoms or from uncertainty in converting Hounsfield values to S.P. The goal of Proton CT is to reconstruct a 3D map of the stopping power within the patient with as fine a voxel size as practical at a minimum dose, using protons (instead of x-rays) in transmission. In a rotational scan the integrated stopping power is determined for every view by a measurement of the energy loss.

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec pCT Challenge #1: Multiple Coulomb Scattering D C Williams Phys. Med. Biol. 49 (2004) 2899–2911 The proton path inside the patient/phantom is not straight  the path of every proton before and after the phantom has to be measured and its path inside the patient reconstructed. M. Bruzzi et al IEEE Trans. Nucl. Sci.,54, 140 (2007) From deflection and displacement, calculate the “Most Likely Path MLP” Beam test with sub-divided phantom: MLP can be predicted with sub-mm precision using tracking detectors with ~ 80  m resolution

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Tracking and measuring the residual energy of every proton requires fast sensors and fast data acquisition (DAQ). Data Flow math: Assuming 100 protons / 1mm voxel and 180 views requires ~ 7*10 8 protons. With 10 kHz data rate, one pCT scan will take 20 hrs (requiring a very patient patient!). A scan with a proton rate of 2 MHz takes 6 min. N.B. such a scan will deliver a dose of 1.5 mGy. Image Reconstruction To reconstruct images with > 10 7 voxels using ~10 9 protons is NOT trivial. Our reconstruction code is already running on GPU’s in anticipation of the much higher data rates of the future. pCT Challenge #1a: Proton Data rate

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec The proton energy loss is not fixed, but is a stochastic process. The straggling error is a function of depth, irreducible when the energy is not measured.  the straggling within the phantom limits the precision of the energy loss measurement. Challenge #2 to pCT: Range / Energy Straggling Range straggling ~ 1% of range ~ 1mm for 100 MeV, ~ 3mm for 200 MeV Range counter always encounters the maximum range straggling: the error is independent of the WEPL of phantom (depends on proton energy) WEPL = Water equivalent Path Length (of proton in phantom) Geant4 Study:

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Efficiency of energy measurement In addition to ionization processes described by the Bethe-Bloch equation, protons undergo processes which remove protons from the peak in the energy spectra useful for the energy determination. pCT Challenge #2a: useful Proton Rate Simulation and data agree well: At 200 MeV, only ~60% of the protons entering the phantom will be in the quasi-Gaussian end peak of the spectrum. Because of non-Gaussian tails, the energy distributions at present are fitted only at the high side, which comes with a loss of precision. With improved modeling of the tails, this might be recoverable. Geant4 Range Counter Data CsI Calorimeter

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Instrument Solutions to the pCT Challenge: ExperimentTrackerEnergy DetectorProton Energy [MeV] TERA / CERN U. Amaldi et al., NIM A 629 (2011) pp GEM ~100  m Range (3mm) + WLSF + MPPC 100 upgrade Firenze / LNS V. Sipala et al., IEEE NSS-MIC 2011, MIC15.S-305 SI SSD 80  m Fast crystal calorimeter + P.D. 68 LLU / UCSC / NIU F. Hurley et al., subm. to MEDICAL PHYSICS Si SSD 80  m CsI + P.D NIU / FNALSciFi +MPCC mm Range (3mm) + WLSF + MPCC under construction LLU /UCSCSi SSD “Slim edges” 80  m Range (>3mm) + direct MPCC or Polystyrene Calorimeter + PMT under construction

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Imaging Results (LLU-UCSC-NIU) B. Colby, D. Fusi, R. Johnson, S. Kashiguine, F. Martinez-McKinney, J. Missaghian, H. F.-W. Sadrozinski, M. Scaringella SCIPP, UC Santa Cruz, CA USA V. Bashkirov, F. Hurley, S. Penfold, R. Schulte Loma Linda University Medical Center, CA USA G. Coutrakon, B. Erdelyi, V. Rykalin Northern Illinois University S. McAllister, K. Schubert CSU San Bernardino

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec The LLU-UCSC-NIU Prototype Scanner R. W. Schulte, et al.,, IEEE Trans. Nucl. Sci., 51,, pp 866, 2004.

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec CT Image Reconstruction 1.WEPL calibration and cut 2.Correction for overlaps in Si tracker 3.Correction matrix with Calorimeter response 4.Angular and spatial binning 5.Filtered Back Projection and Iterative Algebraic reconstruction 6.MLP formalism for final reconstruction 2.5 mm slice 0.65 mm voxels Reality Check: We accumulated data for this reconstructed image during 4 hours at 20 kHz trigger rate. This is not acceptable for clinical applications ! Next development step: 50x faster pCT scanner 11 MaterialPredicted RSPRSP reconstructed from Measurement Polystyrene Bone Lucite Air air bone lucite polyst.

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Increase Size 2x : 40 cm x 10 cm Improve data throughput 50x:  2MHz sustained proton rate with minimal pile-up  Si sensors are intrinsically fast, built faster readout ASIC and distributed DAQ  Data stream uses local FPGA for data collection, formatting and transmission Improve speed of energy detector:  CsI calorimeter replaced with faster plastic scintillator  Both range counter and range counter-calorimeter hybrid under test  Polystyrene Range Counter with direct MPPC readout looks very promising (~3x p.e. wrt to WLSF readout?)  Geant4 results on Range Counter with thicker tiles is intriguing Improve tiling of Si sensors:  Si SSD are attractive since they have low noise at good efficiency, an important factor in a sparse system (no redundant space points)  “slim edges” allow tiling without overlap LLU-UCSC-CSUSB Head Scanner R. Johnson, H. F.-W. Sadrozinski, D. Steinberg, A. Zatserklanyi, V. Bashkirov, F. Hurley, S. Penfold, R. Schulte, S. McAllister, K. Schubert NIH Grant 1R01EB

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec WEPL (not Energy!) Detector Choices Range Counter Direct MCPP readout (signal 3-5x of 3mm+WLSF) Range-Calorimeter Hybrid “Bulky”: 3 Polystyrene 10cmx10cmx40cm + PM 3-4”PMT ~ 30 p.e. SiPM 70 plates, 4 mm, PolystyreneScint. Hodoscopic CsI Calorimeter P.D. Readout Too slow! Proton path

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec WEPL Calibration of “Bulky” 200 MeV Protons, Polystyrene Degraders of known Water Equivalent Thickness WET Comparison of WEPL Resolution Range Counter WEPL RMS is constant ~ 4 mm (as expected, since range counter is dominated by straggling in phantom/degrader + range counter (expect ~ 1mm from plate thickness) “Bulky” appears to be a good choice, if spill-over can be dealt with when range is close to calorimeter interface Goal of WEPL calibration: Determination of WEPL directly from calorimeter response, without converting first to MeV

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Silicon Tracker Improvements Large area coverage requires tiling of sensors. Sensors have ~ 1mm inactive edges which create image artifacts. In the present prototype tracker this is dealt with by shingling sensors, but a reconstruction nightmare. Overlapping sensors introduces additional, non-uniform energy corrections

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec For Tiling with no Overlap: “Slim Edges” see M. Christophersen’s Talk Si SSD with 900  m dead edge XeF2 scribing + Cleaving + N2 PECVD with guard ring Cut within 50  m of Guard Ring Guard Ring Cut (!) without guard ring Reduce dead edge from 1mm to ~ 200  m Excellent breakdown behavior Current at 150V: ~10 nA/cm with guard ring ~100 nA/cm without guard ring

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Charge Collection of “Slim Edge” HPK Sensor GLAST2000, p-on-n; t = 400  m; L = 10 cm; Pitch: 228  m; # of strips:8 Treatment: Laser scribed w 10% intensity Cleaved with tweezers Oxygen PECVD on Sidewall Cut ~ 100 mm from guard ring 1) Measure i-V for entire sensor and for every strip: Is any current leaking into the active region? 2) Investigation of the Charge collection pre- and post- cutting using the AliBaVa analog readout system

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Currents on Individual Strips The currents on the cut sensor are 1000x larger wrt un-cut. The currents on individual strips are measured through the bias resistor voltage measurements. They are generally consistent for both cut and un-cut devices. We do not see an abnormal behavior for the edge strips. Device i-V uncut-cutStrip i-V uncut-cut P-on-n HPK (GLAST), laser scribed, PECVD Oxygen, 96  m from guard

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Outer Strip Signal pre-cut / post-cut Strip # 203 is next to the cut edge Median does not change by more than 5% from before to after cut 150 V bias, 90 Sr source Cutting does not change the noise in the adjacent outer strip Tail in pulse height distribution from partially measured tracks close to the bias ring is unchanged before/after cut Data taken and analyzed by R. Mori (Florence U.) & M. Cartiglia (Milano H.S.)

Hartmut F.-W. Sadrozinski: pCT HSTD8 Dec Conclusions Proton CT has come a long way since I first talked about it at the 2002 IEEE NSS-MIC Symposium in Norfolk, VA. We see very different approaches on instruments, motivated in part by a technology transfer from HEP/Space. This has come with severe limitations (proton rate!). Using our prototype scanner, we are starting to reconstruct very clear and sophisticated radiographs AND CT images, and are actively improving reconstruction algorithms. We are now arriving at a new phase in pCT: we have a dedicated detector development, with focus on speeding up the data taking to be useful in clinical applications, and optimizing the detector systems based on lessons learned. End-to-end simulation of the instrument has been essential for our understanding of the requirements and proper choice of the technical solution, yet many lessons were learned during operation of the scanners Next (big) step: clinical application. Ongoing and unwavering support by Prof. James M. Slater (LLUMC) made this project possible. We acknowledge support from the US National Institute of Health under the grant NIH Grant 1R01EB