A Digital Tracking Calorimeter for Proton Computed Tomography

Cancer is a destructive disease, in which tumor cells grow out-of-control, halting organ function. Its treatment is threefold: Radical surgery, chemotherapy and radiation therapy. Their relative usage is determined by cancer type, stage, organs at risk adjacent to the tumor and progression. There has been a significant increase in the number of cancer patients treated with radiation therapy using particle therapy in the recent decades worldwide. Short term and long term treatment-induced side effects are reduced when applying particle therapy due to the superior dose conformality compared to conventional radiation therapy using photons. The particle species commonly applied during particle therapy is the proton. Proton therapy as of today is performed with the delivery of pre-calculated dose plans for each patient: the applied dose plans are made based on x-ray computed tomography (CT) images. The CT images are reconstructed based upon photon interaction with matter, thus a conversion is required for calculating the Relative Stopping Power for how the protons traverse and deposit dose in the patient’s body during proton therapy. This conversion procedure introduces range uncertainties typically in the order of 2%–3%, corresponding to 4–6 mm at a treatment depth 20 cm into the patient. A proton CT system would yield a means of direct calculation of the RSP map in the patient. During a proton CT scan, a high-energy proton beam is directed at the patient and the proton beam must have sufficient energy to completely pass through the patient being imaged. The protons’ residual energies are measured after they have traversed through the patient and into a detector behind to the patient. The information about the residual energy from each proton can then be used, together with the proton’s estimated path through the patient, as a basis for reconstructing a volumetric RSP. In this thesis, the feasibility of using a purely pixel-based detector, a so-called Digital Tracking Calorimeter (DTC), for proton CT purposes is investigated and its performance quantified through experiments and Monte Carlo simulations. The DTC is designed for tracking and measuring the range and energy of individual protons in a proton beam. The DTC consists of multiple layers of semiconductor pixel sensors with a digital readout, interleaved with a passive absorber material for energy degradation. The sensor chips are considered near optimal for use in the tracking, due to their data processing capacity at the required readout speed, the high granularity of such a detector system and also due to their short radiation thickness. The requirement to this part of the detector is that it must be able to allow for reconstruction of a large amount of proton tracks in each data readout cycle, enabling high proton rate capabilities. The DTC was originally designed for the reconstruction of high-energy electromagnetic showers for the Forward Calorimeter project in the ALICE experiment at CERN. The presented prototype forms the basis for a proton CT system using a single pixelbased technology for both tracking and calorimetry. This advantage simplifies the setup and reduces the cost of a proton CT system assembly, and it is a unique feature of the Digital Tracking Calorimeter concept. Measurement data from the AGORFIRM beamline at KVI-CART in Groningen in the Netherlands and Monte Carlo simulation results are used in order to develop a proton tracking algorithm for the estimation of the residual ranges of a high number of concurrent proton tracks. The range of the individual protons can with the first prototype be estimated with a range resolution of 6–25 mm Water Equivalent Thickness. This relatively low achieved range resolution is expected due to the original design goal of the prototype. The readout system for this prototype is able to handle a proton intensity of 1 million protons/s by using 500 concurrent proton tracks in each readout frame, which is comparable to present similar prototypes. A next prototype of the proton CT setup using the DTC is at present in the construction stage. A DTC further optimized for use in a proton CT system will utilize next-generation Monolithic Active Pixel Sensors with larger sensor areas and a hundredfold increase in readout speeds. The developed proton CT analysis framework is applied on a variety of possible DTC geometries for the next prototype. The potential design choices are evaluated on basis of the reached range accuracy and range uncertainty as well as of the track reconstruction efficiency. A design recommendation is reached where the proposed DTC will have 3.5 mm thick aluminum absorber slabs between each sensor layer. Some of the tools applied in this thesis for the purpose of proton range calculation have been validated through comparison studies: First, different MC programs are compared to each other and to available experimental data. Secondly, the accuracy and usability of several available proton range calculation models are evaluated through their capability of reproducing tabulated energy-range proton values. An accurate model is found and applied throughout this thesis for proton range reconstruction purposes

Enhancing Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer Patients with iCE, a Novel System for Automated Multi-Criterial Treatment Planning Including Beam Angle Optimization

In this study, the novel iCE radiotherapy treatment planning system (TPS) for automated multi-criterial planning with integrated beam angle optimization (BAO) was developed, and applied to optimize organ at risk (OAR) sparing and systematically investigate the impact of beam angles on radiotherapy dose in locally advanced non-small cell lung cancer (LA-NSCLC). iCE consists of an in-house, sophisticated multi-criterial optimizer with integrated BAO, coupled to a broadly used commercial TPS. The in-house optimizer performs fluence map optimization to automatically generate an intensity-modulated radiotherapy (IMRT) plan with optimal beam angles for each patient. The obtained angles and dose-volume histograms are then used to automatically generate the final deliverable plan with the commercial TPS. For the majority of 26 LA-NSCLC patients, iCE achieved improved heart and esophagus sparing compared to the manually created clinical plans, with significant reductions in the median heart Dmean (8.1 vs. 9.0 Gy, p = 0.02) and esophagus Dmean (18.5 vs. 20.3 Gy, p = 0.02), and reductions of up to 6.7 Gy and 5.8 Gy for individual patients. iCE was superior to automated planning using manually selected beam angles. Differences in the OAR doses of iCE plans with 6 beams compared to 4 and 8 beams were statistically significant overall, but highly patient-specific. In conclusion, automated planning with integrated BAO can further enhance and individualize radiotherapy for LA-NSCLC.publishedVersio

Improving knowledge-based treatment planning for lung cancer radiotherapy with automatic multi-criteria optimized training plans

Background: Knowledge-based planning (KBP) is a method for automated radiotherapy treatment planning where appropriate optimization objectives for new patients are predicted based on a library of training plans. KBP can save time and improve organ at-risk sparing and inter-patient consistency compared to manual planning, but its performance depends on the quality of the training plans. We used another system for automated planning, which generates multi-criteria optimized (MCO) plans based on a wish list, to create training plans for the KBP model, to allow seamless integration of knowledge from a new system into clinical routine. Model performance was compared for KBP models trained with manually created and automatic MCO treatment plans. Material and Methods: Two RapidPlan models with the same 30 locally advanced non-small cell lung cancer patients included were created, one containing manually created clinical plans (RP_CLIN) and one containing fully automatic multi-criteria optimized plans (RP_MCO). For 15 validation patients, model performance was compared in terms of dose-volume parameters and normal tissue complication probabilities, and an oncologist performed a blind comparison of the clinical (CLIN), RP_CLIN, and RP_MCO plans. Results: The heart and esophagus doses were lower for RP_MCO compared to RP_CLIN, resulting in an average reduction in the risk of 2-year mortality by 0.9 percentage points and the risk of acute esophageal toxicity by 1.6 percentage points with RP_MCO. The oncologist preferred the RP_MCO plan for 8 patients and the CLIN plan for 7 patients, while the RP_CLIN plan was not preferred for any patients. Conclusion: RP_MCO improved OAR sparing compared to RP_CLIN and was selected for implementation in the clinic. Training a KBP model with clinical plans may lead to suboptimal output plans, and making an extra effort to optimize the library plans in the KBP model creation phase can improve the plan quality for many future patients.</p

Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer

Purpose Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT. Methods and Materials For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LETd) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve. Results An overall statistically significant RAIC association with dose and LETd was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30-2.72) for dose and 2.68 (1.93-4.93) for LETd, respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LETd effect demonstrated increased risk of RAIC with increasing LETd for the majority of patients. Estimated probability of RAIC with LETd = 1 keV/µm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD15 were estimated to be 63.6 and 50.1 Gy with LETd equal to 2 and 5 keV/µm, respectively. Conclusions Our results suggest that the LETd effect could be of clinical significance for some patients; LETd assessment in clinical treatment plans should therefore be taken into consideration.publishedVersio

Image quality of list-mode proton imaging without front trackers

List mode proton imaging relies on accurate reconstruction of the proton most likely path (MLP) through the patient. This typically requires two sets of position sensitive detector systems, one upstream (front) and one downstream (rear) of the patient. However, for a clinical implementation it can be preferable to omit the front trackers (single-sided proton imaging). For such a system, the MLP can be computed from information available through the beam delivery system and the remaining rear tracker set. In this work, we use Monte Carlo simulations to compare a conventional double-sided (using both front and rear detector systems) with a single-sided system (only rear detector system) by evaluating the spatial resolution of proton radiographs (pRad) and proton CT images (pCT) acquired with these set-ups. Both the pencil beam spot size, as well as the spacing between spots was also adjusted to identify the impact of these beam parameters on the image quality. Relying only on the pencil beam central position for computing the MLP resulted in severe image artifacts both in pRad and pCT. Using the recently extended-MLP formalism that incorporate pencil beam uncertainty removed these image artifacts. However, using a more focused pencil beam with this algorithm induced image artifacts when the spot spacing was the same as the beam spot size. The spatial resolution tested with a sharp edge gradient technique was reduced by 40% for single-sided (MTF10% = 3.0 lp/cm) compared to double-sided (MTF10% = 4.9 lp/cm) pRad with ideal tracking detectors. Using realistic trackers the difference decreased to 30%, with MTF10% of 4.0 lp/cm for the realistic double-sided and 2.7 lp/cm for the realistic single-sided setup. When studying an anthropomorphic paediatric head phantom both single- and double-sided set-ups performed similarly where the difference in water equivalent thickness (WET) between the two set-ups were less than 0.01 mm in homogeneous areas of the head. Larger discrepancies between the two set-ups were visible in high density gradients like the facial structures. A complete CT reconstruction of a Catphan$^{\circledR}$ module was performed. Assuming ideal detectors, the obtained spatial resolution was 5.1 lp/cm for double-sided and 3.8 lp/cm for the single-sided setup. Double- and single-sided pRad with realistic tracker properties returned a spatial resolution of 3.8 lp/cm and 3.2 lp/cm, respectively. Future studies should investigate the development of dedicated reconstruction algorithms targeted for single-sided particle imaging.publishedVersio

A hybrid multi-particle approach to range assessment-based treatment verification in particle therapy

Particle therapy (PT) used for cancer treatment can spare healthy tissue and reduce treatment toxicity. However, full exploitation of the dosimetric advantages of PT is not yet possible due to range uncertainties, warranting development of range-monitoring techniques. This study proposes a novel range-monitoring technique introducing the yet unexplored concept of simultaneous detection and imaging of fast neutrons and prompt-gamma rays produced in beam-tissue interactions. A quasimonolithic organic detector array is proposed, and its feasibility for detecting range shifts in the context of proton therapy is explored through Monte Carlo simulations of realistic patient models and detector resolution efects. The results indicate that range shifts of 1 mm can be detected at relatively low proton intensities (22.30(13) × 107 protons/spot) when spatial information obtained through imaging of both particle species are used simultaneously. This study lays the foundation for multiparticle detection and imaging systems in the context of range verifcation in PTpublishedVersio

Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter

Objective. Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots. Approach. We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction. Main results. The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 107 protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm. Significance. Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area.publishedVersio