Particle imaging for daily in-room image guidance in particle therapy

Particle therapy exploits the highly localized depth dose profile of protons and light ions to deliver a high dose to the target while largely sparing surrounding healthy tissue. The steep dose gradient at the end of the ions range, known as the Bragg peak, however, also makes particle therapy sensitive to range uncertainties. In current clinical practice, a major cause of range uncertainties resides in the conversion of the treatment planning x-ray CT to the patient specific relative stopping power (RSP) map that is crucial for accurate treatment planning. By measuring the energy loss of particles after traversing the patient, particle imaging enables a more direct reconstruction of the RSP. In this thesis, different aspects towards the clinical implementation of particle imaging are investigated. First, a theoretical description of the point-spread function for different particle radiography algorithms is developed in order to explain observed limitations. A novel filtering technique to remove nuclear interaction events in particle imaging is proposed and high quality experimental helium ion CTs are demonstrated. First results from an experimental comparison between particle and x-ray CT modalities for RSP prediction in animal tissue samples are presented. Furthermore, a novel technique for intra-treatment helium ion imaging based on a mixed helium/carbon beam is explored with that relative range changes in the millimeter regime were observable. Finally, novel particle imaging detector designs are investigated. The thesis highlights the potential of helium ion imaging for pre- and intra-treatment image guidance in particle therapy

Considerations for Upright Particle Therapy Patient Positioning and Associated Image Guidance

Particle therapy is a rapidly growing field in cancer therapy. Worldwide, over 100 centers are in operation, and more are currently in construction phase. The interest in particle therapy is founded in the superior target dose conformity and healthy tissue sparing achievable through the particles’ inverse depth dose profile. This physical advantage is, however, opposed by increased complexity and cost of particle therapy facilities. Particle therapy, especially with heavier ions, requires large and costly equipment to accelerate the particles to the desired treatment energy and steer the beam to the patient. A significant portion of the cost for a treatment facility is attributed to the gantry, used to enable different beam angles around the patient for optimal healthy tissue sparing. Instead of a gantry, a rotating chair positioning system paired with a fixed horizontal beam line presents a suitable cost-efficient alternative. Chair systems have been used already at the advent of particle therapy, but were soon dismissed due to increased setup uncertainty associated with the upright position stemming from the lack of dedicated image guidance systems. Recently, treatment chairs gained renewed interest due to the improvement in beam delivery, commercial availability of vertical patient CT imaging and improved image guidance systems to mitigate the problem of anatomical motion in seated treatments. In this review, economical and clinical reasons for an upright patient positioning system are discussed. Existing designs targeted for particle therapy are reviewed, and conclusions are drawn on the design and construction of chair systems and associated image guidance. Finally, the different aspects from literature are channeled into recommendations for potential upright treatment layouts, both for retrofitting and new facilities

Considerations for Upright Particle Therapy Patient Positioning and Associated Image Guidance

Particle therapy is a rapidly growing field in cancer therapy. Worldwide, over 100 centers are in operation, and more are currently in construction phase. The interest in particle therapy is founded in the superior target dose conformity and healthy tissue sparing achievable through the particles’ inverse depth dose profile. This physical advantage is, however, opposed by increased complexity and cost of particle therapy facilities. Particle therapy, especially with heavier ions, requires large and costly equipment to accelerate the particles to the desired treatment energy and steer the beam to the patient. A significant portion of the cost for a treatment facility is attributed to the gantry, used to enable different beam angles around the patient for optimal healthy tissue sparing. Instead of a gantry, a rotating chair positioning system paired with a fixed horizontal beam line presents a suitable cost-efficient alternative. Chair systems have been used already at the advent of particle therapy, but were soon dismissed due to increased setup uncertainty associated with the upright position stemming from the lack of dedicated image guidance systems. Recently, treatment chairs gained renewed interest due to the improvement in beam delivery, commercial availability of vertical patient CT imaging and improved image guidance systems to mitigate the problem of anatomical motion in seated treatments. In this review, economical and clinical reasons for an upright patient positioning system are discussed. Existing designs targeted for particle therapy are reviewed, and conclusions are drawn on the design and construction of chair systems and associated image guidance. Finally, the different aspects from literature are channeled into recommendations for potential upright treatment layouts, both for retrofitting and new facilities

Experimental comparison of photon versus particle computed tomography to predict tissue relative stopping powers

Purpose: Measurements comparing relative stopping power (RSP) accuracy of state-of-the-art systems representing single-energy and dual-energy computed tomography (SECT/DECT) with proton CT (pCT) and helium CT (HeCT) in biological tissue samples. Methods: We used 16 porcine and bovine samples of various tissue types and water, covering an RSP range from 0.90urn:x-wiley:00942405:media:mp15283:mp15283-math-00010.06 to 1.78 urn:x-wiley:00942405:media:mp15283:mp15283-math-00020.05. Samples were packed and sealed into 3D-printed cylinders (urn:x-wiley:00942405:media:mp15283:mp15283-math-0003 cm, urn:x-wiley:00942405:media:mp15283:mp15283-math-0004 cm) and inserted into an in-house designed cylindrical polymethyl methacrylate (PMMA) phantom (urn:x-wiley:00942405:media:mp15283:mp15283-math-0005 cm, urn:x-wiley:00942405:media:mp15283:mp15283-math-0006 cm). We scanned the phantom in a commercial SECT and DECT (120 kV; 100 and 140 kV/Sn (tin-filtered)); and acquired pCT and HeCT (urn:x-wiley:00942405:media:mp15283:mp15283-math-0007 MeV/u, 2urn:x-wiley:00942405:media:mp15283:mp15283-math-0008 steps, urn:x-wiley:00942405:media:mp15283:mp15283-math-0009 (p)/urn:x-wiley:00942405:media:mp15283:mp15283-math-0010 (He) particles/projection) with a particle imaging prototype. RSP maps were calculated from SECT/DECT using stoichiometric methods and from pCT/HeCT using the DROP-TVS algorithm. We estimated the average RSP of each tissue per modality in cylindrical volumes of interest and compared it to ground truth RSP taken from peak-detection measurements. Results: Throughout all samples, we observe the following root-mean-squared RSP prediction errors urn:x-wiley:00942405:media:mp15283:mp15283-math-0011 combined uncertainty from reference measurement and imaging: SECT 3.10urn:x-wiley:00942405:media:mp15283:mp15283-math-00122.88%, DECT 0.75urn:x-wiley:00942405:media:mp15283:mp15283-math-00132.80%, pCT 1.19urn:x-wiley:00942405:media:mp15283:mp15283-math-0014 2.81%, and HeCT 0.78urn:x-wiley:00942405:media:mp15283:mp15283-math-00152.81%. The largest mean errors urn:x-wiley:00942405:media:mp15283:mp15283-math-0016 combined uncertainty per modality are SECT 8.22 urn:x-wiley:00942405:media:mp15283:mp15283-math-00172.79% in cortical bone, DECT 1.74urn:x-wiley:00942405:media:mp15283:mp15283-math-00182.00% in back fat, pCT 1.80 urn:x-wiley:00942405:media:mp15283:mp15283-math-00194.27% in bone marrow, and HeCT 1.37urn:x-wiley:00942405:media:mp15283:mp15283-math-00204.25% in bone marrow. Ring artifacts were observed in both pCT and HeCT reconstructions, imposing a systematic shift to predicted RSPs. Conclusion: Comparing state-of-the-art SECT/DECT technology and a pCT/HeCT prototype, DECT provided the most accurate RSP prediction, closely followed by particle imaging. The novel modalities pCT and HeCT have the potential to further improve on RSP accuracies with work focusing on the origin and correction of ring artifacts. Future work will study accuracy of proton treatment plans using RSP maps from investigated imaging modalities

A likelihood-based particle imaging filter using prior information

Background: Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality, especially for multinucleon ions that can fragment, such as helium. Purpose: This work proposes a particle imaging filter, referred to as the Prior Filter, based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise. Methods: The Prior Filter uses Fermi–Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3σ filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories. Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of-the-art method including an implementation of the 3σ filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied. Results: The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5–6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3σ filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of-the-art filtering method. Conclusions: The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction

Investigating Slit-Collimator-Produced Carbon Ion Minibeams with High-Resolution CMOS Sensors

Particle minibeam therapy has demonstrated the potential for better healthy tissue sparing due to spatial fractionation of the delivered dose. Especially for heavy ions, the spatial fractionation could enhance the already favorable differential biological effectiveness at the target and the entrance region. Moreover, spatial fractionation could even be a viable option for bringing ions heavier than carbon back into patient application. To understand the effect of minibeam therapy, however, requires careful conduction of pre-clinical experiments, for which precise knowledge of the minibeam characteristics is crucial. This work introduces the use of high-spatial-resolution CMOS sensors to characterize collimator-produced carbon ion minibeams in terms of lateral fluence distribution, secondary fragments, track-averaged linear energy transfer distribution, and collimator alignment. Additional simulations were performed to further analyze the parameter space of the carbon ion minibeams in terms of beam characteristics, collimator positioning, and collimator manufacturing accuracy. Finally, a new concept for reducing the neutron dose to the patient by means of an additional neutron shield added to the collimator setup is proposed and validated in simulation. The carbon ion minibeam collimator characterized in this work is used in ongoing pre-clinical experiments on heavy ion minibeam therapy at the GSI

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

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

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