Extension and validation of an analytical model forin vivoPET verification of proton therapy—a phantom and clinical study

An inception network for positron emission tomography based dose estimation in carbon ion therapy

Objective. We aim to evaluate a method for estimating 1D physical dose deposition profiles in carbon ion therapy via analysis of dynamic PET images using a deep residual learning convolutional neural network (CNN). The method is validated using Monte Carlo simulations of 12C ion spread-out Bragg peak (SOBP) profiles, and demonstrated with an experimental PET image. Approach. A set of dose deposition and positron annihilation profiles for monoenergetic 12C ion pencil beams in PMMA are first generated using Monte Carlo simulations. From these, a set of random polyenergetic dose and positron annihilation profiles are synthesised and used to train the CNN. Performance is evaluated by generating a second set of simulated 12C ion SOBP profiles (one 116 mm SOBP profile and ten 60 mm SOBP profiles), and using the trained neural network to estimate the dose profile deposited by each beam and the position of the distal edge of the SOBP. Next, the same methods are used to evaluate the network using an experimental PET image, obtained after irradiating a PMMA phantom with a 12C ion beam at QST’s Heavy Ion Medical Accelerator in Chiba facility in Chiba, Japan. The performance of the CNN is compared to that of a recently published iterative technique using the same simulated and experimental 12C SOBP profiles. Main results. The CNN estimated the simulated dose profiles with a mean relative error (MRE) of 0.7% ± 1.0% and the distal edge position with an accuracy of 0.1 mm ± 0.2 mm, and estimate the dose delivered by the experimental 12C ion beam with a MRE of 3.7%, and the distal edge with an accuracy of 1.7 mm. Significance. The CNN was able to produce estimates of the dose distribution with comparable or improved accuracy and computational efficiency compared to the iterative method and other similar PET-based direct dose quantification techniques.

Direct mapping from PET coincidence data to proton-dose and positron activity using a deep learning approach

Objective. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning methods for achieving direct mapping from detector data to the intrinsic dose distribution. Approach. We performed Monte Carlo simulations using GATE/Geant4 10.4 simulation toolkits to generate a dataset using human CT phantom irradiated with high-energy protons and imaged with compact in-beam PET for realistic beam delivery in a single-fraction (∼2 Gy). We developed a neural network model based on conditional generative adversarial networks to generate dose maps conditioned on coincidence distributions in the detector. The model performance is evaluated by the mean relative error, absolute dose fraction difference, and shift in Bragg peak position. Main results. The relative deviation in the dose and range of the distributions predicted by the model from the true values for mono-energetic irradiation between 50 and 122 MeV lie within 1% and 2%, respectively. This was achieved using 105 coincidences acquired five minutes after irradiation. The relative deviation in the dose and range for spread-out Bragg peak distributions were within 1% and 2.6% uncertainties, respectively. Significance. An important aspect of this study is the demonstration of a method for direct mapping from detector counts to dose domain using the low count data of compact detectors suited for practical implementation in particle therapy. Including additional prior information in the future can further expand the scope of our model and also extend its application to other areas of medical imaging.

A Feasibility Study on Proton Range Monitoring Using 13N Peak in Inhomogeneous Targets

Proton irradiations are highly sensitive to spatial variations, mainly due to their high linear energy transfer (LET) and densely ionizing nature. In realistic clinical applications, the targets of ionizing radiation are inhomogeneous in terms of geometry and chemical composition (i.e., organs in the human body). One of the main methods for proton range monitoring is to utilize the production of proton induced positron emitting radionuclides; these could be measured precisely with positron emission tomography (PET) systems. One main positron emitting radionuclide that could be used for proton range monitoring and verification was found to be ¹³N that produces a peak close to the Bragg peak. In the present work, we have employed the Monte Carlo method and Spectral Analysis (SA) technique to investigate the feasibility of utilizing the ¹³N peak for proton range monitoring and verification in inhomogeneous targets. Two different phantom types, namely, (1) ordinary slab and (2) MIRD anthropomorphic phantoms, were used. We have found that the generated ¹³N peak in such highly inhomogeneous targets (ordinary slab and human phantom) is close to the actual Bragg peak, when irradiated by incident proton beam. The feasibility of using the SA technique to estimate the distribution of positron emitter was also investigated. The current findings and the developed tools in the present work would be helpful in proton range monitoring and verification in realistic clinical radiation therapy using proton beams.

Technical Design Report for a Carbon-11 Treatment Facility

Particle therapy relies on the advantageous dose deposition which permits to highly conform the dose to the target and better spare the surrounding healthy tissues and organs at risk with respect to conventional radiotherapy. In the case of treatments with heavier ions (like carbon ions already clinically used), another advantage is the enhanced radiobiological effectiveness due to high linear energy transfer radiation. These particle therapy advantages are unfortunately not thoroughly exploited due to particle range uncertainties. The possibility to monitor the compliance between the ongoing and prescribed dose distribution is a crucial step toward new optimizations in treatment planning and adaptive therapy. The Positron Emission Tomography (PET) is an established quantitative 3D imaging technique for particle treatment verification and, among the isotopes used for PET imaging, the 11C has gained more attention from the scientific and clinical communities for its application as new radioactive projectile for particle therapy. This is an interesting option clinically because of an enhanced imaging potential, without dosimetry drawbacks; technically, because the stable isotope 12C is successfully already in use in clinics. The MEDICIS-Promed network led an initiative to study the possible technical solutions for the implementation of 11C radioisotopes in an accelerator-based particle therapy center. We present here the result of this study, consisting in a Technical Design Report for a 11C Treatment Facility. The clinical usefulness is reviewed based on existing experimental data, complemented by Monte Carlo simulations using the FLUKA code. The technical analysis starts from reviewing the layout and results of the facilities which produced 11C beams in the past, for testing purposes. It then focuses on the elaboration of the feasible upgrades of an existing 12C particle therapy center, to accommodate the production of 11C beams for therapy. The analysis covers the options to produce the 11C atoms in sufficient amounts (as required for therapy), to ionize them as required by the existing accelerator layouts, to accelerate and transport them to the irradiation rooms. The results of the analysis and the identified challenges define the possible implementation scenario and timeline.

Novel On-line PET Imaging for Intra-Beam Range Verification and Delivery Optimization: A Simulation Feasibility Study

On-line PET image-based method uses an initial particle beam to measure the particle beam range (BR) within the same fraction so that any measured range-shift with respect to the predicted BR can be compensated before the rest therapeutic beam deliveries. However, the method requires to use a low-dose initial beam to minimize the risk of beam overshooting, which leads to low image count and inaccurate BR measurement. In this in-silico study, we evaluated the feasibility of a new on-line PET imaging method that measures BR at the mid-plane of a target volume with part of the high-dose therapy beams to verify BR and guide adaptive treatment re-planning. Simulations included various processes of proton beam radiations to a tumor inside a human brain phantom, positron and PET image generation at the mid-plane with initial beams, activity range measurement, and range-shift compensated beam delivery. The results demonstrated that the new method, under the simulated conditions, can achieve ~1.1 mm mid-plane BR measurement accuracy and closely match the delivered range-shift compensated dose distribution with the planned one. Overall, it is promising that this new method may significantly improve particle therapy accuracy.

In Vivo 3-D Dose Verification Using PET/CT Images After Carbon-Ion Radiation Therapy

Objective

To investigate the usefulness of positron emission tomography (PET) images obtained after carbon-ion irradiation for dose verification in carbon-ion radiotherapy.

Methods and Materials

An anthropomorphic head phantom was used in this study. Three cubes with volumes of 1, 4, and 10 ml were contoured as targets in the phantom CT through a treatment planning system. Treatment plans with six prescriptions from 2.5 to 10 Gy (2.5, 3, 5, 6, 8, and 10 Gy effective dose) were designed and delivered by 90° fixed carbon-ion beams, respectively. After irradiation of the phantom, a PET/CT scan was performed to fuse the treatment-planning CT image with the PET/CT image. The relationship between target volume and the standard uptake value (SUV) in PET/CT was evaluated for corresponding plan prescription. The MIM Maestro software was used for the image fusion and data analysis.

Results

SUV in the target had an approximate linear relationship with the effective dose. The same effective dose could generate a roughly equal SUV for different target volumes (p < 0.05).

Conclusions

It is feasible to verify the actual 3-D dose distribution of carbon-ion radiotherapy by the approach in this study.

Denoising PET images for proton therapy using a residual U-net

The use of proton therapy has the advantage of high dose concentration as it is possible to concentrate the dose on the tumor while suppressing damage to the surrounding normal organs. However, the range uncertainty significantly affects the actual dose distribution in the vicinity of the proton range, limiting the benefit of proton therapy for reducing the dose to normal organs. By measuring the annihilation gamma rays from the produced positron emitters, it is possible to obtain a proton induced positron emission tomography (pPET) image according to the irradiation region of the proton beam. Smoothing with a Gaussian filter is generally used to denoise PET images; however, this approach lowers the spatial resolution. Furthermore, other conventional smoothing processing methods may deteriorate the steep region of the pPET images. In this study, we proposed a denoising method based on a Residual U-Net for pPET images. We conducted the Monte Carlo simulation and irradiation experiment on a human phantom to obtain pPET data. The accuracy of the range estimation and the image similarity were evaluated for pPET images using the Residual U-Net, a Gaussian filter, a median filter, the block-matching and 3D-filtering (BM3D), and a total variation (TV) filter. Usage of the Residual U-Net yielded effective results corresponding to the range estimation; however, the results of peak-signal-to-noise ratio were identical to those for the Gaussian filter, median filter, BM3D, and TV filter. The proposed method can contribute to improving the accuracy of treatment verification and shortening the PET measurement time.

A machine learning framework with anatomical prior for online dose verification using positron emitters and PET in proton therapy

We developed a machine learning framework in order to establish the correlation between dose and activity distributions in proton therapy. A recurrent neural network was used to predict dose distribution in three dimensions based on the information of proton-induced positron emitters. Hounsfield Unit (HU) information from CT images and analytically derived stopping power (SP) information were incorporated as auxiliary inputs. Four different scenarios were investigated: Activity only, Activity + HU, Activity + SP and Activity + HU + SP. The performance was quantitatively studied in terms of mean absolute error (MAE) and mean relative error (MRE), under different signal-to-noise ratios (SNRs). In addition to the first dataset of mono-energetic beams, three additional datasets were validated to help evaluate the generalization capability of our proposed model: a dataset of a lower SNR, five reconstructed PET images, and a dataset of spread-out Bragg peaks. Good verification accuracy of dose verification in three dimensions is demonstrated. The inclusion of anatomical information improves both accuracy and generalization. For an activity profile with an SNR of 4 (the mono-energetic case), the framework is able to obtain an MRE of ∼ 0.99% over the whole range and a range uncertainty of ∼ 0.27 mm. The machine learning-based framework may emerge as a useful tool to allow for online dose verification and quality assurance in proton therapy.

Extension of the ML-EM algorithm for dose estimation using PET in proton therapy: application to an inhomogeneous target

Positron emission tomography (PET) has been used for in vivo treatment verification, mainly for range verification, in proton therapy. Evaluating the direct dose from PET measurements remains challenging; however, it is highly desirable from a clinical perspective. In this study, a method for estimating the dose distribution from the positron emitter distributions was developed using the maximum likelihood expectation maximization algorithm. The 1D spatial relationship between positron emitter distributions and a dose distribution in an inhomogeneous target was inputted into the system matrix based on a filter framework. In contrast, spatial resolution of the PET system and total variation regularization (as prior knowledge for dose distribution) were considered in the 3D image-space. The dose estimation was demonstrated using Monte Carlo simulated PET activity distributions with substantial noise in a head and neck phantom. This mimicked the single field irradiation of the spread-out Bragg peak beams at clinical dose levels. Besides the simple implementation of the algorithm, this strategy achieved a high-speed calculation (30 s for a 3D dose estimation) and accurate dose and range estimations (less than 10% and 2 mm errors at 1-σ values, respectively). The proposed method could be key for using PET for in vivo dose monitoring.

Feasibility study of patient-specific dose verification in proton therapy utilizing positron emission tomography (PET) and generative adversarial network (GAN)

PURPOSE

Online dose verification based on proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between dose and the activity distributions, a machine learning-based approach was developed to establish their relationship.

METHODS

Simulations were carried out using a pencil beam scanning system and a computed tomography (CT) image-based phantom. A DiscoGAN model was developed to perform dose verification for both central and off-center lines. Besides the activity as input, HU information from CT images and stopping power (SP) prior were incorporated as auxiliary features for the model. The performance was quantitatively studied in terms of mean absolute error (MAE) and mean relative error (MRE), under different signal-to-noise ratios (SNRs). In addition to a dataset comprising monoenergetic beams, two additional datasets were generated to evaluate the model's generalization capability: five reconstructed PET images based on an in-beam PET system and a dataset comprising spread-out Bragg peaks (SOBPs).

RESULTS

The feasibility of dose verification was successfully demonstrated for all three datasets. For the monoenergetic case (i.e., raw activity of positron emitters), the MRE is found to be <1% for the central lines and 5% for the off-center lines, respectively. The range uncertainty is found to be less than 1 mm. The prediction based on five PET images, which take into account the detection of 511-keV photons and image reconstruction, yields slightly inferior performance. For the SOBP case, the MRE of the center lines is found to be <3% and the range uncertainty is <1 mm. The inclusion of anatomical information (HU and SP) improves both accuracy and generalization of the DiscoGAN model.

CONCLUSION

The combination of proton-induced positron emitters, in-beam PET, and machine learning may become a useful tool allowing for patient-specific online dose verification in proton therapy.

A filtering approach for PET and PG predictions in a proton treatment planning system

Positron emission tomography (PET) and prompt gamma (PG) detection are promising proton therapy monitoring modalities. Fast calculation of the expected distributions is desirable for comparison to measurements and to develop/train algorithms for automatic treatment error detection. A filtering formalism was used for positron-emitter predictions and adapted to allow for its use for the beamline of any proton therapy centre. A novel approach based on a filtering formalism was developed for the prediction of energy-resolved PG distributions for arbitrary tissues. The method estimates PG yields and their energy spectra in the entire treatment field. Both approaches were implemented in a research version of the RayStation treatment planning system. The method was validated against PET monitoring data and Monte Carlo simulations for four patients treated with scanned proton beams. Longitudinal shifts between profiles from analytical and Monte Carlo calculations were within -1.7 and 0.9 mm, with maximum standard deviation of 0.9 mm and 1.1 mm, for positron-emitters and PG shifts, respectively. Normalized mean absolute errors were within 1.2 and 5.3%. When comparing measured and predicted PET data, the same more complex case yielded an average shift of 3 mm, while all other cases were below absolute average shifts of 1.1 mm. Normalized mean absolute errors were below 7.2% for all cases. A novel solution to predict positron-emitter and PG distributions in a treatment planning system is proposed, enabling calculation times of only a few seconds to minutes for entire patient cases, which is suitable for integration in daily clinical routine.

ML-EM algorithm for dose estimation using PET in proton therapy

Positron emission tomography (PET) has been extensively studied and clinically investigated for dose verification in proton therapy. However, the production distributions of positron emitters are not proportional to the dose distribution. Thus, direct dose evaluation is limited when using the conventional PET-based approach. We propose a method for estimating the dose distribution from the positron emitter distributions using the maximum likelihood (ML) expectation maximization (EM) algorithm combined with filtering. In experiments to verify the effectiveness of the proposed method, mono-energetic and spread-out Bragg-peak proton beams were delivered by a synchrotron, and a water target was irradiated at clinical dose levels. Planar PET measurements were performed during beam pauses and after irradiation over a total period of 200 s. In addition, we conducted a Monte Carlo simulation to obtain the required filter functions and analyze the influence of the number of algorithm iterations on estimation. We successfully estimated the 2D dose distributions even under statistical noise in the PET images. The accuracy of the 2D dose estimation was about 10% for both beams at the 1-[Formula: see text] values of relative error. This value is comparable to the deviations in the measured PET activity distributions. For the laterally integrated profile along the beam direction, a low error within 5% was obtained per irradiation value. Moreover, the difference of estimated proton ranges was within 1 mm, and 2D estimation from the PET images was completed in 21 ms. Hence, the proposed algorithm may be applied to real-time dose monitoring. Although this is the first attempt to use the ML-EM algorithm for dose estimation, the proposed method showed high accuracy and speed in the estimation of proton dose distribution from PET data. The proposed method is thus a step forward to exploit the full potential of PET for in vivo dose verification.

Prediction of positron emitter distributions for range monitoring in carbon ion therapy: an analytical approach

Range verification is one of the most relevant tasks in ion beam therapy. In the case of carbon ion therapy, positron emission tomography (PET) is the most widely used method for this purpose, which images the [Formula: see text]-activation following nuclear interactions of the ions with the tissue nuclei. Since the positron emitter activity profile is not directly proportional to the dose distribution, until today only its comparison to a prediction of the PET profile allows for treatment verification. Usually, this prediction is obtained from time-consuming Monte Carlo simulations of high computational effort, which impacts the clinical workflow. To solve this issue in proton therapy, a convolution approach was suggested to predict positron emitter activity profiles from depth dose distributions analytically. In this work, we introduce an approach to predict positron emitter distributions from depth dose profiles in carbon ion therapy. While the distal fall-off position of the positron emitter profile is predicted from a convolution approach similar to the one suggested for protons, additional analytical functions are introduced to describe the characteristics of the positron emitter distribution in tissue. The feasibility of this approach is demonstrated with monoenergetic depth dose profiles and spread out Bragg peaks in homogeneous and heterogeneous phantoms. In all cases, the positron emitter profile is predicted with high precision and the distal fall-off position is reproduced with millimeter accuracy.

Particle beam microstructure reconstruction and coincidence discrimination in PET monitoring for hadron therapy

Positron emission tomography is one of the most mature techniques for monitoring the particles range in hadron therapy, aiming to reduce treatment uncertainties and therefore the extent of safety margins in the treatment plan. In-beam PET monitoring has been already performed using inter-spill and post-irradiation data, i.e. while the particle beam is off or paused. The full beam acquisition procedure is commonly discarded because the particle spills abruptly increase the random coincidence rates and therefore the image noise. This is because random coincidences cannot be separated by annihilation photons originating from radioactive decays and cannot be corrected with standard random coincidence techniques due to the time correlation of the beam-induced background with the ion beam microstructure. The aim of this paper is to provide a new method to recover in-spill data to improve the images obtained with full-beam PET acquisitions. This is done by estimating the temporal microstructure of the beam and thus selecting input PET events that are less likely to be random ones. The PET detector we used was the one developed within the INSIDE project and tested at the CNAO synchrotron-based facility. The data were taken on a PMMA phantom irradiated with 72 MeV proton pencil beams. The obtained results confirm the possibility of improving the acquired PET data without any external signal coming from the synchrotron or ad hoc detectors.

Carbon ions beam therapy monitoring with the INSIDE in-beam PET

In vivo range monitoring techniques are necessary in order to fully take advantage of the high dose gradients deliverable in hadrontherapy treatments. Positron emission tomography (PET) scanners can be used to monitor beam-induced activation in tissues and hence measure the range. The INSIDE (Innovative Solutions for In-beam DosimEtry in Hadrontherapy) in-beam PET scanner, installed at the Italian National Center of Oncological Hadrontherapy (CNAO, Pavia, Italy) synchrotron facility, has already been successfully tested in vivo during a proton therapy treatment. We discuss here the system performance evaluation with carbon ion beams, in view of future in vivo tests. The work is focused on the analysis of activity images obtained with therapeutic treatments delivered to polymethyl methacrylate (PMMA) phantoms, as well as on the test of an innovative and robust Monte Carlo simulation technique for the production of reliable prior activity maps. Images are reconstructed using different integration intervals, so as to monitor the activity evolution during and after the treatment. Three procedures to compare activity images are presented, namely Pearson correlation coefficient, Beam's eye view and overall view. Images of repeated irradiations of the same treatments are compared to assess the integration time necessary to provide reproducible images. The range agreement between simulated and experimental images is also evaluated, so as to validate the simulation capability to provide sound prior information. The results indicate that at treatment end, or at most 20 s afterwards, the range measurement is reliable within 1-2 mm, when comparing both different experimental sessions and data with simulations. In conclusion, this work shows that the INSIDE in-beam PET scanner performance is promising towards its in vivo test with carbon ions.

Online proton therapy monitoring: clinical test of a Silicon-photodetector-based in-beam PET

Particle therapy exploits the energy deposition pattern of hadron beams. The narrow Bragg Peak at the end of range is a major advantage but range uncertainties can cause severe damage and require online verification to maximise the effectiveness in clinics. In-beam Positron Emission Tomography (PET) is a non-invasive, promising in-vivo technique, which consists in the measurement of the β+ activity induced by beam-tissue interactions during treatment, and presents the highest correlation of the measured activity distribution with the deposited dose, since it is not much influenced by biological washout. Here we report the first clinical results obtained with a state-of-the-art in-beam PET scanner, with on-the-fly reconstruction of the activity distribution during irradiation. An automated time-resolved quantitative analysis was tested on a lacrimal gland carcinoma case, monitored during two consecutive treatment sessions. The 3D activity map was reconstructed every 10 s, with an average delay between beam delivery and image availability of about 6 s. The correlation coefficient of 3D activity maps for the two sessions (above 0.9 after 120 s) and the range agreement (within 1 mm) prove the suitability of in-beam PET for online range verification during treatment, a crucial step towards adaptive strategies in particle therapy.

Induced radioactivity of a GSO scintillator by secondary fragments in carbon ion therapy and its effects on in-beam OpenPET imaging

The accumulation of induced radioactivity within in-beam PET scanner scintillators is of concern for its long-term clinical usage in particle therapy. To estimate the effects on OpenPET which we are developing for in-beam PET based on GSOZ (Zi doped Gd2SiO5), we measured the induced radioactivity of GSO activated by secondary fragments in a water phantom irradiation by a (12)C beam with an energy of 290 MeV u(-1). Radioisotopes of Na, Ce, Eu, Gd, Nd, Pm and Tb including positron emitters were observed in the gamma ray spectra of the activated GSO with a high purity Ge detector and their absolute radioactivities were calculated. We used the Monte Carlo simulation platform, Geant4 in which the observed radioactivity was assigned to the scintillators of a precisely reproduced OpenPET and the single and coincidence rates immediately after one treatment and after one-year usage were estimated for the most severe conditions. Comparing the highest coincidence rate originating from the activated scintillators (background) and the expected coincidence rate from an imaging object (signal), we determined the expected signal-to-noise ratio to be more than 7 within 3 min and more than 10 within 1 min from the scan start time. We concluded the effects of scintillator activation and their accumulation on the OpenPET imaging were small and clinical long-term usage of the OpenPET was feasible.

Range Verification Methods in Particle Therapy: Underlying Physics and Monte Carlo Modeling

Hadron therapy allows for highly conformal dose distributions and better sparing of organs-at-risk, thanks to the characteristic dose deposition as function of depth. However, the quality of hadron therapy treatments is closely connected with the ability to predict and achieve a given beam range in the patient. Currently, uncertainties in particle range lead to the employment of safety margins, at the expense of treatment quality. Much research in particle therapy is therefore aimed at developing methods to verify the particle range in patients. Non-invasive in vivo monitoring of the particle range can be performed by detecting secondary radiation, emitted from the patient as a result of nuclear interactions of charged hadrons with tissue, including β (+) emitters, prompt photons, and charged fragments. The correctness of the dose delivery can be verified by comparing measured and pre-calculated distributions of the secondary particles. The reliability of Monte Carlo (MC) predictions is a key issue. Correctly modeling the production of secondaries is a non-trivial task, because it involves nuclear physics interactions at energies, where no rigorous theories exist to describe them. The goal of this review is to provide a comprehensive overview of various aspects in modeling the physics processes for range verification with secondary particles produced in proton, carbon, and heavier ion irradiation. We discuss electromagnetic and nuclear interactions of charged hadrons in matter, which is followed by a summary of some widely used MC codes in hadron therapy. Then, we describe selected examples of how these codes have been validated and used in three range verification techniques: PET, prompt gamma, and charged particle detection. We include research studies and clinically applied methods. For each of the techniques, we point out advantages and disadvantages, as well as clinical challenges still to be addressed, focusing on MC simulation aspects.

Mapping (15)O production rate for proton therapy verification

PURPOSE

This work was a proof-of-principle study for the evaluation of oxygen-15 ((15)O) production as an imaging target through the use of positron emission tomography (PET), to improve verification of proton treatment plans and to study the effects of perfusion.

METHODS AND MATERIALS

Dynamic PET measurements of irradiation-produced isotopes were made for a phantom and rabbit thigh muscles. The rabbit muscle was irradiated and imaged under both live and dead conditions. A differential equation was fitted to phantom and in vivo data, yielding estimates of (15)O production and clearance rates, which were compared to live versus dead rates for the rabbit and to Monte Carlo predictions.

RESULTS

PET clearance rates agreed with decay constants of the dominant radionuclide species in 3 different phantom materials. In 2 oxygen-rich materials, the ratio of (15)O production rates agreed with the expected ratio. In the dead rabbit thighs, the dynamic PET concentration histories were accurately described using (15)O decay constant, whereas the live thigh activity decayed faster. Most importantly, the (15)O production rates agreed within 2% (P>.5) between conditions.

CONCLUSIONS

We developed a new method for quantitative measurement of (15)O production and clearance rates in the period immediately following proton therapy. Measurements in the phantom and rabbits were well described in terms of (15)O production and clearance rates, plus a correction for other isotopes. These proof-of-principle results support the feasibility of detailed verification of proton therapy treatment delivery. In addition, (15)O clearance rates may be useful in monitoring permeability changes due to therapy.

Proton therapy verification with PET imaging

Proton therapy is very sensitive to uncertainties introduced during treatment planning and dose delivery. PET imaging of proton induced positron emitter distributions is the only practical approach for in vivo, in situ verification of proton therapy. This article reviews the current status of proton therapy verification with PET imaging. The different data detecting systems (in-beam, in-room and off-line PET), calculation methods for the prediction of proton induced PET activity distributions, and approaches for data evaluation are discussed.

In vivo proton range verification: a review

Protons are an interesting modality for radiotherapy because of their well defined range and favourable depth dose characteristics. On the other hand, these same characteristics lead to added uncertainties in their delivery. This is particularly the case at the distal end of proton dose distributions, where the dose gradient can be extremely steep. In practice however, this gradient is rarely used to spare critical normal tissues due to such worries about its exact position in the patient. Reasons for this uncertainty are inaccuracies and non-uniqueness of the calibration from CT Hounsfield units to proton stopping powers, imaging artefacts (e.g. due to metal implants) and anatomical changes of the patient during treatment. In order to improve the precision of proton therapy therefore, it would be extremely desirable to verify proton range in vivo, either prior to, during, or after therapy. In this review, we describe and compare state-of-the art in vivo proton range verification methods currently being proposed, developed or clinically implemented.

The effect of anterior proton beams in the setting of a prostate-rectum spacer

Studies suggest that anterior beams with in vivo range verification would improve rectal dosimetry in proton therapy for prostate cancer. We investigated whether prostate-rectum spacers would enhance or diminish the benefits of anterior proton beams in these treatments. Twenty milliliters of hydrogel was injected between the prostate and rectum of a cadaver using a transperineal approach. Computed tomography (CT) and magnetic resonance (MR) images were used to generate 7 uniform scanning (US) and 7 single-field uniform dose pencil-beam scanning (PBS) plans with different beam arrangements. Pearson correlations were calculated between rectal, bladder, and femoral head dosimetric outcomes and beam arrangement anterior scores, which characterize the degree to which dose is delivered anteriorly. The overall quality of each plan was compared using a virtual dose-escalation study. For US plans, rectal mean dose was inversely correlated with anterior score, but for PBS plans there was no association between rectal mean dose and anterior score. For both US and PBS plans, full bladder and empty bladder mean doses were correlated with anterior scores. For both US and PBS plans, femoral head mean doses were inversely correlated with anterior score. For US plans and a full bladder, 4 beam arrangements that included an anterior beam tied for the highest maximum prescription dose (MPD). For US plans and an empty bladder, the arrangement with 1 anterior and 2 anterior oblique beams achieved the highest MPD in the virtual dose-escalation study. The dose-escalation study did not differentiate beam arrangements for PBS. All arrangements in the dose-escalation study were limited by bladder constraints except for the arrangement with 2 posterior oblique beams. The benefits of anterior proton beams in the setting of prostate-rectum spacers appear to be proton modality dependent and may not extend to PBS.