In vivo PET range verification relies on the comparison of measured and simulated activity distributions. The accuracy of the simulated distribution depends on the accuracy of the Monte Carlo code, which is in turn dependent on the accuracy of the available cross-section data for β+ isotope production. We have explored different cross-section data available in the literature for the main reaction channels (16O(p,pn)15O, 12C(p,pn)11C and 16O(p,3p3n)11C) contributing to the production of β+ isotopes by proton beams in patients. Available experimental and theoretical values were implemented in the simulation and compared with measured PET images obtained with a high-resolution PET scanner. Each reaction channel was studied independently. A phantom with three different materials was built, two of them with high carbon or oxygen concentration and a third one with average soft tissue composition. Monoenergetic and SOBP field irradiations of the phantom were accomplished and measured PET images were compared with simulation results. Different cross-section values for the tissue-equivalent material lead to range differences below 1 mm when a 5 min scan time was employed and close to 5 mm differences for a 30 min scan time with 15 min delay between irradiation and scan (a typical off-line protocol). The results presented here emphasize the need of more accurate measurement of the cross-section values of the reaction channels contributing to the production of PET isotopes by proton beams before this in vivo range verification method can achieve mm accuracy.
Purpose: To investigate if MRI of the liver can be used for in-vivo dose verification in proton therapy. Recently it was shown that irradiated healthy liver tissue shows a strong systematic decrease in uptake of a hepatobiliary-directed contrast agent (Gd-EOB-DTPA) six weeks after brachytherapy. In this study it is investigated, if the radiation-related effect is also detectable for hypo-fractionated proton therapy. Methods: For patients who receive liver lesion directed hypo-fractionated proton therapy (5 fractions within 2 weeks) Gd-EOB-DTPA enhanced MRI is performed 10–12 weeks after treatment. MR images are registered to the planning CT and the planned dose map by non-rigid image registration. The reviewer contours the border of hypointensity on T1-w images that indicates the hepatocyte function loss. The threshold dose for this function loss is evaluated as the D90, the dose achieved in at least 90% of the pseudolesion volume. Moreover, irradiated- to-non-irradiated liver contrast and the correlation of detected signal change in MRI with the planned dose map are analyzed. Results: Gd-EOB-DTPA enhanced T1-w MR images taken after hypo-fractionated proton therapy show a hypo-intense area that is correlated to the area of high dose deposition in shape and volume with small deviations in location (up to 5–6 mm) giving information on the actual distal edge position. With Gd-EOB-DTPA enhanced MRI, irradiated-to-non-irradiated contrast is superior to MRI enhanced with extracellular, nonspecific contrast agents used in a previous study. Conclusions: A biomarker for radiation induced changes in liver tissue was identified and promising post-treatment MRI data have been acquired and are currently evaluated. For the next step, a pilot patient study has been set up to investigate, if radiation-induced changes using Gd-EOB-DTPA enhanced MRI can be detected prospectively during fractionated proton therapy and therefore would allow for an immediate assessment of the proton therapy with direct impact on patient safety. The work was partly supported by the German Federal Ministry of Education and Research (BMBF) under contract no. 03ZIK445.
Range uncertainties in proton therapy hamper treatment precision. Prompt gamma-rays were suggested 16 years ago for real-time range verification, and have already shown promising results in clinical studies with collimated cameras. Simultaneously, alternative imaging concepts without collimation are investigated to reduce the footprint and price of current prototypes. In this manuscript, a compact range verification method is presented. It monitors prompt gamma-rays with a single scintillation detector positioned coaxially to the beam and behind the patient. Thanks to the solid angle effect, proton range deviations can be derived from changes in the number of gamma-rays detected per proton, provided that the number of incident protons is well known. A theoretical background is formulated and the requirements for a future proof-of-principle experiment are identified. The potential benefits and disadvantages of the method are discussed, and the prospects and potential obstacles for its use during patient treatments are assessed. The final milestone is to monitor proton range differences in clinical cases with a statistical precision of 1 mm, a material cost of 25000 USD and a weight below 10 kg. This technique could facilitate the widespread application of in vivo range verification in proton therapy and eventually the improvement of treatment quality.
Purpose: To investigate the feasibility of using MRI to verify proton beam range in distal regions for liver tumor treatment. Methods: In the treatment of liver tumors with proton beams, the dose range uncertainty in the distal region can lead to reduced dose in tumor and/or increased dose in the surrounding normal tissue. Due to the increased extracellular fluid after radiation treatment, the irradiated areas in liver usually appear hypo-intense on T1-weighted MR images, and hyper-intense on T2-weighted MR images. This change of MR signal intensity (SI) allows for a quantitative verification of dose range in vivo. To achieve this goal, follow-up T1/T2-weighted MR images are firstly registered to the planning CT images. Then MR SI is correlated to the radiation dose at the superior/inferior penumbra dose fall- off, which includes two penumbrae in two proton beams. This SI-dose correlation is finally employed on MR images to estimate the proton end-of- range. This methodology is being evaluated on a 15-patients database, which is being collected in our institute. Results: The preliminary results were based on three patients who received proton liver treatment. We observed correlations between MR SI and radiation proton dose in superior/inferior penumbra regions, with correlation coefficients (R2) of 0.86, 0.97, and 0.97, respectively. By applying the SI-dose correlation to the distal region of proton beam, the mean distances from the MRI-estimated dose range to the prescribed dose range were −0.4 mm, 2.6 mm, and 2.4 mm, respectively. Conclusions: The preliminary results demonstrate that the proton dose range can be verified in vivo to within 2.6 mm by follow-up MR images after proton liver treatment. This IRB-approved study is being extended to 15 patients with liver cancer treated by proton radiotherapy.
Purpose: To reconstruct the delivered dose distribution in proton therapy from proton beam induced PET signals using deconvolution methods. Methods: Previous verification strategies in proton therapy compared measured PET signals with estimated PET signals created by a convolution of the planned dose distribution with a specific filter function. We now use this filter function to reconstruct the delivered dose distribution from the PET signals by developing suitable de‐convolution methods. Since deconvolution is an ill‐posed inverse problem, regularization is required to obtain a stable solution and to cope with noise found in measured PET signals. The basic filtering approach is developed for homogeneous media and additional procedures are necessary to generalize the PET estimation to inhomogeneous media. We use this generalization formalism in our deconvolution approach to allow a dose reconstruction from measured patient PET data. In addition, the PET image formation includes a convolution with a system response function which has to be removed in an additional deconvolution. To evaluate our reconstruction method, we detect the edge of the distal fall‐off region in the reconstructed dose and compare it with its nominal/planned position. Results: Various simulations demonstrate the potential and robustness of the dose reconstruction. The detection of the distal fall‐off region shows an average accuracy of 2 mm even for strongly degraded measured PET signals. In addition, we use the deconvolution approach to successfully reconstruct the dose distribution from measured PET data for a head and neck patient. Conclusions: This work indicates the potential of dose reconstruction in proton therapy from measured PET signals. The proposed method is able to correctly reconstruct a dose distribution and to cope with noise in the PET images. The shape of the reconstructed dose distribution of a head and neck patient is in good agreement with the treatment planning dose.
