Impact on the estimated dose of different tissue assignment strategies during partial breast irradiations with INTRABEAM.

PURPOSE: Partial breast irradiations with electronic brachytherapy or kilovoltage intraoperative radiotherapy devices such as Axxent or INTRABEAM are becoming more common every day. Breast is mainly composed of glandular and adipose tissues, which are not always clearly disentangled in planning breast CTs. In these cases, breast tissues are replaced with an average soft tissue, or even water. However, at kilovoltage energies, this may lead to large differences in the delivered dose, due to the dominance of photoelectric effect. Therefore, the aim of this work was to study the effect on the dose prescribed in breast with the INTRABEAM device using different soft tissue assignment strategies that would replace the adipose and glandular tissues that constitute the breast in cases where these tissues cannot be adequately distinguished in a CT scan. METHODS AND MATERIALS: Dose was computed with a Monte Carlo code in five patients with a 3 cm diameter INTRABEAM spherical applicator. Tissues within the breast were assigned following six different strategies: one based on the TG-43 recommendations, representing the whole breast as water of unity density, another one also water-based but with CT derived density, and the other four also based on CT-derived densities, using a single tissue resulting from different mixes of glandular and adipose tissues. These were compared against the reference dose computed in an accurately segmented CT, following TG-186 recommendations. Relative differences and dose ratios between the reference and the other tissue assignment strategies were obtained in three regions of interest inside the breast. RESULTS AND CONCLUSIONS: Dose planning in water-based tissues was found inaccurate for breast treatment with INTRABEAM, as it would incur in up to 30% under-prescription of dose. If accurate soft tissue assignments in the breast cannot be safely done, a single-tissue composition of 80% adipose and 20% glandular tissue, or even a 100% adipose tissue, would be recommended to avoid dose under-prescription.

Feasibility study of a SiPM-fiber detector for non-invasive measurement of arterial input function for preclinical and clinical positron emission tomography.

Pharmacokinetic positron emission tomography (PET) studies rely on the measurement of the arterial input function (AIF), which represents the time-activity curve of the radiotracer concentration in the blood plasma. Traditionally, obtaining the AIF requires invasive procedures, such as arterial catheterization, which can be challenging, time-consuming, and associated with potential risks. Therefore, the development of non-invasive techniques for AIF measurement is highly desirable. This study presents a detector for the non-invasive measurement of the AIF in PET studies. The detector is based on the combination of scintillation fibers and silicon photomultipliers (SiPMs) which leads to a very compact and rugged device. The feasibility of the detector was assessed through Monte Carlo simulations conducted on mouse tail and human wrist anatomies studying relevant parameters such as energy spectrum, detector efficiency and minimum detectable activity (MDA). The simulations involved the use of 18F and 68Ga isotopes, which exhibit significantly different positron ranges. In addition, several prototypes were built in order to study the different components of the detector including the scintillation fiber, the coating of the fiber, the SiPMs, and the operating configuration. Finally, the simulations were compared with experimental measurements conducted using a tube filled with both 18F and 68Ga to validate the obtained results. The MDA achieved for both anatomies (approximately 1000 kBq/mL for mice and 1 kBq/mL for humans) falls below the peak radiotracer concentrations typically found in PET studies, affirming the feasibility of conducting non-invasive AIF measurements with the fiber detector. The sensitivity for measurements with a tube filled with 18F (68Ga) was 1.2 (2.07) cps/(kBq/mL), while for simulations, it was 2.81 (6.23) cps/(kBq/mL). Further studies are needed to validate these results in pharmacokinetic PET studies.

UMC-PET: a fast and flexible Monte Carlo PET simulator.

Objective.The GPU-based Ultra-fast Monte Carlo positron emission tomography simulator (UMC-PET) incorporates the physics of the emission, transport and detection of radiation in PET scanners. It includes positron range, non-colinearity, scatter and attenuation, as well as detector response. The objective of this work is to present and validate UMC-PET as a a multi-purpose, accurate, fast and flexible PET simulator.Approach.We compared UMC-PET against PeneloPET, a well-validated MC PET simulator, both in preclinical and clinical scenarios. Different phantoms for scatter fraction (SF) assessment following NEMA protocols were simulated in a 6R-SuperArgus and a Biograph mMR scanner, comparing energy histograms, NEMA SF, and sensitivity for different energy windows. A comparison with real data reported in the literature on the Biograph scanner is also shown.Main results.NEMA SF and sensitivity estimated by UMC-PET where within few percent of PeneloPET predictions. The discrepancies can be attributed to small differences in the physics modeling. Running in a 11 GB GeForce RTX 2080 Ti GPU, UMC-PET is ∼1500 to ∼2000 times faster than PeneloPET executing in a single core Intel(R) Xeon(R) CPU W-2155 @ 3.30 GHz.Significance.UMC-PET employs a voxelized scheme for the scanner, patient adjacent objects (such as shieldings or the patient bed), and the activity distribution. This makes UMC-PET extremely flexible. Its high simulation speed allows applications such as MC scatter correction, faster SRM estimation for complex scanners, or even MC iterative image reconstruction.

Zinc-Doped Iron Oxide Nanoparticles as a Proton-Activatable Agent for Dose Range Verification in Proton Therapy.

Proton therapy allows the treatment of specific areas and avoids the surrounding tissues. However, this technique has uncertainties in terms of the distal dose fall-off. A promising approach to studying the proton range is the use of nanoparticles as proton-activatable agents that produce detectable signals. For this, we developed an iron oxide nanoparticle doped with Zn (IONP@Zn-cit) with a hydrodynamic size of 10 nm and stability in serum. Cytotoxicity, defined as half of the surveillance, was 100 µg Zn/mL in the U251 cell line. The effect on clonogenic cell death was tested after X-ray irradiation, which suggested a radioprotective effect of these nanoparticles at low concentrations (1-10 µg Zn/mL). To evaluate the production of positron emitters and prompt-gamma signals, IONP@Zn-cit was irradiated with protons, obtaining prompt-gamma signals at the lowest measured concentration (10 mg Zn/mL). Finally, 67Ga-IONP@Zn-cit showed accumulation in the liver and spleen and an accumulation in the tumor tissue of 0.95% ID/g in a mouse model of U251 cells. These results suggest the possibility of using Zn nanoparticles as proton-activatable agents to verify the range by prompt gamma detection and face the challenges of prompt gamma detection in a specific biological situation, opening different avenues to go forward in this field.

Technical note: Measurement of the bunch structure of a clinical proton beam using a SiPM coupled to a plastic scintillator with an optical fiber.

BACKGROUND: Recent proposals of high dose rate plans in protontherapy as well as very short proton bunches may pose problems to current beam monitor systems. There is an increasing demand for real-time proton beam monitoring with high temporal resolution, extended dynamic range and radiation hardness. Plastic scintillators coupled to optical fiber sensors have great potential in this context to become a practical solution towards clinical implementation. PURPOSE: In this work, we evaluate the capabilities of a very compact fast plastic scintillator with an optical fiber readout by a SiPM and electronics sensor which has been used to provide information on the time structure at the nanosecond level of a clinical proton beam. MATERIALS AND METHODS: A 3 × 3 × 3 mm3 plastic scintillator (EJ-232Q Eljen Technology) coupled to a 3 × 3 mm2 SiPM (MicroFJ-SMA-30035, Onsemi) has been characterized with a 70 MeV clinical proton beam accelerated in a Proteus One synchrocyclotron. The signal was read out by a high sampling rate oscilloscope (5 GS/s). By exposing the sensor directly to the proton beam, the time beam profile of individual spots was recorded. RESULTS: Measurements of detector signal have been obtained with a time sampling period of 0.8 ns. Proton bunch period (16 ns), spot (10 µs) and interspot (1 ms) time structures could be observed in the time profile of the detector signal amplitude. From this, the RF frequency of the accelerator has been extracted, which is found to be 64 MHz. CONCLUSIONS: The proposed system was able to measure the fine time structure of a clinical proton accelerator online and with ns time resolution.

Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions.

BACKGROUND: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [18F]F-FDG PET/CT images. METHODS: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [18F]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized uptake values normalized for body lean body mass). The lesion pool was divided into training (n = 182) and testing cohorts (n = 46); for each lesion, 101 image features from both PET and CT were extracted (202 features per lesion). These features, along with clinical and pathological information, allowed the prediction model's construction by using seven popular feature selection methods in cross-combination with another seven machine-learning (ML) classifiers. The performance of the different models was investigated with the receiver-operating characteristic curve (ROC) analysis, using the area under the curve (AUC) and accuracy (ACC) metrics. RESULTS: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 ± 0.06 and 0.92 ± 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts. CONCLUSIONS: Image features obtained from a pretreatment [18F]F-FDG PET/CT along with clinical vaiables could predict the metabolic response of metastatic breast cancer lesions, by their incorporation into predictive models, whose performance depends on the selected combination between feature selection and ML classifier methods.

XIORT-MC: A real-time MC-based dose computation tool for low- energy X-rays intraoperative radiation therapy.

PURPOSE: The INTRABEAM system is a miniature accelerator for low-energy X-ray Intra-Operative Radiation Therapy (IORT), and it could benefit from a fast and accurate dose computation tool. With regards to accuracy, dose computed with Monte Carlo (MC) simulations are the gold standard, however, they require a large computational effort and consequently they are not suitable for real-time dose planning. This work presents a comparison of the implementation on Graphics Processing Unit (GPU) of two different dose calculation algorithms based on MC phase-space (PHSP) information to compute dose distributions for the INTRABEAM device within seconds and with the accuracy of realistic MC simulations. METHODS: The MC-based algorithms we present incorporate photoelectric, Compton and Rayleigh effects for the interaction of low-energy X-rays. XIORT-MC (X-ray Intra-Operative Radiation Therapy Monte Carlo) includes two dose calculation algorithms; a Woodcock-based MC algorithm (WC-MC) and a Hybrid MC algorithm (HMC), and it is implemented in CPU and in GPU. Detailed MC simulations have been generated to validate our tool in homogeneous and heterogeneous conditions with all INTRABEAM applicators, including three clinically realistic CT-based simulations. A performance study has been done to determine the acceleration reached with the code, in both CPU and GPU implementations. RESULTS: Dose distributions were obtained with the HMC and the WC-MC and compared to standard reference MC simulations with more than 95% voxels fulfilling a 7%-0.5 mm gamma evaluation in all the cases considered. The CPU-HMC is 100 times more efficient than the reference MC, and the CPU-WC-MC is about 50 times more efficient. With the GPU implementation, the particle tracking of the WC-MC is faster than the HMC, with the extraction of the particle's information from the PHSP file taking a major part of the time. However, thanks to the variance reduction techniques implemented in the HMC, up to 400 times less particles are needed in the HMC to reach the same level of noise than the WC-MC. Therefore, in our implementation for INTRABEAM energies, the HMC is about 1.3 times more efficient than the WC-MC in an NVIDIA GeForce GTX 1080 Ti card and about 5.5 times more efficient in an NVIDIA GeForce RTX 3090. Dose with noise below 5% has been obtained in realistic situations in less than 5 s with the WC-MC and in less than 0.5 s with the HMC. CONCLUSIONS: The XIORT-MC is a dose computation tool designed to take full advantage of modern GPUs, making possible to obtain MC-grade accurate dose distributions within seconds. Its high speed allows a real-time dose calculation that includes the realistic effects of the beam in voxelized geometries of patients. It can be used as a dose-planning tool in the operating room during a XIORT treatment with any INTRABEAM device.

Radiochromic film dosimetry for protons up to 10 MeV with EBT2, EBT3 and unlaminated EBT3 films.

Passive dosimetry with radiochromic films is widely used in proton radiotherapy, both in clinical and scientific environments, thanks to its simplicity, high spatial resolution and dose-rate independence. However, film under-response for low-energy protons, the so-called linear-energy transfer (LET) quenching, must be accounted and corrected for. We perform a meta-analysis on existing film under-response data with EBT, EBT2 and EBT3 GAFchromic™ films and provide a common framework to integrate it, based on the calculation of dose-averaged LET in the active layer of the films. We also report on direct measurements with the 10 MeV proton beam at the Center for Microanalysis of Materials (CMAM) for EBT2, EBT3 and unlaminated EBT3 films, focusing on the 20-80 keVµm-1LET range, where previous data was scarce. Measured film relative efficiency (RE) values are in agreement with previously reported data from the literature. A model on film RE constructed with combined literature and own experimental values in the 5-80 keVµm-1LET range is presented, supporting the hypothesis of a linear decrease of RE with LET, with no remarkable differences between the three types of films analyzed.

Dictionary-based protoacoustic dose map imaging for proton range verification.

Proton radiotherapy has the potential to provide state-of-the-art dose conformality in the tumor area, reducing possible adverse effects on surrounding organs at risk. However, uncertainties in the exact location of the proton Bragg peak inside the patient prevent this technique from achieving full clinical potential. In this context, in vivo verification of the range of protons in patients is key to reduce uncertainty margins. Protoacoustic range verification employs acoustic pressure waves generated by protons due to the radio-induced thermoacoustic effect to reconstruct the dose deposited in a patient during proton therapy. In this paper, we propose to use the a priori knowledge of the shape of the proton dose distribution to create a dictionary with the expected ultrasonic signals at predetermined detector locations. Using this dictionary, the reconstruction of deposited dose is performed by matching pre-calculated dictionary acoustic signals with data acquired online during treatment. The dictionary method was evaluated on a single-field proton plan for a prostate cancer patient. Dose calculation was performed with the open-source treatment planning system matRad, while acoustic wave propagation was carried out with k-Wave. We studied the ability of the proposed dictionary method to detect range variations caused by anatomical changes in tissue density, and alterations of lateral and longitudinal beam position. Our results show that the dictionary-based protoacoustic method was able to identify the changes in range originated by all the alterations introduced, with an average accuracy of 1.4 mm. This procedure could be used for in vivo verification, comparing the measured signals with the precalculated dictionary.

Speed of sound ultrasound transmission tomography image reconstruction based on Bézier curves.

Speed of Sound (SoS) maps from ultrasound tomography (UST) provide valuable quantitative information for soft tissue characterization and identification of lesions, making this technique interesting for breast cancer detection. However, due to the complexity of the processes that characterize the interaction of ultrasonic waves with matter, classic and fast tomographic algorithms such as back-projection are not suitable. Consequently, the image reconstruction process in UST is generally slow compared to other more conventional medical tomography modalities. With the aim of facilitating the translation of this technique into real clinical practice, several reconstruction algorithms are being proposed to make image reconstruction in UST to be a fast and accurate process. The geometrical acoustic approximation is often used to reconstruct SoS with less computational burden in comparison with full-wave inversion methods. In this work, we propose a simple formulation to perform on-the-flight reconstruction for UST using geometrical acoustics with refraction correction based on quadratic Bézier polynomials. Here we demonstrate that the trajectories created with these polynomials are an accurate approximation to reproduce the refracted acoustic paths connecting the emitter and receiver transducers. The method is faster than typical acquisition times in UST. Thus, it can be considered a step towards real-time reconstructions, which may contribute to its future clinical translation.

Biological and Mechanical Synergies to Deal With Proton Therapy Pitfalls: Minibeams, FLASH, Arcs, and Gantryless Rooms.

Proton therapy has advantages and pitfalls comparing with photon therapy in radiation therapy. Among the limitations of protons in clinical practice we can selectively mention: uncertainties in range, lateral penumbra, deposition of higher LET outside the target, entrance dose, dose in the beam path, dose constraints in critical organs close to the target volume, organ movements and cost. In this review, we combine proposals under study to mitigate those pitfalls by using individually or in combination: (a) biological approaches of beam management in time (very high dose rate "FLASH" irradiations in the order of 100 Gy/s) and (b) modulation in space (a combination of mini-beams of millimetric extent), together with mechanical approaches such as (c) rotational techniques (optimized in partial arcs) and, in an effort to reduce cost, (d) gantry-less delivery systems. In some cases, these proposals are synergic (e.g., FLASH and minibeams), in others they are hardly compatible (mini-beam and rotation). Fixed lines have been used in pioneer centers, or for specific indications (ophthalmic, radiosurgery,…), they logically evolved to isocentric gantries. The present proposals to produce fixed lines are somewhat controversial. Rotational techniques, minibeams and FLASH in proton therapy are making their way, with an increasing degree of complexity in these three approaches, but with a high interest in the basic science and clinical communities. All of them must be proven in clinical applications.

MultiRBE: Treatment planning for protons with selective radiobiological effectiveness.

PURPOSE: Clinical treatment planning protocols for protons recommend a uniform value radiobiological effectiveness (RBE) of protons of 1.1 throughout the treatment field, despite evidence from in-vitro and animal studies that proton RBE increases with linear energy transfer (LET), causing tissues placed distally to the target location to receive a presumably higher biological dose than estimated. While several voices in the medical physics community have advocated for variable RBE-based optimization, the uncertainties in RBE models have prevented its implementation in clinical practice, since an overestimation of RBE could cause significant target underdosage. METHODS: We propose a mixed RBE model (MultiRBE), where a uniform RBE is used in the target contours to ensure an adequate tumor coverage in terms of physical dose, but a variable RBE is used elsewhere. Our model was implemented in the open-source treatment planning system matRad and three example cases were planned: a homogeneous phantom, a prostate tumor and a head-and-neck case. MultiRBE was used for plan optimization, and the produced plans were subsequently evaluated in terms of physical dose coverage (V95% ) and variable RBE-weighted dose in organs at risk and normal tissue complication probabilities (NTCP), where prediction models were available. RESULTS: The planning algorithm showed potential for reducing the biological dose in organs surrounding the planning target and thus decreasing the probability for complications in normal tissue (by up to 62% in the prostate case and 37% in the head-and-neck patient). This was achieved without compromising the target coverage or homogeneity in terms of physical dose, as a result of a smarter redistribution of dose among the surrounding tissues with regard to the optimization constraints. CONCLUSIONS: The results prove the ability of the MultiRBE model to reduce biological dose at healthy tissues without compromising the dose coverage of the tumor, with independence of the variable RBE models used.

Improving PET Quantification of Small Animal [68Ga]DOTA-Labeled PET/CT Studies by Using a CT-Based Positron Range Correction.

PURPOSE: Image quality of positron emission tomography (PET) tracers that emits high-energy positrons, such as Ga-68, Rb-82, or I-124, is significantly affected by positron range (PR) effects. PR effects are especially important in small animal PET studies, since they can limit spatial resolution and quantitative accuracy of the images. Since generators accessibility has made Ga-68 tracers wide available, the aim of this study is to show how the quantitative results of [68Ga]DOTA-labeled PET/X-ray computed tomography (CT) imaging of neuroendocrine tumors in mice can be improved using positron range correction (PRC). PROCEDURES: Eighteen scans in 12 mice were evaluated, with three different models of tumors: PC12, AR42J, and meningiomas. In addition, three different [68Ga]DOTA-labeled radiotracers were used to evaluate the PRC with different tracer distributions: [68Ga]DOTANOC, [68Ga]DOTATOC, and [68Ga]DOTATATE. Two PRC methods were evaluated: a tissue-dependent (TD-PRC) and a tissue-dependent spatially-variant correction (TDSV-PRC). Taking a region in the liver as reference, the tissue-to-liver ratio values for tumor tissue (TLRtumor), lung (TLRlung), and necrotic areas within the tumors (TLRnecrotic) and their respective relative variations (ΔTLR) were evaluated. RESULTS: All TLR values in the PRC images were significantly different (p < 0.05) than the ones from non-PRC images. The relative differences of the tumor TLR values, respect to the case with no PRC, were ΔTLRtumor 87 ± 41 % (TD-PRC) and 85 ± 46 % (TDSV-PRC). TLRlung decreased when applying PRC, being this effect more remarkable for the TDSV-PRC method, with relative differences respect to no PRC: ΔTLRlung = - 45 ± 24 (TD-PRC), - 55 ± 18 (TDSV-PRC). TLRnecrotic values also decreased when using PRC, with more noticeable differences for TD-PRC: ΔTLRnecrotic = - 52 ± 6 (TD-PRC), - 48 ± 8 (TDSV-PRC). CONCLUSION: The PRC methods proposed provide a significant quantitative improvement in [68Ga]DOTA-labeled PET/CT imaging of mice with neuroendocrine tumors, hence demonstrating that these techniques could also ameliorate the deleterious effect of the positron range in clinical PET imaging.

Heterogeneity in [18F]fluorodeoxyglucose positron emission tomography/computed tomography of non-small cell lung carcinoma and its relationship to metabolic parameters and pathologic staging.

To investigate the relationships between tumor heterogeneity, assessed by texture analysis of [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET) images, metabolic parameters, and pathologic staging in patients with non-small cell lung carcinoma (NSCLC). A retrospective analysis of 38 patients with histologically confirmed NSCLC who underwent staging FDG-PET/computed tomography was performed. Tumor images were segmented using a standardized uptake value (SUV) cutoff of 2.5. Five textural features, related to the heterogeneity of gray-level distribution, were computed (energy, entropy, contrast, homogeneity, and correlation). Additionally, metabolic parameters such as SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as pathologic staging, histologic subtype, and tumor diameter, were obtained. Finally, a correlation analysis was carried out. Of 38 tumors, 63.2% were epidermoid and 36.8% were adenocarcinomas. The mean ± standard deviation values of MTV and TLG were 30.47 ± 25.17 mL and 197.81 ± 251.11 g, respectively. There was a positive relationship of all metabolic parameters (SUVmax, SUVmean, MTV, and TLG) with entropy, correlation, and homogeneity and a negative relationship with energy and contrast. The T component of the pathologic TNM staging (pT) was similarly correlated with these textural parameters. Textural features associated with tumor heterogeneity were shown to be related to global metabolic parameters and pathologic staging.

Heterogeneity in [¹8F]fluorodeoxyglucose positron emission tomography/computed tomography of non-small cell lung carcinoma and its relationship to metabolic parameters and pathologic staging.

To investigate the relationships between tumor heterogeneity, assessed by texture analysis of [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET) images, metabolic parameters, and pathologic staging in patients with non-small cell lung carcinoma (NSCLC). A retrospective analysis of 38 patients with histologically confirmed NSCLC who underwent staging FDG-PET/computed tomography was performed. Tumor images were segmented using a standardized uptake value (SUV) cutoff of 2.5. Five textural features, related to the heterogeneity of gray-level distribution, were computed (energy, entropy, contrast, homogeneity, and correlation). Additionally, metabolic parameters such as SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as pathologic staging, histologic subtype, and tumor diameter, were obtained. Finally, a correlation analysis was carried out. Of 38 tumors, 63.2% were epidermoid and 36.8% were adenocarcinomas. The mean ± standard deviation values of MTV and TLG were 30.47 ± 25.17 mL and 197.81 ± 251.11 g, respectively. There was a positive relationship of all metabolic parameters (SUVmax, SUVmean, MTV, and TLG) with entropy, correlation, and homogeneity and a negative relationship with energy and contrast. The T component of the pathologic TNM staging (pT) was similarly correlated with these textural parameters. Textural features associated with tumor heterogeneity were shown to be related to global metabolic parameters and pathologic staging.