Physics in medicine and biology最新文献

{"title":"Novel direct alpha spectroscopy technique for²²⁵Ac radiopharmaceutical detection in cancer cells.","authors":"Mahsa Farasat, Behrad Saeedi Saghez, Luke Wharton, Sidney Shapiro, Chris Vinnick, Madison Daignault, Meghan Kostashuk, Nicholas Pranjatno, Myla Weiman, Corina Andreoiu, Hua Yang, Peter Kunz","doi":"10.1088/1361-6560/add987","DOIUrl":"10.1088/1361-6560/add987","url":null,"abstract":"<p><p><i>Objective.</i>Targeted Alpha Therapy (TAT) is a promising approach for treating metastatic cancers, utilizing alpha-emitting radionuclides conjugated to tumor-targeting molecules. Actinium-225 (²²⁵Ac) has emerged as a clinically relevant candidate due to its decay chain, which produces four successive alpha emissions, effectively damaging cancer cells. However, the nuclear recoil effect can lead to off-target redistribution of decay daughters, complicating dosimetry and increasing potential toxicity. This study aims to address these challenges by developing a direct alpha spectroscopy method for<i>in vitro</i>investigations of²²⁵Ac radiopharmaceuticals.<i>Approach.</i>We developed the Bio-Sample Alpha Detector (BAD), a silicon-based detector designed to operate under ambient conditions, enabling direct alpha spectroscopy of cell samples. AR42J rat pancreatic tumor cells, which express somatostatin receptor 2 (SSTR2), were incubated with [²²⁵Ac]Ac-crown-TATE, [²²⁵Ac]Ac-PSMA-617, and [²²⁵Ac]Ac³⁺. The BAD setup allowed radiolabeled cell samples to be positioned within 100<i>µ</i>m of the detector for alpha spectra acquisition with statistical uncertainties of less than 1% in count rates. Geant4 Monte Carlo simulations were employed to validate the experimental results.<i>Main results.</i>Distinct spectral differences between radiolabeled cells and reference samples confirmed the uptake of [²²⁵Ac]Ac-crown-TATE by AR42J cells. Detection of²¹³Po, a decay daughter of²²⁵Ac, indicated partial retention and release of decay products from cells, providing insight into intracellular retention and radionuclide redistribution. Geant4 simulations confirmed the alignment of experimental data with theoretical predictions.<i>Significance.</i>This study introduces a novel method for directly measuring the behavior of²²⁵Ac and its decay daughters in biological samples using alpha spectroscopy. The BAD setup provides a valuable tool for investigating radionuclide retention, redistribution, and microdosimetry in radiopharmaceutical research.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144079582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning positioning algorithms for long semi-monolithic scintillator PET detectors.","authors":"Samuel Mungai Kinyanjui, Zhonghua Kuang, Zheng Liu, Ning Ren, Yongfeng Yang","doi":"10.1088/1361-6560/addbbe","DOIUrl":"10.1088/1361-6560/addbbe","url":null,"abstract":"<p><p><i>Objective.</i>In this work, machine learning positioning algorithms are developed to improve the spatial resolutions of the semi-monolithic scintillator detectors in both monolithic (<i>y</i>) and depth of interaction (<i>z</i>) directions.<i>Approach.</i>Two long semi-monolithic scintillator detectors consisting of 12 lutetium yttrium oxyorthosilicate (LYSO) slabs of 0.96 × 56 × 10 mm³and 14 LYSO slabs of 0.81 × 56 × 10 mm³were manufactured. The scintillator arrays were read out by a 4 × 16 silicon photomultiplier array. 27 × 5 (<i>y, z</i>) positions of each detector were irradiated via a collimated²²Na pencil beam. Extreme gradient boosting (XGBoost) machine learning model was used to predict the interaction positions for<i>y</i>and<i>z</i>. The genetic algorithm (GA) or particle swarm optimization (PSO) algorithm was used to optimize hyperparameters for the XGBoost model. The results of the machine learning positioning algorithms were compared to analytical positioning methods.<i>Main results.</i>The GA and PSO algorithms provided similar results. Compared to the analytical methods, the machine learning positioning methods improved both<i>y</i>and<i>z</i>spatial resolutions especially at both ends of the detectors. The average<i>y</i>spatial resolutions using the machine learning positioning methods were 0.92 ± 0.41 mm and 0.94 ± 0.44 mm as compared to those obtained with the squared center of gravity method of 1.38 ± 0.23 mm and 1.39 ± 0.25 mm for the two detectors, respectively. The average<i>z</i>spatial resolutions obtained with the machine learning positioning methods were 1.67 ± 0.41 mm and 1.68 ± 0.45 mm as compared to those obtained with inverse standard deviation method of 2.09 ± 0.82 mm and 2.14 ± 0.81 mm for the two detectors, respectively.<i>Significance.</i>With the machine learning positioning algorithms, the semi-monolithic scintillator detectors with submillimeter slab thickness evaluated in this work provide less than 1 mm<i>y</i>spatial resolution and less than 2 mm<i>z</i>spatial resolution.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards large nuclear imaging system optical simulations with optiGAN, a generative adversarial network.","authors":"Carlotta Trigila, Guneet Mummaneni, Brahim Mehadji, Brandon Pardi, Emilie Roncali","doi":"10.1088/1361-6560/adde0c","DOIUrl":"https://doi.org/10.1088/1361-6560/adde0c","url":null,"abstract":"<p><p>Optical Monte Carlo (MC) simulations are essential for modeling light transport in radiation detectors used in nuclear imaging and high-energy physics. However, full-system simulations remain computationally prohibitive due to the need to track optical photons across large detector arrays. To address this challenge, we developed optiGAN, a conditional Wasserstein Generative Adversarial Network (GAN) designed to accelerate detailed optical simulations while maintaining high fidelity.&#xD;Our approach trains optiGAN on high-dimensional optical photon distributions generated using GATE 10, the new Python-based version of the well-established MC simulation toolkit. Two datasets were constructed from 511 keV interactions in bismuth germanate crystals: one included multidimensional features (spatial coordinates, kinetic energy, and time), and another focused solely on time distributions. OptiGAN employs a combination of conditional GAN and Wasserstein GAN with gradient penalty (WGAN-GP) to enhance training stability and accuracy. Model performance was evaluated using the Jensen- Shannon distance, achieving similarity scores exceeding 90% for most photon properties, with further improvements when focusing exclusively on timing distributions.&#xD;To validate optiGAN ability to reproduce system-level detector performance, we used its output to generate silicon photomultiplier signals using a validated SiPM simulation toolkit. The resulting energy and timing resolutions closely matched those obtained from full MC simulations, demonstrating that optiGAN preserves key detector characteristics while improving computational efficiency by up to two orders of magnitude.&#xD;These findings establish optiGAN as a promising tool for large-scale detector simulations, enabling rapid evaluation of new detector technologies, also because it has been integrated in the new version of GATE. Future work will focus on further optimizing model performance and extending its applicability to system-level nuclear imaging simulations.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144174362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetry for low energy electrons in the range of 12 to 45 keV with EBT3 GafChromic films.","authors":"Julian Freier, Leon Brückner, Bastian Löhrl, Maya Shariff, Luitpold Distel, Christoph Bert, Peter Hommelhoff","doi":"10.1088/1361-6560/adde28","DOIUrl":"https://doi.org/10.1088/1361-6560/adde28","url":null,"abstract":"<p><p>Objective Low energy electrons (LEE) in the range of tens of keV&#xD;combine high relative biological effectiveness (RBE) with low penetration depth in&#xD;tissue, making them an interesting tool for radiobiological studies. To harness these&#xD;advantages, a reliable and comprehensible dosimetry method is essential.&#xD;Approach Unlaminated EBT3 GafChromic films were evaluated as potential LEE&#xD;dosimeters, given the limitations of other dosimetry tools for LEE applications. The&#xD;depth dose profile of the LEE in the film was simulated and then combined with the&#xD;experimentally determined response of the film to a calibrated radiation source. Using&#xD;this, the total response of the film for a given average dose was calculated.&#xD;Main results A calibration curve for unlaminated EBT3 GafChromic films for LEE in&#xD;the energy range of 12 keV to 45 keV has been successfully developed for a range of&#xD;average doses from 0 Gy to 16 Gy.&#xD;Significance The developed calibration curve enables direct, quantitative comparison&#xD;of biological experiments using LEE with other types of radiation such as x-rays,&#xD;facilitating the adoption of LEE in radiobiological research.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144174357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time integrated modeling of soft tissue deformation and stress based on deep learning.","authors":"Ziyang Hu, Shenghui Liao, Xiaoyan Kui, Renzhong Wu, Feng Yuan, Qiuyang Chen","doi":"10.1088/1361-6560/adde0d","DOIUrl":"https://doi.org/10.1088/1361-6560/adde0d","url":null,"abstract":"<p><strong>Objective: </strong>Accurately and in real-time simulating soft tissue deformation and visualizing stress distribution are crucial for advancing surgical simulators closer to real surgical environments. The concept of using neural networks to accelerate the finite element method has emerged as a powerful approach for real-time physical modeling of soft tissues due to its excellent performance. However, existing models primarily focus on deformation modeling, neglecting the important guiding role of soft tissue stress field modeling in surgical training. Moreover, when modeling multiple physical fields, the vast differences in data distribution between these fields can cause a model to become biased toward features with larger scales if they are simply concatenated and fed into the network for training. This paper aims to address the issue of missing stress rendering in surgical simulators by developing a neural network-based real-time multi-physics modeling framework for soft tissues.&#xD;&#xD;Approach: By compactly encoding the nonlinear relationship between soft tissue boundary conditions and physical fields, the method accelerates the computation of deformation and stress fields. The feature scales of the physical fields are balanced using Z-Score normalization, which mitigates the problem of large-scale features dominating the model training.&#xD;&#xD;Main results: We validated the effectiveness of our method on three-dimensional models of a cantilever beam, liver, spleen, and kidney. Experiments demonstrate that our method achieves an excellent balance between efficiency and accuracy. Compared to traditional methods, it offers a thousand-fold or even ten-thousand-fold improvement in efficiency with only around a 1% loss in accuracy.&#xD;&#xD;Significance: The proposed model effectively predicts the displacement and stress distribution of soft tissue, offering the potential to enhance surgical simulators with the capability to render multiple physical properties.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144174361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Myocardial<i>T</i>₁mapping at 5T using multi-inversion recovery real-time spoiled GRE.","authors":"Linqi Ge, Yihang Zhang, Huibin Zhu, Lang Zhang, Yihang Zhou, Haifeng Wang, Dong Liang, Hairong Zheng, Yanjie Zhu","doi":"10.1088/1361-6560/add986","DOIUrl":"10.1088/1361-6560/add986","url":null,"abstract":"<p><p><i>Objective.</i>To develop an accurate myocardial<i>T</i>₁mapping technique at 5T using Look-Locker-based multiple inversion-recovery with the real-time spoiled gradient echo (GRE) acquisition.<i>Approach.</i>The proposed<i>T</i>₁mapping technique (mIR-rt) samples the recovery of inverted magnetization using the real-time GRE and the images captured during diastole are selected for<i>T</i>₁fitting. Multiple-inversion recoveries are employed to increase the sample size for accurate fitting. The<i>T</i>₁mapping method was validated using Bloch simulation, phantom studies, and in 16 healthy volunteers at 5T.<i>Main Results.</i>In both simulation and phantom studies, the<i>T</i>₁values measured by mIR-rt closely approximate the reference<i>T</i>₁values, with errors less than 3%, while the conventional MOLLI sequence underestimates<i>T</i>₁values. The myocardial<i>T</i>₁values at 5T are 1553 ± 52 ms, 1531 ± 53 ms, and 1526 ± 60 ms (mean ± standard deviation) at the apex, middle, and base, respectively. The<i>T</i>₁values measured by MOLLI (1350 ± 48 ms, 1349 ± 47 ms, and 1354 ± 45 ms at the apex, middle, and base) were significantly lower than those of mIR-rt with<i>p</i>< 0.001 for all three layers. The mIR-rt sequence method used in our study provides high reproducibility, particularly in the middle slices, supporting its practical relevance for myocardial<i>T</i>₁mapping.<i>Significance.</i>The proposed method is feasible for myocardial<i>T</i>₁mapping at 5T and provides better accuracy than the conventional MOLLI sequence.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144079573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetric commissioning of small animal FLASH radiation research platform.","authors":"Mohammad-Ali Tajik Mansoury, Daniel Sforza, John Wong, Iulian Iordachita, Mohammad Rezaee","doi":"10.1088/1361-6560/add641","DOIUrl":"10.1088/1361-6560/add641","url":null,"abstract":"<p><p><i>Objective.</i>The FLASH-SARRP, a new small animal radiation research platform has been designed to support conventional, high and ultrahigh dose-rate kV x-rays for preclinical research. This self-shielded system features two high-capacity x-ray sources with rotating-anode technology. This study characterizes the dosimetric and mechanical performances of the system for preclinical FLASH radiation research.<i>Approach.</i>Mechanical alignment of two x-ray tubes was performed using a custom-designed jig by aligning the outlet ports of the tube housings. Alignment of mechanical and radiation centers was evaluated by scanning a highly-collimated slit across the focal-spot. The linearity of the x-ray tube voltage, current and exposure-time was evaluated using silicon diode and ion-chamber detectors. Dosimetric characteristics of beam e.g. output linearity, depth dose-rate and profiles were measured using calibrated radiochromic films, thermoluminescence, and ion-chamber detectors in kV solid-water phantom or air, with and without external energy filtration. Dose-rate uniformity, flatness, symmetry, beam width, and penumbra were assessed for single and parallel-opposed x-ray beams across various field sizes.<i>Results.</i>The x-ray sources were aligned at 0.3 mm accuracy. The radiation beam center was within 1.0 mm of mechanical center. Beam output was highly linear with wide ranges of tube current (5-630 mA) and exposure-time (5-6300 ms), supporting accurate dose-rate and dose adjustments. The FLASH-SARRP supports a wide range of dose-rates from <1 Gy s^-1to 100 Gy s^-1, depending on field size. The uniformity of the depth and crossbeam dose-rates is ±3.6 Gy s^-1and ±1.5 Gy s^-1between 5-15 mm phantom depth without and with external filter, respectively.<i>Significance.</i>The FLASH-SARRP provides desirable dosimetric performance for small animal irradiation, supporting both conventional and FLASH dose-rate across field sizes from 5 mm-diameter circular to 20 mm-square apertures. This platform enables comparative studies between FLASH and conventional dose-rates in small animal (e.g. mouse) models.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144063552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting and monitoring response to head and neck cancer radiotherapy using multimodality imaging and radiobiological digital twin simulations.","authors":"Eric Aliotta, Jeho Jeong, Ramesh Paudyal, Milan Grkovski, Bill Diplas, James Han, Vaios Hatzoglou, Michalis Aristophanous, Nadeem Riaz, Heiko Schöder, Nancy Y Lee, Amita Shukla-Dave, Joseph O Deasy","doi":"10.1088/1361-6560/add9de","DOIUrl":"10.1088/1361-6560/add9de","url":null,"abstract":"<p><p><i>Objective.</i>To predict radiotherapy treatment response for head and neck cancer (HNC) using multimodality imaging and personalized radiobiological modeling.<i>Approach.</i>A mechanistic radiobiological model was combined with multi-modality imaging data from diffusion weighted-magnetic resonance imaging and positron emission tomography scans with [¹⁸F]Fluorodeoxyglucose (FDG) and [¹⁸F]Fluoromisonidazole (FMISO) tracers to develop personalized treatment response models for human papilloma virus associated HNC patients undergoing chemo-radiotherapy. Models were initialized to incorporate patient-specific imaging and updated to reflect longitudinal measurements of nodal gross tumor volume throughout treatment. Prediction accuracy was assessed based on mean absolute error (MAE) of weekly volume predictions and in predicting locoregional recurrence (LRR) following treatment.<i>Main results.</i>Personalized modeling based on pretreatment imaging significantly improved longitudinal volume prediction accuracy and correlation with measurement compared with a generic population model (MAE = 23.4 ± 10.0% vs 24.9 ± 9.0%,<i>p</i>= 0.002 on paired<i>t</i>-test,<i>R</i>= 0.82 vs 0.72). Adding volume measurements from weeks 1 and 2 further improved prediction accuracy for subsequent weeks (MAE = 12.5 ± 8.1%, 10.7 ± 9.9%). When incorporating feedback with longitudinal measurements, penalizing large deviations from pretreatment model parameters using a variational regularization method was necessary to maintain model stability. Model-predicted volumes based on baseline + week-1 information significantly improved LRR prediction compared with week-1 volume data alone (area under the curve, AUC = 0.83 vs 0.77,<i>p</i>= 0.03) and was similar to prediction using week-3 volume data (AUC = 0.83 vs 0.85,<i>p</i>= non-significant).<i>Significance.</i>The proposed approach, which integrates clinical imaging and radiobiological principles, could be a basis to guide pretreatment prescription personalization as well as on-treatment adaptations.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial dose-distribution-based risk mapping to predict moist desquamation in breast radiotherapy.","authors":"Aria Malhotra, Elisa K Chan, Alan Nichol, Cheryl Duzenli","doi":"10.1088/1361-6560/add985","DOIUrl":"10.1088/1361-6560/add985","url":null,"abstract":"<p><p><i>Objective.</i>A relationship between the regional spatial distribution of skin dose and the development of moist desquamation (MD) was established for patients treated with breast radiotherapy.<i>Approach.</i>A 56-patient dataset was used to develop and validate a dose-distance based metric to predict MD. Dose distributions for the skin were extracted from AcurosXB treatment plans, and patient reported outcomes were used to classify the incidence of MD across the whole breast and then more specifically in the inferior breast. The sensitivity and specificity of the metric was compared against dose-area (A38 Gy ⩽ 50 cm²) and dose-volume (V105% ⩽ 2% of the breast volume) predictive metrics with the same dataset.<i>Main results.</i>With a sensitivity of 70% and a specificity of 72%, the dose-distance metric outperformed the dose-area (45%, 55%) and dose-volume (43%, 56%) predictive metrics. The test performance improves to a sensitivity and specificity of 81% when excluding the full coverage breast support devices that confounded the skin dose identification in the analysis.<i>Significance.</i>This metric offers regional MD prediction and risk mapping to highlight regions at high risk of developing severe skin toxicity and is suitable for implementation within the treatment planning process.This work is based on data acquired for the following clinical trials: ClinicalTrials.gov NCT04543851 and NCT04257396.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144079584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First experimental test of a coded-mask gamma camera for proton therapy monitoring.","authors":"Magdalena Kołodziej, Stephan Brons, Mikołaj Dubiel, George N Farah, Alexander Fenger, Ronja Hetzel, Jonas Kasper, Monika Kercz, Barbara Kołodziej, Linn Mielke, Gabriel Ostrzołek, Magdalena Rafecas, Jorge Roser, Katarzyna Rusiecka, Achim Stahl, Vitalii Urbanevych, Ming-Liang Wong, Aleksandra Wrońska","doi":"10.1088/1361-6560/adc96b","DOIUrl":"10.1088/1361-6560/adc96b","url":null,"abstract":"<p><p><i>Objective.</i>The objective of the presented study was to evaluate the feasibility of a coded-mask (CM) gamma camera for real-time range verification in proton therapy, addressing the need for a precise and efficient method of treatment monitoring.<i>Approach.</i>A CM gamma camera prototype was tested in clinical conditions. The setup incorporated a scintillator-based detection system and a structured tungsten collimator. The experiment consisted of the irradiation of PMMA phantom with proton beams of energies ranging from 70.51 to 108.15 MeV. Experimental data were benchmarked against Monte Carlo simulations. The distal falloff position (DFP) was determined for both experimental data and simulations.<i>Main results.</i>The tested CM camera achieved a statistical precision of DFP determination of 1.7 mm for 10⁸protons, which is consistent with simulation predictions, despite hardware limitations such as non-functional detector pixels. Simulations indicated that a fully operational setup would further improve the performance of the detector. The system demonstrated rate capability sufficient for clinical proton beam intensities and maintained performance without significant dead time.<i>Significance.</i>This study validates the potential of the CM gamma camera for real-time proton therapy monitoring. The technology promises to enhance treatment accuracy and patient safety, offering a competitive alternative to existing approaches such as single-slit and multi-slit systems.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}