Physics in medicine and biology最新文献

{"title":"Pioneering MCF MKM RBE-weighted dose calculation for carbon ion radiotherapy: development of a pencil beam algorithm and validation against Monte Carlo simulation on clinical CT data.","authors":"Jun Tan, Alessio Parisi, Keith M Furutani, Masashi Yagi, Shannon Hartzell, Sridhar Yaddanapudi, Xiaoying Liang, Chunjoo Park, Chris J Beltran, Bo Lu","doi":"10.1088/1361-6560/adf36f","DOIUrl":"10.1088/1361-6560/adf36f","url":null,"abstract":"<p><p><i>Objective.</i>This study aims to develop and validate a pencil beam (PB) algorithm for computing Mayo Clinic Florida microdosimetric kinetic model (MCF MKM)-based relative biological effectiveness (RBE) weighted doses in carbon-ion radiotherapy (CIRT), and to compare its accuracy and efficiency against Monte Carlo (MC) simulations using real patient computed tomography (CT) data.<i>Approach.</i>A PB algorithm was implemented to calculate both physical and microdosimetric parameters-using the abridged microdosimetry distribution methodology (AMDM)-for the MCF MKM model, and subsequently the RBE-weighted dose. Four clinical cases (brain, head and neck, lung and prostate) were planned in-house and computed using the PB algorithm and tool for particle simulation (TOPAS) MC simulations. Dose-volume histograms (DVHs), dose profiles, gamma analysis, and computational times were compared. Monochromatic and polychromatic AMDM kernels were also evaluated to assess any impact on RBE dose distributions.<i>Main results.</i>Except for the lung case, the PB algorithm showed strong agreement with TOPAS MC simulations, with gamma passing rates over 98% at 3%/3 mm and around 90% at 2%/2 mm for the other three cases. DVHs and dose profiles also closely matched. In the lung case, agreement was lower-87.6% at 3%/3 mm and 77.1% at 2%/2 mm-due to PB's limitations in modeling Coulomb scattering in heterogeneous lung tissue. Still, PB calculations were completed in minutes, highlighting its potential for fast, clinically viable RBE dose evaluation.<i>Significance.</i>This study presents the first complete demonstration of an MCF MKM-based RBE dose calculation using a PB algorithm on actual patient CT data, providing a robust balance between accuracy and computational efficiency. Although limitations in PB modeling may introduce larger discrepancies in highly heterogeneous anatomical regions and sites, the overall performance and speed underscore the method's viability for routine clinical CIRT planning.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699225","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":"Last vertex splitting: a new retroactive Monte Carlo splitting technique applied to LINAC out-of-field dose computation.","authors":"Maxime Jacquet, Jean Michel Létang, Thomas Baudier, Philippe Boissard, Meissane M'hamdi, Ibrahima Diallo, Charlotte Robert, David Sarrut","doi":"10.1088/1361-6560/adf1d2","DOIUrl":"10.1088/1361-6560/adf1d2","url":null,"abstract":"<p><p>We propose a new variance reduction technique called last vertex splitting (LVS) designed to reduce computation time in Monte Carlo (MC) simulations for particles traversing high-attenuating media, such as the collimator and other beam-limiting devices in a LINAC head. Combined with a hybrid version of the track length estimator (hTLE), the LVS method accelerates out-of-field (OOF) dose calculations by optimizing photon tracking and interaction modeling. Our analysis indicates that the residual bias introduced by the method remains below one percent, with an estimated efficiency speed-up of ×4.5 for a 10×10 cm²field, to ×10 when combined with hTLE. Typically, this approach enables the rapid generation of extensive MC datasets, facilitating the training of deep learning algorithms to predict OOF doses more efficiently. Beyond radiotherapy applications, the LVS method can be adapted for scenarios requiring computationally efficient simulations of particle transport in complex geometries, such as in radiation shielding assessments.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668128","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":"GPU-accelerated FREDopt package for simultaneous dose and LET_dproton radiotherapy plan optimization via superiorization methods.","authors":"Damian Borys, Jan Gajewski, Tobias Becher, Yair Censor, Renata Kopeć, Marzena Rydygier, Angelo Schiavi, Tomasz Skóra, Anna Spaleniak, Niklas Wahl, Agnieszka Wochnik, Antoni Ruciński","doi":"10.1088/1361-6560/ade841","DOIUrl":"10.1088/1361-6560/ade841","url":null,"abstract":"<p><p>This study presents Fast paRticle thErapy Dose optimizer (FREDopt), a newly developed GPU-accelerated open-source optimization software for simultaneous proton dose and dose-averaged linear energy transfer (LET_d) optimization in intensity-modulated proton therapy treatment planning. FREDopt was implemented entirely in Python, leveraging CuPy for GPU acceleration and incorporating fast Monte Carlo simulations from the FRED code. The treatment plan optimization workflow includes pre-optimization and optimization, the latter equipped with a novel superiorization of feasibility-seeking algorithms. Feasibility-seeking requires finding a point that satisfies prescribed constraints. Superiorization interlaces computational perturbations into iterative feasibility-seeking steps to steer them toward a superior feasible point, replacing the need for costly full-fledged constrained optimization. The method was validated on two treatment plans of patients treated in a clinical proton therapy center, with dose and LET_ddistributions compared before and after reoptimization. Simultaneous dose and LET_doptimization using FREDopt led to a substantial reduction of LET_dand (dose) × (LET_d) in organs at risk while preserving target dose conformity. Computational performance evaluation showed execution times of 14-50 min, depending on the algorithm and target volume size-satisfactory for clinical and research applications while enabling further development of the well-tested, documented open-source software.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144497652","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":"Deep learning-based real-time detection of head and neck tumors during radiation therapy.","authors":"Mark Gardner, Youssef Ben Bouchta, Daniel Truant, Adam Mylonas, Jonathan Sykes, Purnima Sundaresan, Paul J Keall","doi":"10.1088/1361-6560/adf40e","DOIUrl":"10.1088/1361-6560/adf40e","url":null,"abstract":"<p><p><i>Objective.</i>Clinical drivers for real-time head and neck (H&N) tumor tracking during radiation therapy (RT) are accounting for motion caused by changes to the immobilization mask fit, and to reduce mask-related patient distress by replacing the masks with patient motion management methods. The purpose of this paper is to investigate a deep learning-based method to segment H&N tumors in patient kilovoltage (kV) x-ray images to enable real-time H&N tumor tracking during RT.<i>Approach.</i>An ethics-approved clinical study collected data from 17 H&N cancer patients undergoing conventional H&N RT. For each patient, personalized conditional generative adversarial networks (cGANs) were trained to segment H&N tumors in kV x-ray images. Network training data were derived from each patient's planning CT and contoured gross tumor volumes (GTV). For each training epoch, the planning CT and GTV were deformed and forward projected to create the training dataset. The testing data consisted of kV x-ray images used to reconstruct the pre-treatment CBCT volume for the first, middle and end fractions. The ground truth tumor locations were derived by deformably registering the planning CT to the pre-treatment CBCT and then deforming the GTV and forward projecting the deformed GTV. The generated cGAN segmentations were compared to ground truth tumor segmentations using the absolute magnitude of the centroid error and the mean surface distance (MSD) metrics.<i>Main results.</i>The centroid error for the nasopharynx (<i>n</i>= 4), oropharynx (<i>n</i>= 9) and larynx (<i>n</i>= 4) patients was 1.5 ± 0.9 mm, 2.4 ± 1.6 mm, 3.5 ± 2.2 mm respectively and the MSD was 1.5 ± 0.3 mm, 1.9 ± 0.9 mm and 2.3 ± 1.0 mm respectively. There was a weak correlation between the centroid error and the LR tumor location (<i>r</i>= 0.41), which was higher for oropharynx patients (<i>r</i>= 0.77).<i>Significance.</i>The paper reports on markerless H&N tumor detection accuracy using kV images. Accurate tracking of H&N tumors can enable more precise RT leading to mask-free RT enabling better patient outcomes.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144708469","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":"RadiSeq: a single- and bulk-cell whole-genome DNA sequencing simulator for radiation-damaged cell models.","authors":"Felix Mathew, Luc Galarneau, John Kildea","doi":"10.1088/1361-6560/adf689","DOIUrl":"https://doi.org/10.1088/1361-6560/adf689","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;To build and validate a simulation framework to perform single-cell and bulk-cell whole genome sequencing simulation of radiation-exposed Monte Carlo cell models to assist radiation genomics studies. &#xD;Approach:&#xD;Sequencing the genomes of radiation-damaged cells can provide useful insight into radiation action for radiobiology research. However, carrying out post-irradiation sequencing experiments can often be challenging, expensive, and time-consuming. Although computational simulations have the potential to provide solutions to these experimental challenges, and aid in designing optimal experiments, the absence of tools currently limits such application. Monte Carlo toolkits exist to simulate radiation exposures of cell models but there are no tools to simulate single- and bulk-cell sequencing of cell models containing radiation-damaged DNA. Therefore, we aimed to develop a Monte Carlo simulation framework to address this gap by designing a tool capable of simulating sequencing processes for radiation-damaged cells.&#xD;Main Results:&#xD;We developed RadiSeq - a multi-threaded whole-genome DNA sequencing simulator written in C++. RadiSeq can be used to simulate Illumina sequencing of radiation-damaged cell models produced by Monte Carlo simulations. RadiSeq has been validated through comparative analysis, where simulated data were matched against experimentally obtained data, demonstrating reasonable agreement between the two. Additionally, it comes with numerous features designed to closely resemble actual whole-genome sequencing. RadiSeq is also highly customizable with a single input parameter file. &#xD;Significance:&#xD;RadiSeq enables the research community to perform complex simulations of radiation-exposed DNA sequencing, supporting the optimization, planning, and validation of costly and time-intensive radiation biology experiments. This framework provides a powerful tool for advancing radiation genomics research.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144761008","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":"The potential dual role of platinum nanoparticles in dosimetry and cancer treatment.","authors":"Iara Souza Lima, Eder J Guidelli, Juçara Cominal, Maryanne Trafani de Melo, Pietro Ciancaglini, Ana Paula Ramos, Oswaldo Baffa","doi":"10.1088/1361-6560/adf688","DOIUrl":"https://doi.org/10.1088/1361-6560/adf688","url":null,"abstract":"<p><p>To investigate the dual role of platinum nanoparticles (PtNPs) in enhancing radiation dosimetry using alanine-based nanocomposites and in increasing the radiosensitivity of melanoma cancer cells. PtNPs were synthesized and incorporated into L-alanine to form nanocomposites with varying mass percentages (0.5%, 1.0%, and 3.0%). These materials were characterized and tested for their dosimetric response using Electron Spin Resonance (ESR) spectroscopy. Additionally, the cytotoxic and radiosensitizing effects of PtNPs were evaluated in B16F10 melanoma cells exposed to different concentrations of PtNPs and radiation doses. An increase in Pt precursor concentration led to larger nanoparticles without destabilizing the system. PtNPs altered the crystallinity of alanine and enhanced ESR signal intensity. Dose enhancement factors (DEF) of 1.1 ± 0.3, 2.2 ± 0.8, and 2.5 ± 0.9 were observed for 0.5%, 1.0%, and 3.0% PtNPs, respectively. In vitro assays showed a reduction in cell viability at low PtNP concentrations, with maximum radiosensitizing effect observed at 60 µg•mL⁻¹ and 10 Gy radiation after 24 hours.These findings demonstrate that PtNPs can significantly improve both the sensitivity of ESR-based dosimeters and the efficacy of radiotherapy through radiosensitization. The combined dosimetric and therapeutic potential of PtNPs paves the way for integrated applications in radiation oncology and personalized treatment strategies.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144761009","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":"Target density override in pencil beam scanning proton therapy for different motion target delineation: an experimental dosimetry analysis.","authors":"Xianrui Yan, Yungang Wang, Jiabing Gu, Chengqiang Li, Cheng Tao, Antoine Simon, Huazhong Shu, Jian Zhu","doi":"10.1088/1361-6560/adf370","DOIUrl":"10.1088/1361-6560/adf370","url":null,"abstract":"<p><p><i>Objective</i>. This study aims to investigate the impact of target density override (TDO) on delivered dose in pencil beam scanning proton therapy (PBSPT) by experimental measurement.<i>Approach.</i>TDO was developed using the density characteristics of all target voxels, employing a sliding window approach. The density-override value was specified as the statistical mean through traversal calculation. Three motion targets were delineated from 3D plain-scan CT (3DpCT), average intensity projection (AIP), and maximum intensity projection (MIP). The prescription proton dose was 200 cGy (RBE). Dose distribution information at the central coronal plane for all plans was quantified by digitizing the film scan.<i>Main results</i>. The target density of AIP and MIP were -315.59 HU (95% CI: -317.35 HU--313.83 HU), and 76.26 HU (95% CI: 75.59 HU-76.93 HU). The Unconformity Index (UCI_underdose) of the MIP were 0. The MIP and 3DpCT UCI_overdosefailed the Mann-Whitney<i>U</i>test (<i>Z</i>= -3.674, -3.606;<i>p</i>= 0). The AIP and MIP prescription dose area for the initial target were 11.42 cm²(95% CI: 9.71 cm²-13.13 cm²) and 20.90 cm²(95% CI: 20.24 cm²-21.55 cm²), while increased to 20.07 cm²(95% CI: 19.36 cm²-20.77 cm²) and decreased to 18.62 cm²(95% CI: 18.27 cm²-18.98 cm²) for TDO target. The initial and TDO target D₉₈of AIP, 3DpCT were 185.40 cGy (95% CI: 180.19-190.60 cGy) and 204.54 cGy (95% CI: 194.76-214.33 cGy), 142.46 cGy (95% CI: 137.37-147.55 cGy) and 145.28 cGy (95% CI: 140.44-150.11 cGy).<i>Significance</i>. TDO improves dose coverage within the tumor and reduces the out-field dose. This method provides a promising strategy to optimize dose delivery in PBSPT, especially for tumors affected by respiratory motion.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699226","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":"Preliminary assessment of proton linear energy transfer distribution in patients with MRI-guided proton therapy: a simulation study.","authors":"Ye Chen, Masaki Konno, Naoki Saito, Seishin Takao, Naoki Miyamoto, Kohei Yokokawa, Takayuki Hashimoto, Hidefumi Aoyama, Taeko Matsuura","doi":"10.1088/1361-6560/adf60a","DOIUrl":"https://doi.org/10.1088/1361-6560/adf60a","url":null,"abstract":"<p><p><i>Objective</i>. MRI-guided proton therapy is under development as an advanced technique that combines proton therapy with real-time MRI imaging, offering improved tumor targeting and better protection of adjacent healthy tissues. However, clinically relevant interactions between magnetic fields and linear energy transfer (LET) remain unexplored. This study investigated the LET distributions of primary and secondary protons with an emphasis on the influence of magnetic fields on both tumors and organs at risk.<i>Approach</i>. Monte Carlo simulations were performed using the Geant4 software (version 10.1.p01) to calculate the dose-averaged LET (<i>LET_d</i>) at different magnetic field strengths. Treatment plans were designed for three patients with liver, head, and prostate cancers for this study. A homogeneous magnetic field perpendicular to the proton beam direction was assumed throughout.<i>Main results</i>. In conventional proton therapy (without a magnetic field), high<i>LET_d</i>values are concentrated at the distal fall-off region of proton beam. When a magnetic field is applied, these high<i>LET_d</i>regions are rotated along the beam deflection direction. In the prostate case with two opposing beams, the magnetic field preserved these high<i>LET_d</i>regions by reducing the averaging effect, thereby limiting their dilution. This preservation effect became more pronounced with increasing magnetic field strength.<i>Significance</i>. In MRI-guided proton therapy, strategies to address<i>LET_d</i>distribution changes caused by the magnetic field are considered desirable, particularly in high magnetic field environments.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144753991","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":"Efficient motion-corrected image reconstruction for 3D cardiac MRI through stochastic optimisation.","authors":"Letizia Protopapa, Margaret A G Duff, Johannes Mayer, Jeanette Schulz-Menger, Kris Thielemans, Christoph Kolbitsch, Edoardo Pasca","doi":"10.1088/1361-6560/adf609","DOIUrl":"https://doi.org/10.1088/1361-6560/adf609","url":null,"abstract":"<p><p>Objective&#xD;&#xD;Motion-corrected image reconstruction (MCIR) allows for fast and efficient cardiac&#xD;magnetic resonance imaging (MRI) acquisition with predictable scan times. Since&#xD;data obtained in all phases of respiratory and cardiac motion can be exploited, the&#xD;duration of the scan is not affected by changes in heart rate or irregular breathing&#xD;patterns.&#xD;Achieving high-quality reconstructions from MCIR data typically requires iterative&#xD;optimisation algorithms with regularisation. Reconstruction time increases with the&#xD;number of motion states. This is particularly relevant in cardiac MRI, where both&#xD;cardiac and respiratory motion corrections are necessary to minimise motion artefacts.&#xD;&#xD;Approach&#xD;&#xD;In this work, we present a stochastic optimisation approach for cardio-respiratory&#xD;MCIR using the Stochastic Primal Dual Hybrid Gradient (SPDHG) algorithm. We&#xD;compare the convergence rates with deterministic optimisation methods.&#xD;&#xD;Main Results&#xD;In phantom experiments with simulated motion, we demonstrate the improved&#xD;convergence rates of SPDHG with respect to deterministic algorithms, while&#xD;maintaining image quality. Convergence is improved both in terms of reconstruction&#xD;times and computational effort. We validate the method's effectiveness on an in vivo&#xD;3D whole-heart cardiac MR scan. The in vivo method demonstrates that the motion&#xD;compensation method we use allows for non-rigid deformation patterns and irregular&#xD;breathing patterns.&#xD;&#xD;&#xD;Significance&#xD;&#xD;This study demonstrates that stochastic algorithms can converge significantly faster&#xD;than deterministic algorithms for MCIR, especially for a large number of motion&#xD;states. With the proposed approach, increasing the number of motion states reduces&#xD;the number of epochs required to reconstruct the image and therefore it is no longer&#xD;necessary to balance the competing requirements of accurate motion correction and&#xD;computational effort.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144753990","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":"Improving Cherenkov-based radiotherapy dose estimates in diverse patient populations via skin luminance imaging.","authors":"S M Decker, J M Andreozzi, D Hernandez, D A Alexander, V Wickramasinghe, R L Hachadorian, I M Oraiqat, E Chen, I R Washington, J Gui, R Zhang, L A Jarvis, P Bruza, D J Gladstone, B W Pogue","doi":"10.1088/1361-6560/aded68","DOIUrl":"10.1088/1361-6560/aded68","url":null,"abstract":"<p><p><i>Objective</i>. Cherenkov imaging is an emerging technology that detects light naturally emitted from patient tissue during radiation treatment. The initial intensity of Cherenkov light is proportional to radiation dose, but its absorption is highly dependent on patient skin pigmentation, with increasing melanin attenuating more Cherenkov photons per dose. This effect must be calibrated per patient before Cherenkov emission can serve as an accurate surrogate for dose. In this study, we present the first attempt at<i>in vivo</i>Cherenkov imaging of a diverse patient cohort and calibration for the effect of skin pigmentation towards quantitative Cherenkov dosimetry.<i>Approach</i>. A multi-institutional collaboration was designed to increase the diversity of our patient imaging cohort. Cherenkov imaging was completed with a time-gated, iCMOS camera, and color background images were taken with an RGB camera module under standardized lighting. Under an institutional review board-approved retrospective protocol, skin pigmentation was assessed per patient by calculating the relative luminance of their treated area from the color images. Additionally, 2D dose maps were generated by projecting the exponentially-weighted dose from the surface to 5 mm into the body, representative of Cherenkov emission.<i>Main results.</i>Of<i>N</i>_6MV= 23 and<i>N</i>_15MV= 20 breast patients imaged, encompassing a variety of skin pigmentations, Cherenkov intensity varied nearly 5X for the same dose delivered across the examined cohort. Plotting Cherenkov intensity per unit dose revealed a direct correlation with relative luminance, providing a linear calibration factor based on skin pigmentation. Including this calibration factor significantly improved Cherenkov-to-dose linearity, from<i>R</i>²= 0.79-0.96 for 6 MV and<i>R</i>²= 0.19-0.91 for 15 MV (<i>p</i>< 0.05).<i>Significance</i>. This study marks the first assessment of Cherenkov imaging in a diverse, representative patient population. It addresses an integral factor towards achieving quantitative<i>in vivo</i>Cherenkov dosimetry and demonstrates significant mitigation of the effect of skin pigmentation, while preserving the non-contact, real-time benefits of Cherenkov imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591974","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}