Medical physics最新文献

{"title":"Tissue classification from raw diffusion-weighted images using machine learning.","authors":"Guangyu Dan, Cui Feng, Zheng Zhong, Kaibao Sun, Ping-Shou Zhong, Daoyu Hu, Zhen Li, Xiaohong Joe Zhou","doi":"10.1002/mp.17810","DOIUrl":"https://doi.org/10.1002/mp.17810","url":null,"abstract":"<p><strong>Background: </strong>In diffusion-weighted imaging (DWI), a large collection of diffusion models is available to provide insights into tissue characteristics. However, these models are limited by predefined assumptions and computational challenges, potentially hindering the full extraction of information from the diffusion MR signal.</p><p><strong>Purpose: </strong>This study aimed at developing a MOdel-free Diffusion-wEighted MRI (MODEM) method for tissue differentiation by using a machine learning (ML) algorithm based on raw diffusion images without relying on any specific diffusion model. MODEM has been applied to both simulation data and cervical cancer diffusion images and compared with several diffusion models.</p><p><strong>Methods: </strong>With Institutional Review Board approval, 54 cervical cancer patients (median age, 52 years; age range, 29-73 years) participated in the study, including 26 in the early FIGO (International Federation of Gynecology and Obstetrics) stage (IB, 16; IIA, 10) and 28 the late stage (IIB, 8; IIIB, 14; IIIC, 1; IVA, 3; IVB, 2). The participants underwent DWI with 17 b-values (0 to 4500 s/mm²) at 3 Tesla. Synthetic diffusion MRI signals were also generated using Monte-Carlo simulation with Gaussian noise doping under varying substrates. MODEM with multilayer perceptron and five diffusion models (mono-exponential, intra-voxel incoherent-motion, diffusion kurtosis imaging, fractional order calculus, and continuous-time-random-walk models) were employed to distinguish different substrates in the simulation data and differentiate different pathological states (i.e., normal vs. cancerous tissue; and early-stage vs. late-stage cancers) in the cervical cancer dataset. Accuracy and area under the receiver operating characteristic (ROC) curve were evaluated. Mann-Whitney U-test was used to compare the area under the curve (AUC) and accuracy values between MODEM and the five diffusion models.</p><p><strong>Results: </strong>For the simulation dataset, MODEM produced a higher AUC and better accuracy, particularly in scenarios where the noise level exceeded 5%. For the cervical cancer dataset, MODEM yielded the highest AUC and accuracy in cervical cancer detection (AUC, 0.976; accuracy, 91.9%) and cervical cancer staging (AUC, 0.773; accuracy, 69.2%), significantly outperforming any of the diffusion models (p < 0.05).</p><p><strong>Conclusions: </strong>MODEM is useful for cervical cancer detection and staging and offers considerable advantages over analytical diffusion models for tissue characterization.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based estimation of respiration-induced deformation from surface motion: A proof-of-concept study on 4D thoracic image synthesis.","authors":"Jie Zhang, Xue Bai, Guoping Shan","doi":"10.1002/mp.17804","DOIUrl":"https://doi.org/10.1002/mp.17804","url":null,"abstract":"<p><strong>Background: </strong>Four-dimension computed tomography (4D-CT) provides important respiration-related information for thoracic radiotherapy. Its quality is challenged by various respiratory patterns. Its acquisition gives rise to the risk of higher radiation exposure. Based on a continuously estimated deformation, a 4D synthesis by warping a high-quality volumetric image is a possible solution.</p><p><strong>Purpose: </strong>To propose a non-patient-specific cascaded ensemble model (CEM) to estimate respiration-induced thoracic tissue deformation from surface motion.</p><p><strong>Methods: </strong>The CEM is cascaded by three deep learning-based models. By inputting the surface motion, CEM outputs a deformation vector field (DVF) inside thorax. In our work, the surface motion was simulated using the body contours derived from 4D-CT. The CEM was trained on our private database including 62 4D-CT sets, and was tested on a public database encompassing 80 4D-CT sets. To evaluate CEM, we employed the model output DVF to generate a few series of synthesized CTs, and compared them with the ground truth. CEM was also compared with other published works.</p><p><strong>Results: </strong>CEM synthesized CT with an mRMSE (average root mean square error) of 61.06 ± 10.43HU (average ± standard deviation), an mSSIM (average structural similarity index measure) of 0.990 ± 0.004, and an mMAE (average mean absolute error) of 26.80 ± 5.65HU. Compared with other works, CEM showed the best result.</p><p><strong>Conclusions: </strong>The results demonstrated the effectiveness of CEM on estimating tissue DVF inside thorax. CEM requires no patient-specific breathing data sampling and no additional training before treatment. It shows potential for broad applications.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Out-of-field neutron radiation from clinical proton, helium, carbon, and oxygen ion beams.","authors":"Matteo Bolzonella, Marco Caresana, Andrea Cirillo, Josep M Martí-Climent, Evangelina Martínez-Francés, Christina Mooshammer, Stefan Schmidt, Stephan Brons, Marco Silari, Christina Stengl, Liliana Stolarczyk, José Vedelago","doi":"10.1002/mp.17797","DOIUrl":"https://doi.org/10.1002/mp.17797","url":null,"abstract":"<p><strong>Background: </strong>In hadron therapy, out-of-field doses, which may in the long-term cause secondary cancers, are mostly due to neutrons. Very recently, ⁴He and ¹⁶O beams have been added to protons and ¹²C ions for clinical therapy.</p><p><strong>Purpose: </strong>The focus of this article is to compare secondary neutron doses produced by clinical protons, ⁴He, ¹²C, and ¹⁶O ion beams.</p><p><strong>Methods: </strong>Ambient dose equivalent, H*(10), measurements were performed, with five types of rem counters, of the neutron field produced by the four primary ions impinging on a water phantom. This experiment was performed at the Heidelberg Ion Beam Therapy Center (HIT) in the framework of the activities of the European Radiation Dosimetry Group (EURADOS). The experimental data are normalized to both unit primary particle and target dose, and are further compared to Monte Carlo (MC) simulations performed with the FLUKA and MCNP codes.</p><p><strong>Results: </strong>The intensity of the neutron field increases with ion mass, and the trend is more significant in the forward direction. The minimum H*(10) for all ions, 5µSv/Gy to 10µSv/Gy, was measured in the transverse and backward directions, whereas the maximum measured value was about 1.3 mSv/Gy for primary ¹⁶O ions in the forward direction. Additional MC simulations are presented for a more detailed analysis of the rem counters' response in the presence of heavy charged fragments. In the downstream direction, for ¹²C and ¹⁶O ions, approximately only 30% of the instruments' counts are due to neutrons.</p><p><strong>Conclusion: </strong>The four extended-range instruments provide reliable and consistent results, whereas the conventional rem counter underestimates H*(10) in a neutron field with a large high-energy component. FLUKA and MCNP provide consistent predictions, within a factor of 1.6 for the downstream position and lower differences in the other cases, and are in agreement with the experimental data. It was found that under certain conditions neutrons do not represent the only secondary radiation field to be monitored; the presence of charged particles affects the performance of moderator-type neutron detectors.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An isodose-constrained automatic treatment planning strategy using a multicriteria predicted dose rating.","authors":"Zihan Sun, Jiazhou Wang, Weigang Hu, Yongheng Yan, Yuanhua Chen, Guorong Yao, Zhongjie Lu, Senxiang Yan","doi":"10.1002/mp.17795","DOIUrl":"https://doi.org/10.1002/mp.17795","url":null,"abstract":"<p><strong>Background: </strong>Previous knowledge-based planning studies have demonstrated the feasibility of predicting three-dimensional photon dose distributions and subsequently generating treatment plans. The steepness of dose fall-off represents a critical metric for clinical plan evaluation; however, dose fall-off similarity is frequently overlooked in dose prediction tasks. Our study introduces a novel automatic treatment planning methodology that specifically focuses on dose fall-off reconstruction for nasopharyngeal carcinoma (NPC).</p><p><strong>Purpose: </strong>Our study aims to establish an innovative methodology for automatic treatment plan generation that leverages dose fall-off information derived from deep learning-predicted dose distributions. Additionally, we propose and validate a comprehensive multicriteria rating strategy for dose prediction.</p><p><strong>Methods: </strong>We incorporated 120 nasopharyngeal cancer cases in this study, distributing them into training (n = 90), validation (n = 10), and testing (n = 20) cohorts. Three distinct dose prediction models were trained: U-Net, DoseNet, and Transformer. To determine the optimal dose prediction model, we developed a comprehensive multicriteria rating strategy that integrates mean absolute error, dose-volume histogram analysis, and isodose dice similarity coefficients. Based on these predictions, we implemented two automatic planning approaches: (1) IsoPlans, which extracts isodose lines from the predicted dose distribution to generate radiotherapy contours as optimization objectives and (2) DVH-IsoPlans, which enhances the first strategy by incorporating additional dose-volume constraints to further optimize treatment planning parameters.</p><p><strong>Results: </strong>The multicriteria scores for the three dose prediction models (U-Net, DoseNet, and Transformer) were 0.85, 0.84, and 0.82, respectively. The dose prediction model achieved a minimum mean absolute error of 2.71 Gy. In our clinical validation, 4 of the 20 generated IsoPlans failed to meet clinical requirements, whereas all 20 DVH-IsoPlans successfully satisfied clinical requirements. The mean plan optimization time for the 20 test cases was significantly reduced from 870 to 560 s for IsoPlans and to 470 s for DVH-IsoPlans, representing a substantial reduction of 37.5% and 50.5%, respectively.</p><p><strong>Conclusions: </strong>In this study, a multicriteria rating strategy is proposed which combines pixel-wise numerical evaluation, clinical parameter evaluation and physical dose fall-off evaluation in order to rate the dose prediction models. Moreover, an automated planning workflow has been developed, enabling the rapid generation of treatment plans based on the isodose structures of the predicted dose. A self-consistent dose prediction to automatic planning scheme based on isodose lines is proposed, which significantly reduces the time required for plan optimization.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FLASH radiotherapy: Paradigm shift or a passing fad?","authors":"Joao Seco, Joseph O Deasy, Indra J Das","doi":"10.1002/mp.17807","DOIUrl":"https://doi.org/10.1002/mp.17807","url":null,"abstract":"","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent meningioma grading based on medical features.","authors":"Hua Bai, Jieyu Liu, Chen Wu, Zhuo Zhang, Qiang Gao, Yong Yang","doi":"10.1002/mp.17808","DOIUrl":"https://doi.org/10.1002/mp.17808","url":null,"abstract":"<p><strong>Background: </strong>Meningiomas are the most common primary intracranial tumors in adults. Low-grade meningiomas have a low recurrence rate, whereas high-grade meningiomas are highly aggressive and recurrent. Therefore, the pathological grading information is crucial for treatment, as well as follow-up and prognostic guidance. Most previous studies have used radiomics or deep learning methods to extract feature information for grading meningiomas. However, some radiomics features are pixel-level features that can be influenced by factors such as image resolution and sharpness. Additionally, deep learning models that perform grading directly from MRI images often rely on image features that are ambiguous and uncontrollable, which reduces the reliability of the results to a certain extent.</p><p><strong>Purpose: </strong>We aim to validate that combining medical features with deep neural networks can effectively improve the accuracy and reliability of meningioma grading.</p><p><strong>Methods: </strong>We construct a SNN-Tran model for grading meningiomas by analyzing medical features including tumor volume, peritumoral edema volume, dural tail sign, tumor location, the ratio of peritumoral edema volume to tumor volume, age and gender. This method is able to better capture the complex relationships and interactions in the medical features and enhance the reliability of the prediction results.</p><p><strong>Results: </strong>Our model achieve an accuracy of 0.875, sensitivity of 0.886, specificity of 0.847, and AUC of 0.872. And the method is superior to the deep learning, radiomics and SOTA methods.</p><p><strong>Conclusion: </strong>We demonstrate that combining medical features with SNN-Tran can effectively improve the accuracy and reliability of meningioma grading. The SNN-Tran model excel in capturing long-range dependencies in the medical feature sequence.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Point-cloud segmentation with in-silico data augmentation for prostate cancer treatment.","authors":"Jianxin Zhou, Massimiliano Salvatori, Kadishe Fejza, Gregory M Hermann, Angela Di Fulvio","doi":"10.1002/mp.17815","DOIUrl":"https://doi.org/10.1002/mp.17815","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;In external x-ray radiation therapy, the administered dose distribution can deviate from the planned dose due to alterations in patient positioning, changes in intra-fraction anatomy, and the limited precision of the beam delivery system in spatial terms. Adaptive radiation therapy (ART) can potentially improve dose delivery accuracy by re-optimizing the treatment plan before each fraction, maximizing the dose to the target volume while minimizing exposure to surrounding radiosensitive organs. However, to effectively implement ART, the stages of the radiation therapy pipeline, including image acquisition, segmentation, physician directive generation, and treatment plan generation, must be optimized for maximum speed and accuracy to ensure feasibility prior to each treatment fraction. In this work, we focus on image segmentation. By reducing the segmentation computation time, one can reproduce the planning process for each session, enabling routine customization for individual patients, achieving safe dose escalation, better cancer control, and reduced risk of severe radiotoxicity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;The aim of this study is to develop a fast point-cloud-based segmentation model with novel in-silico-aided data augmentation and demonstrate it on pelvic computed tomography (CT) patient data used in prostate cancer (PCa) treatment. This model can be implemented during ART because it requires only a few seconds to perform organ segmentation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this study, a dataset of pelvic CT images was obtained from Order of St. Francis (OSF) Healthcare Hospital (Peoria, IL, USA), comprising 38 images in total. These were divided into 25 for training, seven for validation, and six for testing the developed model. A novel point-cloud-based model was used to reduce the prostate segmentation time, cross-validation was implemented to ensure the robustness of the model. The developed point-cloud-based network is a novel deep-learning (DL) model that adds a loss function that combines region-based with a new boundary loss function. The region-based loss enables the identification of large volumes while the boundary loss, whose relative weight increases with the epochs, increases the network training ability of uneven surfaces, like the interface between the prostate bladder and rectum, which are challenging to resolve. We introduced a new data-augmentation approach to expand the training set. This fully automated method generates synthetic 3-D CT images by creating relevant organs in the extended cardiac-torso (XCAT) computational phantom. The Dice similarity coefficient was used as an assessment metric and compared to state-of-the-art segmentation models. The doses to the prostate and organs at risk (i.e., bladder and rectum) were also calculated for both our automated segmentation and manual expert segmentation to evaluate the practical feasibility of the point-cloud-based approach.&lt;/p&gt;&lt;p&gt;&lt;st","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electron beam reference dosimetry measurements obtained at multiple institutions using the Addendum to AAPM's TG-51 protocol.","authors":"Bryan R Muir, Thomas H Davis, Sandeep Dhanesar, Yair Hillman, Viktor Iakovenko, Yu Lei, Tina Pike, Daniel W Pinkham, Eric Vandervoort, Grace Gwe-Ya Kim","doi":"10.1002/mp.17802","DOIUrl":"https://doi.org/10.1002/mp.17802","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The TG-51 protocol describes methods for obtaining reference dosimetry measurements for external photon and electron beams. Since the publication of TG-51 in 1999, research on reference dosimetry has allowed revisiting the procedures and data recommended in the protocol. An Addendum to TG-51 for electron beam reference dosimetry was published in 2024, which revises the formalism and procedures and provides updated &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;k&lt;/mi&gt; &lt;mi&gt;Q&lt;/mi&gt;&lt;/msub&gt; &lt;annotation&gt;$k_{Q}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To compare clinical reference dosimetry measurements in electron beams obtained using the original American Association of Physicists in Medicine's (AAPM) TG-51 protocol and its associated Addendum (AAPM WGTG51 report 385).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Measurements were performed in electron beams using the data and methods prescribed by TG-51 and its Addendum. Nine participants (eight clinics and one primary standards laboratory) provided data and measurements. Results were obtained with 18 linacs using 87 total beam energies (4-6 energies per linac) between 4-22 MeV, representing the range of electron beam energies used clinically. Various cylindrical (6 types) and parallel-plate (4 types) ionization chamber types were employed, representing most of the chambers commonly used in modern radiation therapy clinics. An analysis was performed to determine if differences arise from the new data recommended for beam quality conversion factors or from changes to the procedure.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Results for dose to water per monitor unit obtained using the Addendum are up to 2.3% higher in low-energy beams and 1.3% higher in high-energy beams compared to results obtained using the original TG-51 protocol. These differences are consistent with what was predicted by the Addendum. Differences arise from both the changes to procedure (up to 0.7% from not requiring the &lt;math&gt; &lt;semantics&gt;&lt;msubsup&gt;&lt;mi&gt;P&lt;/mi&gt; &lt;mi&gt;gr&lt;/mi&gt; &lt;mi&gt;Q&lt;/mi&gt;&lt;/msubsup&gt; &lt;annotation&gt;$P^Q_{rm gr}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; correction for cylindrical chambers, 0.5% from the change in the shift of the point of measurement for parallel-plate chambers) as well as the recommended data (0.8% from differences in &lt;math&gt; &lt;semantics&gt;&lt;msubsup&gt;&lt;mi&gt;k&lt;/mi&gt; &lt;msub&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;50&lt;/mn&gt;&lt;/msub&gt; &lt;mo&gt;'&lt;/mo&gt;&lt;/msubsup&gt; &lt;annotation&gt;$k^prime _{R_{50}}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , 0.5% from differences in &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;k&lt;/mi&gt; &lt;mi&gt;ecal&lt;/mi&gt;&lt;/msub&gt; &lt;annotation&gt;$k_{rm ecal}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This work elucidates where differences arise in results obtained using the original TG-51 protocol and its associated Addendum for electron beam reference dosimetry. The results presented here provide confidence in the new approach and data recommended by the Addendum. Clinical physicists can use these results to ensure that differences are as expecte","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Megavoltage intrafraction monitoring and position uncertainty in gimbaled markerless dynamic tumor tracking treatment of lung tumors.","authors":"Marco Serpa, Tobias Brandt, Simon K B Spohn, Andreas Rimner, Christoph Bert","doi":"10.1002/mp.17740","DOIUrl":"https://doi.org/10.1002/mp.17740","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The clinical realization of markerless dynamic tumor tracking (MLDTT) has prompted a new paradigm shift to intrafraction imaging-based quality assurance (QA). During MLDTT treatment using a gimbaled accelerator, the megavoltage (MV) imager serves as an independent system to leverage real-time intrafraction monitoring. Soft-tissue feature tracking has shown promise for tumor localization in confined MV projections, but studies demonstrating its application in clinical MLDTT treatments are scarse.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To validate MV image-based dense soft-tissue feature tracking for intrafraction position monitoring of lung tumors during MLDTT stereotactic body radiotherapy (SBRT), and report on the resolved geometric uncertainty.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Ten non-small cell lung cancer (NSCLC) patients underwent MLDTT-SBRT using a commercial gimbal-based system. During treatment, beam's-eye-view (BEV) projection images were captured at ∼3 frames s&lt;sup&gt;-1&lt;/sup&gt; (fps) using the electronic portal imaging device (EPID). MV sequences were streamed to a research workstation and processed off-line using a purpose-built algorithm, the soft-tissue feature tracker (SoFT). Both the tumor and dynamic field aperture position were automatically extracted in the pan and tilt directions of the gimbaled x-ray head, corresponding to the in-plane lateral and longitudinal direction of the imager, and compared to ground truth manual tracking. The success, percentage of fields producing an output, and performance of MV tracking under the presence/absence of anatomy-related obstruction and multi-leaf collimator (MLC) occlusion were quantified, including three-dimensional conformal (3DCRT) and step-and-shoot intensity modulated radiotherapy (IMRT) deliveries. In addition, the geometric uncertainty of MLDTT treatment was estimated as the difference between field aperture and target center position in the BEV. The standard deviation of systematic (Σ) and random (σ) errors were determined.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;MV tracking was successful for 89.7% of (unmodulated) 3DCRT fields, as well as 82.4% of (modulated) control points (CPs) and subfields (SFs) for IMRT and field-in-field 3DCRT deliveries. The MV tracking accuracy was dependent on the traversed anatomy, tumor visibility, and occlusion by the MLC. The mean MV tracking accuracy was 1.2 mm (pan) and 1.4 mm (tilt), and a resultant 2D accuracy of 1.8 mm. The MV tracking performance within 2 mm was observed in 92.1% (pan) and 86.6% (tilt), respectively. The mean aperture-target positional uncertainty smaller than 3 mm/5 mm was observed in 94.4/97.9% (pan) and 89.6/96.7% (tilt) of the time. The group Σ and σ were 0.5 mm/0.8 mm (pan), and 0.7 mm/1.2 mm (tilt), compared to 0.3 mm/0.5 mm (pan), and 0.6 mm/0.9 mm (tilt) based on the manual ground truth.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;MV imaging coupled with the soft-tissue feature tracker algorithm constitutes a valua","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small elongated MLC fields: Novel equivalent square field formula and output factors.","authors":"Antonella Fogliata, Antonella Stravato, Marco Pelizzoli, Francesco La Fauci, Pasqualina Gallo, Andrea Bresolin, Luca Cozzi, Giacomo Reggiori","doi":"10.1002/mp.17806","DOIUrl":"https://doi.org/10.1002/mp.17806","url":null,"abstract":"<p><strong>Purpose: </strong>This study evaluates different approaches for estimating the equivalent square field size (ESF) to derive the Output Correction Factors (OCF) according to the IAEA TRS-483 protocol, for small fields, focusing on rectangular fields generated by MLCs. A novel formula is proposed for estimating the ESF to be used alongside the TRS-483 formalism for Field Output Factor (FOF) determination.</p><p><strong>Method: </strong>FOF for fields from 0.5 to 4 cm side shaped with MLC (jaws fixed to 4.4 × 4.4 cm²) were measured using two Varian TrueBeam (with Millennium and HD-MLC), at isocenter, 10 cm depth, with 6 and 10 MV beam energies, both with and without flattening filter, with microDiamond, DiodeE, and PinPoint3D detectors. Measured ratios were corrected using the OCF from the TRS-483 Tables to determine the FOF. The field size for each setting was determined as the FWHM of the scanning profiles acquired with the microDiamond detector. The ESF was determined using three methods: the Equivalent Area method (according to TRS-483), the Sterling Formula, and a new method according to the following formula: <math> <semantics><mrow><mi>E</mi> <mi>q</mi> <mi>S</mi> <mi>q</mi> <mi>F</mi> <mi>S</mi> <mo>=</mo> <mrow><mo>[</mo> <mrow><mn>2</mn> <mo>·</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <msup><mrow><mo>(</mo> <mrow><mi>X</mi> <mo>,</mo> <mi>Y</mi></mrow> <mo>)</mo></mrow> <mi>a</mi></msup> <mo>·</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <msup><mrow><mo>(</mo> <mrow><mi>X</mi> <mo>,</mo> <mi>Y</mi></mrow> <mo>)</mo></mrow> <mrow><mo>(</mo> <mrow><mn>2</mn> <mo>-</mo> <mi>a</mi></mrow> <mo>)</mo></mrow> </msup> </mrow> <mo>]</mo></mrow> <mo>/</mo> <mrow><mo>(</mo> <mrow><mi>X</mi> <mo>+</mo> <mi>Y</mi></mrow> <mo>)</mo></mrow> <mspace></mspace></mrow> <annotation>$EqSqFS = [ {2 cdot min{{( {X,Y} )}^a} cdot max{{( {X,Y} )}^{( {2 - a} )}}} ]/( {X + Y} );$</annotation></semantics> </math> , with <math><semantics><mi>a</mi> <annotation>$a$</annotation></semantics> </math> here empirically set to 1.12.</p><p><strong>Results: </strong>Corrected FOF for square fields showed good agreement among the detectors with the Equivalent Area as ESF, validating the TRS-483 procedure. For even slightly elongated fields data demonstrated the inadequacy of the equivalent area method. The Sterling formula improved the results but still exhibits substantial differences for the smallest fields. The proposed EqSqFS effectively addresses these shortcomings, showing a description very close to the physical one provided by Ringholtz with the pencil beam method, which utilizes a kernel model to characterize both primary and scatter components of the dose.</p><p><strong>Conclusions: </strong>A new approach for ESF estimation is introduced, which is valid for elongated small fields, to be used in combination with TRS-483 OCF.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}