Physics in medicine and biology最新文献

{"title":"Enhancing image quality in fast neutron-based range verification of proton therapy using a deep learning-based prior in LM-MAP-EM reconstruction.","authors":"Lena M Setterdahl, Kyrre Skjerdal, Hunter N Ratliff, Kristian Smeland Ytre-Hauge, William R B Lionheart, Sean Holman, Helge E S Pettersen, Francesco Blangiardi, Danny Lathouwers, Ilker Meric","doi":"10.1088/1361-6560/ade198","DOIUrl":"10.1088/1361-6560/ade198","url":null,"abstract":"<p><p><i>Objective.</i>This study investigates the use of list-mode (LM) maximum<i>a posteriori</i>(MAP) expectation maximization (EM) incorporating prior information predicted by a convolutional neural network for image reconstruction in fast neutron (FN)-based proton therapy range verification.<i>Approach</i>. A conditional generative adversarial network (pix2pix) was trained on progressively noisier data, where detector resolution effects were introduced gradually to simulate realistic conditions. FN data were generated using Monte Carlo simulations of an 85 MeV proton pencil beam in a computed tomography-based lung cancer patient model, with range shifts emulating weight gain and loss. The network was trained to estimate the expected two-dimensional ground truth FN production distribution from simple back-projection images. Performance was evaluated using mean squared error, structural similarity index (SSIM), and the correlation between shifts in predicted distributions and true range shifts.<i>Main results</i>. Our results show that pix2pix performs well on noise-free data but suffers from significant degradation when detector resolution effects are introduced. Among the LM-MAP-EM approaches tested, incorporating a mean prior estimate into the reconstruction process improved performance, with LM-MAP-EM using a mean prior estimate outperforming naïve LM maximum likelihood EM (LM-MLEM) and conventional LM-MAP-EM with a smoothing quadratic energy function in terms of SSIM.<i>Significance</i>. Findings suggest that deep learning techniques can enhance iterative reconstruction for range verification in proton therapy. However, the effectiveness of the model is highly dependent on data quality, limiting its robustness in high-noise scenarios.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234736","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":"High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.","authors":"Victor Murray, Can Wu, Ricardo Otazo","doi":"10.1088/1361-6560/ade195","DOIUrl":"10.1088/1361-6560/ade195","url":null,"abstract":"<p><p><i>Objective.</i>To develop motion-resolved volumetric MRI with 1.1 mm isotropic resolution and scan times <5 min using a combination of 3D radial kooshball acquisition and spatial-temporal deep learning 4D reconstruction for free-breathing high-definition (HD) lung MRI.<i>Approach.</i>Free-breathing lung MRI was conducted on eight healthy volunteers and ten patients with lung tumors on a 3 T MRI scanner using a 3D radial kooshball sequence with half-spoke (ultrashort echo time, UTE, TE = 0.12 ms) and full-spoke (T1-weighted, TE = 1.55 ms) acquisitions. Data were motion-sorted using amplitude-binning on a respiratory motion signal. Two high-definition Movienet (HD-Movienet) deep learning models were proposed to reconstruct 3D radial kooshball data: slice-by-slice reconstruction in the coronal orientation using 2D convolutional kernels (2D-based HD-Movienet) and reconstruction on blocks of eight coronal slices using 3D convolutional kernels (3D-based HD-Movienet). Two applications were considered: (a) anatomical imaging at expiration and inspiration with four motion states and a scan time of 2 min, and (b) dynamic motion imaging with 10 motion states and a scan time of 4 min. The training was performed using XD-GRASP 4D images reconstructed from 4.5 min and 6.5 min acquisitions as references.<i>Main Results.</i>2D-based HD-Movienet achieved a reconstruction time of <6 s, significantly faster than the iterative XD-GRASP reconstruction (>10 min with GPU optimization) while maintaining comparable image quality to XD-GRASP with two extra minutes of scan time. The 3D-based HD-Movienet improved reconstruction quality at the expense of longer reconstruction times (<11 s).<i>Significance.</i>HD-Movienet demonstrates the feasibility of motion-resolved 4D MRI with isotropic 1.1 mm resolution and scan times of only 2 min for four motion states and 4 min for 10 motion states, marking a significant advancement in clinical free-breathing lung MRI.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234737","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":"MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.","authors":"Mojtaba Safari, Shansong Wang, Zach Eidex, Qiang Li, Richard L J Qiu, Erik H Middlebrooks, David S Yu, Xiaofeng Yang","doi":"10.1088/1361-6560/ade049","DOIUrl":"10.1088/1361-6560/ade049","url":null,"abstract":"<p><p><i>Objective.</i>Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning super-resolution (SR) techniques reconstruct high-resolution (HR) images from low-resolution (LR) pairs, but they involve extensive sampling steps, limiting real-time application. To overcome these issues, this study introduces a residual error-shifting mechanism markedly reducing sampling steps while maintaining vital anatomical details, thereby accelerating MRI reconstruction.<i>Approach.</i>We developed Res-SRDiff, a novel diffusion-based SR framework incorporating residual error shifting into the forward diffusion process. This integration aligns the degraded HR and LR distributions, enabling efficient HR image reconstruction. We evaluated Res-SRDiff using ultra-high-field brain T1 MP2RAGE maps and T2-weighted prostate images, benchmarking it against Bicubic, Pix2pix, CycleGAN, SPSR, I²SB, and TM-DDPM methods. Quantitative assessments employed peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), gradient magnitude similarity deviation (GMSD), and learned perceptual image patch similarity. Additionally, we qualitatively and quantitatively assessed the proposed framework's individual components through an ablation study and conducted a Likert-based image quality evaluation.<i>Main results.</i>Res-SRDiff significantly surpassed most comparison methods regarding PSNR, SSIM, and GMSD for both datasets, with statistically significant improvements (p-values≪0.05). The model achieved high-fidelity image reconstruction using only four sampling steps, drastically reducing computation time to under one second per slice. In contrast, traditional methods like TM-DDPM and I²SB required approximately 20 and 38 s per slice, respectively. Qualitative analysis showed Res-SRDiff effectively preserved fine anatomical details and lesion morphologies. The Likert study indicated that our method received the highest scores,4.14±0.77(brain) and4.80±0.40(prostate).<i>Significance.</i>Res-SRDiff demonstrates efficiency and accuracy, markedly improving computational speed and image quality. Incorporating residual error shifting into diffusion-based SR facilitates rapid, robust HR image reconstruction, enhancing clinical MRI workflow and advancing medical imaging research. Code available athttps://github.com/mosaf/Res-SRDiff.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12167925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144216556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing radiosensitivity through sublethal damage recovery: a comparison of survival-based and molecular repair kinetics.","authors":"Naim Chabaytah, Mirta Dumančić, Emmanuel C Asante, Tanner Connell, Michael Witcher, Shirin Abbasinejad Enger","doi":"10.1088/1361-6560/ade221","DOIUrl":"10.1088/1361-6560/ade221","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;i&gt;Objective&lt;/i&gt;. This study aimed to determine whether the kinetics of sublethal damage recovery after x-ray irradiation, quantified as the repair half time (TrepairSLD) derived from split-dose clonogenic survival, correlates with intrinsic radiosensitivity across four human cancer cell lines: HeLa (cervical), PC3 (prostate), and HCT116 and HT29 (colorectal). In addition, the study compared this survival-based indicator with molecular repair kinetics assessed through&lt;i&gt;γ&lt;/i&gt;H2AX and 53BP1 foci clearance.&lt;i&gt;Approach&lt;/i&gt;. By using a phenomenological approach, we assessed sublethal damage recovery kinetics, aiming to determine whether this recovery rate could serve as a biomarker for cancer-specific intrinsic radiosensitivity. Cells were subjected to split-dose 4 Gy irradiation delivered in two fractions of 2 Gy across a 0 to 10 h inter-fraction interval range using a Multi-Rad x-ray irradiator with a peak tube voltage of 225 kV. The clonogenic assay was performed following split-dose irradiation of the experimental groups to assess cell survival. Colonies were fixed, stained, and counted (⩾50 cells/colony viable threshold) to calculate survival fractions (SFs) from the four independent experimental runs completed for each cell line. Unirradiated control cells were used to calculate plating efficiency. The measured SF as a function of inter-fraction time was fitted with the Lea-Catcheside modified linear-quadratic model with a half-life of sublethal damage repair,TrepairSLD, as a free parameter. To compare this approach to molecular DNA repair kinetics, immunofluorescence-based ionizing radiation-induced foci (IRIF) clearance experiments were performed following single 2 Gy irradiation using the same x-ray source.&lt;i&gt;γ&lt;/i&gt;H2AX and 53BP1 foci were quantified from 0.5 to 24 h post-irradiation, and foci clearance half-lives (TrepairγH2AXandTrepair53BP1) were determined by single-phase exponential decay fitting.&lt;i&gt;Main results&lt;/i&gt;. For all measured cell lines, an increase in SF was observed with increasing inter-fraction time. The estimatedTrepairSLDvaried across cell lines, from1.07±0.35 h in HT29, to1.98±0.94 h in HeLa,2.00±0.30 h in PC3, and3.58±1.45 h in HCT116, indicating different capacities for sublethal damage repair. A negative correlation was measured betweenTrepairSLDand clonogenic survival at 2 Gy (SF2Gy) by performing orthogonal distance regression, with a slope of-350±50 min (&lt;i&gt;p&lt;/i&gt; = 0.02).TrepairγH2AXandTrepair53BP1ranged from 3 to 11 h, with HT29 showing the fastest foci resolution. However, these molecular repair kinetics times did not significantly correlate withSF2Gy(&lt;i&gt;p&lt;/i&gt; &gt; 0.05) or follow the same trend asTrepairSLDacross cell lines. For example, PC3 cells exhibited the slowest foci clearance, whereas HCT116 displayed the slowestTrepairSLD, suggesting that IRIF-based measurements do not reliably reflect functional sublethal damage repair.&lt;i&gt;Significance&lt;/i&gt;. Clonogenic survival assays capture the integrated biological ou","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144249178","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":"Current status and challenges of minimally invasive ultrasound thermal ablation technology.","authors":"Shengrong Lin, Kang Chen, Jianming Wen","doi":"10.1088/1361-6560/ade111","DOIUrl":"10.1088/1361-6560/ade111","url":null,"abstract":"<p><p>Image-guided minimally invasive ultrasound thermal ablation has been widely studied for disease treatment due to its unique advantages, such as large treatment volumes and conformal delivery of ablation energy. This review presents the state-of-the-art of this technology and highlights the challenges. Ultrasound applicator designs, common image guidance methods, and treatment sites are first summarized. The recent decade has shown that despite the continuous development of this technology, only limited clinical translations have succeeded. To push this technology forward, challenges regarding the advancement of applicators, precise and real-time imaging guidance methods, and the validation of the clinical effectiveness of potential treatment sites are described. Finally, future directions are discussed. It is envisioned that future development of artificial intelligence-powered applicators with conformal ablation ability and multi-modal image guidance will enable surgeons to perform precise, visible, customizable treatment.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226269","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":"10.1088/1361-6560/adde0d","url":null,"abstract":"<p><p><i>Objective</i>. 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.<i>Approach</i>. 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<i>Z</i>-Score normalization, which mitigates the problem of large-scale features dominating the model training.<i>Main results</i>. 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 1000-fold or even 10 000-fold improvement in efficiency with only around a 1% loss in accuracy.<i>Significance</i>. 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":"2025-06-12","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":"Implicit neural representation for medical image reconstruction.","authors":"Yanjie Zhu, Yuanyuan Liu, Yihang Zhang, Dong Liang","doi":"10.1088/1361-6560/addfa5","DOIUrl":"10.1088/1361-6560/addfa5","url":null,"abstract":"<p><p>Medical image reconstruction aims to generate high-quality images from incompletely sampled raw sensor data, which poses an ill-posed inverse problem. Traditional iterative reconstruction methods rely on prior information to empirically construct regularization terms, a process that is not trivial. While deep learning-based supervised reconstruction has made significant progress in improving image quality, it requires large-scale training data, which is difficult to obtain in medical imaging. Recently, implicit neural representation (INR) has emerged as a promising approach, offering a flexible and continuous representation of images by modeling the underlying signal as a function of spatial coordinates. This allows INR to capture fine details and complex structures more effectively than conventional discrete methods. This paper provides a comprehensive review of INR-based medical image reconstruction techniques, highlighting its growing impact on the field. The benefits of INR in both image and measurement domains are presented, and its advantages, limitations, and future research directions are discussed.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209175","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":"Modeling prompt gamma (PG) emission, detection and imaging in real patient anatomy using a novel Compton camera for dose verification in proton therapy.","authors":"V R Sharma, Z Jiang, S Mossahebi, E Shakeri, A Chalise, M K Gobbert, S W Peterson, J C Polf, L Ren","doi":"10.1088/1361-6560/addf0d","DOIUrl":"10.1088/1361-6560/addf0d","url":null,"abstract":"<p><p><i>Objective</i>. Prompt gamma (PG) imaging is a promising modality for proton dose verification. Currently, there is a lack of effective tools to investigate the entire PG imaging process in patient anatomy, from PG emission to camera detection and image reconstruction, to evaluate and optimize its efficacy for dose verification in proton therapy.<i>Approach</i>. To address this gap, we developed a Monte-Carlo package, POLARIS J Monte Carlo (PJ-MC), that simulates the entire PG emission and imaging workflow in patient anatomy. We utilized Geant4 classes and G4-ancillary tools, employing the DCMTK external tool with G4PhantomParameterisation to convert patient CT data into voxelized geometries. Proton beams were modeled based on medical physics commissioning data. A novel two-stage POLARIS-J3 Compton-Camera was simulated under the patient couch for recording total, double, and triple scattered PG signals. Proton maximum range calculations from the PJ-MC are compared with dose calculations from a clinical treatment planning system. The detected PG signals data in the simulation were used to reconstruct PG images using Kernel- Weighted-Back-Projection algorithm.<i>Main results</i>. Analysis of gamma energy distribution showed a decay pattern with clear emission lines from nuclear reactions involving oxygen, carbon, nitrogen, and calcium. Neutron-induced reactions contribute significantly less-by an order of magnitude-compared to proton-induced reactions in various tissues. Mean absolute percentage error analysis showed that PG range verification was more stable when considering the range at 80%or 50%of<i>D</i>_max, as opposed to the range at the<i>D</i>_max, where energy gating slightly improves accuracy but may reduce localization due to photon loss. Results showed that patient anatomy can impact the location of hot spot in the PG images, affecting its accuracy for localizing Bragg peak.<i>Significance</i>. In summary, our simulation package provides additional insights into PG emission and imaging in patient anatomy and serves as a robust tool for evaluating and optimizing PG imaging, enhancing its precision for dose verification in proton therapy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187577","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":"Improved feedback loop control for ultrasound-assisted blood-brain barrier opening in non-human primates based on the discrimination between intra- and extra-cerebral cavitation.","authors":"Paul Mondou, Gwenaël Pagé, Corentin Cornu, Clémentine Morisset, Elias Djaballah, Audrey Fayard, Sophie Lecourtois, Marion Gay, Maxime Roustan, Julien Flament, Alexandre Vignaud, Sébastien Mériaux, Qi Zhu, Romina Aron Badin, Anthony Novell, Benoit Larrat","doi":"10.1088/1361-6560/addf0c","DOIUrl":"10.1088/1361-6560/addf0c","url":null,"abstract":"<p><p><i>Objective</i>. Temporary, non-invasive, and localized permeabilization of the blood-brain barrier (BBB) can be achieved through focused ultrasound and microbubbles (MB). This technique has been extensively employed in rodent and non-human primate (NHP) studies for testing various drugs but requires precise control of ultrasonic pressure. However, controlling cavitation in NHP is challenging due to their thicker skull inducing strong ultrasonic attenuation. Furthermore, extra-cranial cavitation may occur masking the cavitation signal at the focal region (cerebral cavitation). Particularly in larger male NHP, temporal muscles are highly perfused and filled with MB.<i>Approach</i>. This study proposes a feedback loop control strategy to distinguish between intra- and extra-cerebral cavitation by analyzing broadband noise recorded by passive cavitation detection sensors.<i>Main results</i>. The frequency-dependent low-pass filtering effect by the skull allows differentiation of distinct frequency components, providing insights into cavitation origin. The present study involved 17 BBB opening experiments in NHP.<i>Significance</i>. Although successful BBB disruption can be achieved in NHP with thin temporal muscles (<5 mm) using a regular feedback loop algorithm, NHP having thicker muscles (>15 mm) require the use of an optimized algorithm able to specifically extract the signature of intra-cerebral cavitation.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187576","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":"Afterloader integrated EMT enables improved dwell position model definition and quality assurance in Venezia gynaecological brachytherapy applicators.","authors":"Ioannis Androulakis, Myra van Laar, Jeremy Godart, Robin Straathof, Henrike Westerveld, Remi Nout, Mischa Hoogeman, Inger-Karine K Kolkman-Deurloo","doi":"10.1088/1361-6560/addfa7","DOIUrl":"10.1088/1361-6560/addfa7","url":null,"abstract":"<p><p><i>Objective.</i>In brachytherapy for gynecological cancers using intracavitary applicators, implant reconstruction is commonly performed using applicator libraries. These libraries contain applicator geometry models as well as dwell position (DP) models defined in respect to the applicator geometry. In this study, we investigate whether an afterloader integrated electromagnetic tracking (EMT) system can be utilized for DP model definition and quality assurance in such applicators.<i>Approach.</i>DPs in four sets of two configurations of the Elekta Venezia Advanced Gynaecological Applicator (22 mm ovoids/40 mm intrauterine (IU) and 26 mm ovoids/70 mm IU) were measured using an afterloader integrated EMT system. Measurements were evaluated for reproducibility and compared against manufacturer-specified (MS) DPs and a computed tomography (CT)-corrected DP model.<i>Main Results.</i>Excellent EMT measurement reproducibility was observed, with values of ⩽0.2 mm for both configurations. The overall reproducibility, including applicator geometry reproducibility, was ⩽0.4 mm for both configurations. Significant discrepancies from the manufacturer's DP model were observed, with a mean ± sd deviation of 1.13 ± 0.66 mm (22/40) and 1.37 ± 0.63 (26/70), particularly in the IU channel, where MS DPs were not experimentally defined. Discrepancies were reduced to 0.89 ± 0.41 mm (22/40) and 0.81 ± 0.33 mm (26/70) when the CT-corrected DP model was used as baseline, highlighting the need for experimentally defined DP models. The overall uncertainty of single measurements was below the clinically acceptable 2 mm limit.<i>Significance.</i>This study confirms that afterloader integrated EMT can accurately reconstruct source paths in gynecological brachytherapy applicators and supports its incorporation into clinical workflows for improved quality assurance and treatment precision. The importance of EMT for quality assurance was highlighted by measured deviations from manufacturer's DP model in a clinical relevant part of the IU channel.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209173","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}