Physics in medicine and biology最新文献

{"title":"A robust optimisation genetic algorithm for HDR prostate brachytherapy including all major uncertainties.","authors":"Andrew C Kennedy, Michael J J Douglass, Raghavendra V Gowda, Alexandre M C Santos","doi":"10.1088/1361-6560/addf0b","DOIUrl":"10.1088/1361-6560/addf0b","url":null,"abstract":"<p><p><i>Objective.</i>In high-dose-rate prostate brachytherapy, uncertainties are likely to cause a deviation from the nominal treatment plan, potentially leading to failure in achieving clinical objectives. Robust optimisation has the potential to maximise the probability that objectives are met during treatment despite these uncertainties.<i>Approach.</i>A probabilistic robust optimiser that incorporating fourteen major uncertainty sources was developed and evaluated on 49 patients. Three objective functions were maximised to generate the approximate Pareto front of 200 robust-optimised plans, approximating the robustness of: (1) The minimum dose to the hottest 90% of the prostate (D90P), (2) The maximum dose to the urethra (D0.01 ccU), and (3) The maximum dose to the rectum (D0.1 ccR). Plans were then robustly evaluated using 1000 uncertainty scenarios each simulating a possible deviation from the planned treatment. The percentage of scenarios meeting theD90P,D0.01 ccU, andD0.1 ccRmetrics were determined, along with the overall pass rate, defined as the percentage of scenarios meting all three metrics simultaneously. These pass-rates, along with nominal metrics, were, were used to select the best robust-optimised plan. A radiation oncologist evaluated the best robust-optimised plans against the treatment planning system (TPS)-optimised plan for ten patients. The same selection criteria were then applied to a further cohort of 39 patients and the same plan comparisons performed.<i>Main results</i>. All best robust-optimised plans had higher overall pass-rates (mean: 50.7 ± 1.5%, SD: 14.2%) then TPS-optimised plans (mean: 32.0 ± 1.5%, SD: 12.3%). The meanD0.01 ccUpass-rate was 66.0 ± 1.3% (SD: 12.1) for the robust-optimised plans compared with 47.2 ± 1.3% (SD: 9.3%) for TPS-optimised plans. TheD90Ppass-rates was higher for robust-optimised plans (mean: 85.6 ± 1.1%, SD: 9.5%) then TPS-optimised (mean: 82.2 ± 1.1%, SD: 13.8%) in 36 patients.D0.1 ccRpass-rates remained consistently high for both optimisation methods.<i>Significance</i>. The robust optimisation algorithm generated plans with greater robustness than the TPS-optimised plans for nine out of ten patients evaluated by a radiation oncologist, in an average algorithm runtime of 1-minute-49 s.</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":"144199892","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":"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, optiGAN, a conditional Wasserstein generative adversarial network (GAN) was developed to accelerate detailed optical simulations while maintaining high fidelity. 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. To validate optiGAN ability to reproduce system-level detector performance, its output was used 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. 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 into the new version of GATE. Future work will focus on further optimizing model performance and extending its applicability to system-level nuclear imaging simulations.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-09","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":"optiGAN: A Deep Learning-Based Alternative to Optical Photon Tracking in Python-Based GATE (10+).","authors":"Guneet Mummaneni, Carlotta Trigila, Nils Krah, David Sarrut, Emilie Roncali","doi":"10.1088/1361-6560/ade2b5","DOIUrl":"https://doi.org/10.1088/1361-6560/ade2b5","url":null,"abstract":"<p><strong>Objective: </strong>To accelerate optical photon transport simulations in the GATE medical physics framework using a Generative Adversarial Network (GAN), while ensuring high modeling accuracy. Traditionally, detailed optical Monte Carlo methods have been the gold standard for modeling photon interactions in detectors, but their high computational cost remains a challenge. This study explores the integration of optiGAN, a Generative Adversarial Network (GAN) model into GATE 10, the new Python-based version of the GATE medical physics simulation framework released in November 2024.&#xD;Approach: The goal of optiGAN is to accelerate optical photon transport simulations while maintaining modelling accuracy. The optiGAN model, based on a GAN architecture, was integrated into GATE 10 as a computationally efficient alternative to traditional optical Monte Carlo simulations. To ensure consistency, optical photon transport modules were implemented in GATE 10 and validated against GATE v9.3 under identical simulation conditions. Subsequently, simulations using full Monte Carlo tracking in GATE 10 were compared to those using GATE 10-optiGAN.&#xD;Main results: Validation studies confirmed that GATE 10 produces results consistent with GATE v9.3. Simulations using GATE 10-optiGAN showed over 92% similarity to Monte Carlo-based GATE 10 results, based on the Jensen-Shannon distance across multiple photon transport parameters. optiGAN successfully captured multimodal distributions of photon position, direction, and energy at the photodetector face. Simulation time analysis revealed a reduction of approximately 50% in execution time with GATE 10-optiGAN compared to full Monte Carlo simulations.&#xD;Significance: The study confirms both the fidelity of optical photon transport modeling in GATE 10 and the effective integration of deep learning-based acceleration through optiGAN. This advancement enables large-scale, high-fidelity optical simulations with significantly reduced computational cost, supporting broader applications in medical imaging and detector design.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258717","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":"Development of a breathing lung phantom for proton CT imaging.","authors":"Valentin Wegener, Tobias Fischer, Moritz Rabe, Guillaume Landry, Reinhard W Schulte, Katia Parodi, Jonathan Bortfeldt, Mark Pankuch, Robert P Johnson, Julie Lascaud, George Dedes, Marco Riboldi, Prasannakumar Palaniappan","doi":"10.1088/1361-6560/ade2b6","DOIUrl":"https://doi.org/10.1088/1361-6560/ade2b6","url":null,"abstract":"<p><strong>Objective: </strong>To report on the design of a deformable lung phantom capable of imitating breathing motion with realistic tissue surrogate properties for proton imaging applications.</p><p><strong>Approach: </strong>The phantom was manufactured via 3D printing and silicone moulding, with a customised structural design for motor-controlled breathing motion. The overall size of the phantom was rescaled to fit in the experimental proton CT (pCT) scanner prototype, featuring a 284 mm maximum size for the imaging field-of-view. Several flexible resins were evaluated in perspective of flexibility by varying ultraviolet exposure times, as increased exposure results in resin hardening at each layer. We optimised the structure to achieve ideal lung compression properties, while preserving its integrity to hold the weight of a solid tumour. Phantom material properties were characterised by segmentation of each component in X-ray CT and pCT images, to determine the CT number expressed in Hounsfield units and the relative stopping power (RSP) with respect to water.</p><p><strong>Main results: </strong>We achieved non-homogenous compression in the lung using a grid structure with gradient thickness. The rigid ribcage was 3D printed using granite based material. The tumour motion implemented in the phantom design, as measured using template-matching in fluoroscopic X-ray imaging, revealed hysteretic motion with 10 mm peak-to-peak in the superior-inferior direction.</p><p><strong>Significance: </strong>The developed deformable lung phantom imitated lung motion characteristics, featuring CT number and RSP values in the range comparable to human tissues. The developed breathing phantom is put forward for experimental motion studies in pCT imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258716","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 12-45 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":"10.1088/1361-6560/adde28","url":null,"abstract":"<p><p><i>Objective.</i>Low energy electrons (LEEs) in the range of tens of keV combine high relative biological effectiveness with low penetration depth in tissue, making them an interesting tool for radiobiological studies. To harness these advantages, a reliable and comprehensible dosimetry method is essential.<i>Approach.</i>Unlaminated EBT3 GafChromic films were evaluated as potential LEE dosimeters, given the limitations of other dosimetry tools for LEE applications. The depth dose profile of the LEE in the film was simulated and then combined with the experimentally determined response of the film to a calibrated radiation source. Using this, the total response of the film for a given average dose was calculated.<i>Main results.</i>A calibration curve for unlaminated EBT3 GafChromic films for LEE in the energy range of 12-45 keV has been successfully developed for a range of average doses from 0 Gy to 16 Gy.<i>Significance.</i>The developed calibration curve enables direct, quantitative comparison of biological experiments using LEE with other types of radiation such as x-rays, facilitating the adoption of LEE in radiobiological research.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-09","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":"CT-Denoimer: efficient contextual transformer network for low-dose CT denoising.","authors":"Yuanke Zhang, Fan Xu, Rui Zhang, Yanfei Guo, Hanxiang Wang, Bingbing Wei, Fei Ma, Jing Meng, Jianlei Liu, Hongbing Lu, Yang Chen","doi":"10.1088/1361-6560/addea6","DOIUrl":"10.1088/1361-6560/addea6","url":null,"abstract":"<p><p><i>Objective</i>. Low-dose computed tomography (LDCT) effectively reduces radiation exposure to patients, but introduces severe noise artifacts that affect diagnostic accuracy. Recently, Transformer-based network architectures have been widely applied to LDCT image denoising, generally achieving superior results compared to traditional convolutional methods. However, these methods are often hindered by high computational costs and struggles in capturing complex local contextual features, which negatively impact denoising performance<i>Approach</i>. In this work, we propose CT-Denoimer, an efficient CT Denoising Transformer network that captures both global correlations and intricate, spatially varying local contextual details in CT images, enabling the generation of high-quality images. The core of our framework is a Transformer module that consists of two key components: the multi-Dconv head transposed attention (MDTA) and the mixed contextual feed-forward network (MCFN). The MDTA block captures global correlations in the image with linear computational complexity, while the MCFN block manages multi-scale local contextual information, both static and dynamic, through a series of Enhanced Contextual Transformer modules. In addition, we incorporate operation-wise attention layers to enable collaborative refinement in the proposed CT-Denoimer, enhancing its ability to more effectively handle complex and varying noise patterns in LDCT images<i>Main results</i>. Extensive experimental validation on both the AAPM-Mayo public dataset and a real-world clinical dataset demonstrated the state-of-the-art performance of the proposed CT-Denoimer. It achieved a peak signal-to-noise ratio of 33.681 dB, a structural similarity index measure of 0.921, an information fidelity criterion of 2.857 and a visual information fidelity of 0.349. Subjective assessment by radiologists gave an average score of 4.39, confirming its clinical applicability and clear advantages over existing methods<i>Significance</i>. This study presents an innovative CT denoising Transformer network that sets a new benchmark in LDCT image denoising, excelling in both noise reduction and fine structure preservation.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182788","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 applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.","authors":"Runyu Jiang, Malvern Madondo, Xiaoman Zhang, Yuan Shao, Mohammadamin Moradi, James J Sohn, Tianming Wu, Xiaofeng Yang, Yasmin Hasan, Zhen Tian","doi":"10.1088/1361-6560/addea5","DOIUrl":"10.1088/1361-6560/addea5","url":null,"abstract":"<p><p><i>Objective.</i>High-dose-rate (HDR) brachytherapy is integral to the standard-of-care for locally advanced cervical cancer (LACC). Currently, selection of brachytherapy applicators relies on physician's clinical experience, which can lead to variability in treatment quality and outcomes. This study presents a deep learning-based decision-support tool for selecting between interstitial Syed applicators and intracavitary tandem & ovoids applicators.<i>Approach.</i>The network architecture consists of six 3D convolutional-pooling-rectified linear unit blocks, followed by a fully connected block. The input to the network includes three channels: a 3D contour mask of clinical target volume (CTV), organs at risk (OAR), and central tandem, and two 3D distance maps of CTV and OAR voxels relative to the tandem's central axis. The network outputs a probability score, indicating the suitability of Syed applicators. Binary cross-entropy loss combined with<i>L</i>₁regularization was used for network training.<i>Main results.</i>A retrospective study was performed on 184 LACC patients with 422 instances of applicator insertion. The data was divided into three sets: Dataset-1 of 163 patients with 372 insertions for training and hyperparameter tuning, Dataset-2 of 17 patients with 36 insertions and Dataset-3 of four complex cases with 14 insertions for testing. Five-fold cross-validation was performed on Dataset-1, during which hyperparameters were heuristically tuned to optimize classification accuracy across the folds. The highest average accuracy was 92.1 ± 3.8%. Using the hyperparameters that resulted in this highest accuracy, the final model was then trained on the full Dataset-1, and evaluated on the other two independent datasets, achieving 96.0% accuracy, 90.9% sensitivity, and 97.4% specificity.<i>Significance.</i>These results demonstrate the potential of our model as a quality assurance tool in LACC HDR brachytherapy, providing feedback on physicians' applicator choice and supporting continuous improvement in decision-making. Future work will focus on collecting more data for further validation and extending its application for prospective applicator selection.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12142457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183303","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":"Roadmap: medical physics technologies in brachytherapy.","authors":"William Youngjae Song, Michael Roumeliotis, Junghoon Lee, Aaron Fenster, Jessica Rodgers, Tiana Trumpour, Rachel Glicksman, Alejandro Berlin, Åsa Carlsson Tedgren, Javier Vijande, Luc Beaulieu, Björn Morén, Sandra Michelle Meyers, Alexandra Rink, Yin Gao, Benjamin Fahimian, Debarghya China, Kai Ding, Tarun Podder, Eric Carver, Mark J Rivard, Susan Richardson, Milan Grkovski, Moeen Meftahi, Ulysses Gardner, Rachit Kumar, Daniel Scanderbeg, Catheryn Yashar, Issam El Naqa, Akila N Viswanathan, Xun Jia","doi":"10.1088/1361-6560/ade21f","DOIUrl":"https://doi.org/10.1088/1361-6560/ade21f","url":null,"abstract":"<p><p>Brachytherapy is a crucial modality of radiotherapy for cancer, known for its effectiveness in delivering high doses of radiation directly to tumors while sparing surrounding healthy tissues. Despite its clinical importance, recent years have witnessed a concerning decline in its utilization, which negatively impacts patient outcomes. This decline is attributed to several factors, with the inherent complexity of brachytherapy, fair reimbursement policies, and high dexterity being significant barriers. There are silver linings, however, as growing number of applications are seen in continents such as Africa, as well as advances in medical physics technology offering promising solutions to these challenges. This roadmap paper aims to provide a comprehensive overview and preview of advancements in brachytherapy, as well as strategies to address key challenges in four critical areas: 'Imaging and Image Guidance,' 'Treatment Planning,' 'Treatment Delivery,' and 'Brachytherapy Outcomes.' We anticipate that these advances will enhance therapeutic efficacy, equipping clinicians worldwide with the tools needed to deliver state-of-the-art cancer treatments and fostering a promising future in oncology care.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144249182","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":"Quantitative and automatic plan-of-the-day assessment to facilitate adaptive radiotherapy in cervical cancer.","authors":"Sarah A Mason, Lei Wang, Sophie E Alexander, Susan Lalondrelle, Helen A McNair, Emma J Harris","doi":"10.1088/1361-6560/ade197","DOIUrl":"https://doi.org/10.1088/1361-6560/ade197","url":null,"abstract":"<p><strong>Objective: </strong>To facilitate implementation of plan-of-the-day (POTD) selection for treating locally advanced cervical cancer (LACC), we developed a POTD assessment tool for CBCT-guided radiotherapy (RT). A female pelvis segmentation model (U-Seg3) is combined with a quantitative standard operating procedure (qSOP) to identify optimal and acceptable plans. &#xD;&#xD;Approach: The planning CT[i], corresponding structure set[ii], and manually contoured CBCTs[iii] (n=226) from 39 LACC patients treated with POTD (n=11) or non-adaptive RT (n=28) were used to develop U-Seg3, an algorithm incorporating deep-learning and deformable image registration techniques to segment the low-risk clinical target volume (LR-CTV), high-risk CTV (HR-CTV), bladder, rectum, and bowel bag. A single-channel input model (iii only, U-Seg1) was also developed. Contoured CBCTs from the POTD patients were (a) reserved for U-Seg3 validation/testing, (b) audited to determine optimal and acceptable plans, and (c) used to empirically derive a qSOP that maximised classification accuracy. &#xD;&#xD;Main Results: The median [interquartile range] DSC between manual and U-Seg3 contours was 0.83 [0.80], 0.78 [0.13], 0.94 [0.05], 0.86[0.09], and 0.90 [0.05] for the LR-CTV, HR-CTV, bladder, rectum, and bowel bag. These were significantly higher than U-Seg1 in all structures but bladder. The qSOP classified plans as acceptable if they met target coverage thresholds (LR-CTV≧99%, HR-CTV≧99.8%), with lower LR-CTV coverage (≧95%) sometimes allowed. The acceptable plan minimising bowel irradiation was considered optimal unless substantial bladder sparing could be achieved. With U-Seg3 embedded in the qSOP, optimal and acceptable plans were identified in 46/60 and 57/60 cases. &#xD;&#xD;Significance: U-Seg3 outperforms U-Seg1 and all known CBCT-based female pelvis segmentation models. The tool combining U-Seg3 and the qSOP identifies optimal plans with equivalent accuracy as two observers. In an implementation strategy whereby this tool serves as the second observer, plan selection confidence and decision-making time could be improved whilst simultaneously reducing the required number of POTD-trained radiographers by 50%.&#xD;&#xD;&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234738","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":"Visualising ventilation changes following endobronchial valve placement with x-ray velocimetry functional lung imaging.","authors":"Ronan Smith, Charlotte Thomas, Phan Nguyen, Arash Badiei, Nina Eikelis, Kristopher Nilsen, Piraveen Pirakalathanan, David Parsons, Martin Donnelley","doi":"10.1088/1361-6560/ade196","DOIUrl":"https://doi.org/10.1088/1361-6560/ade196","url":null,"abstract":"<p><strong>Objective: </strong>Endobronchial Valves are a minimally invasive treatment for emphysema. After bronchoscopic placement the valves reduce the flow of air into targeted areas of the lung, causing collapse, and allowing the remainder of the lung to function more effectively. This&#xD;pilot study aims to demonstrate the capability and potential of a new imaging modality - X-ray Velocimetry - for detecting these changes to lung function.&#xD;Approach: X-ray Velocimetry is a novel method that uses X-ray images taken during a breath to track lung motion, producing 3D maps of local ventilation. Healthy sheep received a CT scan and underwent X-ray Velocimetry imaging before and after endobronchial valves were placed in the lung. Sheep were imaged again when the endobronchial valves were removed after 14 days.&#xD;Main results: X-ray Velocimetry enabled visualisation and quantification of a reduction of airflow to the areas downstream of the endobronchial valves, both in areas where collapse was and was not visible in CT. Changes to ventilation were also clearly visible in the remainder of the lungs.&#xD;Significance: This preclinical study has shown X-ray Velocimetry is capable of detecting changes to ventilation caused by endobronchial valve placement, paving the way towards use in patients.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234739","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}