Physics in medicine and biology最新文献

{"title":"Low dose contrast enhancement of biodegradable low-density stents by an approach balancing radiopaque coatings and beam filtration.","authors":"Samira Ravanbakhsh, Souheib Zekraoui, Theophraste Lescot, Magdalena Bazalova-Carter, Diego Mantovani, Marc-André Fortin","doi":"10.1088/1361-6560/ad9e7b","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9e7b","url":null,"abstract":"<p><strong>Objective: </strong>Biodegradable cardiovascular stents made of thin, low atomic number metals (e.g. Zn, Mg, Fe) are now approved for clinical use. However, poor contrast under X-ray imaging leads to longer surgical times, high patient exposure, and sometimes stent misplacement. This study aimed at enhancing the visibility of low-Z metal stents under X-ray imaging, by combining high-Z metal coatings and beam filtration.</p><p><strong>Approach: </strong>Photon energy spectra from W-anode X-ray beams operated at 80 and 120 kVp, were generated by the SpekCalc and BEAMnrc softwares. The contrast produced by Fe stent struts (50-µm; 10 m W coatings), as well as dose and air kerma values (by BEAMnrc), were simulated. Several types of beam hardening filters (Sn: 0.1, 0.2 mm; Cu: 0.2, 0.7 mm) were also applied. Then, Fe foils (50-µm) with W coatings (2-3 µm-thick) were fabricated by magnetosputtering. These samples were X-ray visualised, for quantification of contrast between W-coated and uncoated Fe samples. Fe struts (50-µm) were also coated with W (3.8 ± 0.2 µm), and stent-like objects were X-ray visualised.</p><p><strong>Main results: </strong>Fe samples attenuate 6.4% (120 kVp) and 10.1% (80 kVp) spectra photons, and 25% and 34.5% for W-coated Fe samples (SpekCalc). BEAMnrc calculations revealed the highest contrast improvement in a 120 kVp beam (36.4, and 38.5%) for W-coated and uncoated Fe samples with Sn (0.2 mm), and Cu + Sn (0.2 + 0.2 mm) filters. Experimentally, the highest contrasts between Fe and W-Fe foils, were obtained with 0.2 mm Sn (580  5 % increase). The dose was also strongly reduced (70 and 75%, for 80 and 120 kVp beams). Finally, for 3D Fe stents visualised at 80 kVp, the highest CNR and CNRD values were achieved with 0.1 mm Sn (18.5 x and 20.1 mGy⁻¹; compared to 15.0 x and 12.0 mGy⁻¹ in no-filter condition).</p><p><strong>Significance: </strong>The contrast of Fe-based stents in X-ray imaging is improved by addition of a thin layer of W and beam filtration with Sn. The precision and rapidity of biodegradable stents implantation would be improved thereby, as well as the dose to patients.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818963","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":"Second Comment on 'Modeling for predicting survival fraction of cells after ultra-high dose rate irradiation'.","authors":"Hans Liew, Andrea Mairani","doi":"10.1088/1361-6560/ad997c","DOIUrl":"https://doi.org/10.1088/1361-6560/ad997c","url":null,"abstract":"<p><p>We comment on the reply by Shiraishi<i>et al</i>to our comments regarding their recently published study 'Modeling for Predicting Survival Fraction of Cells after Ultra-High Dose Rate Irradiation'. While we appreciate the effort of the authors to consider our comments, we see ourselves compelled to add another short comment as we believe that some of our suggestions have been misrepresented. This may have resulted in a misguiding re-evaluation of the model.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"69 24","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812990","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 role of effective dose in medicine now and into the future.","authors":"Colin John Martin, Abdullah Abuhaimed","doi":"10.1088/1361-6560/ad9e68","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9e68","url":null,"abstract":"<p><p>Effective dose was created as a radiological protection dose quantity linked to risk to enable planning of radiological protection for the control of exposure. Its application and use has evolved from occupational and public exposure during work with radiation sources to medicine and applications in patient dosimetry. Effective dose is the sum of doses to organs and tissues within the body weighted according to their sensitivity to radiation for induction of stochastic effects determined from epidemiological studies of exposed populations. It is based on radiation risks averaged over the population and formulated using reference phantoms. Effective dose has been adopted by the medical community for application to patients and has been instrumental in raisingawareness of doses from medical imaging. However, although effective dose can beused for comparison of doses from different medical procedures, it is not designed forapplication to individual patients. The reasons being that organ doses vary with the stature of the patient and the radiation risks depend on the age and sex of the patient. Moves to more personalised medicine have created a desire for a more individualised approach to patient dosimetry, although support for this progression is not universal. This paper traces the evolution of effective dose and its applications. It reflects on how well effective dose provides a measure of risk for individual patients and examines ways in which a more personalised approach might be developed with reference to computed tomography (CT). It considers differences in dose relating to the sizes of patients and looks at variations in risks of cancer incidence within a population with an age distribution typical of patients and examines how this relates to the risk profile. Possible options for improving the individualisation of dosimetry are discussed.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818912","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":"Investigating the impact of the effective point of measurement for plane-parallel ionization chambers in clinical proton beams.","authors":"Kilian-Simon Baumann, Ana Lourenço, Jörg Wulff, Gloria Vilches-Freixas, Hugo Palmans","doi":"10.1088/1361-6560/ad9e7c","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9e7c","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the impact of the positioning of plane-parallel ionization chambers in proton beams on the calculation of the chamber-specific factor f_Qand, hence, the beam quality correction factor k_Q,Q0.&#xD; Approach: Monte Carlo simulations were performed to calculate the chamber-specific factor f_Qin monoenergetic proton beams for six different plane-parallel ionization chambers while positioning the chambers with a) their reference point and b) their effective point of measurement accounting for the water equivalent thickness of the entrance window.&#xD;Main results: For all ionization chamber models investigated in this study, the difference in f_Qbetween both positioning approaches was larger for steeper dose gradients and bigger differences between the geometrical thickness and water-equivalent thickness of the entrance window. The largest effect was 1.2% for the IBA PPC-05 ionization chamber at an energy of 60 MeV.&#xD;Significance: The positioning of plane-parallel ionization chambers in proton beams has a systematic impact on the f_Qfactor. This is especially of relevance for the k_Q,Q0factors presented in the recently updated TRS-398 Code of Practice (CoP) from IAEA. The background is that a positioning with the effective point of measurement is prescribed in TRS-398 CoP, however, all Monte Carlo derived data that have been employed for the update are based on a positioning of the ionization chambers with their reference point. Hence, the updated k_Q,Q0factors for plane-parallel ionization chambers in proton beams are subject to systematic errors that are as large as 0.5%.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818962","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":"An efficient deep unrolling network for sparse-view CT reconstruction via alternating optimization of dense-view sinograms and images.","authors":"Chang Sun, Yitong Liu, Hongwen Yang","doi":"10.1088/1361-6560/ad9dac","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9dac","url":null,"abstract":"<p><strong>Objective: </strong>There have been many advancements in deep unrolling methods for sparse-view computed tomography (SVCT) reconstruction. These methods combine model-based and deep learning-based reconstruction techniques, improving the interpretability and achieving significant results. However, they are often computationally expensive, particularly for clinical raw projection data with large sizes. This study aims to address this issue while maintaining the quality of the reconstructed image.</p><p><strong>Approach: </strong>The SVCT reconstruction task is decomposed into two subproblems using the proximal gradient method: optimizing dense-view sinograms and optimizing images. Then dense-view sinogram inpainting, image-residual learning, and image-refinement modules are performed at each iteration stage using deep neural networks. Unlike previous unrolling methods, the proposed method focuses on optimizing dense-view sinograms instead of full-view sinograms. This approach not only reduces computational resources and runtime but also minimizes the challenge for the network to perform sinogram inpainting when the sparse ratio is extremely small, thereby decreasing the propagation of estimation error from the sinogram domain to the image domain.</p><p><strong>Main results: </strong>The proposed method successfully reconstructs an image (512×512 pixels) from real-size (2304×736) projection data, with 3.39 M training parameters and an inference time of 0.09 seconds per slice on a GPU. The proposed method also achieves superior quantitative and qualitative results compared with state-of-the-art deep unrolling methods on datasets with sparse ratios of 1/12 and 1/18, especially in suppressing artifacts and preserving structural details. Additionally, results show that using dense-view sinogram inpainting not only accelerates the computational speed but also leads to faster network convergence and further improvements in reconstruction results.</p><p><strong>Significance: </strong>This research presents an efficient dual-domain deep unrolling technique that produces excellent results in SVCT reconstruction while requiring small computational resources. These findings have important implications for speeding up deep unrolling CT reconstruction methods and making them more practical for processing clinical CT projection data.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814100","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":"MRgRT real-time target localization using foundation models for contour point tracking and promptable mask refinement.","authors":"Tom Julius Blöcker, Elia Lombardo, Sebastian Marschner, Claus Belka, Stefanie Corradini, Miguel A Palacios, Marco Riboldi, Christopher Kurz, Guillaume Landry","doi":"10.1088/1361-6560/ad9dad","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9dad","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to evaluate two real-time target tracking approaches for magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) based on foundation artificial intelligence (AI) models.&#xD;&#xD;Approach: The first approach used a point-tracking model that propagates points from a reference contour. The second approach used a video-object-segmentation model, based on Segment Anything Model 2 (SAM2). Both approaches were evaluated and compared against each other, inter-observer variability, and a transformer-based image registration model, TransMorph, with and without patient-specific (PS) fine-tuning. The evaluation was carried out on 2D cine MRI datasets from two institutions, containing scans from 33 patients with 8060 labeled frames, with annotations from 2 to 5 observers per frame, totaling 29179 ground truth segmentations. The segmentations produced were assessed using the Dice similarity coefficient (DSC), 50% and 95% Hausdorff distances (HD50 / HD95), and the Euclidean center distance (ECD).&#xD;&#xD;Main results: The results showed that the contour tracking (median DSC 0.92 ± 0.04 and ECD 1.9 ± 1.0 mm) and SAM2-based (median DSC 0.93 ± 0.03 and ECD 1.6 ± 1.1 mm) approaches produced target segmentations comparable or superior to TransMorph without PS fine-tuning (median DSC 0.91 ± 0.07 and ECD 2.6 ± 1.4 mm) and slightly inferior to TransMorph with PS fine-tuning (median DSC 0.94 ± 0.03 and ECD 1.4 ± 0.8 mm). Between the two novel approaches, the one based on SAM2 performed marginally better at a higher computational cost (inference times 92 ms for contour tracking and 109 ms for SAM2). Both approaches and TransMorph with PS fine-tuning exceeded inter-observer variability (median DSC 0.90 ± 0.06 and ECD 1.7 ± 0.7 mm).&#xD;&#xD;Significance: This study demonstrates the potential of foundation models to achieve high-quality real-time target tracking in MRgRT, offering performance that matches state-of-the-art methods without requiring PS fine-tuning.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814125","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":"Validation of the generalized stochastic microdosimetric model (GSM²) over a broad range of LET and particle beam type: a unique model for accurate description of (therapy relevant) radiation qualities.","authors":"Giulio Bordieri, Marta Missiaggia, Giorgio Cartechini, Marco Battestini, Lawrence Bronk, Fada Guan, David R Grosshans, Priyamvada Rai, Emanuele Scifoni, Chiara La Tessa, Gianluca Lattanzi, Francesco G Cordoni","doi":"10.1088/1361-6560/ad9dab","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9dab","url":null,"abstract":"<p><p>The present work shows the first extensive validation of the<i>Generalized Stochastic Microdosimetric Model</i>(GSM²). This mechanistic and probabilistic model is trained and tested over cell survival experiments conducted with two cell lines (H460 and H1437), three different types of radiation (protons, helium, and carbon ions), spanning a very broad LET range from 1 keV/μm up to more than 300 keV/μm.&#xD;&#xD;Currently, the existing mechanistic radiation biophysical models show some limitations in describing cell killing without the addition of ad hoc corrections, especially in the high-LET regime, where the overkill effect is observed.&#xD;&#xD;The experimental irradiation conditions have been accurately reproduced with Monte Carlo simulations using the GEANT4-based TOPAS computational toolkit. We show the main and unique features of GSM², i.e., how it can accurately predict the biological response by considering the full information on the stochasticity of radiation through the microdosimetric spectrum, which is supposed to be the best descriptor of radiation quality. &#xD;&#xD;Well-matching results for different biological endpoints with the natural presence of the overkill effect fully display the predictive power of GSM².&#xD;This study shows the complete generality and flexibility of GSM²and its ability to successfully predict the cell survival probability from very different particle radiation fields. Consequently, we demonstrate the dependence of the relative biological effectiveness on the whole microdosimetric spectrum, which fully includes the stochasticity inherently given by radiation-matter interaction.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814142","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":"Longitudinal interpretability of deep learning based breast cancer risk prediction.","authors":"Zan Klanecek, Yao-Kuan Wang, Tobias Wagner, Lesley Cockmartin, Nicholas W Marshall, Brayden Schott, Alison Deatsch, Andrej Studen, Katja Jarm, Mateja Krajc, Miloš Vrhovec, Hilde Bosmans, Robert Jeraj","doi":"10.1088/1361-6560/ad9db3","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9db3","url":null,"abstract":"<p><strong>Objective: </strong>Deep-learning-based models have achieved state-of-the-art breast cancer risk (BCR) prediction performance. However, these models are highly complex, and the underlying mechanisms of BCR prediction are not fully understood. Key questions include whether these models can detect breast morphologic changes that lead to cancer. These findings would boost confidence in utilizing BCR models in practice and provide clinicians with new perspectives. In this work, we aimed to determine when oncogenic processes in the breast provide sufficient signal for the models to detect these changes.&#xD;&#xD;Approach: In total, 1210 screening mammograms were collected for patients screened at different times before the cancer was screen-detected and 2400 mammograms for patients with at least ten years of follow-up. MIRAI, a BCR risk prediction model, was used to estimate the BCR. Attribution Heterogeneity was defined as the relative difference between the attributions obtained from the right and left breasts using one of the eight interpretability techniques. Model reliance on the side of the breast with cancer was quantified with AUC. The Mann-Whitney U test was used to check for significant differences in median absolute Attribution Heterogeneity between cancer patients and healthy individuals.&#xD;&#xD;Results: All tested attribution methods showed a similar longitudinal trend, where the model reliance on the side of the breast with cancer was the highest for the 0-1 Years-To-Cancer interval (AUC=0.85-0.95), dropped for the 1-3 Years-To-Cancer interval (AUC=0.64-0.71), and remained above the threshold for random performance for the 3-5 Years-To-Cancer interval (AUC=0.51-0.58). For all eight attribution methods, the median values of absolute attribution heterogeneity were significantly larger for patients diagnosed with cancer at one point (p<0.01).&#xD;&#xD;Significance: Interpretability of BCR prediction has revealed that long-term predictions (beyond three years) are most likely based on typical breast characteristics, such as breast density; for mid-term predictions (one to three years), the model appears to detect early signs of tumor development, while for short-term predictions (up to a year), the BCR model essentially functions as a breast cancer detection model.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814122","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":"A comprehensive dual energy method for CBCT metal artifact reduction.","authors":"Weiwei Ge, Zihao Liu, Hehe Cui, Xiaogang Yuan, Yidong Yang","doi":"10.1088/1361-6560/ad9db1","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9db1","url":null,"abstract":"<p><strong>Objective: </strong>A major limitation in CBCT application is the presence of metal artifacts when scanning metal-embedded objects or high attenuation materials. This study aims to develop a dual-energy based method for effective metal artifact reduction.</p><p><strong>Approach: </strong>The proposed method comprised three steps. Initially, the virtual monoenergetic (VM) projections were generated by combining high- and low-energy projections to mitigate metal artifacts caused by the beam hardening effect. Subsequently, the normalized metal artifact reduction (NMAR) projections were created using the VM projections through the NMAR method. Then, the NMAR CBCT was produced by reintegrating metal into the CBCT reconstructed from NMAR projections. Finally, the iterative reconstruction was employed to obtain the final CBCT, utilizing VM projections and the NMAR CBCT as the initial input. Validation of the proposed method was achieved through Monte Carlo (MC) simulations on digital dental and abdominal phantoms, and CBCT scanning on CIRS Model 062M head and body phantoms. The Structural Similarity Index Measurement (SSIM) and the Root Mean Square Error (RMSE) were employed for image quality evaluation.</p><p><strong>Main results: </strong>Both the MC simulation and phantom scanning demonstrated that the proposed method was superior to the frequency split metal artifact reduction (FSMAR) method in mitigating artifacts and preserving anatomic details around metal. Averaged over four phantoms, the SSIM was enhanced from 98.48% with FSMAR to 99.86% with our proposed method, and the RMSE was reduced from 93.62 HU to 71.05 HU. Furthermore, the proposed method could be implemented with less than two minutes after GPU acceleration.</p><p><strong>Significance: </strong>The proposed dual-energy based metal artifact correction method effectively corrects metal artifacts and preserves tissue details surrounding the metal region by leveraging the strengths of VM, projection interpolation and iterative reconstruction techniques. It has strong potential of clinical implementation due to the superior performance in image quality and process efficiency.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814097","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":"Quantifying the spatial distribution of the accumulated dose uncertainty using the novel delta index.","authors":"Madelon van den Dobbelsteen, Sara L Hackett, Lando S Bosma, Renate J A van Doormaal, Bram van Asselen, Martin F Fast","doi":"10.1088/1361-6560/ad9dae","DOIUrl":"https://doi.org/10.1088/1361-6560/ad9dae","url":null,"abstract":"<p><strong>Objective: </strong>Inter- and intra-fractional anatomical changes during a radiotherapy treatment can cause differences between the initially planned dose and the delivered dose. The total delivered dose can be accumulated over all fractions by using Deformable Image Registration (DIR). However, there is uncertainty in this process which should be accounted for. The aim of this study is to propose a novel metric estimating the spatial distribution of the accumulated dose uncertainty and to evaluate its performance for multi-fraction online adaptive treatments.&#xD;&#xD;Approach: We postulate a new metric, the delta (δ) index, to estimate the uncertainties associated with the dose accumulation process. This metric is calculated for each voxel and takes into account the spatial uncertainty in DIR and local dose differences. For the spatial uncertainty of the DIR, the Distance Discordance Metric (DDM) was used. The accumulated dose and the δ index were determined for ten lung Stereotactic Body Radiation Therapy (SBRT) patients. The δ index was complemented by a more understandable metric, the δ index passing rate, which is the percentage of points satisfying the passing criteria in a region. &#xD;&#xD;Main Results: The spatial distribution of the δ index and the δ index passing rates showed that voxels failing the criteria were predominantly in lower-dose regions. The mean percentage of voxels passing the criterion increased from 65% to 78%, for threshold doses of 20% and 90% of the prescription doses, respectively. &#xD;&#xD;Significance: The δ index was postulated to quantify the spatial distribution of the uncertainties associated with the dose accumulation process. The metric gives an intuitive understanding of the reliability of accumulated dose distributions and derived DVH metrics. The performance of the δ index was evaluated for multi-fraction online adaptive treatments, where a case of sub-optimal image registration was identified by the metric.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814129","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}