Physics in medicine and biology最新文献

{"title":"Low-dose CT reconstruction using cross-domain deep learning with domain transfer module.","authors":"Yoseob Han","doi":"10.1088/1361-6560/adb932","DOIUrl":"10.1088/1361-6560/adb932","url":null,"abstract":"<p><p><i>Objective</i>. X-ray computed tomography employing low-dose x-ray source is actively researched to reduce radiation exposure. However, the reduced photon count in low-dose x-ray sources leads to severe noise artifacts in analytic reconstruction methods like filtered backprojection. Recently, deep learning (DL)-based approaches employing uni-domain networks, either in the image-domain or projection-domain, have demonstrated remarkable effectiveness in reducing image noise and Poisson noise caused by low-dose x-ray source. Furthermore, dual-domain networks that integrate image-domain and projection-domain networks are being developed to surpass the performance of uni-domain networks. Despite this advancement, dual-domain networks require twice the computational resources of uni-domain networks, even though their underlying network architectures are not substantially different.<i>Approach</i>. The U-Net architecture, a type of Hourglass network, comprises encoder and decoder modules. The encoder extracts meaningful representations from the input data, while the decoder uses these representations to reconstruct the target data. In dual-domain networks, however, encoders and decoders are redundantly utilized due to the sequential use of two networks, leading to increased computational demands. To address this issue, this study proposes a cross-domain DL approach that leverages analytical domain transfer functions. These functions enable the transfer of features extracted by an encoder trained in input domain to target domain, thereby reducing redundant computations. The target data is then reconstructed using a decoder trained in the corresponding domain, optimizing resource efficiency without compromising performance.<i>Main results</i>. The proposed cross-domain network, comprising a projection-domain encoder and an image-domain decoder, demonstrated effective performance by leveraging the domain transfer function, achieving comparable results with only half the trainable parameters of dual-domain networks. Moreover, the proposed method outperformed conventional iterative reconstruction techniques and existing DL approaches in reconstruction quality.<i>Significance</i>. The proposed network leverages the transfer function to bypass redundant encoder and decoder modules, enabling direct connections between different domains. This approach not only surpasses the performance of dual-domain networks but also significantly reduces the number of required parameters. By facilitating the transfer of primal representations across domains, the method achieves synergistic effects, delivering high quality reconstruction images with reduced radiation doses.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472829","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":"Power absorption and temperature rise in deep learning based head models for local radiofrequency exposures.","authors":"Sachiko Kodera, Reina Yoshida, Essam A Rashed, Yinliang Diao, Hiroyuki Takizawa, Akimasa Hirata","doi":"10.1088/1361-6560/adb935","DOIUrl":"10.1088/1361-6560/adb935","url":null,"abstract":"<p><p><i>Objective.</i>Computational uncertainty and variability of power absorption and temperature rise in humans for radiofrequency (RF) exposure is a critical factor in ensuring human protection. This aspect has been emphasized as a priority. However, accurately modeling head tissue composition and assigning tissue dielectric and thermal properties remains a challenging task. This study investigated the impact of segmentation-based versus segmentation-free models for assessing localized RF exposure.<i>Approach.</i>Two computational head models were compared: one employing traditional tissue segmentation and the other leveraging deep learning to estimate tissue dielectric and thermal properties directly from magnetic resonance images. The finite-difference time-domain method and the bioheat transfer equation was solved to assess temperature rise for local exposure. Inter-subject variability and dosimetric uncertainties were analyzed across multiple frequencies.<i>Main results.</i>The comparison between the two methods for head modeling demonstrated strong consistency, with differences in peak temperature rise of 7.6 ± 6.4%. The segmentation-free model showed reduced inter-subject variability, particularly at higher frequencies where superficial heating dominates. The maximum relative standard deviation in the inter-subject variability of heating factor was 15.0% at 3 GHz and decreased with increasing frequencies.<i>Significance.</i>This study highlights the advantages of segmentation-free deep-learning models for RF dosimetry, particularly in reducing inter-subject variability and improving computational efficiency. While the differences between the two models are relatively small compared to overall dosimetric uncertainty, segmentation-free models offer a promising approach for refining individual-specific exposure assessments. These findings contribute to improving the accuracy and consistency of human protection guidelines against RF electromagnetic field exposure.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472644","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 patient-specific deep learning markerless lung tumor tracking aided by 4DCBCT.","authors":"L Huang, A Thummerer, C I Papadopoulou, S Corradini, C Belka, M Riboldi, C Kurz, G Landry","doi":"10.1088/1361-6560/adb89c","DOIUrl":"10.1088/1361-6560/adb89c","url":null,"abstract":"<p><p><i>Objective</i>. Tracking tumors with multi-leaf collimators and x-ray imaging can be a cost-effective motion management method to reduce internal target volume margins for lung cancer patients, sparing normal tissues while ensuring target coverage. To realize that, accurate tumor localization on x-ray images is essential. We aimed to develop a systematic method for automatically generating tumor segmentation ground truth (GT) on cone-beam computed tomography (CBCT) projections and use it to help refine and validate our patient-specific AI-based tumor localization model.<i>Approach</i>. To obtain the tumor segmentation GT on CBCT projections, we propose a 4DCBCT-aided GT generation pipeline consisting of three steps: breathing phase extraction and 10-phase 4DCBCT reconstruction, manual segmentation on phase 50% followed by deformable contour propagation to other phases, and forward projection of the 3D segmentation to the CBCT projection of the corresponding phase. We then used the CBCT projections from one fraction in the angular range of [-10∘, 10^∘] and [80^∘, 100^∘] to refine a Retina U-Net baseline model, which was pretrained on 1140231 digitally reconstructed radiographs generated from a public lung dataset for automatic tumor delineation on projections, and used later-fraction CBCT projections in the same angular range for testing. Six LMU University Hospital patient CBCT projection sets were reserved for validation and 11 for testing. Tracking accuracy was evaluated as the center-of-mass (COM) error and the Dice similarity coefficient (DSC) between the predicted and ground-truth segmentations.<i>Main results</i>. Over the 11 testing patients, each with around 40 CBCT projections tested, the patient refined models had a mean COM error of 2.3 ± 0.9 mm/4.2 ± 1.7 mm and a mean DSC of 0.83 ± 0.06/0.72 ± 0.13 for angles within [-10∘, 10^∘] / [80^∘, 100^∘]. The mean inference time was 68 ms/frame. The patient-specific training segmentation loss was found to be correlated to the segmentation performance at [-10∘, 10^∘].<i>Significance</i>. Our proposed approach allows patient-specific real-time markerless lung tumor tracking, which could be validated thanks to the novel 4DCBCT-aided GT generation approach.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468800","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":"Dose measurement of ophthalmic Ru-106/Rh-106 applicators with a diamond detector calibrated in a clinical megavoltage electron beam.","authors":"Assi Valve, Vappu Reijonen, Anna Rintala, Satu Strengell, Katri Nousiainen, Mikko Tenhunen","doi":"10.1088/1361-6560/adb9b1","DOIUrl":"10.1088/1361-6560/adb9b1","url":null,"abstract":"<p><p><i>Objective.</i>Uveal melanomas and retinoblastomas can be treated with ophthalmic beta-emitting ruthenium-106/rhodium-106 applicators. The applicator manufacturer provides a datasheet of the dosimetric properties of each applicator set, but the source strengths and 3D dose distributions should be verified by the end user with independent measurements.<i>Approach.</i>The purpose of this work was to calibrate diamond detector against low energy electron beam and determine necessary correction factors in the geometry of ophthalmic applicators to be able to perform quality assurance (QA) measurements for the applicators. Two separate sets of applicators were evaluated.<i>Main results.</i>The results showed good agreement with manufacturers' specifications. An average agreement of 3% to the manufacturer's reference data was observed: measured dose rate/reference = 0.97 ± 0.04 (mean ± SD), range 0.90-1.05.<i>Significance.</i>It can be concluded that megavoltage electron beam is suitable for calibration of a diamond detector. After calibration, detector can be used for an absolute dose measurement of a ruthenium-106/rhodium-106 applicator with sufficient performance to detect deviations larger than 10% in the QA before clinical use.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493225","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 deep learning model for inter-fraction head and neck anatomical changes in proton therapy.","authors":"Tiberiu Burlacu, Mischa Hoogeman, Danny Lathouwers, Zoltán Perkó","doi":"10.1088/1361-6560/adba39","DOIUrl":"10.1088/1361-6560/adba39","url":null,"abstract":"<p><p><i>Objective.</i>To assess the performance of a probabilistic deep learning based algorithm for predicting inter-fraction anatomical changes in head and neck patients.<i>Approach.</i>A probabilistic daily anatomy model (DAM) for head and neck patients DAM (DAM_HN) is built on the variational autoencoder architecture. The model approximates the generative joint conditional probability distribution of the repeat computed tomography (rCT) images and their corresponding masks on the planning CT images (pCT) and their masks. The model outputs deformation vector fields, which are used to produce possible rCTs and associated masks. The dataset is composed of 93 patients (i.e. 315 pCT-rCT pairs), 9 (i.e. 27 pairs) of which were set aside for final testing. The performance of the model is assessed based on the reconstruction accuracy and the generative performance for the set aside patients.<i>Main results.</i>The model achieves a DICE score of 0.83 and an image similarity score normalized cross-correlation of 0.60 on the test set. The generated parotid glands, spinal cord and constrictor muscle volume change distributions and center of mass shift distributions were also assessed. For all organs, the medians of the distributions are close to the true ones, and the distributions are broad enough to encompass the real observed changes. Moreover, the generated images display anatomical changes in line with the literature reported ones, such as the medial shifts of the parotids glands.<i>Significance.</i>DAM_HNis capable of generating realistic anatomies observed during the course of the treatment and has applications in anatomical robust optimization, treatment planning based on plan library approaches and robustness evaluation against inter-fractional changes.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143502922","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":"Metal artifacts correction based on a physics-informed nonlinear sinogram completion model.","authors":"Shuqiong Fan, Mengfei Li, Chuwen Huang, Xiaojuan Deng, Hongwei Li","doi":"10.1088/1361-6560/adbaad","DOIUrl":"10.1088/1361-6560/adbaad","url":null,"abstract":"<p><p><i>Objective.</i>Metal artifacts seriously deteriorate CT image quality. Current metal artifacts reduction (MAR) methods suffer from insufficient correction or easily introduce secondary artifacts. To better suppress metal artifacts, we propose a sinogram completion approach extracting and utilizing useful information that contained in the corrupted metal trace projections.<i>Approach.</i>Our method mainly contains two stages: sinogram interpolation by an improved normalization technique for initial correction and physics-informed nonlinear sinogram decomposition for further improvement. In the first stage, different from the popular normalized metal artifact reduction method, we propose a more meaningful normalization scheme for the interpolation procedure. In the second stage, instead of performing a linear sinogram decomposition as done in the physics-informed sinogram completion method, we introduce a nonlinear decomposition model that can accurately separate the sinogram into metal and non-metal contributions by better modeling the physical scanning process. The interpolated sinogram and physics-informed correction compensate each other to reach the optimal correction results.<i>Main results.</i>Experimental results on simulated and real data indicate that, in terms of both structures preservation and detail recovery, the proposed physics-informed nonlinear sinogram completion method achieves very competitive performance for MAR compared to existing methods.<i>Significance.</i>According to our knowledge, it is for the first time that a nonlinear sinogram decomposition model is proposed in the literature for metal artifacts correction. It might motivate further research exploring this idea for various sinogram processing tasks.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143516514","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":"Self-supervised U-transformer network with mask reconstruction for metal artifact reduction.","authors":"Fanning Kong, Zaifeng Shi, Huaisheng Cao, Yudong Hao, Qingjie Cao","doi":"10.1088/1361-6560/adbaae","DOIUrl":"10.1088/1361-6560/adbaae","url":null,"abstract":"<p><p><i>Objective</i>. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning has been widely explored for the metal artifact reduction (MAR) task. Nevertheless, paired metal artifact CT datasets suitable for training do not exist in reality. Although the synthetic CT image dataset provides additional training data, the trained networks still generalize poorly to real metal artifact data.<i>Approach.</i>A self-supervised U-shaped transformer network is proposed to focus on model generalizability enhancement in MAR tasks. This framework consists of a self-supervised mask reconstruction pre-text task and a down-stream task. In the pre-text task, the CT images are randomly corrupted by masks. They are recovered with themselves as the label, aiming at acquiring the artifacts and tissue structure of the actual physical situation. Down-stream task fine-tunes MAR target through labeled images. Utilizing the multi-layer long-range feature extraction capabilities of the Transformer efficiently captures features of metal artifacts. The incorporation of the MAR bottleneck allows for the distinction of metal artifact features through cross-channel self-attention.<i>Main result</i>. Experiments demonstrate that the framework maintains strong generalization ability in the MAR task, effectively preserving tissue details while suppressing metal artifacts. The results achieved a peak signal-to-noise ratio of 43.86 dB and a structural similarity index of 0.9863 while ensuring the efficiency of the model inference. In addition, the Dice coefficient and mean intersection over union are improved by 11.70% and 9.51% in the segmentation of the MAR image, respectively.<i>Significance.</i>The combination of unlabeled real-artifact CT images and labeled synthetic-artifact CT images facilitates a self-supervised learning process that positively contributes to model generalizability.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143516518","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":"Determination of output correction factors in magnetic fields using two methods for two detectors at the central axis.","authors":"Stephan Frick, Moritz Schneider, Daniela Thorwarth, Ralf-Peter Kapsch","doi":"10.1088/1361-6560/adb934","DOIUrl":"10.1088/1361-6560/adb934","url":null,"abstract":"<p><p><i>Objective.</i>Commissioning an MR-linac treatment planning system requires output correction factors,kB→,Qclin,Qmsrfclin,fmsr, for detectors to accurately measure the linac's output at various field sizes. In this study,kB→,Qclin,Qmsrfclin,fmsrwas determined at the central axis using two methods: one that combines the corrections for the influence of the magnetic field and the small field in a single factor (kB→,Qclin,Qmsrfclin,fmsr), and a second that isolates the magnetic field's influence, allowing the use of output correction factors without a magnetic field,kQclin,Qmsrfclin,fmsr, from literature for determiningkB→,Qclin,Qmsrfclin,fmsr.<i>Approach.</i>To determinekB→,Qclin,Qmsrfclin,fmsrand examine its behaviour across different photon energies and magnetic flux densitiesBin small fields, measurements with an ionization chamber (0.07 cm³sensitive volume) and a solid-state detector were carried out at an experimental facility for both approaches. Changes in absorbed dose to water with field size were determined via Monte Carlo simulations. To evaluate clinical applicability, additional measurements were conducted on a 1.5 T MR-linac.<i>Main results.</i>Both methods determined comparablekB→,Qclin,Qmsrfclin,fmsrresults. For field sizes >3 × 3 cm²,Branging from -1.5 to 1.5 T and photon energies of 6 and 8 MV, no change ofkQclin,Qmsrfclin,fmsras a function of the magnetic field was observed. Comparison with measurement results from the 1.5 T MR-linac confirm this. For ⩽3 × 3 cm²,kB→,Qclin,Qmsrfclin,fmsrdepends on photon energy andB. For 1.5 T and 6 MV,BreduceskQclin,Qmsrfclin,fmsrup to 3% for the ionization chamber and up to 7% for the solid-state detector.<i>Significance.</i>kB→,Qclin,Qmsrfclin,fmsrwere successfully determined for two detectors, enabling their use at a 1.5 T MR-linac. For field sizes of >3 × 3 cm²,kB→,Qclin,Qmsrfclin,fmsris one for most detectors suitable for small field dosimetry for all available perpendicular MR-linac systems, as confirmed in the literature. For these field sizes and detectors, the correction factor accounting for the dosimeter response change in the reference field due to the magnetic field,kB→,Qmsrfmsr, can be used for cross-calibration. Therefore, future research may only focus on small field sizes.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472827","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":"Guided synthesis of annotated lung CT images with pathologies using a multi-conditioned denoising diffusion probabilistic model (mDDPM).","authors":"Arjun Krishna, Ge Wang, Klaus Mueller","doi":"10.1088/1361-6560/adb9b3","DOIUrl":"10.1088/1361-6560/adb9b3","url":null,"abstract":"<p><p><i>Objective</i>. The training of AI models for medical image diagnostics requires highly accurate, diverse, and large training datasets with annotations and pathologies. Unfortunately, due to privacy and other constraints the amount of medical image data available for AI training remains limited, and this scarcity is exacerbated by the high overhead required for annotation. We address this challenge by introducing a new controlled framework for the generation of synthetic images complete with annotations, incorporating multiple conditional specifications as inputs.<i>Approach</i>. Using lung CT as a case study, we employ a denoising diffusion probabilistic model to train an unconditional large-scale generative model. We extend this with a classifier-free sampling strategy to develop a robust generation framework. This approach enables the generation of constrained and annotated lung CT images that accurately depict anatomy, successfully deceiving experts into perceiving them as real. Most notably, we demonstrate the generalizability of our multi-conditioned sampling approach by producing images with specific pathologies, such as lung nodules at designated locations, within the constrained anatomy.<i>Main results</i>. Our experiments reveal that our proposed approach can effectively produce constrained, annotated and diverse lung CT images that maintain anatomical consistency and fidelity, even for annotations not present in the training datasets. Moreover, our results highlight the superior performance of controlled generative frameworks of this nature compared to nearly every state-of-the-art image generative model when trained on comparable large medical datasets. Finally, we highlight how our approach can be extended to other medical imaging domains, further underscoring the versatility of our method.<i>Significance</i>. The significance of our work lies in its robust approach for generating synthetic images with annotations, facilitating the creation of highly accurate and diverse training datasets for AI applications and its wider applicability to other imaging modalities in medical domains. Our demonstrated capability to faithfully represent anatomy and pathology in generated medical images holds significant potential for various medical imaging applications, with high promise to lead to improved diagnostic accuracy and patient care.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493230","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":"Anticipating potential bottlenecks in adaptive proton FLASH therapy: a ridge filter reuse strategy.","authors":"Benjamin Roberfroid, Macarena S Chocan Vera, Camille Draguet, John A Lee, Ana M Barragán-Montero, Edmond Sterpin","doi":"10.1088/1361-6560/adb9b2","DOIUrl":"10.1088/1361-6560/adb9b2","url":null,"abstract":"<p><p><i>Objective.</i>Achieving FLASH dose rate with pencil beam scanning intensity modulated proton therapy is challenging. However, utilizing a single energy layer with a ridge filter (RF) can maintain dose rate and conformality. Yet, changes in patient anatomy over the treatment course can render the RF obsolete. Unfortunately, creating a new RF is time-consuming, thus, incompatible with online adaptation. To address this, we propose to re-optimize the spot weights while keeping the same initial RF.<i>Approach.</i>Data from six head and neck cancer patients with a repeated computed tomography (CT₂) were used. FLASH treatment plans were generated with three methods on CT₂: 'full-adaptation' (FA), optimized from scratch with a new RF; 'spot-adaptation only' (SAO), re-using initial RF but adjusting plan spot weights; and 'no adaptation' (NoA) where the dose from initial plans on initial CT (CT₁) was recomputed on CT₂. The prescribed dose per fraction was 9 Gy. Different beam angles were tested for each CT₂(1 beam per fraction). The FA, SAO and NoA plans were then compared on CT₂.<i>Main results.</i>Fractions with SAO showed a median decrease of 0.05 Gy for<i>D</i>98% and a median increase of 0.03 Gy for<i>D</i>2% of CTV when compared to their homologous FA plans on nominal case. Median conformity number decreased by 0.03. Median max dose to spinal cord increased by 0.09 Gy. The largest median increase in mean dose to organs was 0.03 Gy to the mandible. The largest observed median difference in organs receiving a minimal dose rate of 40 Gy s^-1was 0.5% for the mandible. Up to 16 of the 20 evaluated SAO fractions were thus deemed clinically acceptable, with up to 8 NoA plans already acceptable before adaptation.<i>Significance.</i>Proposed SAO workflow showed that for most of our evaluated plans, daily reprinting of RF was not necessary.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493222","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}