Physics in medicine and biology最新文献

{"title":"Anatomy-wise lung ventilation imaging for precise functional lung avoidance radiation therapy.","authors":"Zhi Chen, Zihan Li, Yu-Hua Huang, Xinzhi Teng, Jiang Zhang, Tianyu Xiong, Yanjing Dong, Liming Song, Ge Ren, Jing Cai","doi":"10.1088/1361-6560/adb123","DOIUrl":"10.1088/1361-6560/adb123","url":null,"abstract":"<p><p><i>Objective.</i>This study aimed to propose a method for obtaining anatomy-wise lung ventilation image (VI_aw) that enables functional assessment of lung parenchyma and tumor-blocked pulmonary segments. The VI_awwas used to define multiple functional volumes of the lung and thereby support radiation treatment planning.<i>Approach.</i>A super-voxel-based method was employed for functional assessment of lung parenchyma to generate VI_svd. In the VI_svdof the 11 patients with tumor blockage of the airway, the functional value in tumor-blocked segments was set to 0 to generate the VI_aw. The lung was divided into regions of high functional volume (HFV), unrecoverable low functional volume (LFV), and recoverable LFV (rLFV, the region in the tumor-blocked segment with a high function value based on the VI_svd) to design three intensity-modulated photon plans for five patients. These plans were an anatomical-lung-guided plan (aPlan), a functional-lung-guided plan (fPlan), and a recoverable functional-lung-guided plan (rfPlan) where the latter protected both HFV and rLFV.<i>Main results.</i>The LFV in the reference ventilation images and the tumor-blocked segments had a high overlap similarity coefficient value of 0.90 ± 0.07. The mean Spearman correlation between the VI_awand reference ventilation images was 0.72 ± 0.05 for the patient with tumor blockage of the airway. The<i>V</i>20 and mean dose of rLFV in rfPlan were lower than those in aPlan by 12.1 ± 8.4% and 13.0 ± 6.4%, respectively, and lower than those in fPlan by 14.9 ± 9.8% and 15.9 ± 6.5%, respectively.<i>Significance.</i>The VI_awcan reach a moderate-strong correlation with reference ventilation images and thus can identify rLFV to support treatment planning to preserve lung function.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075088","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 quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients.","authors":"Ahmet Ahunbay, Eric Paulson, Ergun Ahunbay, Ying Zhang","doi":"10.1088/1361-6560/adb099","DOIUrl":"10.1088/1361-6560/adb099","url":null,"abstract":"<p><p><i>Objective.</i>One bottleneck of magnetic resonance imaging (MRI)-guided online adaptive radiotherapy is the time-consuming daily online replanning process. The current leaf sequencing method takes up to 10 min, with potential dosimetric degradation and small segment openings that increase delivery time. This work aims to replace this process with a fast deep learning-based method to provide deliverable MLC sequences almost instantaneously, potentially accelerating and enhancing online adaption.<i>Approach.</i>Daily MRIs and plans from 242 daily fractions of 49 abdomen cancer patients on a 1.5 T MR-Linac were used. The architecture included: (1) a recurrent conditional generative adversarial network model to predict segment shapes from a fluence map (FM), recurrently predicting each segment's shape; and (2) a linear matrix equation module to optimize the monitor units (MUs) weights of segments. Multiple models with different segment numbers per beam (4-7) were trained. The final MLC sequences with the smallest relative absolute errors were selected. The predicted MLC sequence was imported into treatment planning system for dose calculation and compared with the original plans.<i>Main results.</i>The gamma passing rate for all fractions was 99.7 ± 0.2% (2%/2 mm criteria) and 92.7 ± 1.6% (1%/1 mm criteria). The average number of segments per beam in the proposed method was 6.0 ± 0.6 compared to 7.5 ± 0.3 in the original plan. The average total MUs were reduced from 1641 ± 262 to 1569.5 ± 236.7 in the predicted plans. The estimated delivery time was reduced from 9.7 min to 8.3 min, an average reduction of 14% and up to 25% for individual plans. Execution time for one plan was less than 10 s using a GTX1660TIGPU.<i>Significance.</i>The developed models can quickly and accurately generate an optimized, deliverable leaf sequence from a FM with fewer segments. This can seamlessly integrate into the current online replanning workflow, greatly expediting the daily plan adaptation process.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143067295","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":"Enhancing U-Net-based Pseudo-CT generation from MRI using CT-guided bone segmentation for radiation treatment planning in head & neck cancer patients.","authors":"Ama Katseena Yawson, Habiba Sallem, Katharina Seidensaal, Thomas Welzel, Sebastian Klüter, Katharina Maria Paul, Stefan Dorsch, Cedric Beyer, Jürgen Debus, Oliver Jäkel, Julia Bauer, Kristina Giske","doi":"10.1088/1361-6560/adb124","DOIUrl":"10.1088/1361-6560/adb124","url":null,"abstract":"<p><p><i>Objective.</i>This study investigates the effects of various training protocols on enhancing the precision of MRI-only Pseudo-CT generation for radiation treatment planning and adaptation in head & neck cancer patients. It specifically tackles the challenge of differentiating bone from air, a limitation that frequently results in substantial deviations in the representation of bony structures on Pseudo-CT images.<i>Approach.</i>The study included 25 patients, utilizing pre-treatment MRI-CT image pairs. Five cases were randomly selected for testing, with the remaining 20 used for model training and validation. A 3D U-Net deep learning model was employed, trained on patches of size 64³with an overlap of 32³. MRI scans were acquired using the Dixon gradient echo (GRE) technique, and various contrasts were explored to improve Pseudo-CT accuracy, including in-phase, water-only, and combined water-only and fat-only images. Additionally, bone extraction from the fat-only image was integrated as an additional channel to better capture bone structures on Pseudo-CTs. The evaluation involved both image quality and dosimetric metrics.<i>Main results.</i>The generated Pseudo-CTs were compared with their corresponding registered target CTs. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the base model using combined water-only and fat-only images were 19.20 ± 5.30 HU and 57.24 ± 1.44 dB, respectively. Following the integration of an additional channel using a CT-guided bone segmentation, the model's performance improved, achieving MAE and PSNR of 18.32 ± 5.51 HU and 57.82 ± 1.31 dB, respectively. The measured results are statistically significant, with a<i>p</i>-value<0.05. The dosimetric assessment confirmed that radiation treatment planning on Pseudo-CT achieved accuracy comparable to conventional CT.<i>Significance.</i>This study demonstrates improved accuracy in bone representation on Pseudo-CTs achieved through a combination of water-only, fat-only and extracted bone images; thus, enhancing feasibility of MRI-based simulation for radiation treatment planning.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080856","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":"Initial results of the Hyperion II^<i>D</i>PET insert for simultaneous PET-MRI applied to atherosclerotic plaque imaging in New-Zealand white rabbits.","authors":"P Gebhardt, B Lavin, A Phinikaridou, J MacKewn, M Henningsson, D Schug, A Salomon, P K Marsden, V Schulz, R M Botnar","doi":"10.1088/1361-6560/ad8c1f","DOIUrl":"10.1088/1361-6560/ad8c1f","url":null,"abstract":"<p><p><i>Objective.</i>In preclinical research,<i>in vivo</i>imaging of mice and rats is more common than any other animal species, since their physiopathology is very well-known and many genetically altered disease models exist. Animal studies based on small rodents are usually performed using dedicated preclinical imaging systems with high spatial resolution. For studies that require animal models such as mini-pigs or New-Zealand White (NZW) rabbits, imaging systems with larger bore sizes are required. In case of hybrid imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI), clinical systems have to be used, as these animal models do not typically fit in preclinical simultaneous PET-MRI scanners.<i>Approach.</i>In this paper, we present initial imaging results obtained with the Hyperion II^DPET insert which can accommodate NZW rabbits when combined with a large volume MRI RF coil. First, we developed a rabbit-sized image quality phantom of comparable size to a NZW rabbit in order to evaluate the PET imaging performance of the insert under high count rates. For this phantom, radioactive spheres with inner diameters between 3.95 and7.86mm were visible in a warm background with a tracer activity ratio of 4.1 to 1 and with a total¹⁸F activity in the phantom of58MBq at measurement start. Second, we performed simultaneous PET-MR imaging of atherosclerotic plaques in a rabbit<i>in vivo</i>using a single injection containing¹⁸F-FDG for detection of inflammatory activity, and Gd-ESMA for visualization of the aortic vessel wall and plaques with MRI.<i>Main results.</i>The fused PET-MR images reveal¹⁸F-FDG uptake within an active plaques with plaque thicknesses in the sub-millimeter range. Histology showed colocalization of¹⁸F-FDG uptake with macrophages in the aortic vessel wall lesions.<i>Significance.</i>Our initial results demonstrate that this PET insert is a promising system for simultaneous high-resolution PET-MR atherosclerotic plaque imaging studies in NZW rabbits.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142522674","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 multiplexing method based on multidimensional readout method<sup />.","authors":"Xin Yu, Han Liu, Huiping Zhao, Jinyong Tao, Da Liang, Jiayang Zeng, Jianfeng Xu, Siwei Xie, Qiyu Peng","doi":"10.1088/1361-6560/adae4c","DOIUrl":"10.1088/1361-6560/adae4c","url":null,"abstract":"<p><p><i>Objective.</i>To develop and validate a novel multidimensional readout method that significantly reduces the number of readout channels (NRC) in PET detectors while maintaining high spatial and energy performance.<i>Approach.</i>We arranged a3×3×4SiPM array in multiple dimensions and employed row/column/layer summation with a resistor-based splitting circuit. We then applied denoising methods to enhance the peak-to-valley ratio in the decoding map, ensuring accurate crystal-position determination. Additionally, we investigated the system's energy response at 511 keV and evaluated the suitability for both clinical and research PET systems.<i>Main results.</i>The proposed multidimensional readout method achieved a favorable multiplexing ratio, lowering the total NRCs without compromising energy resolution at 511 keV. Our tests demonstrated that a SiPM bias voltage of 31 V effectively balances gain and saturation effects, resulting in reliable energy measurements.<i>Significance.</i>By reducing system complexity, cost, and power consumption, the multidimensional readout method presents a practical alternative to conventional readout schemes for PET and other large-scale sensor arrays. Additionally, the approach can manage simultaneous multi-layer hits by arranging detector layers and, when needed, uses ICS detection to correct for scatter events. Its adaptable architecture allows scaling to higher dimensions for broader applications (e.g. SPECT, CT, LiDAR). These features make it a valuable contribution toward more efficient, high-performance imaging technologies in both clinical and industrial settings.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143041063","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 dual-domain network with division residual connection and feature fusion for CBCT scatter correction.","authors":"Shuo Yang, Zhe Wang, Linjie Chen, Ying Cheng, Huamin Wang, Xiao Bai, Guohua Cao","doi":"10.1088/1361-6560/adaf06","DOIUrl":"10.1088/1361-6560/adaf06","url":null,"abstract":"<p><p><i>Objective.</i>This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in cone-beam computed tomography (CBCT).<i>Approach.</i>The proposed network comprises a projection-domain sub-network and an image-domain sub-network. The projection-domain sub-network utilizes a division residual network to amplify the difference between scatter signals and imaging signals, facilitating the learning of scatter signals. The image-domain sub-network contains dual encoders and a single decoder. The dual encoders extract features from two inputs parallelly, and the decoder fuses the extracted features from the two encoders and maps the fused features back to the final high-quality image. Of the two input images to the image-domain sub-network, one is the scatter-contaminated image analytically reconstructed from the scatter-contaminated projections, and the other is the pre-processed image reconstructed from the pre-processed projections produced by the projection-domain sub-network.<i>Main results.</i>Experimental results on both synthetic and real data demonstrate that our method can effectively reduce scatter artifacts and restore image details. Quantitative analysis using synthetic data shows the mean absolute error was reduced by 74% and peak signal-to-noise ratio increased by 57% compared to the scatter-contaminated ones. Testing on real data found a 38% increase in contrast-to-noise ratio with our method compared to the scatter-contaminated image. Additionally, our method consistently outperforms comparative methods such as U-Net, DSE-Net, deep residual convolution neural network (DRCNN) and the collimator-based method.<i>Significance.</i>A dual-domain network that leverages projection-domain division residual connection and image-domain feature fusion has been proposed for CBCT scatter correction. It has potential applications for reducing scatter artifacts and preserving image details in CBCT.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052619","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":"Diffusion transformer model with compact prior for low-dose PET reconstruction.","authors":"Bin Huang, Xubiao Liu, Lei Fang, Qiegen Liu, Bingxuan Li","doi":"10.1088/1361-6560/adac25","DOIUrl":"10.1088/1361-6560/adac25","url":null,"abstract":"<p><p><i>Objective.</i>Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure through low-dose PET scans is beneficial for patient safety, it often results in insufficient statistical data. This scarcity of data poses significant challenges for accurately reconstructing high-quality images, which are essential for reliable diagnostic outcomes.<i>Approach.</i>In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior to enhance the reconstruction quality of low-dose PET imaging. In light of current research findings, we present a pioneering PET reconstruction model that integrates diffusion and transformer models for joint optimization. This model combines the powerful distribution mapping abilities of diffusion model with the capacity of transformers to capture long-range dependencies, offering significant advantages for low-dose PET reconstruction. Additionally, the incorporation of the lesion refining block and alternating direction method of multipliers enhance the recovery capability of lesion regions and preserves detail information, solving blurring problems in lesion areas and texture details of most deep learning frameworks.<i>Main results</i>. Experimental results validate the effectiveness of DTM in reconstructing low-dose PET image quality. DTM achieves state-of-the-art performance across various metrics, including PSNR, SSIM, NRMSE, CR, and COV, demonstrating its ability to reduce noise while preserving critical clinical details such as lesion structure and texture. Compared with baseline methods, DTM delivers best results in denoising and lesion preservation across various low-dose levels, including 10%, 25%, 50%, and even ultra-low-dose level such as 1%. DTM shows robust generalization performance on phantom and patient datasets, highlighting its adaptability to varying imaging conditions.<i>Significance</i>. This approach reduces radiation exposure while ensuring reliable imaging for early disease detection and clinical decision-making, offering a promising tool for both clinical and research applications.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009996","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":"Interactive segmentation for accurately isolating metastatic lesions from low-resolution, large-size bone scintigrams.","authors":"Xiaoqiang Ma, Qiang Lin, Xianwu Zeng, Yongchun Cao, Zhengxing Man, Caihong Liu, Xiaodi Huang","doi":"10.1088/1361-6560/adaf07","DOIUrl":"10.1088/1361-6560/adaf07","url":null,"abstract":"<p><p><i>Objective.</i>Bone is a common site for the metastasis of malignant tumors, and single photon emission computed tomography (SPECT) is widely used to detect these metastases. Accurate delineation of metastatic bone lesions in SPECT images is essential for developing treatment plans. However, current clinical practices rely on manual delineation by physicians, which is prone to variability and subjective interpretation. While computer-aided diagnosis systems have the potential to improve diagnostic efficiency, fully automated segmentation approaches frequently suffer from high false positive rates, limiting their clinical utility.<i>Approach.</i>This study proposes an interactive segmentation framework for SPECT images, leveraging the deep convolutional neural networks to enhance segmentation accuracy. The proposed framework incorporates a U-shaped backbone network that effectively addresses inter-patient variability, along with an interactive attention module that enhances feature extraction in densely packed bone regions.<i>Main results.</i>Extensive experiments using clinical data validate the effectiveness of the proposed framework. Furthermore, a prototype tool was developed based on this framework to assist in the clinical segmentation of metastatic bone lesions and to support the creation of a large-scale dataset for bone metastasis segmentation.<i>Significance.</i>In this study, we proposed an interactive segmentation framework for metastatic lesions in bone scintigraphy to address the challenging task of labeling low-resolution, large-size SPECT bone scans. The experimental results show that the model can effectively segment the bone metastases of lung cancer interactively. In addition, the prototype tool developed based on the model has certain clinical application value.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052791","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":"Pulse-by-pulse treatment planning and its application to generic observations of ultra-high dose rate (FLASH) radiotherapy with photons and protons.","authors":"James L Bedford","doi":"10.1088/1361-6560/adaf04","DOIUrl":"10.1088/1361-6560/adaf04","url":null,"abstract":"<p><p><i>Objective.</i>The exact temporal characteristics of beam delivery affect the efficacy and outcome of ultra-high dose rate (UHDR or 'FLASH') radiotherapy, mainly due to the influence of the beam pulse structure on mean dose rate. Single beams may also be delivered in separate treatment sessions to elevate mean dose rate. This paper therefore describes a model for pulse-by-pulse treatment planning and demonstrates its application by making some generic observations of the characteristics of FLASH radiotherapy with photons and protons.<i>Approach.</i>A beam delivery model was implemented into the AutoBeam (v6.3) inverse treatment planning system, so that the individual pulses of the delivery system could be explicitly described during optimisation. The delivery model was used to calculate distributions of time-averaged and dose-averaged mean dose rate and the dose modifying factor for FLASH was then determined and applied to dose calculated by a discrete ordinates Boltzmann solver. The method was applied to intensity-modulated radiation therapy with photons as well as to passive scattering and pencil beam scanning with protons for the case of a simple phantom geometry with a prescribed dose of 36 Gy in 3 fractions.<i>Main results.</i>Dose and dose rate are highest in the target region, so FLASH sparing is most pronounced around the planning target volume (PTV). When using a treatment session per beam, OAR sparing is possible more peripherally. The sparing with photons is higher than with protons because the dose to OAR is higher with photons.<i>Significance.</i>The framework provides an efficient method to determine the optimal technique for delivering clinical dose distributions using FLASH. The most sparing occurs close to the PTV for hypofractionated treatments.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143053175","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 simple plan strategy to optimize the biological effective dose delivered in robotic radiosurgery of vestibular schwannomas.","authors":"A Moutsatsos, E Pantelis","doi":"10.1088/1361-6560/adaf72","DOIUrl":"10.1088/1361-6560/adaf72","url":null,"abstract":"<p><p>Using the concept of biologically effective dose (BED), the effect of sublethal DNA damage repair (SLR) on the bio-efficacy of prolonged radiotherapy treatments can be quantified (BED_SLR). Such treatments, lasting more than 20 min, are typically encountered in stereotactic radiosurgery (SRS) applications using the CyberKnife (CK) and Gamma knife systems. Evaluating the plan data from 45 Vestibular Schwannoma (VS) cases treated with single fraction CK-SRS, this work demonstrates a statistically significant correlation between the marginal BED_SLRdelivered to the target (<i>m-</i>BED_SLR) and the ratio of the mean collimator size weighted by the fraction of total beams delivered with each collimator (wmCs), to the tumor volume (Tv). The correlation between<i>m-</i>BED_SLRandwmCsTvdatasets was mathematically expressed by the power functionm-BEDSLR=85.21 (±1.7%)⋅(wmCsTv)(0.05±7%) enabling continuous<i>m-</i>BED_SLRpredictions. Using this formula, a specific range of<i>m-</i>BED_SLRlevels can<i>a priori</i>be targeted during treatment planning through proper selection of collimator size(s) for a given tumor volume. Inversely, for a selected set of collimators, the optimization range of<i>m-</i>BED_SLRcan be determined assuming that all beams are delivered with the smallest and largest collimator size. For single collimator cases or when the relative usage of each collimator size is known or estimated, a specific<i>m-</i>BED_SLRlevel can be predicted within 3% uncertainty. The proposed equation is valid for the fixed CK collimators and a physical dose prescription (<i>D</i>_pr) of 13 Gy. For alternate<i>D</i>_prin the range of 11-14 Gy, a linear relationship was found between relative changes of<i>m-</i>BED_SLR(<i>D</i>_pr) and<i>D</i>_prwith respect to<i>m-</i>BED_SLR(13 Gy) and 13 Gy, respectively. The proposed methodology is simple and easy to implement in the clinical setting allowing for optimization of the treatment's bio-effectiveness, in terms of the delivered BED, during treatment planning.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143060194","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}