arXiv - PHYS - Medical Physics最新文献_第7页

CBCT scatter correction with dual-layer flat-panel detector 使用双层平板探测器进行 CBCT 散射校正

arXiv - PHYS - Medical Physics Pub Date : 2024-08-09 DOI: arxiv-2408.04943

Xin Zhang, Jixiong Xie, Ting Su, Jiongtao Zhu, Han Cui, Yuhang Tan, Dongmei Xia, Hairong Zheng, Dong Liang, Yongshuai Ge

{"title":"CBCT scatter correction with dual-layer flat-panel detector","authors":"Xin Zhang, Jixiong Xie, Ting Su, Jiongtao Zhu, Han Cui, Yuhang Tan, Dongmei Xia, Hairong Zheng, Dong Liang, Yongshuai Ge","doi":"arxiv-2408.04943","DOIUrl":"https://doi.org/arxiv-2408.04943","url":null,"abstract":"Background: Recently, the popularity of dual-layer flat-panel detector\u0000(DL-FPD) based dual-energy cone-beam CT (DE-CBCT) imaging has been increasing.\u0000However, the image quality of DE-CBCT remains constrained by the Compton\u0000scattered X-ray photons. Purpose: The objective of this study is to develop an energy-modulated\u0000scatter correction method for DL-FPD based CBCT imaging. Methods: The DLFPD can measure primary and Compton scattered X-ray photons\u0000having dfferent energies: X-ray photons with lower energies are predominantly\u0000captured by the top detector layer, while X-ray photons with higher energies\u0000are primarily collected by the bottom detector layer. Afterwards, the scattered\u0000X-ray signals acquired on both detector layers can be analytically retrieved\u0000via a simple model along with several pre-calibrated parameters. Both Monte\u0000Carlo simulations and phantom experiments are performed to verify this\u0000energy-modulated scatter correction method utilizing DL-FPD. Results: Results demonstrate that the proposed energy-modulated scatter\u0000correction method can signficantly reduce the shading artifacts of both\u0000low-energy and high-energy CBCT images acquired from DL-FPD. On average, the\u0000image non-uniformity is reduce by over 77% in the low-energy CBCT image and by\u0000over 66% in the high-energy CBCT image. Moreover, the accuracy of the\u0000decomposed multi-material results is also substantially improved. Conclusion: In the future, Compton scattered X-ray signals can be easily\u0000corrected for CBCT systems using DL-FPDs.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the Impact of Cochlear Implant Stimulation Artefacts in EEG Recordings: Unveiling Potential Benefits 探索脑电图记录中人工耳蜗刺激伪影的影响：揭示潜在优势

arXiv - PHYS - Medical Physics Pub Date : 2024-08-08 DOI: arxiv-2408.04429

Hongmei Hu, Ben Williges, Deborah Vickers

引用次数: 0

Cold plasma with zirconia nanoparticles for lung cancer via TGF-b{eta} signaling pathway 冷等离子与氧化锆纳米颗粒通过 TGF-b{eta} 信号通路治疗肺癌

arXiv - PHYS - Medical Physics Pub Date : 2024-08-07 DOI: arxiv-2408.11838

Yueye Huang, Rui Zhang, Xiao Chen, Fei Cao, Qiujie Fang, Qingnan Xu, Shicong Huang, Yufan Wang, Guojun Chen, Zhitong Chen

引用次数: 0

Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models 通过浅层扩散模型的潜变量优化进行迭代 CT 重建

arXiv - PHYS - Medical Physics Pub Date : 2024-08-06 DOI: arxiv-2408.03156

Sho Ozaki, Shizuo Kaji, Toshikazu Imae, Kanabu Nawa, Hideomi Yamashita, Keiichi Nakagawa

{"title":"Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models","authors":"Sho Ozaki, Shizuo Kaji, Toshikazu Imae, Kanabu Nawa, Hideomi Yamashita, Keiichi Nakagawa","doi":"arxiv-2408.03156","DOIUrl":"https://doi.org/arxiv-2408.03156","url":null,"abstract":"Image generative AI has garnered significant attention in recent years. In\u0000particular, the diffusion model, a core component of recent generative AI,\u0000produces high-quality images with rich diversity. In this study, we propose a\u0000novel CT reconstruction method by combining the denoising diffusion\u0000probabilistic model with iterative CT reconstruction. In sharp contrast to\u0000previous studies, we optimize the fidelity loss of CT reconstruction with\u0000respect to the latent variable of the diffusion model, instead of the image and\u0000model parameters. To suppress anatomical structure changes produced by the\u0000diffusion model, we shallow the diffusion and reverse processes, and fix a set\u0000of added noises in the reverse process to make it deterministic during\u0000inference. We demonstrate the effectiveness of the proposed method through\u0000sparse view CT reconstruction of 1/10 view projection data. Despite the\u0000simplicity of the implementation, the proposed method shows the capability of\u0000reconstructing high-quality images while preserving the patient's anatomical\u0000structure, and outperforms existing methods including iterative reconstruction,\u0000iterative reconstruction with total variation, and the diffusion model alone in\u0000terms of quantitative indices such as SSIM and PSNR. We also explore further\u0000sparse view CT using 1/20 view projection data with the same trained diffusion\u0000model. As the number of iterations increases, image quality improvement\u0000comparable to that of 1/10 sparse view CT reconstruction is achieved. In\u0000principle, the proposed method can be widely applied not only to CT but also to\u0000other imaging modalities such as MRI, PET, and SPECT.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery 在机器人手术中利用视觉特质先验进行个性化联合器械分割

arXiv - PHYS - Medical Physics Pub Date : 2024-08-06 DOI: arxiv-2408.03208

Jialang Xu, Jiacheng Wang, Lequan Yu, Danail Stoyanov, Yueming Jin, Evangelos B. Mazomenos

{"title":"Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery","authors":"Jialang Xu, Jiacheng Wang, Lequan Yu, Danail Stoyanov, Yueming Jin, Evangelos B. Mazomenos","doi":"arxiv-2408.03208","DOIUrl":"https://doi.org/arxiv-2408.03208","url":null,"abstract":"Personalized federated learning (PFL) for surgical instrument segmentation\u0000(SIS) is a promising approach. It enables multiple clinical sites to\u0000collaboratively train a series of models in privacy, with each model tailored\u0000to the individual distribution of each site. Existing PFL methods rarely\u0000consider the personalization of multi-headed self-attention, and do not account\u0000for appearance diversity and instrument shape similarity, both inherent in\u0000surgical scenes. We thus propose PFedSIS, a novel PFL method with visual trait\u0000priors for SIS, incorporating global-personalized disentanglement (GPD),\u0000appearance-regulation personalized enhancement (APE), and shape-similarity\u0000global enhancement (SGE), to boost SIS performance in each site. GPD represents\u0000the first attempt at head-wise assignment for multi-headed self-attention\u0000personalization. To preserve the unique appearance representation of each site\u0000and gradually leverage the inter-site difference, APE introduces appearance\u0000regulation and provides customized layer-wise aggregation solutions via\u0000hypernetworks for each site's personalized parameters. The mutual shape\u0000information of instruments is maintained and shared via SGE, which enhances the\u0000cross-style shape consistency on the image level and computes the\u0000shape-similarity contribution of each site on the prediction level for updating\u0000the global parameters. PFedSIS outperforms state-of-the-art methods with +1.51%\u0000Dice, +2.11% IoU, -2.79 ASSD, -15.55 HD95 performance gains. The corresponding\u0000code and models will be released at https://github.com/wzjialang/PFedSIS.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling power-law ultrasound absorption using a time-fractional, static memory, Fourier pseudo-spectral method 使用时间分数、静态记忆、傅立叶伪谱法模拟幂律超声吸收

arXiv - PHYS - Medical Physics Pub Date : 2024-08-05 DOI: arxiv-2408.02541

Matthew. J. King, Timon. S. Gutleb, B. E. Treeby, B. T. Cox

引用次数: 0

Self-calibrating Intelligent OCT-SLO SystemMayank Goswami 自校准智能 OCT-SLO 系统Mayank Goswami

arXiv - PHYS - Medical Physics Pub Date : 2024-08-04 DOI: arxiv-2408.02703

Mayank Goswami

{"title":"Self-calibrating Intelligent OCT-SLO SystemMayank Goswami","authors":"Mayank Goswami","doi":"arxiv-2408.02703","DOIUrl":"https://doi.org/arxiv-2408.02703","url":null,"abstract":"A unique sample independent 3D self calibration methodology is tested on a\u0000unique optical coherence tomography and multi-spectral scanning laser\u0000ophthalmoscope (OCT-SLO) hybrid system. Operators visual cognition is replaced\u0000by computer vision using the proposed novel fully automatic AI-driven system\u0000design. Sample specific automatic contrast adjustment of the beam is achieved\u0000on the pre-instructed region of interest. The AI model deduces infrared,\u0000fluorescence, and visual spectrum optical alignment by estimating\u0000pre-instructed features quantitatively. The tested approach, however, is\u0000flexible enough to utilize any apt AI model. Relative comparison with classical\u0000signal-to-noise-driven automation is shown to be 200 percent inferior and 130\u0000percent slower than the AI-driven approach. The best spatial resolution of the\u0000system is found to be (a) 2.41 microns in glass bead eye phantom, 0.76 with STD\u00000.46 microns in the mouse retina in the axial direction, and (b) better than\u0000228 line pair per millimeter (lp per mm) or 2 microns for all three spectrums,\u0000i.e., 488 nm, 840 nm, and 520 to 550 nm emission in coronal, frontal or x-y\u0000plane. Intelligent automation reduces the possibility of developing cold\u0000cataracts (especially in mouse imaging) and patient-associated discomfort due\u0000to delay during manual alignment by facilitating easy handling for swift ocular\u0000imaging and better accuracy. The automatic novel tabletop compact system\u0000provides true functional 3D images in three different spectrums for dynamic\u0000sample profiles. This is especially useful for photodynamic imaging treatment.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive characterization of tumor therapeutic response with simultaneous mapping cell size, density, and transcytolemmal water exchange 通过同时绘制细胞大小、密度和跨细胞膜水交换图，全面描述肿瘤治疗反应的特征

arXiv - PHYS - Medical Physics Pub Date : 2024-08-04 DOI: arxiv-2408.01918

Diwei Shi, Sisi Li, Fan Liu, Xiaoyu Jiang, Lei Wu, Li Chen, Quanshui Zheng, Haihua Bao, Hua Guo, Junzhong Xu

{"title":"Comprehensive characterization of tumor therapeutic response with simultaneous mapping cell size, density, and transcytolemmal water exchange","authors":"Diwei Shi, Sisi Li, Fan Liu, Xiaoyu Jiang, Lei Wu, Li Chen, Quanshui Zheng, Haihua Bao, Hua Guo, Junzhong Xu","doi":"arxiv-2408.01918","DOIUrl":"https://doi.org/arxiv-2408.01918","url":null,"abstract":"Early assessment of tumor therapeutic response is an important topic in\u0000precision medicine to optimize personalized treatment regimens and reduce\u0000unnecessary toxicity, cost, and delay. Although diffusion MRI (dMRI) has shown\u0000potential to address this need, its predictive accuracy is limited, likely due\u0000to its unspecific sensitivity to overall pathological changes. In this work, we\u0000propose a new quantitative dMRI-based method dubbed EXCHANGE (MRI of water\u0000Exchange, Confined and Hindered diffusion under Arbitrary Gradient waveform\u0000Encodings) for simultaneous mapping of cell size, cell density, and\u0000transcytolemmal water exchange. Such rich microstructural information\u0000comprehensively evaluates tumor pathologies at the cellular level. Validations\u0000using numerical simulations and in vitro cell experiments confirmed that the\u0000EXCHANGE method can accurately estimate mean cell size, density, and water\u0000exchange rate constants. The results from in vivo animal experiments show the\u0000potential of EXCHANGE for monitoring tumor treatment response. Finally, the\u0000EXCHANGE method was implemented in breast cancer patients with neoadjuvant\u0000chemotherapy, demonstrating its feasibility in assessing tumor therapeutic\u0000response in clinics. In summary, a new, quantitative dMRI-based EXCHANGE method\u0000was proposed to comprehensively characterize tumor microstructural properties\u0000at the cellular level, suggesting a unique means to monitor tumor treatment\u0000response in clinical practice.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterization of 3D printed micro-blades for cutting tissue-embedding material 用于切割组织包埋材料的 3D 打印微型刀片的特性分析

arXiv - PHYS - Medical Physics Pub Date : 2024-08-03 DOI: arxiv-2408.03155

Saisneha Koppaka, David Doan, Wei Cai, Wendy Gu, Sindy K. Y. Tang

{"title":"Characterization of 3D printed micro-blades for cutting tissue-embedding material","authors":"Saisneha Koppaka, David Doan, Wei Cai, Wendy Gu, Sindy K. Y. Tang","doi":"arxiv-2408.03155","DOIUrl":"https://doi.org/arxiv-2408.03155","url":null,"abstract":"Cutting soft materials on the microscale has emerging applications in\u0000single-cell studies, tissue microdissection for organoid culture, drug screens,\u0000and other analyses. However, the cutting process is complex and remains\u0000incompletely understood. Furthermore, precise control over blade geometries,\u0000such as the blade tip radius, has been difficult to achieve. In this work, we\u0000use the Nanoscribe 3D printer to precisely fabricate micro-blades (i.e., blades\u0000<1 mm in length) and blade grid geometries. This fabrication method enables a\u0000systematic study of the effect of blade geometry on the indentation cutting of\u0000paraffin wax, a common tissue-embedding material. First, we print straight\u0000micro-blades with tip radius ranging from ~100 nm to 10 um. The micro-blades\u0000are mounted in a custom nanoindentation setup to measure the cutting energy\u0000during indentation cutting of paraffin. Cutting energy, measured as the\u0000difference in dissipated energy between the first and second loading cycles,\u0000decreases as blade tip radius decreases, until ~357 nm when the cutting energy\u0000plateaus despite further decrease in tip radius. Second, we expand our method\u0000to blades printed in unconventional configurations, including parallel blade\u0000structures and blades arranged in a square grid. Under the conditions tested,\u0000the cutting energy scales approximately linearly with the total length of the\u0000blades comprising the blade structure. The experimental platform described can\u0000be extended to investigate other blade geometries and guide the design of\u0000microscale cutting of soft materials.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GAMBAS -- Fast Beam Arrangement Selection for Proton Therapy using a Nearest Neighbour Model GAMBAS -- 利用近邻模型为质子治疗快速选择射束排列

arXiv - PHYS - Medical Physics Pub Date : 2024-08-02 DOI: arxiv-2408.01206

Renato Bellotti, Nicola Bizzocchi, Antony J. Lomax, Andreas Adelmann, Damien C. Weber, Jan Hrbacek

{"title":"GAMBAS -- Fast Beam Arrangement Selection for Proton Therapy using a Nearest Neighbour Model","authors":"Renato Bellotti, Nicola Bizzocchi, Antony J. Lomax, Andreas Adelmann, Damien C. Weber, Jan Hrbacek","doi":"arxiv-2408.01206","DOIUrl":"https://doi.org/arxiv-2408.01206","url":null,"abstract":"Purpose: Beam angle selection is critical in proton therapy treatment\u0000planning, yet automated approaches remain underexplored. This study presents\u0000and evaluates GAMBAS, a novel, fast machine learning model for automatic beam\u0000angle selection. Methods: The model extracts a predefined set of anatomical features from a\u0000patient's CT and structure contours. Using these features, it identifies the\u0000most similar patient from a training database and suggests that patient's beam\u0000arrangement. A retrospective study with 19 patients was conducted, comparing\u0000this model's suggestions to human planners' choices and randomly selected beam\u0000arrangements from the training dataset. An expert treatment planner evaluated\u0000the plans on quality (scale 1-5), ranked them, and guessed the method used. Results: The number of acceptable (score 4 or 5) plans was comparable between\u0000human-chosen 17 (89%) and model-selected 16(84%) beam arrangements. The fully\u0000automatic treatment planning took between 4 - 7 min (mean 5 min). Conclusion: The model produces beam arrangements of comparable quality to\u0000those chosen by human planners, demonstrating its potential as a fast tool for\u0000quality assurance and patient selection, although it is not yet ready for\u0000clinical use.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0