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PETformer network enables ultra-low-dose total-body PET imaging without structural prior Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 Yuxiang Li, Yusheng Li
Objective. Positron emission tomography (PET) is essential for non-invasive imaging of metabolic processes in healthcare applications. However, the use of radiolabeled tracers exposes patients to ionizing radiation, raising concerns about carcinogenic potential, and warranting efforts to minimize doses without sacrificing diagnostic quality. Approach. In this work, we present a novel neural network
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A carbon nanotube x-ray source array designed for a new multisource cone beam computed tomography scanner Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 Boyuan Li, Christina R Inscoe, Shuang Xu, Timothy Capo, Donald A Tyndall, Yueh Z Lee, Jianping Lu, Otto Zhou
Cone beam computed tomography (CBCT) is known to suffer from strong scatter and cone beam artifacts. The purpose of this study is to develop and characterize a rapidly scanning carbon nanotube (CNT) field emission x-ray source array to enable a multisource CBCT (ms-CBCT) image acquisition scheme which has been demonstrated to overcome these limitations. A CNT x-ray source array with eight evenly spaced
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Determination of lutetium density in LYSO crystals: methodology and PET detector applications Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 T C Thien, M V Nemallapudi
Objective. Lutetium yttrium oxyorthosilicate (LYSO) scintillation crystals are used in positron emission tomography (PET) due to their high gamma attenuation, fair energy resolution, and fast scintillation decay time. The enduring presence of the 176Lu isotope, characterized by a half-life of 37.9 billion years, imparts a consistent radiation background (BG) profile that depends on the geometry and
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Polyp segmentation with interference filtering and dynamic uncertainty mining Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 Yunhua Zhang, Gang Yang, Congjin Gong, Jianhao Zhang, Shuo Wang, Yutao Wang
Objective. Accurate polyp segmentation from colo-noscopy images plays a crucial role in the early diagnosis and treatment of colorectal cancer. However, existing polyp segmentation methods are inevitably affected by various image noises, such as reflections, motion blur, and feces, which significantly affect the performance and generalization of the model. In addition, coupled with ambiguous boundaries
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A blurring correction method suitable to analyze quantitative x-ray images derived from energy-resolving photon counting detector Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 Daiki Kobayashi, Hiroaki Hayashi, Rina Nishigami, Tatsuya Maeda, Takashi Asahara, Yuki Kanazawa, Akitoshi Katsumata, Natsumi Kimoto, Shuichiro Yamamoto
Objective. The purpose of this study is to propose a novel blurring correction method that enables accurate quantitative analysis of the object edge when using energy-resolving photon counting detectors (ERPCDs). Although the ERPCDs have the ability to generate various quantitative analysis techniques, such as the derivations of effective atomic number (Z eff) and bone mineral density values, at the
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In situ tumor model for longitudinal in silico imaging trials Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 Aunnasha Sengupta, Miguel A Lago, Aldo Badano
Objective. In this article, we introduce a computational model for simulating the growth of breast cancer lesions accounting for the stiffness of surrounding anatomical structures. Approach. In our model, ligaments are classified as the most rigid structures while the softer parts of the breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly
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Measurement of the 12C(p,n)12N reaction cross section below 150 MeV Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 Brian Zapien-Campos, Zahra Ahmadi Ganjeh, Stefan Both, Peter Dendooven
Objective. Proton therapy currently faces challenges from clinical complications on organs-at-risk due to range uncertainties. To address this issue, positron emission tomography (PET) of the proton-induced 11C and 15O activity has been used to provide feedback on the proton range. However, this approach is not instantaneous due to the relatively long half-lives of these nuclides. An alternative nuclide
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Motion compensated cone-beam CT reconstruction using an a priori motion model from CT simulation: a pilot study Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 Michael Lauria, Claudia Miller, Kamal Singhrao, John Lewis, Weicheng Lin, Dylan O’Connell, Louise Naumann, Bradley Stiehl, Anand Santhanam, Peter Boyle, Ann C Raldow, Jonathan Goldin, Igor Barjaktarevic, Daniel A Low
Objective. To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) was previously developed. MC-SART employs a 4D-CBCT reconstruction to obtain an initial model, which suffers from a lack of sufficient projections in each bin. The purpose of this study is to demonstrate
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Self-supervised dual-domain balanced dropblock-network for low-dose CT denoising Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-26 Ran An, Ke Chen, Hongwei Li
Objective. Self-supervised learning methods have been successfully applied for low-dose computed tomography (LDCT) denoising, with the advantage of not requiring labeled data. Conventional self-supervised methods operate only in the image domain, ignoring valuable priors in the sinogram domain. Recently proposed dual-domain methods address this limitation but encounter issues with blurring artifacts
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New optically stimulated luminescence dosimetry film optimized for energy dependence guided by Monte Carlo simulations Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-22 Marijke De Saint-Hubert, Marco Caprioli, Luana de Freitas Nascimento, Laurence Delombaerde, Katleen Himschoot, Dirk Vandenbroucke, Paul Leblans, Wouter Crijns
Optically stimulated luminescence (OSL) film dosimeters, based on BaFBr:Eu2+ phosphor material, have major dosimetric advantages such as dose linearity, high spatial resolution, film re-usability, and immediate film readout. However, they exhibit an energy-dependent over-response at low photon energies because they are not made of tissue-equivalent materials. In this work, the OSL energy-dependent
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Machine learning approach for proton range verification using real-time prompt gamma imaging with Compton cameras: addressing the total deposited energy information gap Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-21 Majid Kazemi Kozani
Objective. Compton camera imaging shows promise as a range verification technique in proton therapy. This work aims to assess the performance of a machine learning model in Compton camera imaging for proton beam range verification improvement. Approach. The presented approach was used to recognize Compton events and estimate more accurately the prompt gamma (PG) energy in the Compton camera to reconstruct
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Nonlocal based FISTA network for noninvasive cardiac transmembrane potential imaging Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-21 Ao Ran, Linsheng Cheng, Shuting Xie, Muqing Liu, Cailing Pu, Hongjie Hu, Huafeng Liu
Objective. The primary aim of our study is to advance our understanding and diagnosis of cardiac diseases. We focus on the reconstruction of myocardial transmembrane potential (TMP) from body surface potential mapping. Approach. We introduce a novel methodology for the reconstruction of the dynamic distribution of TMP. This is achieved through the integration of convolutional neural networks with conventional
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Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-21 Xueli Chen, Yu Meng, Lin Wang, Wangting Zhou, Duofang Chen, Hui Xie, Shenghan Ren
Objective. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed problem to a certain extent, but its accuracy is highly dependent on a prior information, resulting in a less stable and adaptable method. Data-driven
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DRFNet: a deep radiomic fusion network for nAMD/PCV differentiation in OCT images Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-21 Erwei Shen, Zhenmao Wang, Tian Lin, Qingquan Meng, Weifang Zhu, Fei Shi, Xinjian Chen, Haoyu Chen, Dehui Xiang
Objective. Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) present many similar clinical features. However, there are significant differences in the progression of nAMD and PCV. and it is crucial to make accurate diagnosis for treatment. In this paper, we propose a structure-radiomic fusion network (DRFNet) to differentiate PCV and nAMD in optical coherence
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Validation of complex radiotherapy techniques using polymer gel dosimetry Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-20 Christian P Karger, Alina Elter, Stefan Dorsch, Philipp Mann, Evangelos Pappas, Mark Oldham
Modern radiotherapy delivers highly conformal dose distributions to irregularly shaped target volumes while sparing the surrounding normal tissue. Due to the complex planning and delivery techniques, dose verification and validation of the whole treatment workflow by end-to-end tests became much more important and polymer gel dosimeters are one of the few possibilities to capture the delivered dose
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Split-based elevational localization of photoacoustic guidewire tip by 1D array probe using spatial impulse response Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-19 Tomohiko Tanaka, Ryo Imai, Hirozumi Takeshima
Objective. Photoacoustic emitters on the tip of a therapeutic device have been intensively studied for echo-guided intervention purposes. In this study, a novel method for localizing the guidewire tip emitter in the elevation direction using a 1D array probe is proposed to resolve the issue of the tip potentially deviating from the ultrasound-imaged plane. Approach. Our method uses the ‘interference
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Automated target placement for VMAT lattice radiation therapy: enhancing efficiency and consistency Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-18 Christopher Deufel, Christopher Dodoo, James Kavanaugh, Randi Finley, Karen Lang, Kasie Sorenson, Sheri Spreiter, Jamison Brooks, Douglas Moseley, Safia K Ahmed, Michael G Haddock, Daniel Ma, Sean S Park, Ivy A Petersen, Dawn W Owen, Michael P Grams
Objective. An algorithm was developed for automated positioning of lattice points within volumetric modulated arc lattice radiation therapy (VMAT LRT) planning. These points are strategically placed within the gross tumor volume (GTV) to receive high doses, adhering to specific separation rules from adjacent organs at risk (OARs). The study goals included enhancing planning safety, consistency, and
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Imaging and characterization of optical emission from ex vivo tissue during conventional and UHDR PBS proton therapy Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-18 Roman Vasyltsiv, Mahbubur Rahman, Joseph Harms, Megan Clark, David J Gladstone, Brian W Pogue, Rongxiao Zhang, Petr Bruza
Objective. Imaging of optical photons emitted from tissue during radiotherapy is a promising technique for real-time visualization of treatment delivery, offering applications in dose verification, treatment monitoring, and retrospective treatment plan comparison. This research aims to explore the feasibility of intensified imaging of tissue luminescence during proton therapy (PT), under both conventional
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Celiac disease diagnosis from endoscopic images based on multi-scale adaptive hybrid architecture model Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-18 Yilei Wang, Tian Shi, Feng Gao, Shengwei Tian, Long Yu
Objective. Celiac disease (CD) has emerged as a significant global public health concern, exhibiting an estimated worldwide prevalence of approximately 1%. However, existing research pertaining to domestic occurrences of CD is confined mainly to case reports and limited case analyses. Furthermore, there is a substantial population of undiagnosed patients in the Xinjiang region. This study endeavors
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Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-18 Bingxi He, Caixia Sun, Hailin Li, Yongbo Wang, Yunlang She, Mengmeng Zhao, Mengjie Fang, Yongbei Zhu, Kun Wang, Zhenyu Liu, Ziqi Wei, Wei Mu, Shuo Wang, Zhenchao Tang, Jingwei Wei, Lizhi Shao, Lixia Tong, Feng Huang, Mingze Tang, Yu Guo, Huimao Zhang, Di Dong, Chang Chen, Jianhua Ma, Jie Tian
Objective. In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of ‘signal-image-knowledge’ has remained unchanged. However, the process of ‘signal to image’ inevitably introduces information distortion, ultimately leading to irrecoverable biases in the ‘image to knowledge’ process. Our goal is to skip reconstruction and build a diagnostic model directly from
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A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-14 Michelle Oud, Sebastiaan Breedveld, Jesús Rojo-Santiago, Marta Krystyna Giżyńska, Michiel Kroesen, Steven Habraken, Zoltán Perkó, Ben Heijmen, Mischa Hoogeman
Objective. In head-and-neck cancer intensity modulated proton therapy, adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was
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Multileaf collimator characterization and modeling for a 1.5 T MR-linac using static synchronous and asynchronous sweeping gaps Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-14 Roel G J Kierkels, Victor Hernandez, Jordi Saez, Agnes Angerud, Guido C Hilgers, Kathrin Surmann, Danny Schuring, André W H Minken
Objective. The Elekta unity MR-linac delivers step-and-shoot intensity modulated radiotherapy plans using a multileaf collimator (MLC) based on the Agility MLC used on conventional Elekta linacs. Currently, details of the physical Unity MLC and the computational model within its treatment planning system (TPS) Monaco are lacking in published literature. Recently, a novel approach to characterize the
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Ultrasound image segmentation of renal tumors based on UNet++ with fusion of multiscale residuals and dual attention Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-14 Hui Qi, Zhen Wang, Xiaobo Qi, Ying Shi, Tianwu Xie
Objective. Laparoscopic renal unit-preserving resection is a routine and effective means of treating renal tumors. Image segmentation is an essential part before tumor resection. The current segmentation method mainly relies on doctors manual delineation, which is time-consuming, labor-intensive, and influenced by their personal experience and ability. And the image quality of segmentation is low,
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Embedding machine learning based toxicity models within radiotherapy treatment plan optimization Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-14 Donato Maragno, Gregory Buti, Ş. İlker Birbil, Zhongxing Liao, Thomas Bortfeld, Dick den Hertog, Ali Ajdari
Objective. This study addresses radiation-induced toxicity (RIT) challenges in radiotherapy (RT) by developing a personalized treatment planning framework. It leverages patient-specific data and dosimetric information to create an optimization model that limits adverse side effects using constraints learned from historical data. Approach. The study uses the optimization with constraint learning (OCL)
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Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-14 Kun Yang, Qiang Li, Hengxin Liu, Qingxuan Zeng, Dejia Cai, Jiahong Xu, Yingying Zhou, Po-Hsiang Tsui, Xiaowei Zhou
Objective. One big challenge with high-intensity focused ultrasound (HIFU) is that the intense acoustic interference generated by HIFU irradiation overwhelms the B-mode monitoring images, compromising monitoring effectiveness. This study aims to overcome this problem using a one-dimensional (1D) deep convolutional neural network. Approach. U-Net-based networks have been proven to be effective in image
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Effect of interstitial fluid pressure on shear wave elastography: an experimental and computational study Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-14 Ariana Cihan, Kristyna Holko, Luxi Wei, Hendrik J Vos, Charlotte Debbaut, Annette Caenen, Patrick Segers
Objective. An elevated interstitial fluid pressure (IFP) can lead to strain-induced stiffening of poroelastic biological tissues. As shear wave elastography (SWE) measures functional tissue stiffness based on the propagation speed of acoustically induced shear waves, the shear wave velocity (SWV) can be used as an indirect measurement of the IFP. The underlying biomechanical principle for this stiffening
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Mutually enhanced multi-view information learning for segmentation of lung tumor in CT images Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-14 Ping Xuan, Yinfeng Xu, Hui Cui, Qiangguo Jin, Linlin Wang, Toshiya Nakaguchi, Tiangang Zhang
Objective. The accurate automatic segmentation of tumors from computed tomography (CT) volumes facilitates early diagnosis and treatment of patients. A significant challenge in tumor segmentation is the integration of the spatial correlations among multiple parts of a CT volume and the context relationship across multiple channels. Approach. We proposed a mutually enhanced multi-view information model
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A kV–MV approach to CBCT metal artifact reduction using multi-layer MV-CBCT Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-14 Matthew W Jacobson, Tom Harris, Marios Myronakis, Mathias Lehmann, Pascal Huber, Ikechi Ozoemelam, Yue-Houng Hu, Dianne Ferguson, Rony Fueglistaller, Daniel Morf, Ross Berbeco
Objective. To demonstrate that complete cone beam CT (CBCT) scans from both MV-energy and kV-energy LINAC sources can reduce metal artifacts in radiotherapy guidance, while maintaining standard-of-care x-ray doses levels. Approach. MV-CBCT and kV-CBCT scans are acquired at half normal dose. The impact of lowered dose on MV-CBCT data quality is mitigated by the use of a 4-layer MV-imager prototype and
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Synthetic CT imaging for PET monitoring in proton therapy: a simulation study Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-13 Martina Moglioni, Pietro Carra, Silvia Arezzini, Nicola Belcari, Davide Bersani, Andrea Berti, Maria Giuseppina Bisogni, Marco Calderisi, Ilaria Ceppa, Piergiorgio Cerello, Mario Ciocca, Veronica Ferrero, Elisa Fiorina, Aafke Christine Kraan, Enrico Mazzoni, Matteo Morrocchi, Francesco Pennazio, Alessandra Retico, Valeria Rosso, Francesca Sbolgi, Viviana Vitolo, Giancarlo Sportelli
Objective. This study addresses a fundamental limitation of in-beam positron emission tomography (IB-PET) in proton therapy: the lack of direct anatomical representation in the images it produces. We aim to overcome this shortcoming by pioneering the application of deep learning techniques to create synthetic control CT images (sCT) from combining IB-PET and planning CT scan data. Approach. We conducted
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Superficial white matter microstructural imaging method based on time-space fractional-order diffusion Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-13 Jianglin He, Yuanjun Wang
Objective. Microstructure imaging based on diffusion magnetic resonance signal is an advanced imaging technique that enables in vivo mapping of the brain’s microstructure. Superficial white matter (SWM) plays an important role in brain development, maturation, and aging, while fewer microstructure imaging methods address the SWM due to its complexity. Therefore, this study aims to develop a diffusion
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Characterization of real-time cine MR imaging distortion on 0.35 T MRgRT with concentric cine imaging QA phantom Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-13 Shanti Marasini, Mike Cole, Austen Curcuru, Lara M Dyke, H Michael Gach, Rocco Flores, Taeho Kim
Objective. Real-time MRgRT uses 2D-cine imaging for target tracking and motion evaluation. Rotation of gantry induced B 0 off-resonance, resulting in image artifacts and imaging isocenter-shift precluding MR-guided arc therapy. Standard MRI phantoms designed for higher resolution images face challenges when low-resolution cine imaging is needed to achieve high frame rates. This work aimed to examine
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MDT: semi-supervised medical image segmentation with mixup-decoupling training Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-13 Jianwu Long, Yan Ren, Chengxin Yang, Pengcheng Ren, Ziqin Zeng
Objective. In the field of medicine, semi-supervised segmentation algorithms hold crucial research significance while also facing substantial challenges, primarily due to the extreme scarcity of expert-level annotated medical image data. However, many existing semi-supervised methods still process labeled and unlabeled data in inconsistent ways, which can lead to knowledge learned from labeled data
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Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-12 Ting Pang, Jeannie Hsiu Ding Wong, Wei Lin Ng, Chee Seng Chan, Chang Wang, Xuezhi Zhou, Yi Yu
Objective. Generally, due to a lack of explainability, radiomics based on deep learning has been perceived as a black-box solution for radiologists. Automatic generation of diagnostic reports is a semantic approach to enhance the explanation of deep learning radiomics (DLR). Approach. In this paper, we propose a novel model called radiomics-reporting network (Radioport), which incorporates text attention
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Optimizing the traversal time for gantry trajectories for proton arc therapy treatment plans Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-12 V Wase, O Marthin, A Fredriksson, A Finnson
Background. Proton arc therapy (PAT) is an emerging radiation therapy technique where either the gantry or the patient continuously rotates during the irradiation treatment. One of the perceived advantages of PAT is the reduced treatment time, but it is still unclear exactly how long these treatment times will be, given that no machine capable of its delivery is available on the market at the time
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Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-12 Xiao Luo, Peiwen Li, Hongyi Chen, Kun Zhou, Sirong Piao, Liqin Yang, Bin Hu, Daoying Geng
Objective. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE. Approach:
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A multi-modal vision-language pipeline strategy for contour quality assurance and adaptive optimization Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-06 Shunyao Luan, Jun Ou-yang, Xiaofei Yang, Wei Wei, Xudong Xue, Benpeng Zhu
Objective. Accurate delineation of organs-at-risk (OARs) is a critical step in radiotherapy. The deep learning generated segmentations usually need to be reviewed and corrected by oncologists manually, which is time-consuming and operator-dependent. Therefore, an automated quality assurance (QA) and adaptive optimization correction strategy was proposed to identify and optimize ‘incorrect’ auto-segmentations
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THUBreast: an open-source breast phantom generation software for x-ray imaging and dosimetry Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-06 Jiahao Wang, Yeqi Liu, Ankang Hu, Zhen Wu, Hui Zhang, Junli Li, Rui Qiu
Objective. Establishing realistic phantoms of human anatomy is a continuing concern within virtual clinical trials of breast x-ray imaging. However, little attention has been paid to glandular distribution within these phantoms. The principal objective of this study was to develop breast phantoms considering the clinical glandular distribution. Approach. This research introduces an innovative method
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Coupling speckle noise suppression with image classification for deep-learning-aided ultrasound diagnosis Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-04 Ruixin Wang, Xiaohui Liu, Guoping Tan
Objective. During deep-learning-aided (DL-aided) ultrasound (US) diagnosis, US image classification is a foundational task. Due to the existence of serious speckle noise in US images, the performance of DL models may be degraded. Pre-denoising US images before their use in DL models is usually a logical choice. However, our investigation suggests that pre-speckle-denoising is not consistently advantageous
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Voxel-wise dose rate calculation in clinical pencil beam scanning proton therapy Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-04 Juliane Daartz, Thomas M Madden, Arthur Lalonde, Ethan Cascio, Joost Verburg, Helen Shih, Shannon MacDonald, Rachael Hachadorian, Jan Schuemann
Objective. Clinical outcomes after proton therapy have shown some variability that is not fully understood. Different approaches have been suggested to explain the biological outcome, but none has yet provided a comprehensive and satisfactory rationale for observed toxicities. The relatively recent transition from passive scattering (PS) to pencil beam scanning (PBS) treatments has significantly increased
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A simple and precise alignment calibration method for cone-beam computed tomography with the verifications Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-04 Kun-Long Shih, David Shih-Chun Jin, Yu-Hong Wang, Trang Thi Ngoc Tran, Jyh-Cheng Chen
Cone-beam computed tomography (CBCT) is widely used in dental imaging, small animal imaging, radiotherapy, and non-destructive industrial inspection. The quality of CBCT images depends on the precise knowledge of the CBCT system’s alignment. We introduce a distinct procedure, ‘precision alignment loop (PAL)’, to calibrate any CBCT system with a circular trajectory. We describe the calibration procedure
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OPTIma: simplifying calorimetry for proton computed tomography in high proton flux environments Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-29 A Winter, B Vorselaars, M Esposito, A Badiee, T Price, P Allport, N Allinson
Objective. Proton computed tomography (pCT) offers a potential route to reducing range uncertainties for proton therapy treatment planning, however the current trend towards high current spot scanning treatment systems leads to high proton fluxes which are challenging for existing systems. Here we demonstrate a novel approach to energy reconstruction, referred to as ‘de-averaging’, which allows individual
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Biomechanical imaging biomarker during chemoradiotherapy predicts treatment response in head and neck squamous cell carcinoma Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-29 Koya Fujimoto, Takehiro Shiinoki, Yusuke Kawazoe, Yuki Yuasa, Wataru Mukaidani, Yuki Manabe, Miki Kajima, Hidekazu Tanaka
Objective. For response-adapted adaptive radiotherapy (R-ART), promising biomarkers are needed to predict post-radiotherapy (post-RT) responses using routine clinical information obtained during RT. In this study, a patient-specific biomechanical model (BM) of the head and neck squamous cell carcinoma (HNSCC) was proposed using the pre-RT maximum standardized uptake value (SUVmax) of 18F-fluorodeoxyglucose
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SISS-MCO: large scale sparsity-induced spot selection for fast and fully-automated robust multi-criteria optimisation of proton plans Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-29 W Kong, M Oud, S J M Habraken, M Huiskes, E Astreinidou, C R N Rasch, B J M Heijmen, S Breedveld
Objective. Intensity modulated proton therapy (IMPT) is an emerging treatment modality for cancer. However, treatment planning for IMPT is labour-intensive and time-consuming. We have developed a novel approach for multi-criteria optimisation (MCO) of robust IMPT plans (SISS-MCO) that is fully automated and fast, and we compare it for head and neck, cervix, and prostate tumours to a previously published
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Treatment planning for photodynamic therapy of abscess cavities using patient-specific optical properties measured prior to illumination Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-28 Zihao Li, Md Nafiz Hannan, Ashwani K Sharma, Timothy M Baran
Photodynamic therapy (PDT) is an effective antimicrobial therapy that we used to treat human abscess cavities in a Phase 1 clinical trial. This trial included pre-PDT measurements of abscess optical properties, which affect light dose (light fluence) at the abscess wall and PDT response. This study simulated PDT treatment planning for 13 subjects that received optical spectroscopy prior to clinical
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An adaptive h-refinement method for the boundary element fast multipole method for quasi-static electromagnetic modeling Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-28 William A Wartman, Konstantin Weise, Manas Rachh, Leah Morales, Zhi-De Deng, Aapo Nummenmaa, Sergey N Makaroff
Objective. In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates
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Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-28 Juhi Raj, Maël Millardet, Srilalan Krishnamoorthy, Joel S Karp, Suleman Surti, Samuel Matej
Dual panel PET systems, such as Breast-PET (B-PET) scanner, exhibit strong asymmetric and anisotropic spatially-variant deformations in the reconstructed images due to the limited-angle data and strong depth of interaction effects for the oblique LORs inherent in such systems. In our previous work, we studied time-of-flight (TOF) effects and image-based spatially-variant PSF resolution models within
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Affine medical image registration with fusion feature mapping in local and global Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-28 Wei Ji, Feng Yang
Objective. Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible with most real-time medical applications. On the other hand, convolutional neural networks are limited in modeling long-range spatial relationships
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Multiparametric MR-based radiomics fusion combined with quantitative stratified ADC-defined tumor habitats for differentiating TNBC versus non-TNBC Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-28 Wanli Zhang, Fangrong Liang, Yue Zhao, Jiamin Li, Chutong He, Yandong Zhao, Shengsheng Lai, Yongzhou Xu, Wenshuang Ding, Xinhua Wei, Xinqing Jiang, Ruimeng Yang, Xin Zhen
Objective. To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model. Approach. 466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively
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An iterative reconstruction algorithm for unsupervised PET image Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-27 Siqi Wang, Bing Liu, Furan Xie, Li Chai
Objective. In recent years, convolutional neural networks (CNNs) have shown great potential in positron emission tomography (PET) image reconstruction. However, most of them rely on many low-quality and high-quality reference PET image pairs for training, which are not always feasible in clinical practice. On the other hand, many works improve the quality of PET image reconstruction by adding explicit
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Towards liquid EPR dosimetry using nitroxides in aqueous solution Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-27 Sebastian Höfel, Felix Zwicker, Michael K Fix, Malte Drescher
Objective. Water-equivalent dosimeters are desirable for dosimetry in radiotherapy. The present work investigates basic characteristics of novel aqueous detector materials and presents a signal loss approach for electron paramagnetic resonance (EPR) dosimetry. Approach. The proposed principle is based on the radiation dose dependent annihilation of EPR active nitroxides (NO·) in aqueous solutions.
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Time-of-flight scatter rejection in x-ray radiography Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-27 J Rossignol, G Bélanger, D Gaudreault, A C Therrien, Y Bérubé-Lauziére, R Fontaine
Objective. Time-of-flight (TOF) scatter rejection allows for identifying and discarding scattered photons without the use of an anti-scatter grid (ASG). Although TOF scatter rejection was initially presented for cone-beam computed tomography, we propose, herein, to extend this approach to x-ray radiography. This work aims to evaluate with simulations if TOF scatter rejection can outperform ASGs for
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Integrated-mode proton radiography with 2D lateral projections Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-27 Mikaël Simard, Daniel G Robertson, Ryan Fullarton, Gary Royle, Sam Beddar, Charles-Antoine Collins-Fekete
Integrated-mode proton radiography leading to water equivalent thickness (WET) maps is an avenue of interest for motion management, patient positioning, and in vivo range verification. Radiographs can be obtained using a pencil beam scanning setup with a large 3D monolithic scintillator coupled with optical cameras. Established reconstruction methods either (1) involve a camera at the distal end of
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Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-26 Guangcan Qu, Beichen Lu, Jialin Shi, Ziyi Wang, Yaping Yuan, Yifan Xia, Zhifang Pan, Yezhi Lin
MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors’ workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to
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Impact of MRI RF coil design on the RF-induced heating of medical implants: fixed B 1 + rms exposure versus normal operating mode Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-26 Aiping Yao, Zihan Li, Mingjuan Ma
A direct comparison of the impact of RF coil design under specific absorption rate and B1+rms limitations are investigated and quantified using RF coils of different geometries and topologies at 64 MHz and 128 MHz. The RF-induced in vivo electric field and power deposition of a 50 cm long pacemaker and 55 cm long deep brain stimulator (DBS) are evaluated within two anatomical models exposed with these
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Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-26 Qiu Hu, Weiming Cai, Shuwen Xu, Shaohai Hu, Lang Wang, Xinyi He
Objective. Multimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and
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Discrete residual diffusion model for high-resolution prostate MRI synthesis Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-26 Zhitao Han, Wenhui Huang
Objective. High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to process low-resolution (LR) images and create a higher-resolution version. However, existing medical SR models suffer
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TdDS-UNet: top-down deeply supervised U-Net for the delineation of 3D colorectal cancer Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-23 Shuchao Chen, Fei Xie, Shenghuan Chen, Shanshan Liu, Haojiang Li, Qiong Gong, Guangying Ruan, Lizhi Liu, Hongbo Chen
Automatically delineating colorectal cancers with fuzzy boundaries from 3D images is a challenging task, but the problem of fuzzy boundary delineation in existing deep learning-based methods have not been investigated in depth. Here, an encoder–decoder-based U-shaped network (U-Net) based on top-down deep supervision (TdDS) was designed to accurately and automatically delineate the fuzzy boundaries
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Impact of and interplay between proton arc therapy and range uncertainties in proton therapy for head-and-neck cancer Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-23 Sebastian Tattenberg, Peilin Liu, Anthony Mulhem, Xiaoda Cong, Christopher Thome, Xuanfeng Ding
Objective. Proton therapy reduces the integral dose to the patient compared to conventional photon treatments. However, in vivo proton range uncertainties remain a considerable hurdle. Range uncertainty reduction benefits depend on clinical practices. During intensity-modulated proton therapy (IMPT), the target is irradiated from only a few directions, but proton arc therapy (PAT), for which the target
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Semi-supervised low-dose SPECT restoration using sinogram inner-structure aware graph neural network Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-23 Si Li, Keming Chen, Xiangyuan Ma, Zengguo Liang
Objective. To mitigate the potential radiation risk, low-dose single photon emission computed tomography (SPECT) is of increasing interest. Numerous deep learning-based methods have been developed to perform low-dose imaging while maintaining image quality. However, most existing methods seldom explore the unique inner-structure inherent within sinograms. In addition, traditional supervised learning
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Automated classification of ulcerative lesions in small intestine using densenet with channel attention and residual dilated blocks Phys. Med. Biol. (IF 3.5) Pub Date : 2024-02-23 Xudong Guo, Lei Xu, Zhang Liu, Youguo Hao, Peng Wang, Huiyun Zhu, Yiqi Du
Objective. Ulceration of the small intestine, which has a high incidence, includes Crohn’s disease (CD), intestinal tuberculosis (ITB), primary small intestinal lymphoma (PSIL), cryptogenic multifocal ulcerous stenosing enteritis (CMUSE), and non-specific ulcer (NSU). However, the ulceration morphology can easily be misdiagnosed through enteroscopy. Approach. In this study, DRCA-DenseNet169, which