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Nonunion scaphoid bone shape prediction using iterative kernel principal polynomial shape analysis Med Phys (IF 3.8) Pub Date : 2024-03-18 Junjun Zhu, Junhao Zhao, Xianggeng Luo, Zikai Hua
The scaphoid is an important mechanical stabilizer for both the proximal and distal carpal columns. The precise estimation of the complete scaphoid bone based on partial bone geometric information is a crucial factor in the effective management of scaphoid nonunion. Statistical shape model (SSM) could be utilized to predict the complete scaphoid shape based on the defective scaphoid. However, traditional
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First Monte Carlo beam model for ultra-high dose rate radiotherapy with a compact electron LINAC Med Phys (IF 3.8) Pub Date : 2024-03-17 Tianyuan Dai, Austin M. Sloop, Mahbubur R. Rahman, Jacob P. Sunnerberg, Megan A. Clark, Ralph Young, Sebastian Adamczyk, Philip Von Voigts-Rhetz, Chris Patane, Michael Turk, Lesley Jarvis, Brian W. Pogue, David J. Gladstone, Petr Bruza, Rongxiao Zhang
FLASH radiotherapy based on ultra-high dose rate (UHDR) is actively being studied by the radiotherapy community. Dedicated UHDR electron devices are currently a mainstay for FLASH studies.
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A comprehensive lung CT landmark pair dataset for evaluating deformable image registration algorithms Med Phys (IF 3.8) Pub Date : 2024-03-13 Edward R. Criscuolo, Yabo Fu, Yao Hao, Zhendong Zhang, Deshan Yang
PurposeDeformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high‐quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by the National Institute of Biomedical Imaging and Bioengineering
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Adapting low‐dose CT denoisers for texture preservation using zero‐shot local noise‐level matching Med Phys (IF 3.8) Pub Date : 2024-03-13 Youngjun Ko, Seongjong Song, Jongduk Baek, Hyunjung Shim
BackgroundOn enhancing the image quality of low‐dose computed tomography (LDCT), various denoising methods have achieved meaningful improvements. However, they commonly produce over‐smoothed results; the denoised images tend to be more blurred than the normal‐dose targets (NDCTs). Furthermore, many recent denoising methods employ deep learning(DL)‐based models, which require a vast amount of CT images
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Quantitative measurement of the ureter on three‐dimensional magnetic resonance urography images using deep learning Med Phys (IF 3.8) Pub Date : 2024-03-13 Rile Nai, Kexin Wang, Xiaoqing Li, Shangsong Du, Tuya E, He Xiao, Shuo Quan, Yaofeng Zhang, Junhua Yu, Jialun Li, Xiaodong Zhang, Xiaoying Wang
BackgroundAccurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three‐dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor‐intensive procedures and inherent variability. In the realm of
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Multi‐energy blended CBCT spectral imaging and scatter‐decoupled material decomposition using a spectral modulator with flying focal spot (SMFFS) Med Phys (IF 3.8) Pub Date : 2024-03-13 Yifan Deng, Hao Zhou, Zhilei Wang, Adam S. Wang, Hewei Gao
BackgroundCone‐beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image‐guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity.PurposeThe
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Experimental demonstration of real‐time cardiac physiology‐based radiotherapy gating for improved cardiac radioablation on an MR‐linac Med Phys (IF 3.8) Pub Date : 2024-03-13 Osman Akdag, Pim T. S. Borman, Stefano Mandija, Peter L. Woodhead, Prescilla Uijtewaal, Bas W. Raaymakers, Martin F. Fast
BackgroundCardiac radioablation is a noninvasive stereotactic body radiation therapy (SBRT) technique to treat patients with refractory ventricular tachycardia (VT) by delivering a single high‐dose fraction to the VT isthmus. Cardiorespiratory motion induces position uncertainties resulting in decreased dose conformality. Electocardiograms (ECG) are typically used during cardiac MRI (CMR) to acquire
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Physics and small‐scale dosimetry of α$\alpha$‐emitters for targeted radionuclide therapy: The case of 211At$^{211}{\rm At}$ Med Phys (IF 3.8) Pub Date : 2024-03-13 Mario Enrique Alcocer‐Ávila, Alexandre Larouze, Jean‐Emmanuel Groetz, Elif Hindié, Christophe Champion
BackgroundMonte Carlo simulations have been considered for a long time the gold standard for dose calculations in conventional radiotherapy and are currently being applied for the same purpose in innovative radiotherapy techniques such as targeted radionuclide therapy (TRT).PurposeWe present in this work a benchmarking study of the latest version of the Transport d'Ions Lourds Dans l'Aqua & Vivo (TILDA‐V
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Technical note: Cryostat transmission characterization for MR linac – temporal stability, clinical impact and change implementation Med Phys (IF 3.8) Pub Date : 2024-03-11 Urszula Jelen, Claire Pagulayan, Zoë Moutrie, Jason Arts, Armia George, Michael G. Jameson
BackgroundIn the Unity MR linac (Elekta AB, Stockholm, Sweden), the radiation beam traverses the cryostat and the coil support structure. The resulting beam attenuation must be considered for output calibration and its variation with gantry angle must be characterized in the treatment planning system (TPS).PurposeThe aim of this work was to investigate the impact of a change of the cryostat transmission
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Robust treatment planning for small animal radio‐neuromodulation using focused kV x‐ray beams Med Phys (IF 3.8) Pub Date : 2024-03-10 Chenhui Qiu, Wenbo Gu, Huagang Yan, Weiyuan Sun, Yuanyuan Wang, Qiang Wen, Ke Sheng, Wu Liu
BackgroundIn preclinical radio‐neuromodulation research, small animal experiments are pivotal for unraveling radiobiological mechanism, investigating prescription and planning techniques, and assessing treatment effects and toxicities. However, the target size inside a rat brain is typically in the order of sub‐millimeters. The small target inside the visual cortex neural region in rat brain with a
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A prototype scintillator real‐time beam monitor for ultra‐high dose rate radiotherapy Med Phys (IF 3.8) Pub Date : 2024-03-08 Daniel S. Levin, Peter S. Friedman, Claudio Ferretti, Nicholas Ristow, Monica Tecchio, Dale W. Litzenberg, Vladimir Bashkirov, Reinhard Schulte
BackgroundFLASH Radiotherapy (RT) is an emergent cancer RT modality where an entire therapeutic dose is delivered at more than 1000 times higher dose rate than conventional RT. For clinical trials to be conducted safely, a precise and fast beam monitor that can generate out‐of‐tolerance beam interrupts is required. This paper describes the overall concept and provides results from a prototype ultra‐fast
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Millimeter wave‐based patient setup verification and motion tracking during radiotherapy Med Phys (IF 3.8) Pub Date : 2024-03-08 Max Bressler, Jingxuan Zhu, Joshua Olick‐Gibson, Jonathan Haefner, Shuang Zhou, Qinghao Chen, Thomas Mazur, Yao Hao, Paul Carter, Tiezhi Zhang
BackgroundPosition verification and motion monitoring are critical for safe and precise radiotherapy (RT). Existing approaches to these tasks based on visible light or x‐ray are suboptimal either because they cannot penetrate obstructions to the patient's skin or introduce additional radiation exposure. The low‐cost mmWave radar is an ideal solution for these tasks as it can monitor patient position
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A comprehensive quality assurance protocol for electromagnetic tracking in brachytherapy Med Phys (IF 3.8) Pub Date : 2024-03-08 Christopher Dürrbeck, Isaac Neri Gomez‐Sarmiento, Ioannis Androulakis, Birte Christina Sauer, Inger‐Karine Kolkman‐Deurloo, Christoph Bert, Luc Beaulieu
BackgroundElectromagnetic tracking (EMT) systems have proven to be a valuable source of information regarding the location and geometry of applicators in patients undergoing brachytherapy (BT). As an important element of an enhanced and individualized pre‐treatment verification, EMT can play a pivotal role in detecting treatment errors and uncertainties to increase patient safety.PurposeThe purpose
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An analytical form of ring artifact correction for computed tomography based on directional gradient domain optimization Med Phys (IF 3.8) Pub Date : 2024-03-07 Yuang Wang, Zhiqiang Chen, Hewei Gao, Yifan Deng, Li Zhang
BackgroundRing artifact is a common problem in Computed Tomography (CT), which can lead to inaccurate diagnoses and treatment plans. It can be caused by various factors such as detector imperfections, anti‐scatter grids, or other nonuniform filters placed in the x‐ray beam. Physics‐based corrections for these x‐ray source and detector non‐uniformity, in general cannot completely get rid of the ring
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Demonstration of real‐time positron emission tomography biology‐guided radiotherapy delivery to targets Med Phys (IF 3.8) Pub Date : 2024-03-07 Oluwaseyi M. Oderinde, Manoj Narayanan, Peter Olcott, Yevgen Voronenko, Jon Burns, Shiyu Xu, Ling Shao, Karine A. Al Feghali, Shervin M. Shirvani, Murat Surucu, Gopinath Kuduvalli
BackgroundBiology‐guided radiotherapy (BgRT) is a novel technology that uses positron emission tomography (PET) data to direct radiotherapy delivery in real‐time. BgRT enables the precise delivery of radiation doses based on the PET signals emanating from PET‐avid tumors on the fly. In this way, BgRT uniquely utilizes radiotracer uptake as a biological beacon for controlling and adjusting dose delivery
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A semi‐analytical procedure to determine the ion recombination correction factor in high dose‐per‐pulse beams Med Phys (IF 3.8) Pub Date : 2024-03-06 Julien Bancheri, Jan Seuntjens
BackgroundThe conventional theories and methods of determining the ion recombination correction factor, such as Boag theory and the related two voltage method and Jaffé plot extrapolation, do not seem to yield accurate results in FLASH /high dose per pulse (DPP) beams (10 mGy DPP). This is due to the presence of a large free electron fraction that distorts the electric field inside the chamber sensitive
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Pulmonary MRI with hyperpolarized xenon-129 demonstrates novel alterations in gas transfer across the air-blood barrier in asthma Med Phys (IF 3.8) Pub Date : 2024-03-03 Kun Qing, Talissa A. Altes, John P. Mugler, Nicholas J. Tustison, Mata, Kai Ruppert, Peter Komlosi, Xue Feng, Ke Nie, Li Zhao, Zhixing Wang, F. William Hersman, Iulian C. Ruset, Bo Liu, Yun Michael Shim, William Gerald Teague
Individuals with asthma can vary widely in clinical presentation, severity, and pathobiology. Hyperpolarized xenon-129 (Xe129) MRI is a novel imaging method to provide 3-D mapping of both ventilation and gas exchange in the human lung.
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Pan‐cancer image segmentation based on feature pyramids and Mask R‐CNN framework Med Phys (IF 3.8) Pub Date : 2024-03-04 Juan Wang, Jian Zhou, Man Wang
BackgroundCancer, a disease with a high mortality rate, poses a great threat to patients' physical and mental health and can lead to huge medical costs and emotional damage. With the continuous development of artificial intelligence technologies, deep learning‐based cancer image segmentation techniques are becoming increasingly important in cancer detection and accurate diagnosis. However, in segmentation
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Linear Boltzmann equation solver for voxel‐level dosimetry in radiopharmaceutical therapy: Comparison with Monte Carlo and kernel convolution Med Phys (IF 3.8) Pub Date : 2024-03-04 Gunjan Kayal, Benjamin Van, George Andl, Cheng Tu, Todd Wareing, Scott Wilderman, Justin Mikell, Yuni K. Dewaraja
BackgroundWith recent interest in patient‐specific dosimetry for radiopharmaceutical therapy (RPT) and selective internal radiation therapy (SIRT), an increasing number of voxel‐based algorithms are being evaluated. Monte Carlo (MC) radiation transport, generally considered to be the most accurate among different methods for voxel‐level absorbed dose estimation, can be computationally inefficient for
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Classification of multi‐feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks Med Phys (IF 3.8) Pub Date : 2024-03-04 Pengfei Xu, Jing Zhao, Mingxi Wan, Qing Song, Qiang Su, Diya Wang
BackgroundBreast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI‐RADS) category 4 has the highest false‐positive value of about 30% among five categories. The classification task in BI‐RADS category 4 is challenging and has not been fully studied.PurposeThis work aimed
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Optimizing dual‐energy CT technique for iodine‐based contrast‐to‐noise ratio, a theoretical study Med Phys (IF 3.8) Pub Date : 2024-03-04 Fatma Terzioglu, Emil Y. Sidky, John Paul Phillips, Ingrid S. Reiser, Guillaume Bal, Xiaochuan Pan
BackgroundDual‐energy CT (DECT) systems provide valuable material‐specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast‐to‐noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x‐ray tube settings and fluence, is critical for the stability of the reconstruction
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Characterization of a flexible a‐Si:H detector for in vivo dosimetry in therapeutic x‐ray beams Med Phys (IF 3.8) Pub Date : 2024-03-03 Matthew James Large, Aishah Bashiri, Yashiv Dookie, Joanne McNamara, Luca Antognini, Saba Aziz, Lucio Calcagnile, Anna Paola Caricato, Roberto Catalano, Deborah Chila, Giuseppe Antonio Pablo Cirrone, Tomasso Croci, Giacomo Cuttone, Sylvain Dunand, Michele Fabi, Luca Frontini, Catia Grimani, Maria Ionica, Keida Kanxheri, Valentino Liberali, Martino Maurizio, Giuseppe Maruccio, Giovanni Mazza, Mauro
BackgroundThe increasing use of complex and high dose‐rate treatments in radiation therapy necessitates advanced detectors to provide accurate dosimetry. Rather than relying on pre‐treatment quality assurance (QA) measurements alone, many countries are now mandating the use of in vivo dosimetry, whereby a dosimeter is placed on the surface of the patient during treatment. Ideally, in vivo detectors
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Two‐stage adversarial learning based unsupervised domain adaptation for retinal OCT segmentation Med Phys (IF 3.8) Pub Date : 2024-03-01 Shengyong Diao, Ziting Yin, Xinjian Chen, Menghan Li, Weifang Zhu, Muhammad Mateen, Xun Xu, Fei Shi, Ying Fan
BackgroundDeep learning based optical coherence tomography (OCT) segmentation methods have achieved excellent results, allowing quantitative analysis of large‐scale data. However, OCT images are often acquired by different devices or under different imaging protocols, which leads to serious domain shift problem. This in turn results in performance degradation of segmentation models.PurposeAiming at
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Transformaer‐based model for lung adenocarcinoma subtypes Med Phys (IF 3.8) Pub Date : 2024-03-01 Fawen Du, Huiyu Zhou, Yi Niu, Zeyu Han, Xiaodan Sui
BackgroundLung cancer has the highest morbidity and mortality rate among all types of cancer. Histological subtypes serve as crucial markers for the development of lung cancer and possess significant clinical values for cancer diagnosis, prognosis, and prediction of treatment responses. However, existing studies only dichotomize normal and cancerous tissues, failing to capture the unique characteristics
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Synthesis of gadolinium‐enhanced glioma images on multisequence magnetic resonance images using contrastive learning Med Phys (IF 3.8) Pub Date : 2024-02-29 Qian Xie, Yusong Lin, Meiyun Wang, Yaping Wu
BackgroundGadolinium‐based contrast agents are commonly used in brain magnetic resonance imaging (MRI), however, they cannot be used by patients with allergic reactions or poor renal function. For long‐term follow‐up patients, gadolinium deposition in the body can cause nephrogenic systemic fibrosis and other potential risks.PurposeDeveloping a new method of enhanced image synthesis based on the advantages
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PET/SPECT/spectral‐CT/CBCT imaging in a small‐animal radiation therapy platform: A Monte Carlo study—Part I: Quad‐modal imaging Med Phys (IF 3.8) Pub Date : 2024-02-29 Hui Wang, Xiadong Li, Lixia Xu, Yu Kuang
BackgroundIn spite of the tremendous potential of game‐changing biological image‐ and/or biologically guided radiation therapy (RT) and adaptive radiation therapy for cancer treatment, existing limited strategies for integrating molecular imaging and/or biological information with RT have impeded the translation of preclinical research findings to clinical applications. Additionally, there is an urgent
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Initial validation of a diaphragm tracking system for multiple breath‐hold volumetric modulated arc therapy of abdominal tumors: A phantom study Med Phys (IF 3.8) Pub Date : 2024-02-29 Yuki Nozawa, Takeshi Ohta, Atsuto Katano, Kanabu Nawa, Shingo Ohira, Hideomi Yamashita, Keiichi Nakagawa
BackgroundThe breath‐hold radiotherapy has been increasingly used to mitigate interfractional and intrafractional breathing impact on treatment planning and beam delivery. Previous techniques include body surface measurements or radiopaque metal markers, each having known disadvantages.PurposeWe recently proposed a new markerless technique without the disadvantages, where diaphragm was registered between
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Technical note: Workload and transmission data for mobile C‐arm fluoroscopy in gastrointestinal endoscopy Med Phys (IF 3.8) Pub Date : 2024-02-29 Xinhua Li, Theodore A. Marschall, Kai Yang, Bob Liu
BackgroundMobile C‐arms may be used in fixed locations, and it is recommended that qualified experts evaluate structural shielding.PurposeTo assess clinical workload distributions for mobile C‐arms used in gastrointestinal endoscopy and determine the Archer equation parameters for the C‐arm beam spectra.MethodsConsecutive (30 months) gastrointestinal endoscopic procedures on two Cios Alpha systems
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Deep learning‐based harmonization of trabecular bone microstructures between high‐ and low‐resolution CT imaging Med Phys (IF 3.8) Pub Date : 2024-02-28 Indranil Guha, Syed Ahmed Nadeem, Xiaoliu Zhang, Paul A. DiCamillo, Steven M. Levy, Ge Wang, Punam K. Saha
BackgroundOsteoporosis is a bone disease related to increased bone loss and fracture‐risk. The variability in bone strength is partially explained by bone mineral density (BMD), and the remainder is contributed by bone microstructure. Recently, clinical CT has emerged as a viable option for in vivo bone microstructural imaging. Wide variations in spatial‐resolution and other imaging features among
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Impact of cone‐beam computed tomography artifacts on dose calculation accuracy for lung cancer Med Phys (IF 3.8) Pub Date : 2024-02-27 Lukas Schröder, Gregory Bootsma, Uros Stankovic, Lennert Ploeger, Jan‐Jakob Sonke
BackgroundTo implement image‐guided adaptive radiotherapy (IGART), many studies investigated dose calculations on cone‐beam computed tomography (CBCT). A high HU accuracy is crucial for a high dose calculation accuracy and many imaging sites showed satisfactory results. It has been shown that the dose calculation accuracy for lung cancer lags behind.PurposeTo examine why the dose calculation accuracy
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Technical note: TIGRE‐DE for the creation of virtual monoenergetic images from dual‐energy cone‐beam CT Med Phys (IF 3.8) Pub Date : 2024-02-26 Andrew Keeler, Mathias Lehmann, Jason Luce, Mandeep Kaur, John Roeske, Hyejoo Kang
BackgroundDual‐energy (DE)‐CBCT represents a promising imaging modality that can produce virtual monoenergetic (VM) CBCT images. VM images, which provide enhanced contrast and reduced imaging artifacts, can be used to assist in soft‐tissue visualization during image‐guided radiotherapy.PurposeThis work reports the development of TIGRE‐DE, a module in the open‐source TIGRE toolkit for the performance
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Deep‐learning‐based joint rigid and deformable contour propagation for magnetic resonance imaging‐guided prostate radiotherapy Med Phys (IF 3.8) Pub Date : 2024-02-26 Iris D. Kolenbrander, Matteo Maspero, Allard A. Hendriksen, Ryan Pollitt, Jochem R. N. van der Voort van Zyp, Cornelis A. T. van den Berg, Josien P. W. Pluim, Maureen A. J. M. van Eijnatten
BackgroundDeep learning‐based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging‐guided radiotherapy (MRgRT) workflow.PurposeIn this paper, we design an unsupervised, joint rigid, and deformable registration framework for contour propagation in MRgRT of prostate cancer.MethodsThree‐dimensional
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Primary study of the relative and compound biological effectiveness model for boron neutron capture therapy based on nanodosimetry Med Phys (IF 3.8) Pub Date : 2024-02-26 Haijun Mao, Hui Zhang, Ying Luo, Jingfen Yang, Yinuo Liu, Shichao Zhang, Weiqiang Chen, Qiang Li, Zhongying Dai
BackgroundThe current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of
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Line‐based iterative geometric calibration method for a tomosynthesis system Med Phys (IF 3.8) Pub Date : 2024-02-23 Chloe J. Choi, Trevor L. Vent, Raymond J. Acciavatti, Andrew D. A. Maidment
BackgroundA next generation tomosynthesis (NGT) system, capable of two‐dimensional source motion, detector motion in the perpendicular direction, and magnification tomosynthesis, was constructed to investigate different acquisition geometries. Existing position‐based geometric calibration methods proved ineffective when applied to the NGT geometries.PurposeA line‐based iterative calibration method
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A systematic assessment and optimization of photon-counting CT for lung density quantifications Med Phys (IF 3.8) Pub Date : 2024-02-18 Saman Sotoudeh-Paima, W. Paul Segars, Dhrubajyoti Ghosh, Sheng Luo, Ehsan Samei, Ehsan Abadi
Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners.
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Reconstruction of multi‐phase parametric maps in 4D‐magnetic resonance fingerprinting (4D‐MRF) by optimization of local T1 and T2 sensitivities Med Phys (IF 3.8) Pub Date : 2024-02-22 Yat Lam Wong, Tian Li, Chenyang Liu, Ho‐Fun Victor Lee, Lai‐Yin Andy Cheung, Edward Sai Kam Hui, Peng Cao, Jing Cai
BackgroundTime‐resolved magnetic resonance fingerprinting (MRF), or 4D‐MRF, has been demonstrated its feasibility in motion management in radiotherapy (RT). However, the prohibitive long acquisition time is one of challenges of the clinical implementation of 4D‐MRF. The shortening of acquisition time causes data insufficiency in each respiratory phase, leading to poor accuracies and consistencies of
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Creating uniform cluster dose spread‐out Bragg peaks for proton and carbon beams Med Phys (IF 3.8) Pub Date : 2024-02-20 Ramon Ortiz, Bruce Faddegon
BackgroundPreliminary data have shown a close association of the generalized ionization cluster size dose (in short, cluster dose) with cell survival, independent of particle type, and energy, when cluster dose is derived from an ionization detail parameter preferred for its association with cell survival. Such results suggest cluster dose has the potential to replace RBE‐weighted dose in proton and
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Transfer learning for auto‐segmentation of 17 organs‐at‐risk in the head and neck: Bridging the gap between institutional and public datasets Med Phys (IF 3.8) Pub Date : 2024-02-20 Brett Clark, Nicholas Hardcastle, Leigh A. Johnston, James Korte
BackgroundAuto‐segmentation of organs‐at‐risk (OARs) in the head and neck (HN) on computed tomography (CT) images is a time‐consuming component of the radiation therapy pipeline that suffers from inter‐observer variability. Deep learning (DL) has shown state‐of‐the‐art results in CT auto‐segmentation, with larger and more diverse datasets showing better segmentation performance. Institutional CT auto‐segmentation
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Motion correction of 3D dynamic contrast‐enhanced ultrasound imaging without anatomical B‐Mode images: Pilot evaluation in eight patients Med Phys (IF 3.8) Pub Date : 2024-02-20 Jia‐Shu Chen, Maged Goubran, Gaeun Kim, Matthew J. Kim, Jürgen K. Willmann, Michael Zeineh, Dimitre Hristov, Ahmed El Kaffas
BackgroundDynamic contrast‐enhanced ultrasound (DCE‐US) is highly susceptible to motion artifacts arising from patient movement, respiration, and operator handling and experience. Motion artifacts can be especially problematic in the context of perfusion quantification. In conventional 2D DCE‐US, motion correction (MC) algorithms take advantage of accompanying side‐by‐side anatomical B‐Mode images
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An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation Med Phys (IF 3.8) Pub Date : 2024-02-19 Yingqian Liu, Min Wang, Xianfeng Yu, Ying Han, Jiehui Jiang, Zhuangzhi Yan
BackgroundGiven the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmost importance.ObjectiveOur aim is to propose
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Thin slice photon-counting CT coronary angiography compared to conventional CT: Objective image quality and clinical radiation dose assessment Med Phys (IF 3.8) Pub Date : 2024-02-15 Judith van der Bie, Daniel Bos, Marcel L. Dijkshoorn, Ronald Booij, Ricardo P. J. Budde, Marcel van Straten
Photon-counting CT (PCCT) is the next-generation CT scanner that enables improved spatial resolution and spectral imaging. For full spectral processing, higher tube voltages compared to conventional CT are necessary to achieve the required spectral separation. This generated interest in the potential influence of thin slice high tube voltage PCCT on overall image quality and consequently on radiation
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Deep convolutional-neural-network-based metal artifact reduction for CT-guided interventional oncology procedures (MARIO) Med Phys (IF 3.8) Pub Date : 2024-02-14 Wenchao Cao, Ahmad Parvinian, Daniel Adamo, Brian Welch, Matthew Callstrom, Liqiang Ren, Andrew Missert, Christopher P. Favazza
Computed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT-guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal-induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable
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Spectral information content of Compton scattering events in silicon photon counting detectors Med Phys (IF 3.8) Pub Date : 2024-02-14 Scott S. Hsieh, Katsuyuki Taguchi
Silicon (Si) is a possible sensor material for photon counting detectors (PCDs). A major drawback of Si is that roughly two-thirds of x-ray interactions in the diagnostic energy range are Compton scattering. Because Compton scattering is an energy-insensitive process, it is commonly assumed that Compton events retain little spectral information.
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Objective image quality assurance in cone-beam CT: Test methods, analysis, and workflow in longitudinal studies Med Phys (IF 3.8) Pub Date : 2024-02-14 Ashley Johnston, Mahadevappa Mahesh, Ali Uneri, Tatiana A. Rypinski, John M. Boone, Jeffrey H. Siewerdsen
Standards for image quality evaluation in multi-detector CT (MDCT) and cone-beam CT (CBCT) are evolving to keep pace with technological advances. A clear need is emerging for methods that facilitate rigorous quality assurance (QA) with up-to-date metrology and streamlined workflow suitable to a range of MDCT and CBCT systems.
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UDRSNet: An unsupervised deformable registration module based on image structure similarity Med Phys (IF 3.8) Pub Date : 2024-02-14 Yun Wang, Chongfei Huang, Wanru Chang, Wenliang Lu, Qinglei Hui, Siyuan Jiang, Xiaoping Ouyang, Dexing Kong
Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field.
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Training of a deep learning based digital subtraction angiography method using synthetic data Med Phys (IF 3.8) Pub Date : 2024-02-14 Lizhen Duan, Elias Eulig, Michael Knaup, Ralf Adamus, Michael Lell, Marc Kachelrieß
Digital subtraction angiography (DSA) is a fluoroscopy method primarily used for the diagnosis of cardiovascular diseases (CVDs). Deep learning-based DSA (DDSA) is developed to extract DSA-like images directly from fluoroscopic images, which helps in saving dose while improving image quality. It can also be applied where C-arm or patient motion is present and conventional DSA cannot be applied. However
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Equivalent uniform RBE-weighted dose in eye plaque brachytherapy Med Phys (IF 3.8) Pub Date : 2024-02-14 Alexei V. Chvetsov
Brachytherapy for ocular melanoma is based on the application of eye plaques with different spatial dose nonuniformity, time-dependent dose rates and relative biological effectiveness (RBE).
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Development of the quantitative PET prostate phantom (Q3P) for improved quality assurance of 18F-PSMA PET imaging in metastatic prostate cancer Med Phys (IF 3.8) Pub Date : 2024-02-13 Roberto Fedrigo, Robin Coope, Arman Rahmim, François Bénard, Carlos F. Uribe
Phantoms are commonly used to evaluate and compare the performance of imaging systems given the known ground truth. Positron emission tomography (PET) scanners are routinely validated using the NEMA image quality phantom, in which lesions are modeled using 10 to 37 mm fillable spheres. The NEMA phantom neglects, however, to model focal (3–10-mm), high-uptake lesions that are increasingly observed in
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First direct machine-specific parameters incorporated in Spot-scanning Proton Arc (SPArc) optimization algorithm Med Phys (IF 3.8) Pub Date : 2024-02-10 Gang Liu, Qingkun Fan, Lewei Zhao, Peilin Liu, Xiaoda Cong, Di Yan, Xiaoqiang Li, Xuanfeng Ding
Spot-scanning Proton Arc (SPArc) has been of significant interest in recent years because of its superior plan quality. Currently, the primary focus of research and development is on deliverability and treatment efficiency.
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Simplified image-based dosimetry using planar images and patient-specific S-values Med Phys (IF 3.8) Pub Date : 2024-02-10 Keon Min Kim, Minseok Suh, Gi Jeong Cheon, Min Sun Lee, Jae Sung Lee
Single time point measurement approach and hybrid dosimetry were proposed to simplify the dosimetry process. It is anticipated that utilizing patient-specific S-value would enable more accurate dosimetry assessment based on imaging compared to using the conventional MIRD S-values.
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Hypoxia-informed RBE-weighted beam orientation optimization for intensity modulated proton therapy Med Phys (IF 3.8) Pub Date : 2024-02-12 Pavitra Ramesh, Dan Ruan, S. John Liu, Youngho Seo, Steve Braunstein, Ke Sheng
Variable relative biological effectiveness (RBE) models in treatment planning have been proposed to optimize the therapeutic ratio of proton therapy. It has been reported that proton RBE decreases with increasing tumor oxygen level, offering an opportunity to address hypoxia-related radioresistance with RBE-weighted optimization.
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Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset Med Phys (IF 3.8) Pub Date : 2024-02-09 Isaac Shiri, Yazdan Salimi, Nasim Sirjani, Behrooz Razeghi, Sara Bagherieh, Masoumeh Pakbin, Zahra Mansouri, Ghasem Hajianfar, Atlas Haddadi Avval, Dariush Askari, Mohammadreza Ghasemian, Saleh Sandoughdaran, Ahmad Sohrabi, Elham Sadati, Somayeh Livani, Pooya Iranpour, Shahriar Kolahi, Bardia Khosravi, Salar Bijari, Sahar Sayfollahi, Mohammad Reza Atashzar, Mohammad Hasanian, Alireza Shahhamzeh, Arash
Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model.
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Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT–SEQUOIA Med Phys (IF 3.8) Pub Date : 2024-02-07 Gijs D. van Praagh, Pieter H. Nienhuis, Melanie Reijrink, Mirjam E. J. Davidse, Lisa M. Duff, Bruce S. Spottiswoode, Douwe J. Mulder, Niek H. J. Prakken, Andy F. Scarsbrook, Ann W. Morgan, Charalampos Tsoumpas, Jelmer M. Wolterink, Kim B. Mouridsen, Ronald J. H. Borra, Bhanu Sinha, Riemer H. J. A. Slart
Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT,
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CycleSeg: Simultaneous synthetic CT generation and unsupervised segmentation for MR-only radiotherapy treatment planning of prostate cancer Med Phys (IF 3.8) Pub Date : 2024-02-07 Huan Minh Luu, Gyu Sang Yoo, Won Park, Sung-Hong Park
MR-only radiotherapy treatment planning is an attractive alternative to conventional workflow, reducing scan time and ionizing radiation. It is crucial to derive the electron density map or synthetic CT (sCT) from MR data to perform dose calculations to enable MR-only treatment planning. Automatic segmentation of relevant organs in MR images can accelerate the process by preventing the time-consuming
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Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography Med Phys (IF 3.8) Pub Date : 2024-02-07 Stephen Z. Liu, Magdalena Herbst, Jamin Schaefer, Thomas Weber, Sebastian Vogt, Ludwig Ritschl, Steffen Kappler, Christopher E. Kawcak, Holly L. Stewart, Jeffrey H. Siewerdsen, Wojciech Zbijewski
Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition
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Technical note: Errors introduced when using Dose Voxel Kernels for estimating absorbed dose from radiopharmaceutical therapies involving alpha emitters Med Phys (IF 3.8) Pub Date : 2024-02-05 Jonathan Tranel, Stig Palm, Felix Y. Feng, Sara St. James, Thomas A. Hope
In radiopharmaceutical therapies (RPT) involving beta emitters, absorbed dose (Dabs) calculations often employ the use of dose voxel kernels (DVK). Such methods are faster and easier to implement than Monte Carlo (MC) simulations. Using DVK methods implies a non-stochastic distribution of particles. This is a valid assumption for betas where thousands to tens of thousands of particles traversing the
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Technical note: Generalizable and promptable artificial intelligence model to augment clinical delineation in radiation oncology Med Phys (IF 3.8) Pub Date : 2024-02-06 Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, William W. Wong, Sujay A. Vora, Dajiang Zhu, Tianming Liu, Wei Liu
Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction