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An interactive method based on multi-objective optimization for limited-angle CT reconstruction Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-23 Chengxiang Wang, Yuanmei Xia, Jiaxi Wang, Kequan Zhao, Wei Peng and Wei Yu
Objective. Limited-angle x-ray computed tomography (CT) is a typical ill-posed inverse problem, leading to artifacts in the reconstructed image due to the incomplete projection data. Most iteration CT reconstruction methods involve optimization for a single object. This paper explores a multi-objective optimization model and an interactive method based on multi-objective optimization to suppress the
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Joint reconstruction and segmentation in undersampled 3D knee MRI combining shape knowledge and deep learning Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-23 A Kofler, C Wald, C Kolbitsch, C V Tycowicz and F Ambellan
Objective. Task-adapted image reconstruction methods using end-to-end trainable neural networks (NNs) have been proposed to optimize reconstruction for subsequent processing tasks, such as segmentation. However, their training typically requires considerable hardware resources and thus, only relatively simple building blocks, e.g. U-Nets, are typically used, which, albeit powerful, do not integrate
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Automation to facilitate optimisation of breast radiotherapy treatments using EPID-based in vivo dosimetry Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-23 Joshua Kirby and Katherine Chester
Objective. To use automation to facilitate the monitoring of each treatment fraction using an electronic portal imaging device (EPID) based in vivo dosimetry (IVD) system, allowing optimisation of breast radiotherapy delivery for individual patients and cohorts. Approach. A suite of in-house software was developed to reduce the number of manual interactions with the commercial IVD system, dosimetry
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New semi-analytical method for fast transcranial ultrasonic field simulation Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-23 C Angla, H Chouh, P Mondou, G Toullelan, K Perlin, V Brulon, E De Schlichting, B Larrat, J-L Gennisson and S Chatillon
Objective. To optimize and ensure the safety of ultrasound brain therapy, personalized transcranial ultrasound simulations are very useful. They allow to predict the pressure field, depending on the patient skull and probe position. Most transcranial ultrasound simulations are based on numerical methods which have a long computation time and a high memory usage. The goal of this study is to develop
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Implementation of a new EGSnrc particle source class for computed tomography: validation and uncertainty quantification Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-23 Marie-Luise Kuhlmann and Stefan Pojtinger
Objective. Personalized dose monitoring and risk management are of increasing significance with the growing number of computer tomography (CT) examinations. These require high-quality Monte Carlo (MC) simulations that are of the utmost importance for the new developments in personalized CT dosimetry. This work aims to extend the MC framework EGSnrc source code with a new particle source. This, in turn
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SAPPHIRE —establishment of small animal proton and photon image-guided radiation experiments Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-23 Moritz Schneider, Joshua D Schilz, Michael Schürer, Sebastian Gantz, Anne Dreyer, Gert Rothe, Falk Tillner, Elisabeth Bodenstein, Felix Horst and Elke Beyreuther
The in vivo evolution of radiotherapy necessitates innovative platforms for preclinical investigation, bridging the gap between bench research and clinical applications. Understanding the nuances of radiation response, specifically tailored to proton and photon therapies, is critical for optimizing treatment outcomes. Within this context, preclinical in vivo experimental setups incorporating image
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Development of non-invasive flexible directional microwave ablation for central lung cancer: a simulation study Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-21 Zheng Fang, Chen Wu, Lin Cao, Tao Wang, Xiaowu Hong, Michael A.J. Moser, Wenjun Zhang and Bing Zhang
Transbronchial microwave ablation (MWA) with flexible antennas has gradually become an attractive alternative to percutaneous MWA for lung cancer due to its characteristic of non-invasiveness. However, flexible antennas for the precision ablation of lung tumors that are adjacent to critical bronchial structures are still not available. In this study, a non-invasive flexible directional (FD) antenna
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Glioma segmentation based on dense contrastive learning and multimodal features recalibration Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-21 Xubin Hu, Lihui Wang, Li Wang, Qijian Chen, Licheng Zheng and Yuemin Zhu
Accurate segmentation of different regions of gliomas from multimodal magnetic resonance (MR) images is crucial for glioma grading and precise diagnosis, but many existing segmentation methods are difficult to effectively utilize multimodal MR image information to recognize accurately the lesion regions with small size, low contrast and irregular shape. To address this issue, this work proposes a novel
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Markerless liver online adaptive stereotactic radiotherapy: feasibility analysis Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-21 Julien Pierrard, Stéphanie Deheneffe, David Dechambre, Edmond Sterpin, Xavier Geets and Geneviève Van Ooteghem
Objective. Radio-opaque markers are recommended for image-guided radiotherapy in liver stereotactic ablative radiotherapy (SABR), but their implantation is invasive. We evaluate in this in-silico study the feasibility of cone-beam computed tomography-guided stereotactic online-adaptive radiotherapy (CBCT-STAR) to propagate the target volumes without implanting radio-opaque markers and assess its consequence
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State-of-the-art silicon carbide diode dosimeters for ultra-high dose-per-pulse radiation at FLASH radiotherapy Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-19 Celeste Fleta, Giulio Pellegrini, Philippe Godignon, Faustino Gómez Rodríguez, José Paz-Martín, Rafael Kranzer, Andreas Schüller
Objective. The successful implementation of FLASH radiotherapy in clinical settings, with typical dose rates >40 Gy s−1, requires accurate real-time dosimetry. Approach. Silicon carbide (SiC) p–n diode dosimeters designed for the stringent requirements of FLASH radiotherapy have been fabricated and characterized in an ultra-high pulse dose rate electron beam. The circular SiC PiN diodes were fabricated
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Dosimetric validation of SmART-RAD Monte Carlo modelling for x-ray cabinet radiobiology irradiators Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-18 Mark A Hill, Nick Staut, James M Thompson and Frank Verhaegen
Objective. Accuracy and reproducibility in the measurement of radiation dose and associated reporting are critically important for the validity of basic and preclinical radiobiological studies performed with kilovolt x-ray radiation cabinets. This is essential to enable results of radiobiological studies to be repeated, as well as enable valid comparisons between laboratories. In addition, the commonly
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Improving hybrid image and structure-based deformable image registration for large internal deformations Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-17 A Lorenzo Polo, M Nix, C Thompson, C O’Hara, J Entwisle, L Murray, A Appelt, O Weistrand, S Svensson
Objective. Deformable image registration (DIR) is a widely used technique in radiotherapy. Complex deformations, resulting from large anatomical changes, are a regular challenge. DIR algorithms generally seek a balance between capturing large deformations and preserving a smooth deformation vector field (DVF). We propose a novel structure-based term that can enhance the registration efficacy while
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Cavitation-induced pressure saturation: a mechanism governing bubble nucleation density in histotripsy Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-17 Adam D Maxwell, Eli Vlaisavljevich
Objective. Histotripsy is a noninvasive focused ultrasound therapy that mechanically disintegrates tissue by acoustic cavitation clouds. In this study, we investigate a mechanism limiting the density of bubbles that can nucleate during a histotripsy pulse. In this mechanism, the pressure generated by the initial bubble expansion effectively negates the incident pressure in the vicinity of the bubble
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Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-17 Yin Gao, Yesenia Gonzalez, Chika Nwachukwu, Kevin Albuquerque, Xun Jia
Objective. Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy
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Impact of iodinated oil in proton therapy on relative stopping power of liver post-cTACE Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-17 Jiong Shu, Jianguang Zhang, Kyungwook Jee, LingLing Liu, Man Hu, Wanli Huo, Xiangli Cui, Hongzhi Wang, Hsiao-ming Lu
Objective. Conventional transarterial chemoembolization (cTACE) is a common treatment for hepatocellular carcinoma (HCC), often with unsatisfactory local controls. Combining cTACE with radiotherapy shows a promise for unresectable large HCC, with proton therapy preserving healthy liver tissue. However, the proton therapy benefits are subject to the accuracy of tissue relative stopping power (RSP) prediction
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Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-17 Wenyu Xing, Chao He, Yebo Ma, Yiman Liu, Zhibin Zhu, Qingli Li, Wenfang Li, Jiangang Chen, Dean Ta
Objective. Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line. Approach. The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose
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A photon source model for alpha-emitter radionuclides Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-17 D Sarrut, A Etxebeste, J M Létang
Objective. A Monte Carlo virtual source model named PHID (photon from Ion decay) that generates photons emitted in the complex decay chain process of alpha-emitter radionuclides is proposed, typically for use during the simulation of SPECT image acquisition. Approach. Given an alpha-emitter radionuclide, the PHID model extracts from Geant4 databases the photon emission lines from all decaying daughters
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Three-row MRI receive array with remote circuitry to preserve radiation transparency Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-17 Karthik Lakshmanan, Bili Wang, Jerzy Walczyk, Christopher M Collins, Ryan Brown
Objective. Up to this point, 1.5 T linac-compatible coil array layouts have been restricted to one or two rows of coils because of the desire to place radiation-opaque circuitry adjacent to the coils and outside the window through which the linac beam travels. Such layouts can limit parallel imaging performance. The purpose of this work was to design and build a three-row array in which remotely located
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An automated technique for global noise level measurement in CT image with a conjunction of image gradient Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-15 Hsiang-Chi Kuo, Usman Mahmood, Assen S Kirov, James Mechalakos, Cesar Della Biancia, Laura I Cerviño, Seng Boh Lim
Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical
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Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-15 Mahima Merin Philip, Jessica Watts, Seyedeh Niki Mir Moeini, Mohammed Musheb, Fergus McKiddie, Andy Welch, Mintu Nath
Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation
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The impact of motion on onboard MRI-guided pencil beam scanned proton therapy treatments Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-15 Alisha Duetschler, Sairos Safai, Damien C Weber, Antony J Lomax, Ye Zhang
Objective. Online magnetic resonance imaging (MRI) guidance could be especially beneficial for pencil beam scanned (PBS) proton therapy of tumours affected by respiratory motion. For the first time to our knowledge, we investigate the dosimetric impact of respiratory motion on MRI-guided proton therapy compared to the scenario without magnetic field. Approach. A previously developed analytical proton
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A cardiac MRI motion artifact reduction method based on edge enhancement network Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-15 Nanhe Jiang, Yucun Zhang, Qun Li, Xianbin Fu, Dongqing Fang
Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly
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Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-15 Ming Fan, Xuan Cao, Fuqing Lü, Sangma Xie, Zhou Yu, Yuanlin Chen, Zhong Lü, Lihua Li
Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires gadolinium-based contrast agents, which carry risks such as brain retention and astrocyte migration. Contrast-free MRI is thus preferable for patients with renal impairment or who are pregnant. This study aimed to investigate the feasibility
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3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR) Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-15 Hua-Chieh Shao, Tielige Mengke, Jie Deng, You Zhang
Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial
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CT respiratory motion synthesis using joint supervised and adversarial learning Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-15 Y-H Cao, V Bourbonne, F Lucia, U Schick, J Bert, V Jaouen, D Visvikis
Objective. Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery
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Path planning algorithm for percutaneous puncture lung mass biopsy procedure based on the multi-objective constraints and fuzzy optimization Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-15 Jiayu Zhang, Jing Zhang, Ping Han, Xin-Zu Chen, Yu Zhang, Wen Li, Jing Qin, Ling He
Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT (Computed Tomography) images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the
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Dual-space disentangled-multimodal network (DDM-net) for glioma diagnosis and prognosis with incomplete pathology and genomic data Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-10 Lu Qiu, Lu Zhao, Wangyuan Zhao, Jun Zhao
Objective. Effective fusion of histology slides and molecular profiles from genomic data has shown great potential in the diagnosis and prognosis of gliomas. However, it remains challenging to explicitly utilize the consistent-complementary information among different modalities and create comprehensive representations of patients. Additionally, existing researches mainly focus on complete multi-modality
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Convolutional LSTM model for cine image prediction of abdominal motion Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-09 J Weng, S H V Bhupathiraju, T Samant, A Dresner, J Wu, S S Samant
Objective. In this study, we tackle the challenge of latency in magnetic resonance linear accelerator (MR-Linac) systems, which compromises target coverage accuracy in gated real-time radiotherapy. Our focus is on enhancing motion prediction precision in abdominal organs to address this issue. We developed a convolutional long short-term memory (convLSTM) model, utilizing 2D cine magnetic resonance
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Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-09 Francisco Berumen, Samuel Ouellet, Shirin Enger, Luc Beaulieu
Objective. In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability. Approach
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3D whole heart k-space-based super-resolution cardiac T1 mapping using rotated stacks Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-09 Simone Hufnagel, Patrick Schuenke, Jeanette Schulz-Menger, Tobias Schaeffter, Christoph Kolbitsch
Objective. To provide three-dimensional (3D) whole-heart high-resolution isotropic cardiac T1 maps using a k-space-based through-plane super-resolution reconstruction (SRR) with rotated multi-slice stacks. Approach. Due to limited SNR and cardiac motion, often only 2D T1 maps with low through-plane resolution (4–8 mm) can be obtained. Previous approaches used SRR to calculate 3D high-resolution isotropic
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Neural blind deconvolution for deblurring and supersampling PSMA PET Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-09 Caleb Sample, Arman Rahmim, Carlos Uribe, François Bénard, Jonn Wu, Roberto Fedrigo, Haley Clark
Objective. To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution. Approach. Blind deconvolution is a method of estimating the hypothetical ‘deblurred’ image along with the blur kernel (related to the point spread function) simultaneously. Traditional maximum a posteriori blind deconvolution methods
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SM-GRSNet: sparse mapping-based graph representation segmentation network for honeycomb lung lesion Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-09 Yuanrong Zhang, Xiufang Feng, Yunyun Dong, Ying Chen, Zian Zhao, Bingqian Yang, Yunqing Chang, Yujie Bai
Objective. Honeycomb lung is a rare but severe disease characterized by honeycomb-like imaging features and distinct radiological characteristics. Therefore, this study aims to develop a deep-learning model capable of segmenting honeycomb lung lesions from Computed Tomography (CT) scans to address the efficacy issue of honeycomb lung segmentation. Methods. This study proposes a sparse mapping-based
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Improving respiratory signal prediction with a deep neural network and simple changes to the input and output data format Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-08 Ryusuke Hirai, Shinichiro Mori, Hiroki Suyari, Hitoshi Ishikawa
Objective. To improve respiratory gating accuracy and radiation treatment throughput, we developed a generalized model based on a deep neural network (DNN) for predicting any given patient’s respiratory motion. Approach. Our model uses long short-term memory (LSTM) based on a recurrent neural network (RNN), and improves upon common techniques. The first improvement is that the data input is not a one-dimensional
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Benchmarking proton RBE models Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-08 Lydia L Gardner, John D O’Connor, Stephen J McMahon
Objective. To biologically optimise proton therapy, models which can accurately predict variations in proton relative biological effectiveness (RBE) are essential. Current phenomenological models show large disagreements in RBE predictions, due to different model assumptions and differences in the data to which they were fit. In this work, thirteen RBE models were benchmarked against a comprehensive
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S-Net: an S-shaped network for nodule detection in 3D CT images Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-05 JingYu Zhang, Wei Zou, Nan Hu, Bin Zhang, Jiajun Wang
Objective. Accurate and automatic detection of pulmonary nodules is critical for early lung cancer diagnosis, and promising progress has been achieved in developing effective deep models for nodule detection. However, most existing nodule detection methods merely focus on integrating elaborately designed feature extraction modules into the backbone of the detection network to extract rich nodule features
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Impact of glioma peritumoral edema, tumor size, and tumor location on alternating electric fields (AEF) therapy in realistic 3D rat glioma models: a computational study Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-04 Ha Nguyen, Keith E Schubert, Christoph Pohling, Edwin Chang, Vicky Yamamoto, Yuping Zeng, Ying Nie, Samuel Van Buskirk, Reinhard W Schulte, Chirag B Patel
Objective. Alternating electric fields (AEF) therapy is a treatment modality for patients with glioblastoma. Tumor characteristics such as size, location, and extent of peritumoral edema may affect the AEF strength and distribution. We evaluated the sensitivity of the AEFs in a realistic 3D rat glioma model with respect to these properties. Approach. The electric properties of the peritumoral edema
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Edge-illumination spectral phase-contrast tomography Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Luca Brombal, Fulvia Arfelli, Francesco Brun, Vittorio Di Trapani, Marco Endrizzi, Ralf H Menk, Paola Perion, Luigi Rigon, Mara Saccomano, Giuliana Tromba, Alessandro Olivo
Following the rapid, but independent, diffusion of x-ray spectral and phase-contrast systems, this work demonstrates the first combination of spectral and phase-contrast computed tomography (CT) obtained by using the edge-illumination technique and a CdTe small-pixel (62 μm) spectral detector. A theoretical model is introduced, starting from a standard attenuation-based spectral decomposition and leading
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Development of a novel fibre optic beam profile and dose monitor for very high energy electron radiotherapy at ultrahigh dose rates Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Joseph J Bateman, Emma Buchanan, Roberto Corsini, Wilfrid Farabolini, Pierre Korysko, Robert Garbrecht Larsen, Alexander Malyzhenkov, Iñaki Ortega Ruiz, Vilde Rieker, Alexander Gerbershagen, Manjit Dosanjh
Objective. Very high energy electrons (VHEE) in the range of 50–250 MeV are of interest for treating deep-seated tumours with FLASH radiotherapy (RT). This approach offers favourable dose distributions and the ability to deliver ultra-high dose rates (UHDR) efficiently. To make VHEE-based FLASH treatment clinically viable, a novel beam monitoring technology is explored as an alternative to transmission
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2.5D UNet with context-aware feature sequence fusion for accurate esophageal tumor semantic segmentation Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Kai Xu, Feixiang Zhang, Yong Huang, Xiaoyu Huang
Segmenting esophageal tumor from computed tomography (CT) sequence images can assist doctors in diagnosing and treating patients with this malignancy. However, accurately extracting esophageal tumor features from CT images often present challenges due to their small area, variable position, and shape, as well as the low contrast with surrounding tissues. This results in not achieving the level of accuracy
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Verification of neuronavigated TMS accuracy using structured-light 3D scans Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Noora Matilainen, Juhani Kataja, Ilkka Laakso
Objective. To investigate the reliability and accuracy of the manual three-point co-registration in neuronavigated transcranial magnetic stimulation (TMS). The effect of the error in landmark pointing on the coil placement and on the induced electric and magnetic fields was examined. Approach. The position of the TMS coil on the head was recorded by the neuronavigation system and by 3D scanning for
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Semi-supervised iterative adaptive network for low-dose CT sinogram recovery Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Lei Wang, Mingqiang Meng, Shixuan Chen, Zhaoying Bian, Dong Zeng, Deyu Meng, Jianhua Ma
Background. Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of deep learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms
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Evaluation of monolithic crystal detector with dual-ended readout utilizing multiplexing method Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Xiangtao Zeng, Zhiming Zhang, Daowu Li, Xianchao Huang, Zhuoran Wang, Yingjie Wang, Wei Zhou, Peilin Wang, Meiling Zhu, Qing Wei, Huixing Gong, Long Wei
Objective. Monolithic crystal detectors are increasingly being applied in positron emission tomography (PET) devices owing to their excellent depth-of-interaction (DOI) resolution capabilities and high detection efficiency. In this study, we constructed and evaluated a dual-ended readout monolithic crystal detector based on a multiplexing method. Approach. We employed two 12 × 12 silicon photomultiplier
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Evaluating the relationship between contouring variability and modelled treatment outcome for prostate bed radiotherapy Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Viet Le Bao, Annette Haworth, Jason Dowling, Amy Walker, Sankar Arumugam, Michael Jameson, Phillip Chlap, Kirsty Wiltshire, Sarah Keats, Kirrily Cloak, Mark Sidhom, Andrew Kneebone, Lois Holloway
Objectives. Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation
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Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Yankun Lang, Zhuoran Jiang, Leshan Sun, Liangzhong Xiang, Lei Ren
Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue. Approach. We proposed
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Equivalent uniform aerobic dose in radiotherapy for hypoxic tumors Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Alexei V Chvetsov, Mark Muzi
Objective. Equivalent uniform aerobic dose (EUAD) is proposed for comparison of integrated cell survival in tumors with different distributions of hypoxia and radiation dose. Approach. The EUAD assumes that for any non-uniform distributions of radiation dose and oxygen enhancement ratio (OER) within a tumor, there is a uniform distribution of radiation dose under hypothetical aerobic conditions with
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Magneto-acousto-electrical tomography using nonlinearly frequency-modulated ultrasound Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Zhizhuo Cheng, Zhishen Sun, Jianfei Wang, Kebin Jia
Objective. In this study, nonlinearly frequency-modulated (NLFM) ultrasound was applied to magneto-acousto-electrical tomography (MAET) to increase the dynamic range of detection. Approach. Generation of NLFM signals using window function method—based on the principle of stationary phase—and piecewise linear frequency modulation method—based on the genetic algorithm—was discussed. The MAET experiment
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XTransCT: ultra-fast volumetric CT reconstruction using two orthogonal x-ray projections for image-guided radiation therapy via a transformer network Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Chulong Zhang, Lin Liu, Jingjing Dai, Xuan Liu, Wenfeng He, Yinping Chan, Yaoqin Xie, Feng Chi, Xiaokun Liang
Objective. The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy. Approach. Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which
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Hierarchical decomposed dual-domain deep learning for sparse-view CT reconstruction Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Yoseob Han
Objective. X-ray computed tomography employing sparse projection views has emerged as a contemporary technique to mitigate radiation dose. However, due to the inadequate number of projection views, an analytic reconstruction method utilizing filtered backprojection results in severe streaking artifacts. Recently, deep learning (DL) strategies employing image-domain networks have demonstrated remarkable
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IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Yanghui Yan, Tiejun Yang, Chunxia Jiao, Aolin Yang, Jianyu Miao
Objective. Multi-contrast magnetic resonance imaging (MC MRI) can obtain more comprehensive anatomical information of the same scanning object but requires a longer acquisition time than single-contrast MRI. To accelerate MC MRI speed, recent studies only collect partial k-space data of one modality (target contrast) to reconstruct the remaining non-sampled measurements using a deep learning-based
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Cross noise level PET denoising with continuous adversarial domain generalization Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Xiaofeng Liu, Samira Vafay Eslahi, Thibault Marin, Amal Tiss, Yanis Chemli, Yongsong Huang, Keith A Johnson, Georges El Fakhri, Jinsong Ouyang
Objective. Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution
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Multi-scale adversarial learning with difficult region supervision learning models for primary tumor segmentation Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Shenhai Zheng, Qiuyu Sun, Xin Ye, Weisheng Li, Lei Yu, Chaohui Yang
Objective. Recently, deep learning techniques have found extensive application in accurate and automated segmentation of tumor regions. However, owing to the variety of tumor shapes, complex types, and unpredictability of spatial distribution, tumor segmentation still faces major challenges. Taking cues from the deep supervision and adversarial learning, we have devised a cascade-based methodology
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Fano cavity test and investigation of the response of the Roos chamber irradiated by proton beams in perpendicular magnetic fields up to 1 T Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Isabel Blum, Jing Syuen Wong, Krishna Godino Padre, Jessica Stolzenberg, Hermann Fuchs, Kilian-Simon Baumann, Björn Poppe, Hui Khee Looe
Objective. The aim of this work is to investigate the response of the Roos chamber (type 34001) irradiated by clinical proton beams in magnetic fields. Approach. At first, a Fano test was implemented in Monte Carlo software package GATE version 9.2 (based on Geant4 version 11.0.2) using a cylindrical slab geometry in a magnetic field up to 1 T. In accordance to an experimental setup (Fuchs et al )
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An automated methodology for whole-body, multimodality tracking of individual cancer lesions Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Victor Santoro-Fernandes, Daniel T Huff, Luciano Rivetti, Alison Deatsch, Brayden Schott, Scott B Perlman, Robert Jeraj
Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities. Approach. This study introduces a lesion tracking methodology and benchmarked
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Beam monitor chamber calibration of a synchro-cyclotron high dose rate per pulse pulsed scanned proton beam Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-03 Marie Vidal, Anaïs Gérard, Vincent Floquet, Julien Forthomme, Jeppe Brage Christensen, Erik Almhagen, Erik Grusell, Vincent Heymans, Séverine Rossomme, Serge Dumas, Richard Trimaud, Joël Hérault
Objective. Ionization chambers, mostly used for beam calibration and for reference dosimetry, can show high recombination effects in pulsed high dose rate proton beams. The aims of this paper are: first, to characterize the linearity response of newly designed asymmetrical beam monitor chambers (ABMC) in a 100–226 MeV pulsed high dose rate per pulse scanned proton beam; and secondly, to calibrate the
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Spectrum learning for super-resolution tomographic reconstruction Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-02 Zirong Li, Kang An, Hengyong Yu, Fulin Luo, Jiayi Pan, Shaoyu Wang, Jianjia Zhang, Weiwen Wu, Dingyue Chang
Objective. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information. Approach
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Robust, planning-based targeted locoregional tumour heating in small animals Phys. Med. Biol. (IF 3.5) Pub Date : 2024-04-02 Jort A Groen, Johannes Crezee, Hanneke W M van Laarhoven, Bram F Coolen, Gustav J Strijkers, Maarten F Bijlsma, H Petra Kok
Objective. To improve hyperthermia in clinical practice, pre-clinical hyperthermia research is essential to investigate hyperthermia effects and assess novel treatment strategies. Translating pre-clinical hyperthermia findings into clinically viable protocols requires laboratory animal treatment techniques similar to clinical hyperthermia techniques. The ALBA micro8 electromagnetic heating system (Med-logix
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HGCMorph: joint discontinuity-preserving and pose-learning via GNN and capsule networks for deformable medical images registration Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-28 Zhiyue Yan, Jianhua Ji, Jia Ma, Wenming Cao
Objective. This study aims to enhance medical image registration by addressing the limitations of existing approaches that rely on spatial transformations through U-Net, ConvNets, or Transformers. The objective is to develop a novel architecture that combines ConvNets, graph neural networks (GNNs), and capsule networks to improve the accuracy and efficiency of medical image registration, which can
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Sliding transformer with uncertainty estimation for vestibular schwannoma automatic segmentation Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-28 Yang Liu, Mengjun Li, Mingchu Li, Xu Wang, Jiantao Liang, Ge Chen, Yuanjing Feng, Zan Chen
Objective. Automated segmentation of vestibular schwannoma (VS) using magnetic resonance imaging (MRI) can enhance clinical efficiency. Though many advanced methods exist for automated VS segmentation, the accuracy is hindered by ambivalent tumor borders and cystic regions in some patients. In addition, these methods provide results that do not indicate segmentation uncertainty, making their translation
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First experimental time-of-flight-based proton radiography using low gain avalanche diodes Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-28 Felix Ulrich-Pur, Thomas Bergauer, Tetyana Galatyuk, Albert Hirtl, Matthias Kausel, Vadym Kedych, Mladen Kis, Yevhen Kozymka, Wilhelm Krüger, Sergey Linev, Jan Michel, Jerzy Pietraszko, Adrian Rost, Christian Joachim Schmidt, Michael Träger, Michael Traxler
Objective. Ion computed tomography (iCT) is an imaging modality for the direct determination of the relative stopping power (RSP) distribution within a patient’s body. Usually, this is done by estimating the path and energy loss of ions traversing the scanned volume utilising a tracking system and a separate residual energy detector. This study, on the other hand, introduces the first experimental
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A combined analytical and Monte Carlo method for detailed simulations of antiscatter grids in x-ray medical imaging: implementing scatter within the grid Phys. Med. Biol. (IF 3.5) Pub Date : 2024-03-28 Rodrigo T Massera, Hilde Bosmans, Sunay Rodriguez Perez, Nicholas Marshall
Objective. To implement a hybrid method, which combines analytical tracking and interaction simulation using Monte Carlo (MC) techniques, in order to model photon transport inside antiscatter grids (ASG) for x-ray imaging. Approach. A new tally was developed for PENELOPE (v.2018) and penEasy (v. 2020) MC code to simulate photon transmission through ASGs. Two established analytical algorithms from the