Medical physics最新文献

{"title":"Monte Carlo study of gold nanoparticle-mediated radiosensitization effects using nanoscale cell model combined with fractal-based DNA model.","authors":"Amrit Kaphle, Sandun Jayarathna, Sunil Krishnan, Sang Hyun Cho","doi":"10.1002/mp.17676","DOIUrl":"https://doi.org/10.1002/mp.17676","url":null,"abstract":"<p><strong>Background: </strong>Gold nanoparticles (GNPs) are promising radiosensitizers in radiation therapy, yet the exact mechanisms behind their effectiveness remain not fully understood. Monte Carlo (MC) simulations have been used to study extra energy deposition and increased DNA damage by the secondary electrons from intracellularly present GNPs, which are believed to be the key physical mechanisms responsible for the radiosensitization effects observed in many radiobiological studies. However, discrepancies between experimental results and computational predictions persist. While often attributed to purely biological effects, such discrepancies, from a physical modeling point of view, can also be due to the use of MC models constructed with simplified cellular/DNA geometries and unrealistic GNP distributions. To address this challenge, higher-resolution nanoscale models with realistic GNP distributions and detailed cellular/DNA structures are needed. In principle, computational results from such nanoscale models can be not only more accurate but also directly correlated with experimental results for biological outcome modeling.</p><p><strong>Purpose: </strong>The main purpose of this MC study was to investigate the potential increase in radiation-induced DNA damage due to internalized GNPs by using a nanoscale cell model including realistic GNP distributions and detailed cellular/DNA structures.</p><p><strong>Methods: </strong>Two high-resolution nanoscale cellular geometry models, featuring the nucleus filled with fractal-patterned DNA fibers, were constructed from transmission electron microscopy (TEM) images of GNP-laden human colorectal tumor cells. These models were used to simulate the initial yield of single- and double-strand breaks (SSBs and DSBs) of DNA under orthovoltage (250 kVp) and megavoltage (6 MV) photon beam irradiation. In-depth Geant4 MC simulations were conducted to assess radiation-induced effects due to intracellular GNP presence and absence, focusing on the computation of SSBs/DSBs and their causative mechanisms - direct or indirect effects of ionizing radiation. Penelope and Geant4-DNA for Gold (G4_DNA_Au) physics models were employed for GNPs, and the difference between those two physics models were also evaluated.</p><p><strong>Results: </strong>The simulation results revealed a notable enhancement in the nucleus dose and DNA damage due to intracellular GNP presence, with maximum dose enhancements observed at 4.24% and 4.34% for 250 kVp, and 3.04% and 3.22% for 6 MV irradiation using the Penelope and G4_DNA_Au physics models, respectively. Crucially, this study found that indirect yields of both SSB and DSB were significantly higher than their direct counterparts, emphasizing the dominance of indirect DNA damage mechanisms. SSB enhancements were recorded between 2.36% and 3.46%, while DSB enhancements were more significant, ranging from 7.36% to 10.33%, across various scenarios and photon energies under the G4_D","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel pre-spatial data fusion deep learning approach for multimodal volumetric outcome prediction models in radiotherapy.","authors":"John C Asbach, Anurag K Singh, Austin J Iovoli, Mark Farrugia, Anh H Le","doi":"10.1002/mp.17672","DOIUrl":"https://doi.org/10.1002/mp.17672","url":null,"abstract":"<p><strong>Background: </strong>Given the recent increased emphasis on multimodal neural networks to solve complex modeling tasks, the problem of outcome prediction for a course of treatment can be framed as fundamentally multimodal in nature. A patient's response to treatment will vary based on their specific anatomy and the proposed treatment plan-these factors are spatial and closely related. However, additional factors may also have importance, such as non-spatial descriptive clinical characteristics, which can be structured as tabular data. It is critical to provide models with as comprehensive of a patient representation as possible, but inputs with differing data structures are incompatible in raw form; traditional models that consider these inputs require feature engineering prior to modeling. In neural networks, feature engineering can be organically integrated into the model itself, under one governing optimization, rather than performed prescriptively beforehand. However, the native incompatibility of different data structures must be addressed. Methods to reconcile structural incompatibilities in multimodal model inputs are called data fusion. We present a novel joint early pre-spatial (JEPS) fusion technique and demonstrate that differences in fusion approach can produce significant model performance differences even when the data is identical.</p><p><strong>Purpose: </strong>To present a novel pre-spatial fusion technique for volumetric neural networks and demonstrate its impact on model performance for pretreatment prediction of overall survival (OS).</p><p><strong>Methods: </strong>From a retrospective cohort of 531 head and neck patients treated at our clinic, we prepared an OS dataset of 222 data-complete cases at a 2-year post-treatment time threshold. Each patient's data included CT imaging, dose array, approved structure set, and a tabular summary of the patient's demographics and survey data. To establish single-modality baselines, we fit both a Cox Proportional Hazards model (CPH) and a dense neural network on only the tabular data, then we trained a 3D convolutional neural network (CNN) on only the volume data. Then, we trained five competing architectures for fusion of both modalities: two early fusion models, a late fusion model, a traditional joint fusion model, and the novel JEPS, where clinical data is merged into training upstream of most convolution operations. We used standardized 10-fold cross validation to directly compare the performance of all models on identical train/test splits of patients, using area under the receiver-operator curve (AUC) as the primary performance metric. We used a two-tailed Student t-test to assess the statistical significance (p-value threshold 0.05) of any observed performance differences.</p><p><strong>Results: </strong>The JEPS design scored the highest, achieving a mean AUC of 0.779 ± 0.080. The late fusion model and clinical-only CPH model scored second and third highest with 0.74","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Region-guided focal adversarial learning for CT-to-MRI translation: A proof-of-concept and validation study in hepatocellular carcinoma.","authors":"Yi-Fan Xia, Meng Zeng, Shu-Wen Sun, Qiu-Ping Liu, Jiu-Lou Zhang, Rui Zhi, Fei-Yu Lu, Wei Chen, Yu-Dong Zhang","doi":"10.1002/mp.17674","DOIUrl":"https://doi.org/10.1002/mp.17674","url":null,"abstract":"<p><strong>Background: </strong>Generative adversarial networks (GANs) have recently demonstrated significant potential for producing virtual images with the same characteristics as real-life landscapes, thereby enhancing various medical tasks.</p><p><strong>Purpose: </strong>To design a region-guided focal GAN (Focal-GAN) for translating images between CT and MRI and test its clinical applicability in patients with hepatocellular carcinoma (HCC).</p><p><strong>Methods: </strong>Between January 2012 and October 2021, two cohorts of patients with HCC who underwent contrast-enhanced CT (Center 1, n = 685) and MRI (Center 1, n = 516; Center 2, n = 318) were retrospectively enrolled. We trained the Focal-GAN model by adding tumor regions to a baseline Cycle-GAN framework to steer the model toward focal attention learning. The quality of the images generated was assessed using an open-source MRQy tool. The clinical applicability of the Focal-GAN was evaluated by applying the nnUNet and ResNet-50 model for tumor segmentation and microvascular invasion (MVI) prediction in HCC on the generated images.</p><p><strong>Results: </strong>In the ablation tests, Focal-GAN achieved a higher fidelity than the conventional Cycle-GAN in the generated image quality assessment with MRQy. Regarding applicability, regardless of tumor size, nnUNet trained with focal-GAN-generated images achieved higher Dice scores than nnUNet trained using Cycle-GAN-generated images for HCC segmentation in both internal (0.607 vs. 0.341, p < 0.01) and external (0.796 vs. 0.753, p < 0.001) validation. Additionally, ResNet-50 trained with Focal-GAN-generated images produced higher areas-under-curve (AUCs) than ResNet-50 trained with real images for MVI prediction in both internal (0.754 vs. 0.665, p = 0.048) and external (0.670 vs. 0.579, p < 0.001) validation.</p><p><strong>Conclusions: </strong>The designed Focal-GAN model can generate virtual MR images from unpaired CT images, thereby extending the clinical applicability of CT in the liver tumor diagnostic pathway.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised adversarial diffusion models for fast MRI reconstruction.","authors":"Mojtaba Safari, Zach Eidex, Shaoyan Pan, Richard L J Qiu, Xiaofeng Yang","doi":"10.1002/mp.17675","DOIUrl":"https://doi.org/10.1002/mp.17675","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Magnetic resonance imaging (MRI) offers excellent soft tissue contrast essential for diagnosis and treatment, but its long acquisition times can cause patient discomfort and motion artifacts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To propose a self-supervised deep learning-based compressed sensing MRI method named \"Self-Supervised Adversarial Diffusion for MRI Accelerated Reconstruction (SSAD-MRI)\" to accelerate data acquisition without requiring fully sampled datasets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;We used the fastMRI multi-coil brain axial &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt; &lt;annotation&gt;$text{T}_{2}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -weighted ( &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt; &lt;annotation&gt;$text{T}_{2}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -w) dataset from 1376 cases and single-coil brain quantitative magnetization prepared 2 rapid acquisition gradient echoes &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;annotation&gt;$text{T}_{1}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; maps from 318 cases to train and test our model. Robustness against domain shift was evaluated using two out-of-distribution (OOD) datasets: multi-coil brain axial postcontrast &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;annotation&gt;$text{T}_{1}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -weighted ( &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;T&lt;/mi&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;mi&gt;c&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$text{T}_{1}text{c}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ) dataset from 50 cases and axial T1-weighted (T1-w) dataset from 50 patients. Data were retrospectively subsampled at acceleration rates &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mo&gt;∈&lt;/mo&gt; &lt;mo&gt;{&lt;/mo&gt; &lt;mn&gt;2&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;4&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;8&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mo&gt;}&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$ R in lbrace 2times, 4times, 8times rbrace $&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; . SSAD-MRI partitions a random sampling pattern into two disjoint sets, ensuring data consistency during training. We compared our method with ReconFormer Transformer and SS-MRI, assessing performance using normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Statistical tests included one-way analysis of variance and multi-comparison Tukey's honesty significant difference (HSD) tests.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;SSAD-MRI preserved fine structures and brain abnormalities visually better than comparative methods at &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;8&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$ R=8times$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; for both multi-coil and single-coil datasets. It achieved the lowest NMSE at &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mo&gt;∈&lt;/mo&gt; &lt;mo&gt;{&lt;/mo&gt; &lt;mn&gt;4&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mn&gt;8&lt;/mn&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mo&gt;}&lt;/mo&gt;&lt;/mrow&gt; &lt;annotation&gt;$ R in lbrace 4times, 8times rbrace $&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , and the highest PSNR and SSIM values at all acceleration rates for the multi-coil dataset. Simil","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A database of magnetic resonance imaging-transcranial ultrasound co-registration.","authors":"Maryam Alizadeh, D Louis Collins, Marta Kersten-Oertel, Yiming Xiao","doi":"10.1002/mp.17666","DOIUrl":"https://doi.org/10.1002/mp.17666","url":null,"abstract":"<p><strong>Purpose: </strong>As a portable and cost-effective imaging modality with better accessibility than Magnetic Resonance Imaging (MRI), transcranial sonography (TCS) has demonstrated its flexibility and potential utility in various clinical diagnostic applications, including Parkinson's disease and cerebrovascular conditions. To better understand the information in TCS for data analysis and acquisition, MRI can provide guidance for efficient imaging with neuronavigation systems and the confirmation of disease-related abnormality. In these cases, MRI-TCS co-registration is crucial, but relevant public databases are scarce to help develop the related algorithms and software systems.</p><p><strong>Acquisition and validation methods: </strong>This dataset comprises manually registered MRI and transcranial ultrasound volumes from eight healthy subjects. Three raters manually registered each subject's scans, based on visual inspection of image feature correspondence. Average transformation matrices were computed from all raters' alignments for each subject. Inter- and intra-rater variability in the transformations conducted by raters are presented to validate the accuracy and consistency of manual registration. In addition, a population-averaged MRI brain vascular atlas is provided to facilitate the development of computer-assisted TCS acquisition software.</p><p><strong>Data format and usage notes: </strong>The dataset is provided in both NIFTI and MINC formats and is publicly available on the OSF data repository: https://osf.io/zdcjb/.</p><p><strong>Potential applications: </strong>This dataset provides the first public resource for the development and assessment of MRI-TCS registration with manual ground truths, as well as resources for establishing neuronavigation software in data acquisition and analysis of TCS. These technical advancements could greatly boost TCS as an imaging tool for clinical applications in the diagnosis of neurological conditions such as Parkinson's disease and cerebrovascular disorders.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chinese reference population: Open-source age-dependent computational phantoms of reference Chinese population.","authors":"Siyi Huang, Qian Liu, Tianwu Xie","doi":"10.1002/mp.17670","DOIUrl":"https://doi.org/10.1002/mp.17670","url":null,"abstract":"<p><strong>Purpose: </strong>Computational phantoms have been widely used in radiation protection, radiotherapy, medical imaging, surgery navigation, and digital anatomy. However, current Chinese phantoms lack representation for all sensitive groups including adults, children, and pregnant women. This manuscript aims to address this gap by developing novel open-access computational phantoms representing the Chinese population.</p><p><strong>Acquisition and validation methods: </strong>The Chinese reference population (CRP) developed in this study includes 30 phantoms, available in both voxel and nonuniform rational B-spline (NURBS) formats, with ages in 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 18 years, and adult male and female, as well as four pregnant women in early pregnancy, first trimester, second trimester, and third trimester. The development process involved image segmentation, NURBS reconstruction, and voxelization based on whole-body computed tomography (CT) scans of 22 original individual patients. Reference organ masses were directly obtained from the Chinese Reference Human Anatomical Physiological and Metabolic Data, as well as international commission on radiological protection (ICRP) Publication 89.</p><p><strong>Data format and usage notes: </strong>Voxelized phantoms are accessible in DAT format as raw data, which can be opened by medical imaging softwares such as a medical image data analysis tool (AMIDE). Excel files contain descriptive information (ages, genders, phantom sizes, voxel sizes, organ masses, densities) and organ absorbed doses on <math> <semantics> <mrow><msup><mrow></mrow> <mn>18</mn></msup> <mi>F</mi> <mo>-</mo> <mi>F</mi> <mi>D</mi> <mi>G</mi></mrow> <annotation>$^{18}F-FDG$</annotation></semantics> </math> application. All data in this study can be obtained from our official website (https://alldigitaltwins.com) and Zenodo (https://zenodo.org/records/14268606).</p><p><strong>Potential applications: </strong>This work offers a collection of open-source age-dependent phantoms featuring anatomical data specific to the Chinese population. Researchers can utilize this dataset to modify and adapt the phantoms for specific applications, fostering innovation and progress, and enhancing accuracy and applicability in various fields.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technique selection and technical developments for 2D dual-energy subtraction angiography on an interventional C-arm.","authors":"Ethan P Nikolau, Joseph F Whitehead, Martin G Wagner, James R Scheuermann, Paul F Laeseke, Michael A Speidel","doi":"10.1002/mp.17661","DOIUrl":"https://doi.org/10.1002/mp.17661","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Dual-energy (DE) x-ray image acquisition has the potential to provide material-specific angiographic images in the interventional suite. This approach can be implemented with novel detector technologies, such as dual-layer and photon-counting detectors. Alternatively, DE imaging can be implemented on existing systems using fast kV-switching. Currently, there are no commercially available DE options for interventional platforms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study reports on the development of a prototype fast kV-switching DE subtraction angiography system. In contrast to alternative approaches to DE imaging in the interventional suite, this prototype uses a clinically available interventional C-arm equipped with special x-ray tube control software. An automatic exposure control algorithm and technical features needed for such a system in the interventional setting are developed and validated in phantom studies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Fast kV-switching was implemented on an interventional C-arm platform using software that enables frame-by-frame specification of x-ray tube techniques (e.g., tube voltage/kV, pulse width/ms, tube current/mA). A real-time image display was developed on a portable workstation to display DE subtraction images in real-time (nominal 15 frame/s). An empirical CNR-driven automatic exposure control (AEC) algorithm was created to guide DE tube technique selection (kV pair, ms pair, mA). The AEC model contained a look-up table which related DE tube technique parameters and air kerma to iodine CNR, which was measured in acrylic phantom models containing an iodine-equivalent reference object. For a given iodine CNR request, the AEC algorithm estimated patient thickness and then selected the DE tube technique expected to deliver the requested CNR at the minimum air kerma. The AEC algorithm was developed for DE imaging performed without and with the application of anti-correlated noise reduction (ACNR). Validation of the AEC model was performed by comparing the AEC-predicted iodine CNR values with directly measured values in a separate phantom study. Both dose efficiency (CNR&lt;sup&gt;2&lt;/sup&gt;/kerma) and maximum achievable iodine CNR (within tube technique constraints) were quantified. Finally, improvements in DE iodine CNR were quantified using a novel variant to the ACNR approach, which used machine-learning image denoising (ACNR-ML).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The prototype system provided a continuous display of DE subtraction images. For standard DE imaging, the AEC-predicted iodine CNR values agreed with directly measured values to within 3.5% ± 1.6% (mean ± standard deviation). When ACNR was applied, predicted iodine CNR agreed with measurement to within 2.1% ± 3.3%. AEC-generated DE techniques were typically (low/high energy): 63/125 kV, 10/3.2 ms, with varying mA values. When ACNR was applied, dose efficiency was increased by a factor of 9.37 ± 2.08 and maximum CNR was incre","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Will large language model AI (ChatGPT) be a benefit or a risk to quality for submission of medical physics manuscripts?","authors":"Daniel A Low, Per H Halvorsen, Samantha G Hedrick","doi":"10.1002/mp.17657","DOIUrl":"https://doi.org/10.1002/mp.17657","url":null,"abstract":"","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143256558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beam collimation and filtration optimization for a novel orthovoltage radiotherapy system.","authors":"Nathan Clements, Olivia Masella, Deae-Eddine Krim, Lane Braun, Magdalena Bazalova-Carter","doi":"10.1002/mp.17662","DOIUrl":"https://doi.org/10.1002/mp.17662","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The inaccessibility of clinical linear accelerators in low- and middle-income countries creates a need for low-cost alternatives. Kilovoltage (kV) x-ray tubes have shown promise as a source that could meet this need. However, performing radiotherapy with a kV x-ray tube has numerous difficulties, including high skin dose, rapid dose fall-off, and low dose rates. These limitations create a need for highly effective beam collimation and filtration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To improve the treatment potential of a novel kV x-ray system by optimizing an iris collimator and beam filtration using Bayesian techniques and Monte Carlo (MC) simulations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The Kilovoltage Optimized AcceLerated Adaptive therapy system's current beam configuration consists of a 225 kVp x-ray tube, a 12-leaflet tungsten iris collimator, and a 0.1 mm copper filter. A Bayesian optimization was performed for the large and small focal spot sizes of the kV x-ray tube source at 220 kVp using TopasOpt, an open-source library for optimization in TOPAS. Collimator thickness, copper filter thickness, source-to-collimator distance (SCD), and source-to-surface distance (SSD) were the variables considered in the optimization. The objective function was designed to maximize the dose rate and the dose at a depth of 5 cm while minimizing the beam penumbra width and the out-of-field dose (OFD), all evaluated in a water phantom. Post-optimization, the optimal beam configuration was simulated and compared to the existing configuration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The optimal collimation setup consisted of 2.5 mm thick tungsten leaflets for the iris collimator and a 350 mm SSD for both focal spot sizes. The optimal copper filtration was 0.22 mm for the large focal spot and 0.15 mm for the small focal spot, with a SCD of 148.5 mm for the large focal spot and 125.8 mm for the small focal spot. For the large focal spot, the surface dose rate decreased by 9.4%, while the PDD at 5cm depth ( &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mtext&gt;PDD&lt;/mtext&gt; &lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt; &lt;mi&gt;c&lt;/mi&gt; &lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;annotation&gt;$text{PDD}_{5textnormal {cm}}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ) increased by 7.7% compared to the existing iris collimator. Additionally, the surface beam penumbra width was reduced by 31.3%, and no significant changes in the OFD were observed. For the small focal spot, the surface dose rate for the new collimator increased by 3.7% and the &lt;math&gt; &lt;semantics&gt;&lt;msub&gt;&lt;mtext&gt;PDD&lt;/mtext&gt; &lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt; &lt;mi&gt;c&lt;/mi&gt; &lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;annotation&gt;$text{PDD}_{5textnormal {cm}}$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; increased by 5.3%, with no statistically significant changes in the beam penumbra width or OFD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The optimal beam collimation and filtration for both x-ray tube focal spot sizes of a kV radiotherapy system was determined using Bayesian optimization and MC simulations and resulted in improved dose ","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing gamma-ray detection: Processing grooved microstructures on LYSO crystal with femtosecond laser.","authors":"Xi Zhang, Xin Yu, Hua Cheng, Yuli Wang, Hamid Sabet, Siwei Xie, Jianfeng Xu, Qiyu Peng","doi":"10.1002/mp.17665","DOIUrl":"https://doi.org/10.1002/mp.17665","url":null,"abstract":"<p><strong>Background: </strong>Gamma-ray detection plays a crucial role in the fields of biomedicine, space exploration, national defense, and security. High-precision gamma photon detection relies on scintillation crystals, which attenuate gamma rays through mechanisms such as photoelectric effect and Compton scattering. These interactions generate light signals within the scintillation crystal, which are subsequently converted into electronic signals using photodetectors, enabling accurate readout, and analysis.</p><p><strong>Purpose: </strong>Improving the readout efficiency of visible photons in crystal detectors can significantly improve the efficiency of gamma-ray detection. Scintillator crystals are usually hard and brittle materials, which are difficult to process. In this paper, we innovatively propose the method of using a femtosecond laser to process grooved microstructures on the light output surface of scintillator crystals to improve the detection efficiency, and thus enhance the comprehensive performance of gamma-ray detection.</p><p><strong>Methods: </strong>Optical simulation software is first used to explore the enhancement of the light output performance by the grooved microstructures. Subsequently, a 5-dimension system for femtosecond laser processing of scintillator crystals was constructed, which can achieve accurate processing of grooved structures. Finally, the feasibility of the study was verified by applying grooved microstructure on crystal bars and crystal arrays.</p><p><strong>Results: </strong>TracePro simulation results showed an average efficiency improvement in light output of 33.56% within the groove parameters: spacing from 20 to 140 µm, depth from 8 to 28 µm, and width from 10 to 30 µm. A custom-designed readout electronic system for gamma detection and a laser processing platform was then constructed to evaluate the feasibility of applying grooved structures to the lutetium-yttrium oxyorthosilicate (LYSO) crystal surface. According to the simulation results, 12 groups of crystal bars were fabricated with spacings from 60 to 140 µm, depths from 7 to 16 µm, and widths from 11 to 14 µm. Experimental results showed an average improvement of 20.4% in light output for the crystal bars, and that of the crystal arrays can be improved by 6.85% on average.</p><p><strong>Conclusions: </strong>This study introduces a method of using femtosecond lasers to fabricate grooved microstructures on LYSO crystal surfaces, which has demonstrated a significant improvement in light output in both simulation and experimentation. This method can be applied to the production of crystal arrays at a low cost and on a large scale, showing promising potential for common gamma detection applications, such as medical imaging, industry, and astrophysics.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}