Medical physics最新文献

{"title":"Asymmetry analysis of nuclear Overhauser enhancement effect at -1.6 ppm in ischemic stroke","authors":"Yu Zhao,&nbsp;Aqeela Afzal,&nbsp;Zhongliang Zu","doi":"10.1002/mp.17677","DOIUrl":"10.1002/mp.17677","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The nuclear Overhauser enhancement (NOE)-mediated saturation transfer effect at -1.6 ppm, termed NOE(-1.6 ppm), has demonstrated potential for detecting ischemic stroke. However, the quantification of the NOE(-1.6 ppm) effect usually relies on a multiple-pool Lorentzian fit method, which necessitates a time-consuming acquisition of the entire chemical exchange saturation transfer (CEST) Z-spectrum with high-frequency resolution, thus hindering its clinical applications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to assess the feasibility of employing asymmetry analysis, a rapid CEST data acquisition and analysis method, for quantifying the NOE(-1.6 ppm) effect in an animal model of ischemic stroke.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We examined potential contaminations from guanidinium/amine CEST, NOE(-3.5 ppm), and asymmetric magnetization transfer (MT) effects, which could reduce the specificity of the asymmetry analysis of NOE(-1.6 ppm). First, a Lorentzian difference (LD) analysis was used to mitigate direct water saturation and MT effects, providing separate estimations of the contributions from the guanidinium/amine CEST and NOE effects. Then, the asymmetry analysis of the LD fitted spectrum was compared with the asymmetry analysis of the raw CEST Z-spectrum to evaluate the contribution of the asymmetric MT effect at -1.6 ppm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Results show that the variations of the LD quantified NOE(-1.6 ppm) in stroke lesions are much greater than that of the CEST signals at +1.6 ppm and NOE(-3.5 ppm), suggesting that NOE(-1.6 ppm) has a dominating contribution to the asymmetry analysis at -1.6 ppm compared with the guanidinium/amine CEST and NOE(-3.5 ppm) in ischemic stroke. The NOE(-1.6 ppm) variations in the asymmetry analysis of the raw CEST Z-spectrum are close to those in the asymmetry analysis of the LD fitted spectrum, revealing that the NOE(-1.6 ppm) dominates over the asymmetric MT effects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our study demonstrates that the asymmetry analysis can quantify the NOE(-1.6 ppm) contrast in ischemic stroke with high specificity, thus presenting a viable alternative for rapid mapping of ischemic stroke.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 5","pages":"2922-2937"},"PeriodicalIF":3.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural architecture search with Deep Radon Prior for sparse-view CT image reconstruction","authors":"Jintao Fu,&nbsp;Peng Cong,&nbsp;Shuo Xu,&nbsp;Jiahao Chang,&nbsp;Ximing Liu,&nbsp;Yuewen Sun","doi":"10.1002/mp.17685","DOIUrl":"10.1002/mp.17685","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Sparse-view computed tomography (CT) reduces radiation exposure but suffers from severe artifacts caused by insufficient sampling and data scarcity, which compromise image fidelity. Recent advancements in deep learning (DL)-based methods for inverse problems have shown promise for CT reconstruction but often require high-quality paired datasets and lack interpretability.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This paper aims to advance the field of CT reconstruction by introducing a novel unsupervised deep learning method. It builds on the foundation of Deep Radon Prior (DRP), which utilizes an untrained encoder–decoder network to extract implicit features from the Radon domain, and leverages Neural Architecture Search (NAS) to optimize network structures.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We propose a novel unsupervised deep learning method for image reconstruction, termed NAS-DRP. This method leverages reinforcement learning-based NAS to explore diverse architectural spaces and integrates reinforcement learning with data inconsistency in the Radon domain. Building on previous DRP research, NAS-DRP utilizes an untrained encoder–decoder network to extract implicit features from the Radon domain. It further incorporates insights from studies on Deep Image Prior (DIP) regarding the critical impact of upsampling layers on image quality restoration. The method employs NAS to search for the optimal network architecture for upsampling unit tasks, while using Recurrent Neural Networks (RNNs) to constrain the optimization process, ensuring task-specific improvements in sparse-view CT image reconstruction.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Extensive experiments demonstrate that the NAS-DRP method achieves significant performance improvements in multiple CT image reconstruction tasks. The proposed method outperforms traditional reconstruction methods and other DL-based techniques in terms of both objective metrics (PSNR, SSIM, and LPIPS) and subjective visual quality. By automatically optimizing network structures, NAS-DRP effectively enhances the detail and accuracy of reconstructed images while minimizing artifacts.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;NAS-DRP represents a significant advancement in the field of CT image reconstruction. By integrating NAS with deep learning and leveraging Radon domain-specific adaptations, this method effectively addresses the inherent challenges of sparse-view CT imaging. Additionally, it reduces the cost and complexity of data acquisition, d","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 5","pages":"3044-3058"},"PeriodicalIF":3.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","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,&nbsp;Anurag K. Singh,&nbsp;Austin J. Iovoli,&nbsp;Mark Farrugia,&nbsp;Anh H. Le","doi":"10.1002/mp.17672","DOIUrl":"10.1002/mp.17672","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;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.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;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).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;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.","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2675-2687"},"PeriodicalIF":3.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral performance for iodine quantification of a dual-source, dual-kV photon counting detector CT","authors":"Tim Winfree,&nbsp;Kevin Treb,&nbsp;Cynthia McCollough,&nbsp;Shuai Leng","doi":"10.1002/mp.17679","DOIUrl":"10.1002/mp.17679","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Multi-energy CT (MECT) enables quantification of material concentrations by measuring linear attenuation coefficient line integrals with multiple x-ray spectra. Photon counting detector (PCD)-CT utilizes a detector-based approach for MECT that can suffer from substantial spectral overlap, resulting in amplified material quantification noise. Dual-source dual-kV approaches for MECT are currently utilized in some energy-integrating detector (EID)-CT systems and can potentially be utilized with PCD-CT for improved spectral separation.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To evaluate the iodine quantification performance of a novel dual-source (DS)-PCD-CT scan mode and compare to single-source (SS)-PCD-CT and DS-EID-CT.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Materials and Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A 30 cm × 40 cm solid water phantom with four iodine inserts (2, 5, 10, and 15 mg/mL) was scanned with the three spectral modalities: SS-PCD-CT with two energy thresholds, DS-PCD-CT (90/Sn150 kV), and DS-EID-CT (90/Sn150 kV). For each modality, full-dose (12 mGy) and half-dose scans were acquired, and images were reconstructed with filtered back-projection using a quantitative (Qr40) kernel. When scanning in a DS configuration, the total radiation dose budget is split between two detectors, increasing the strength of a signal-dependent filter compared to a SS acquisition. To account for this effect, the modulation transfer function (MTF) for each modality was measured from a 0.05 mm tungsten wire. A linear spatial filter was applied to the SS images to match their MTF to that of the DS images. The resulting high- and low-energy images were input into an image-domain least squares material decomposition algorithm with iodine and water as the two basis materials. Iodine quantification accuracy and noise measured from the iodine basis images were used as figures of merit, and t-tests used to compare between modalities.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The 10% MTF cutoffs were 0.56, 0.57, 0.60, and 0.57 lp/mm for DS-EID-CT, DS-PCD-CT, SS-PCD-CT, and SS-PCD-CT after MTF-matching, respectively, with the SS-PCD-CT MTF cutoff dropping to 0.58 lp/mm at half-dose. Without accounting for the signal-dependent filter by matching the MTFs, the noise in iodine material basis images from SS-PCD-CT was 10% higher than that of DS-EID-CT. After matching the MTFs, the noise in the SS-PCD-CT iodine image was 9%–22% lower than that of the DS-EID-CT. The lowest iodine image noise was from the DS-PCD-CT, which was 39%–41% lower than the DS-EID-CT. The DS-PCD-CT noise magnitude was significantly different from the other mod","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 5","pages":"2824-2831"},"PeriodicalIF":3.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency-aware denoising using a diffusion model for enhanced band-limited and white noise removal in x-ray acoustic computed tomography","authors":"Jiayuan Peng,&nbsp;Mengyang Lu,&nbsp;Boyi Li,&nbsp;Jiazhou Wang,&nbsp;Weigang Hu,&nbsp;Xin Liu","doi":"10.1002/mp.17681","DOIUrl":"10.1002/mp.17681","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Radiation therapy delivers precise doses to tumors, but accurately measuring internal tissue doses remains a challenge. Current methods, such as ionization chambers and radiographic films, rely on external measurements, which cannot provide direct, in vivo dose feedback. X-ray acoustic computed tomography (XACT) was developed to generate thermoacoustic signals when x-rays deposit energy into water or tissue, enabling the reconstruction of dose distribution patterns through acoustic signals. However, the longer pulse width of x-rays from linear accelerators reduces the efficiency of thermoacoustic signal conversion, lowering the signal-to-noise ratio (SNR) of radiofrequency (RF) signals. This noise significantly affects the quality of reconstructed XACT images. Overcoming the impact of noise is essential for advancing XACT toward accurate dose detection.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study aims to develop a frequency-aware denoising (FAD) method for overcoming the impact of band-limited and white noise in RF signals for XACT.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Real RF signals were acquired from an XACT system using radiotherapy megavoltage (MV) x-rays, a water tank, and an ultrasound transducer. To capture the frequency characteristics of these RF signals, we first estimated the probability density function (PDF) of their frequency spectrum. To generate synthetic RF data that closely approximates realistic noisy signals for model training, noise was sampled from this PDF, incorporating both magnitude and random phase components, and combined with simulated signals and white noise. A conditional diffusion model was trained on these synthetic signals to obtain the FAD model. A total of 3150 frequency-aware RF data samples were used to train the FAD model. For testing, acoustic RF signal data excited by five different x-ray shapes were measured, denoised by the FAD model, and finally reconstructed into XACT. The performance of the method was evaluated based on XACT image quality using SNR analysis and γ passing rate, and compared with results from Raw-RF and background noise-removed (BNR-RF) methods.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The FAD-RF method produced XACT images with clearer structural details and fewer artifacts. It achieved the highest SNR among the tested methods, with a mean SNR of 27.6 ± 5.0, outperforming both Raw-RF (22.9 ± 2.2, &lt;i&gt;p&lt;/i&gt; &lt; 0.05) and BNR-RF (22.0 ± 3.0, &lt;i&gt;p&lt;/i&gt; &lt; 0.05). In terms of spatial accuracy, the FAD-RF method also outperformed in γ analysis, achieving a mean γ passing rate of 79.0% ± 2.4%, significantly h","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 5","pages":"3325-3335"},"PeriodicalIF":3.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","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,&nbsp;Meng Zeng,&nbsp;Shu-Wen Sun,&nbsp;Qiu-Ping Liu,&nbsp;Jiu-Lou Zhang,&nbsp;Rui Zhi,&nbsp;Fei-Yu Lu,&nbsp;Wei Chen,&nbsp;Yu-Dong Zhang","doi":"10.1002/mp.17674","DOIUrl":"10.1002/mp.17674","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Between January 2012 and October 2021, two cohorts of patients with HCC who underwent contrast-enhanced CT (Center 1, <i>n</i> = 685) and MRI (Center 1, <i>n</i> = 516; Center 2, <i>n</i> = 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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>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, <i>p</i> &lt; 0.01) and external (0.796 vs. 0.753, <i>p</i> &lt; 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, <i>p</i> = 0.048) and external (0.670 vs. 0.579, <i>p</i> &lt; 0.001) validation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 5","pages":"2861-2873"},"PeriodicalIF":3.2,"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":2,"RegionCategory":"医学","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,&nbsp;D. Louis Collins,&nbsp;Marta Kersten-Oertel,&nbsp;Yiming Xiao","doi":"10.1002/mp.17666","DOIUrl":"10.1002/mp.17666","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Acquisition and validation methods</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data format and usage notes</h3>\u0000 \u0000 <p>The dataset is provided in both NIFTI and MINC formats and is publicly available on the OSF data repository: https://osf.io/zdcjb/.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Potential applications</h3>\u0000 \u0000 <p>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>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 5","pages":"3481-3486"},"PeriodicalIF":3.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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,&nbsp;Qian Liu,&nbsp;Tianwu Xie","doi":"10.1002/mp.17670","DOIUrl":"10.1002/mp.17670","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;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.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Acquisition and validation methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;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.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data format and usage notes&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;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 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;&lt;/mrow&gt;\u0000 &lt;mn&gt;18&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;mi&gt;G&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$^{18}F-FDG$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; application. All data in this study can be obtained from our official website (https://alldigitaltwins.com) and Zenodo (https://zenodo.org/records/14268606).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Potential applications&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;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 f","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2688-2696"},"PeriodicalIF":3.2,"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":2,"RegionCategory":"医学","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,&nbsp;Joseph F. Whitehead,&nbsp;Martin G. Wagner,&nbsp;James R. Scheuermann,&nbsp;Paul F. Laeseke,&nbsp;Michael A. Speidel","doi":"10.1002/mp.17661","DOIUrl":"10.1002/mp.17661","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&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;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The prototype system provided a continuous display of DE subtraction image","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 5","pages":"3228-3242"},"PeriodicalIF":3.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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,&nbsp;Per H. Halvorsen,&nbsp;Samantha G. Hedrick","doi":"10.1002/mp.17657","DOIUrl":"10.1002/mp.17657","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"1974-1977"},"PeriodicalIF":3.2,"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":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}