Medical physics最新文献

{"title":"Evaluating the theranostic potential of ferumoxytol when combined with radiotherapy in a mammary dual tumor mouse model.","authors":"Deng-Yuan Chang, Joseph P Speth, Matthew L Scarpelli","doi":"10.1002/mp.17888","DOIUrl":"https://doi.org/10.1002/mp.17888","url":null,"abstract":"<p><strong>Background: </strong>The radiation-induced abscopal effect (RIAE) is a desirable phenomenon involving radiation-induced activation of the immune system and regression of metastatic disease after local radiotherapy. However, the majority of patients undergoing radiotherapy do not experience abscopal responses. One potential barrier to the RIAE is tumor-associated macrophages (TAMs), which can be recruited to the tumor after radiotherapy and have an immunosuppressive effect on the tumor microenvironment (TME).</p><p><strong>Purpose: </strong>We aim to evaluate the dual capabilities of the FDA-approved iron nanoparticle ferumoxytol for (1) enhancing the RIAE and (2) measuring TAMs by magnetic resonance imaging (MRI). We hypothesized that (1) the immunomodulating effect of ferumoxytol could enhance the RIAE by repolarizing the M2 TAMs to M1 TAMs, and (2) the TAMs could be non-invasively imaged by ferumoxytol-MRI.</p><p><strong>Methods: </strong>Twenty-eight BALB/c mice were subcutaneously implanted with 4T1 primary orthotopic tumor (mammary fat pad) and flank tumor (abscopal tumor). At 14 days post-implantation, mice were separated into four groups: control (Ctrl), radiotherapy (RT) only (8-Gy×3), ferumoxytol only (FMX; 40 mg/kg) and combined (Comb) group (a single dose of 40 mg/kg FMX 24 h prior to 8-Gy×3) (n = 7 mice per group; 56 tumors). At 23- and 24-day post-implantation the pre- and post-FMX injection MRI was performed for mice in FMX and Comb group. The percent change in transverse relaxation time (%T2*) from pre to post ferumoxytol injection was calculated from MR images for both tumors and lymph nodes (LNs). At 25 days post-implantation, both tumors were harvested, and the TAMs were analyzed by flow cytometry.</p><p><strong>Results: </strong>At 25 days post-implantation, the primary tumor volume in the RT and Comb groups was significantly lower than the Ctrl and FMX groups (p < 0.05). No significant size difference of abscopal tumors was observed among all groups. In addition, there was no significant difference in lung metastasis nodules. A significant decrease in %T2* values of tumors and LNs in the FMX and Comb group 24 h post-ferumoxytol injection was observed, suggesting ferumoxytol uptake in TAMs. The flow cytometry result showed that the CD80<sup>+</sup> CD206<sup>-</sup> M1 macrophage population was similar among all tumors and groups. The CD80<sup>-</sup> CD206<sup>+</sup> M2 macrophage population was also similar in all tumors and groups, with the exception of the FMX group, where the M2 tumor macrophage levels were significantly higher when compared to the Ctrl group (p < 0.05). Tumors in the FMX group had a significant negative Pearson correlation between tumor %T2* change and M1 tumor macrophage levels (r = -0.76, p < 0.05) but this correlation was not significant in any other treatment group.</p><p><strong>Conclusions: </strong>Radiotherapy combined with ferumoxytol led to significant growth delays of irradiated tum","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121819","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":"Diffusion Schrödinger bridge models for high-quality MR-to-CT synthesis for proton treatment planning.","authors":"Muheng Li, Xia Li, Sairos Safai, Antony J Lomax, Ye Zhang","doi":"10.1002/mp.17898","DOIUrl":"https://doi.org/10.1002/mp.17898","url":null,"abstract":"<p><strong>Background: </strong>In recent advancements in proton therapy, magnetic resonance (MR)-based treatment planning is gaining momentum due to its excellent soft tissue contrast and high potential to minimize extra radiation exposure compared to traditional computed tomography (CT)-based methods. This transition underscores the critical need for accurate MR-to-CT image synthesis, which is essential for precise proton dose calculations.</p><p><strong>Purpose: </strong>This study aims to introduce and evaluate the diffusion Schrödinger bridge models (DSBM), an innovative approach for high-quality and efficient MR-to-CT synthesis, in order to improve both the quality and speed of synthetic CT (sCT) image generation.</p><p><strong>Methods: </strong>The DSBM learns the nonlinear diffusion processes between MR and CT data distributions. Unlike traditional diffusion models (DMs), which start synthesis from a Gaussian distribution, DSBM starts from the prior distribution, enabling more direct and efficient synthesis. The model was trained on 46 head-and-neck (HN) MR-CT pairs and 77 brain tumor MR-CT pairs, with 8 and 10 scans used for testing, respectively. Comprehensive evaluations were conducted at both image and dosimetric levels, using metrics such as mean absolute error (MAE), Dice score, voxel-wise proton dose differences, gamma pass rates of clinical plans, and typical dose indices.</p><p><strong>Results: </strong>For the HN dataset, DSBM achieved a lower MAE of 72.42 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 9.78 Hounsfield unit (HU) compared to 77.72 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 9.11 HU with the best baseline approach, and a higher Dice score for bone of 83.32 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 3.25% compared to 82.55 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 3.62%, indicating superior anatomical accuracy. Dosimetric evaluations showed a 1%/1 mm gamma pass rate of 95.85 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 2.99%, surpassing the 95.25 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 3.09% achieved by the baseline model. For the brain tumor dataset, DSBM outperformed the baseline with an MAE of 91.73 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 6.86 HU compared to 103.25 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 9.58 HU, and a Dice score for bone of 82.85 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 3.88% compared to 81.27 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 4.59%. DSBM also demonstrated a higher 1%/1 mm gamma pass rate of 97.93 <math><semantics><mo>±</mo> <annotation>$pm$</annotation></semantics> </math> 1.82%, confirming its robustness across different anatomical regi","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121817","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":"Automatic x-ray to CT registration using embedding reconstruction and lite cross-attention.","authors":"Tonglong Li, Minheng Chen, Mingying Li, Chuanyou Li, Youyong Kong","doi":"10.1002/mp.17896","DOIUrl":"https://doi.org/10.1002/mp.17896","url":null,"abstract":"<p><strong>Background: </strong>The registration of intraoperative x-ray images with preoperative CT images is an important step in image-guided surgery. However, existing regression-based methods lack an interpretable and stable mechanism when fusing information from intraoperative images and preoperative CT volumes. In addition, existing feature extraction and fusion methods limit the accuracy of pose regression.</p><p><strong>Purpose: </strong>The objective of this study is to develop a method that leverages both x-ray and computed tomography (CT) images to rapidly and robustly estimate an accurate initial registration within a broad search space. This approach integrates the strengths of learning-based registration with those of traditional registration methodologies, enabling the acquisition of registration outcomes across a wide search space at an accelerated pace.</p><p><strong>Methods: </strong>We introduce a regression-based registration framework to address the aforementioned issues. We constrain the feature fusion process by training the network to reconstruct the high-dimensional feature representation vector of the preoperative CT volume in the embedding space from the input single-view x-ray, thereby enhancing the interpretability of feature extraction. Also, in order to promote the effective fusion and better extraction of local texture features and global information, we propose a lightweight cross-attention mechanism named lite cross-attention(LCAT). Besides, to meet the intraoperative requirements, we employ the intensity-based registration method CMA-ES to refine the result of pose regression.</p><p><strong>Results: </strong>Our approach is verified on both real and simulated x-ray data. Experimental results show that compared with the existing learning-based registration methods, the median rotation error of our method can reach 1.9 <math> <semantics><msup><mrow></mrow> <mo>∘</mo></msup> <annotation>$^circ$</annotation></semantics> </math> and the median translation error can reach 5.6 mm in the case of a large search range. When evaluated on 52 real x-ray images, we have a median rotation error of 1.6 <math> <semantics><msup><mrow></mrow> <mo>∘</mo></msup> <annotation>$^circ$</annotation></semantics> </math> and a median translation error of 3.8 mm due to the smaller search range. We also verify the role of the LCAT and embedding reconstruction modules in our registration framework. If they are not used, our registration performance will be reduced to approximately random initialization results.</p><p><strong>Conclusions: </strong>During the experiments, our method demonstrates higher accuracy and larger capture range on both simulated images and real x-ray images compared to existing methods. The inspiring experimental results indicate the potential for future clinical application of our method.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121810","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 3D deep learning model based on MRI for predicting lymphovascular invasion in rectal cancer.","authors":"Tangjuan Wang, Chuanyu Chen, Chang Liu, Shaopeng Li, Peng Wang, Dawei Yin, Ying Liu","doi":"10.1002/mp.17882","DOIUrl":"https://doi.org/10.1002/mp.17882","url":null,"abstract":"<p><strong>Background: </strong>The assessment of lymphovascular invasion (LVI) is crucial in the management of rectal cancer; However, accurately evaluating LVI preoperatively using imaging remains challenging. Recent advances in radiomics have created opportunities for developing more accurate diagnostic tools.</p><p><strong>Purpose: </strong>This study aimed to develop and validate a deep learning model for predicting LVI in rectal cancer patients using preoperative MR imaging.</p><p><strong>Methods: </strong>These cases were randomly divided into a training cohort (n = 233) and an validation cohort (n = 101) at a ratio of 7:3. Based on the pathological reports, the patients were classified into positive and negative groups according to their LVI status. Based on the preoperative MRI T2WI axial images, the regions of interest (ROI) were defined from the tumor itself and the edges of the tumor extending outward by 5 pixels, 10 pixels, 15 pixels, and 20 pixels. The 2D and 3D deep learning features were extracted using the DenseNet121 architecture, and the deep learning models were constructed, including a total of ten models: GTV (the tumor itself), GPTV5 (the tumor itself and the tumor extending outward by 5 pixels), GPTV10, GPTV15, and GPTV20. To assess model performance, we utilized the area under the curve (AUC) and conducted DeLong test to compare different models, aiming to identify the optimal model for predicting LVI in rectal cancer.</p><p><strong>Results: </strong>In the 2D deep learning model group, the 2D GPTV10 model demonstrated superior performance with an AUC of 0.891 (95% confidence interval [CI] 0.850-0.933) in the training cohort and an AUC of 0.841 (95% CI 0.767-0.915) in the validation cohort. The difference in AUC between this model and other 2D models was not statistically significant based on DeLong test (p > 0.05); In the group of 3D deep learning models, the 3D GPTV10 model had the highest AUC, with a training cohort AUC of 0.961 (95% CI 0.940-0.982) and a validation cohort AUC of 0.928 (95% CI 0.881-0.976). DeLong test demonstrated that the performance of the 3D GPTV10 model surpassed other 3D models as well as the 2D GPTV10 model (p < 0.05).</p><p><strong>Conclusion: </strong>The study developed a deep learning model, namely 3D GPTV10, utilizing preoperative MRI data to accurately predict the presence of LVI in rectal cancer patients. By training on the tumor itself and its surrounding margin 10 pixels as the region of interest, this model achieved superior performance compared to other deep learning models. These findings have significant implications for clinicians in formulating personalized treatment plans for rectal cancer patients.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113176","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":"Correction to \"Development, validation, and simplification of a scanner-specific CT simulator\".","authors":"","doi":"10.1002/mp.17839","DOIUrl":"https://doi.org/10.1002/mp.17839","url":null,"abstract":"","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103441","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":"Medical Physics is transitioning to double blinded review.","authors":"John M Boone, Stanley H Benedict","doi":"10.1002/mp.17858","DOIUrl":"https://doi.org/10.1002/mp.17858","url":null,"abstract":"","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103442","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":"Fusing radiomics and deep learning features for automated classification of multi-type pulmonary nodule.","authors":"Lingyan Du, Guozhi Tang, Yue Che, Shihai Ling, Xin Chen, Xingliang Pan","doi":"10.1002/mp.17901","DOIUrl":"https://doi.org/10.1002/mp.17901","url":null,"abstract":"<p><strong>Background: </strong>The accurate classification of lung nodules is critical to achieving personalized lung cancer treatment and prognosis prediction. The treatment options for lung cancer and the prognosis of patients are closely related to the type of lung nodules, but there are many types of lung nodules, and the distinctions between certain types are subtle, making accurate classification based on traditional medical imaging technology and doctor experience challenging.</p><p><strong>Purpose: </strong>In this study, a novel method was used to analyze quantitative features in CT images using CT radiomics to reveal the characteristics of pulmonary nodules, and then feature fusion was used to integrate radiomics features and deep learning features to improve the accuracy of classification.</p><p><strong>Methods: </strong>This paper proposes a fusion feature pulmonary nodule classification method that fuses radiomics features with deep learning neural network features, aiming to automatically classify different types of pulmonary nodules (such as Malignancy, Calcification, Spiculation, Lobulation, Margin, and Texture). By introducing the Discriminant Correlation Analysis feature fusion algorithm, the method maximizes the complementarity between the two types of features and the differences between different classes. This ensures interaction between the information, effectively utilizing the complementary characteristics of the features. The LIDC-IDRI dataset is used for training, and the fusion feature model has been validated for its advantages and effectiveness in classifying multiple types of pulmonary nodules.</p><p><strong>Results: </strong>The experimental results show that the fusion feature model outperforms the single-feature model in all classification tasks. The AUCs for the tasks of classifying Calcification, Lobulation, Margin, Spiculation, Texture, and Malignancy reached 0.9663, 0.8113, 0.8815, 0.8140, 0.9010, and 0.9316, respectively. In tasks such as nodule calcification and texture classification, the fusion feature model significantly improved the recognition ability of minority classes.</p><p><strong>Conclusions: </strong>The fusion of radiomics features and deep learning neural network features can effectively enhance the overall performance of pulmonary nodule classification models while also improving the recognition of minority classes when there is a significant class imbalance.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112116","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":"Thymoma habitat segmentation and risk prediction model using CT imaging and K-means clustering.","authors":"Zhu Liang, Jiamin Li, Shuyan He, Siyuan Li, Runzhi Cai, Chunyuan Chen, Yan Zhang, Biao Deng, Yanxia Wu","doi":"10.1002/mp.17892","DOIUrl":"https://doi.org/10.1002/mp.17892","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Thymomas, though rare, present a wide range of clinical behaviors, from indolent to aggressive forms, making accurate risk stratification crucial for treatment planning. Traditional methods such as histopathology and radiological assessments often lack the ability to capture tumor heterogeneity, which can impact prognosis. Radiomics, combined with machine learning, provides a method to extract and analyze quantitative imaging features, offering the potential to improve tumor classification and risk prediction. By segmenting tumors into distinct habitat zones, it becomes possible to assess intratumoral heterogeneity more effectively. This study employs radiomics and machine learning techniques to enhance thymoma risk prediction, aiming to improve diagnostic consistency and reduce variability in radiologists' assessments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to identify different habitat zones within thymomas through CT imaging feature analysis and to establish a predictive model to differentiate between high and low-risk thymomas. Additionally, the study explores how this model can assist radiologists.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We obtained CT imaging data from 133 patients with thymoma who were treated at the Affiliated Hospital of Guangdong Medical University from 2015 to 2023. Images from the plain scan phase, venous phase, arterial phase, and their differential images (subtracted images) were used. Tumor regions were segmented into three habitat zones using K-Means clustering. Imaging features from each habitat zone were extracted using the PyRadiomics (van Griethuysen, 2017) library. The 28 most distinguishing features were selected through Mann-Whitney U tests (Mann, 1947) and Spearman's correlation analysis (Spearman, 1904). Five predictive models were built using the same machine learning algorithm (Support Vector Machine [SVM]): Habitat1, Habitat2, Habitat3 (trained on features from individual tumor habitat regions), Habitat All (trained on combined features from all regions), and Intra (trained on intratumoral features), and their performances were evaluated for comparison. The models' diagnostic outcomes were compared with the diagnoses of four radiologists (two junior and two experienced physicians).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The AUC (area under curve) for habitat zone 1 was 0.818, for habitat zone 2 was 0.732, and for habitat zone 3 was 0.763. The comprehensive model, which combined data from all habitat zones, achieved an AUC of 0.960, outperforming the model based on traditional radiomic features (AUC of 0.720). The model significantly improved the diagnostic accuracy of all four radiologists. The AUCs for junior radiologists 1 and 2 increased from 0.747 and 0.775 to 0.932 and 0.972, respectively, while for experienced radiologists 1 and 2, the AUCs increased from 0.932 and 0.859 to 0.977 and 0.972, respectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This study successfully identi","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096515","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":"Transformer model based on Sonazoid contrast-enhanced ultrasound for microvascular invasion prediction in hepatocellular carcinoma.","authors":"Qiong Qin, Jinshu Pang, Jingdan Li, Ruizhi Gao, Rong Wen, Yuquan Wu, Li Liang, Qiao Que, Changwen Liu, Jinbo Peng, Yun Lv, Yun He, Peng Lin, Hong Yang","doi":"10.1002/mp.17895","DOIUrl":"https://doi.org/10.1002/mp.17895","url":null,"abstract":"<p><strong>Background: </strong>Microvascular invasion (MVI) is strongly associated with the prognosis of patients with hepatocellular carcinoma (HCC).</p><p><strong>Purpose: </strong>To evaluate the value of Transformer models with Sonazoid contrast-enhanced ultrasound (CEUS) in the preoperative prediction of MVI.</p><p><strong>Methods: </strong>This retrospective study included 164 HCC patients. Deep learning features and radiomic features were extracted from arterial and Kupffer phase images, alongside the collection of clinicopathological parameters. Normality was assessed using the Shapiro-Wilk test. The Mann‒Whitney U-test and least absolute shrinkage and selection operator algorithm were applied to screen features. Transformer, radiomic, and clinical prediction models for MVI were constructed with logistic regression. Repeated random splits followed a 7:3 ratio, with model performance evaluated over 50 iterations. The area under the receiver operating characteristic curve (AUC, 95% confidence interval [CI]), sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), decision curve, and calibration curve were used to evaluate the performance of the models. The DeLong test was applied to compare performance between models. The Bonferroni method was used to control type I error rates arising from multiple comparisons. A two-sided p-value of < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>In the training set, the diagnostic performance of the arterial-phase Transformer (AT) and Kupffer-phase Transformer (KT) models were better than that of the radiomic and clinical (Clin) models (p < 0.0001). In the validation set, both the AT and KT models outperformed the radiomic and Clin models in terms of diagnostic performance (p < 0.05). The AUC (95% CI) for the AT model was 0.821 (0.72-0.925) with an accuracy of 80.0%, and the KT model was 0.859 (0.766-0.977) with an accuracy of 70.0%. Logistic regression analysis indicated that tumor size (p = 0.016) and alpha-fetoprotein (AFP) (p = 0.046) were independent predictors of MVI.</p><p><strong>Conclusions: </strong>Transformer models using Sonazoid CEUS have potential for effectively identifying MVI-positive patients preoperatively.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096516","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 ¹H/¹⁹F double-tuned coil using an asymmetrical butterfly coil.","authors":"Suk-Min Hong, Chang-Hoon Choi, Jörg Felder, N Jon Shah","doi":"10.1002/mp.17890","DOIUrl":"https://doi.org/10.1002/mp.17890","url":null,"abstract":"<p><strong>Background: </strong>Fluorine-19 (¹⁹F) magnetic resonance imaging (MRI) is a non-invasive imaging tool for the targeted application of fluorinated agents, such as cell tracking, and for the demonstration of oximetry. However, as the SNR of ¹⁹F is significantly weaker than that of proton (¹H) imaging, the ¹⁹F coil must be combined with ¹H coils for anatomical co-registration and B₀ shimming. This is difficult due to the strong coupling between the coils when they are in proximity, and is problematic since the Larmor frequency of ¹⁹F is 94% that of ¹H, further increasing the potential for coupling between the ¹H and ¹⁹F elements.</p><p><strong>Purpose: </strong>Conventional double-tuned coil methods tend to generate loss compared to single-tuned reference coils. The asymmetrical butterfly coil has a split resonance peak, which can cover frequencies of ¹H and ¹⁹F without losses arising from lossy traps or switching circuits. In this study, the use of an asymmetrical butterfly coil was evaluated for ¹H/¹⁹F applications.</p><p><strong>Methods: </strong>To increase quadrature efficiency at both the ¹H and ¹⁹F frequencies, the left and right loops of the butterfly coil were tuned asymmetrically. The coil's tuning and performance were evaluated in simulations and MR measurements, and the results were compared to a dimension-matched single-tuned loop coil.</p><p><strong>Results: </strong>The split resonance peak of the asymmetrical butterfly coil successfully spanned the ¹⁹F to ¹H frequency. It operated with higher quadrature efficiency at both ¹H and ¹⁹F frequencies and demonstrated superior receive sensitivity and SNR compared to the dimension-matched single-tuned loop coil.</p><p><strong>Conclusions: </strong>The split resonance peak of the asymmetrical butterfly coil supported both ¹H and ¹⁹F frequencies, delivering a higher SNR than that of the single-tuned loop coil. Since the asymmetrical butterfly coil can cover ¹H and ¹⁹F frequencies without loss and provides higher efficiency than the reference single-tuned coil, it can be effectively utilized for ¹H/¹⁹F MRI applications.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096506","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}