Medical physics最新文献

{"title":"Deep-learning based multibeat echocardiographic cardiac phase detection.","authors":"Hanlin Cheng, Zhongqing Shi, Zhanru Qi, Xiaoxian Wang, Guanjun Guo, Aijuan Fang, Zhibin Jin, Chunjie Shan, Yue Du, Ruiyang Chen, Sunnan Qian, Shouhua Luo, Jing Yao","doi":"10.1002/mp.17733","DOIUrl":"https://doi.org/10.1002/mp.17733","url":null,"abstract":"<p><strong>Background: </strong>End-to-end automatic detection of cardiac phase in multibeat echocardiograms is crucial for measuring cardiac parameters in clinical scenarios. However, existing studies face limitations due to the high cost of data annotation and collection, and time-consuming detection processes.</p><p><strong>Purpose: </strong>This study introduces a novel multibeat echocardiographic cardiac phase detection network, EchoPhaseNet, to perform fast and accurate cardiac phase detection of variable-length multibeat echocardiographic sequences, with low annotation costs and limited data.</p><p><strong>Materials and methods: </strong>Five echocardiographic datasets were used in this study, including a small-scale private dataset, Echo-DT (DrumTower), a medium-scale publicly available dataset, PhaseDetection, and three additional publicly available datasets: EchoNet-Dynamic, CAMUS, and EchoNet-Dynamic-MultiBeat. EchoPhaseNet and four other deep learning-based cardiac phase detection methods were trained and internally validated on the Echo-DT and PhaseDetection datasets (with sample ratios for training, validation, and testing set at 60%:20%:20% and 80%:0%:20%, respectively), and then externally validated on the other three datasets. Model performance was evaluated using GradCAM for qualitative visualization and absolute frame difference (aFD) for quantitative accuracy, with statistical significance assessed using Tukey's test and Benjamini-Hochberg correction, considering corrected p-values <math><semantics><mo><</mo> <annotation>$<$</annotation></semantics> </math> 0.05 as significant.</p><p><strong>Results: </strong>The annotation costs and accuracy of end-diastolic (ED) and end-systolic (ES) detection using EchoPhaseNet were compared with those of four other comparison methods. EchoPhaseNet achieves effective specific phase detections using only ED/ES labels, reducing annotation costs and making it applicable to a wider range of detection scenarios compared to all the comparison methods. On the Echo-DT dataset, EchoPhaseNet's mean aFD values for ED and ES detection in the A4C view samples were 1.08 and 1.04, respectively, significantly outperforming three comparison methods in ED detection accuracy (p-values <math><semantics><mo><</mo> <annotation>$<$</annotation></semantics> </math> 0.01) and comparable to the remaining one (p-values <math><semantics><mo>></mo> <annotation>$>$</annotation></semantics> </math> 0.05). On the PhaseDetection dataset, EchoPhaseNet's mean aFD values for ED and ES detection were 1.67 and 2.19, respectively, comparable to the detection accuracies of all four comparison methods (p-values <math><semantics><mo>></mo> <annotation>$>$</annotation></semantics> </math> 0.05). In addition, EchoPhaseNet showed strong generalization ability on multiple external validation datasets. After training on the small-scale Echo-DT dataset, EchoPhaseNet significantly outperformed the four comparison methods (p-values <math><","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665762","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":"Dual branch segment anything model-transformer fusion network for accurate breast ultrasound image segmentation.","authors":"Yu Li, Jin Huang, Yimin Zhang, Jingwen Deng, Jingwen Zhang, Lan Dong, Du Wang, Liye Mei, Cheng Lei","doi":"10.1002/mp.17751","DOIUrl":"https://doi.org/10.1002/mp.17751","url":null,"abstract":"<p><strong>Background: </strong>Precise and rapid ultrasound-based breast cancer diagnosis is essential for effective treatment. However, existing ultrasound image segmentation methods often fail to capture both global contextual features and fine-grained boundary details.</p><p><strong>Purpose: </strong>This study proposes a dual-branch network architecture that combines the Swin Transformer and Segment Anything Model (SAM) to enhance breast ultrasound image (BUSI) segmentation accuracy and reliability.</p><p><strong>Methods: </strong>Our network integrates the global attention mechanism of the Swin Transformer with fine-grained boundary detection from SAM through a multi-stage feature fusion module. We evaluated our method against state-of-the-art methods on two datasets: the Breast Ultrasound Images dataset from Wuhan University (BUSI-WHU), which contains 927 images (560 benign and 367 malignant) with ground truth masks annotated by radiologists, and the public BUSI dataset. Performance was evaluated using mean Intersection-over-Union (mIoU), 95th percentile Hausdorff Distance (HD95) and Dice Similarity coefficients, with statistical significance assessed using two-tailed independent t-tests with Holm-Bonferroni correction ( <math> <semantics><mrow><mi>α</mi> <mo>=</mo> <mn>0.05</mn></mrow> <annotation>$alpha =0.05$</annotation></semantics> </math> ).</p><p><strong>Results: </strong>On our proposed dataset, the network achieved a mIoU of 90.82% and a HD95 of 23.50 pixels, demonstrating significant improvements over current state-of-the-art methods with effect sizes for mIoU ranging from 0.38 to 0.61 (p <math><semantics><mo><</mo> <annotation>$<$</annotation></semantics> </math> 0.05). On the BUSI dataset, the network achieved a mIoU of 82.83% and a HD95 of 71.13 pixels, demonstrating comparable improvements with effect sizes for mIoU ranging from 0.45 to 0.58 (p <math><semantics><mo><</mo> <annotation>$<$</annotation></semantics> </math> 0.05).</p><p><strong>Conclusions: </strong>Our dual-branch network leverages the complementary strengths of Swin Transformer and SAM through a fusion mechanism, demonstrating superior breast ultrasound segmentation performance. Our code is publicly available at https://github.com/Skylanding/DSATNet.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660087","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":"Remote sensing of high energy particle current generated by megavoltage x-rays.","authors":"Arith Rajapakse, Coral Outwater, Davide Brivio, Erno Sajo, Piotr Zygmanski","doi":"10.1002/mp.17756","DOIUrl":"https://doi.org/10.1002/mp.17756","url":null,"abstract":"<p><strong>Background: </strong>In x-ray radiography and computed tomography (CT), absorbed dose is deposited in a radiation detector array in the form of charge carriers and collected. While these modalities are the standard for clinical imaging during the radiation therapy process, they require the use of bulk materials and adequate operating voltages. These constraints leave space for an imaging/dosimetry niche favoring low profile, low power, and non-invasive modalities.</p><p><strong>Purpose: </strong>The conversion of therapeutic radiation to absorbed dose begins with the generation of high energy electrons. If utilized correctly, the high energy particle currents (HEC) offer a unique prospect for a novel form of imaging and dosimetry. In this paper, we establish the theoretical and experimental framework behind the sensing of HEC by measuring currents in various homogeneous and heterogeneous phantoms and comparing the measured signals to both one-dimensional particle transport and Monte Carlo (MC) based simulations.</p><p><strong>Methods: </strong>The experimental setup for HEC sensing consists of pairs of complementary electrodes placed upstream and downstream of the object or phantom in question. When irradiated with 6MV x-rays, two signals, s₁, and s₂, were collected with zero external bias. These signals are coupled to each other due to the distribution of HEC inside the phantom. Both homogeneous (water) and heterogeneous (water and bone) phantoms were irradiated, and the measured signals were reviewed against simulations (MCNP6, CEPXS).</p><p><strong>Results: </strong>The measured signals s₁ and s₂ (as a function of water equivalent thickness [WET]) for homogeneous phantoms matched the trends established by the corresponding radiation transport simulations; indicating that these signals convey information about the distribution of HEC inside the phantoms. Based on these findings, new signal metrics, α and β, were formalized and used to quantify the scanning of heterogeneous phantoms.</p><p><strong>Conclusion: </strong>In this work, we demonstrated that information about the internal composition of an object can be obtained through HEC sensing. Specifically, the distribution of HEC inside of an object resulting from x-ray irradiation was measured using a simple system of planar electrodes and agreed well with radiation transport simulations. HEC sensing has the potential to be a disruptive method of imaging with its low power, low profile, and non-invasive nature.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660160","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":"Three-dimensional regional evaluation of right ventricular myocardial work from cine computed tomography: A pilot study.","authors":"Amanda Craine, Anderson Scott, Dhruvi Desai, Seth Kligerman, Eric Adler, Nick H Kim, Laith Alshawabkeh, Francisco Contijoch","doi":"10.1002/mp.17738","DOIUrl":"10.1002/mp.17738","url":null,"abstract":"<p><strong>Background: </strong>Evaluating regional variations in right ventricular (RV) performance can be challenging, particularly in patients with significant impairments due to the need for 3D spatial coverage with high spatial resolution. ECG-gated cineCT can fully visualize the RV and be used to quantify regional strain with high spatial resolution. However, strain is influenced by loading conditions. Myocardial work (MW)-measured clinically as the ventricular pressure-strain loop area-is considered a more comprehensive metric due to its independence of preload and afterload. In this study, we sought to develop regional RV MW assessments in 3D with high spatial resolution by combining cineCT-derived regional strain with RV pressure waveforms from right heart catheterization (RHC).</p><p><strong>Purpose: </strong>Regional MW is not measured in the right ventricle (RV) due to a lack of high spatial resolution regional strain (RS) estimates throughout the ventricle. We present a cineCT-based approach to evaluate regional RV performance and demonstrate its ability to phenotype three complex populations: end-stage LV failure (HF), chronic thromboembolic pulmonary hypertension (CTEPH), and repaired tetralogy of Fallot (rTOF).</p><p><strong>Methods: </strong>Forty-nine patients (19 HF, 11 CTEPH, 19 rTOF) underwent cineCT and RHC. RS was estimated as the regional change in the endocardial surface from full-cycle ECG-gated cineCT and combined with RHC pressure waveforms to create regional pressure-strain loops; endocardial MW was measured as the loop area. Detailed, 3D mapping of RS and MW enabled spatial visualization of strain and work strength, and phenotyping of patients.</p><p><strong>Results: </strong>HF patients demonstrated more overall impaired strain and work compared to the CTEPH and rTOF cohorts. For example, the HF patients had more akinetic areas (median: 9%) than CTEPH (median: < 1%, p = 0.02) and rTOF (median: 1%, p < 0.01) and performed more low work (median: 69%) than the rTOF cohort (median: 38%, p < 0.01). The CTEPH cohort had more impairment in the septal wall; < 1% of the free wall and 16% of the septal wall performed negative work. The rTOF cohort demonstrated a wide distribution of strain and work, ranging from hypokinetic to hyperkinetic strain and low to medium-high work. Impaired strain (-0.15 ≤ RS) and negative work were strongly-to-very strongly correlated with RVEF (R = -0.89, p < 0.01; R = -0.70, p < 0.01, respectively), while impaired work (MW ≤ 5 mmHg) was moderately correlated with RVEF (R = -0.53, p < 0.01).</p><p><strong>Conclusion: </strong>Regional RV MW maps can be derived from clinical CT and RHC studies and can provide patient-specific phenotyping of RV function in complex heart disease patients.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660167","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":"Histogram matching-enhanced adversarial learning for unsupervised domain adaptation in medical image segmentation.","authors":"Xiaoxue Qian, Hua-Chieh Shao, Yunxiang Li, Weiguo Lu, You Zhang","doi":"10.1002/mp.17757","DOIUrl":"https://doi.org/10.1002/mp.17757","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Unsupervised domain adaptation (UDA) seeks to mitigate the performance degradation of deep neural networks when applied to new, unlabeled domains by leveraging knowledge from source domains. In medical image segmentation, prevailing UDA techniques often utilize adversarial learning to address domain shifts for cross-modality adaptation. Current research on adversarial learning tends to adopt increasingly complex models and loss functions, making the training process highly intricate and less stable/robust. Furthermore, most methods primarily focused on segmentation accuracy while neglecting the associated confidence levels and uncertainties.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To develop a simple yet effective UDA method based on histogram matching-enhanced adversarial learning (HMeAL-UDA), and provide comprehensive uncertainty estimations of the model predictions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Aiming to bridge the domain gap while reducing the model complexity, we developed a novel adversarial learning approach to align multi-modality features. The method, termed HMeAL-UDA, integrates a plug-and-play histogram matching strategy to mitigate domain-specific image style biases across modalities. We employed adversarial learning to constrain the model in the prediction space, enabling it to focus on domain-invariant features during segmentation. Moreover, we quantified the model's prediction confidence using Monte Carlo (MC) dropouts to assess two voxel-level uncertainty estimates of the segmentation results, which were subsequently aggregated into a volume-level uncertainty score, providing an overall measure of the model's reliability. The proposed method was evaluated on three public datasets (Combined Healthy Abdominal Organ Segmentation [CHAOS], Beyond the Cranial Vault [BTCV], and Abdominal Multi-Organ Segmentation Challenge [AMOS]) and one in-house clinical dataset (UTSW). We used 30 MRI scans (20 from the CHAOS dataset and 10 from the in-house dataset) and 30 CT scans from the BTCV dataset for UDA-based, cross-modality liver segmentation. Additionally, 240 CT scans and 60 MRI scans from the AMOS dataset were utilized for cross-modality multi-organ segmentation. The training and testing sets for each modality were split with ratios of approximately 4:1-3:1.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Extensive experiments on cross-modality medical image segmentation demonstrated the superiority of HMeAL-UDA over two state-of-the-art approaches. HMeAL-UDA achieved a mean (± s.d.) Dice similarity coefficient (DSC) of 91.34% ± 1.23% and an HD95 of 6.18 ± 2.93 mm for cross-modality (from CT to MRI) adaptation of abdominal multi-organ segmentation, and a DSC of 87.13% ± 3.67% with an HD95 of 2.48 ± 1.56 mm for segmentation adaptation in the opposite direction (MRI to CT). The results are approaching or even outperforming those of supervised methods trained with \"ground-truth\" labels in the target domain. In addition, we provide a","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660092","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":"Study of linear energy transfer effect on rib fracture in breast cancer patients receiving pencil-beam-scanning proton therapy.","authors":"Yunze Yang, Kimberly R Gergelis, Jiajian Shen, Arslan Afzal, Trey C Mullikin, Robert W Gao, Khaled Aziz, Dean A Shumway, Kimberly S Corbin, Wei Liu, Robert W Mutter","doi":"10.1002/mp.17745","DOIUrl":"10.1002/mp.17745","url":null,"abstract":"<p><strong>Background: </strong>In breast cancer patients treated with pencil-beam scanning proton therapy (PBS), the increased linear energy transfer (LET) near the end of the proton range can affect nearby ribs. This may associate with a higher risk of rib fractures.</p><p><strong>Purpose: </strong>To study the effect of LET on rib fracture in breast cancer patients treated with PBS using a novel tool of dose-LET volume histogram (DLVH).</p><p><strong>Methods: </strong>From a prospective registry of patients treated with post-mastectomy proton therapy to the chest wall and regional lymph nodes for breast cancer between 2015 and 2020, we retrospectively identified rib fracture cases detected after completing treatment. Contemporaneously treated control patients who did not develop rib fracture were matched to patients 2:1 considering prescription dose, boost location, reconstruction status, laterality, chest wall thickness, and treatment year. The DLVH index, V(d, l), defined as volume(V) of the structure with at least dose(d) and dose-averaged LET (l) (LETd), was calculated. DLVH plots between the fracture and control group were compared. Conditional logistic regression (CLR) model was used to establish the relation of V(d, l) and the observed fracture at each combination of d and l. The p-value derived from CLR model shows the statistical difference between fracture patients and the matched control group. Using the 2D p-value map derived from CLR model, the DLVH features associated with the patient outcomes were extracted.</p><p><strong>Results: </strong>Seven rib fracture patients were identified, and fourteen matched patients were selected for the control group. The median time from the completion of proton therapy to rib fracture diagnosis was 12 months (range 5-14 months). Two patients had grade 2 symptomatic rib fracture while the remaining 5 were grade 1 incidentally detected on imaging. The derived p-value map demonstrated larger V(0-36 Gy[RBE], 4.0-5.0 keV/µm) in patients experiencing fracture (p < 0.1). For example, the p-value for V(30 Gy[RBE], 4.0 keV/um) was 0.069.</p><p><strong>Conclusion: </strong>In breast cancer patients receiving PBS, a larger volume of chest wall receiving moderate dose and high LETd may result in an increased risk of rib fracture.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660164","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":"An unsupervised approach for projection binning to reduce motion artifacts in free-breathing animal models.","authors":"Mostafa K Ismail, Kai Ruppert, Marco Caballo, Hooman Hamedani, Ian Duncan, Stephen Kadlecek, Rahim Rizi","doi":"10.1002/mp.17762","DOIUrl":"https://doi.org/10.1002/mp.17762","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Dynamic imaging holds great potential in the diagnosis and comprehensive evaluation of different diseases by capturing mechanical and dynamic characteristics of moving organs. Nonetheless, motion artifacts notably impair image quality, hindering accurate and localized analysis-particularly in free-breathing scenarios. In preclinical studies, traditional methods often necessitate artificial breathing control or use invasive techniques that do not permit functional lung assessment under normal physiological conditions, potentially biasing results and restricting longitudinal studies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study aimed to mitigate motion artifacts and preserve temporal information, thus enhancing the spatiotemporal resolution of dynamic micro-CT images in free-breathing animals. We sought to combine the benefits of standard amplitude and phase binning within an unsupervised learning approach, without the need for iterative methods, prior knowledge, or alteration of the reconstruction process. Our approach facilitates accurate imaging of free-breathing animals under various protocols, without requiring artificial breathing control or invasive interventions, through a straightforward, immediately applicable retrospective analysis method.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A novel periodic line-constrained K-means clustering technique was developed as an unsupervised method for projection/interleave binning. To validate this technique on preclinical micro-CT images, a syringe-spring system was engineered to simulate respiratory motion. Imaging was performed on this moving phantom with various breathing rates and inhale-to-exhale (I/E) ratios, as well as in free-breathing rats and rabbits. Additionally, we detail a method for extracting the breathing signal directly from the x-ray projection images and introduce a systematic approach for data imputation in limited-angle scenarios. We also established a metric for quantifying motion artifacts in our 4DCT images.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The clustering method effectively integrated the benefits of both amplitude and phase binning, leading to a marked reduction in motion artifacts across all tests. Notably, our method yielded enhanced image clarity and improved accuracy in capturing dynamic lung volumes, evidenced by sharper diaphragm edges, better visibility of blood vessels, and diminished blurring and motion artifacts. Quantitative analysis using linear regression of diaphragm speed versus blur measure showed a near-zero slope for both rats and rabbits, indicating a substantial decrease in motion artifact presence compared to traditional binning methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The periodic line-constrained K-means clustering method provides a robust solution for enhancing the quality of dynamic micro-CT imaging in preclinical studies. By reducing motion artifacts and improving image resolution, this approach enables more precise evaluations ","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660073","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":"Impact of nuclear fragmentation and irradiation scenarios on the dose-averaged LET, the RBE, and their relationship for H, He, C, O, and Ne ions.","authors":"Alessio Parisi, Keith M Furutani, Chris J Beltran","doi":"10.1002/mp.17755","DOIUrl":"https://doi.org/10.1002/mp.17755","url":null,"abstract":"<p><strong>Background: </strong>Projectile and target fragmentation are nuclear phenomena that can influence the computation of the linear energy transfer (LET) and the relative biological effectiveness (RBE) in external radiotherapy with accelerated ions. Correlations between these two quantities are routinely established during radiobiological experiments to interpret the results and to develop and calibrate RBE models.</p><p><strong>Purpose: </strong>This study systematically evaluates the impact of secondary fragments and irradiation scenarios on the dose-averaged LET, the RBE, and their correlation in the case of exposures to clinically relevant ion beams.</p><p><strong>Methods: </strong>57 600 microdosimetric lineal energy spectra and corresponding LET distributions were simulated with the Monte Carlo code PHITS across different scenarios, including track segment calculations, pristine, and spread-out Bragg peaks of ¹H, ⁴He, ¹²C, ¹⁶O, and ²⁰Ne ions within water phantoms. The LET distributions were analyzed to calculate the dose-average LET, both including or excluding the contribution of secondary ions of an element different from the primary beam. Similarly, the lineal energy distributions were processed in conjunction with the Mayo Clinic Florida microdosimetric kinetic model to compute the RBE for two theoretical cell lines (α/β = 2 and 10 Gy in the case of 6 MV x-rays). Correlations between the RBE and dose-averaged LET were established by analyzing the simulation results within water phantoms and then compared to the corresponding trends from the track segment calculations.</p><p><strong>Results: </strong>Excluding secondary fragments had a pronounced impact on the dose-averaged LET and the RBE, particularly in the entrance regions of proton beams and close to the distal edge of heavier ions. The correlations between the RBE and the dose-averaged LET were not universal, but highly dependent on the irradiation scenario. For proton beams only, the dose-averaged LET of hydrogen ions served as a practical first-order descriptor of the RBE. Track segment simulations, commonly used for calibrating and benchmarking RBE models, provided a reasonable approximation for low-energy beams but failed to fully capture the complexity of polyenergetic radiation fields.</p><p><strong>Conclusions: </strong>Secondary fragments can substantially affect the dose-averaged LET and the RBE, even in proton beams. The dose-averaged LET, including or not the contributions from secondary fragments, was generally unable to adequately capture RBE variations across different scenarios. A more comprehensive approach, considering microdosimetric distributions, is necessary to accurately describe RBE variations in ion therapy.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660146","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":"Multimodal feature-guided diffusion model for low-count PET image denoising.","authors":"Gengjia Lin, Yuxi Jin, Zhenxing Huang, Zixiang Chen, Haizhou Liu, Chao Zhou, Xu Zhang, Wei Fan, Na Zhang, Dong Liang, Peng Cao, Zhanli Hu","doi":"10.1002/mp.17764","DOIUrl":"https://doi.org/10.1002/mp.17764","url":null,"abstract":"<p><strong>Background: </strong>To minimize radiation exposure while obtaining high-quality Positron Emission Tomography (PET) images, various methods have been developed to derive standard-count PET (SPET) images from low-count PET (LPET) images. Although deep learning methods have enhanced LPET images, they rarely utilize the rich complementary information from MR images. Even when MR images are used, these methods typically employ early, intermediate, or late fusion strategies to merge features from different CNN streams, failing to fully exploit the complementary properties of multimodal fusion.</p><p><strong>Purpose: </strong>In this study, we introduce a novel multimodal feature-guided diffusion model, termed MFG-Diff, designed for the denoising of LPET images with the full utilization of MRI.</p><p><strong>Methods: </strong>MFG-Diff replaces random Gaussian noise with LPET images and introduces a novel degradation operator to simulate the physical degradation processes of PET imaging. Besides, it uses a novel cross-modal guided restoration network to fully exploit the modality-specific features provided by the LPET and MR images and utilizes a multimodal feature fusion module employing cross-attention mechanisms and positional encoding at multiple feature levels for better feature fusion.</p><p><strong>Results: </strong>Under four counts (2.5%, 5.0%, 10%,  and 25%), the images generated by our proposed network showed superior performance compared to those produced by other networks in both qualitative and quantitative evaluations, as well as in statistical analysis. In particular, the peak-signal-to-noise ratio of the generated PET images improved by more than 20% under a 2.5% count, the structural similarity index improved by more than 16%, and the root mean square error reduced by nearly 50%. On the other hand, our generated PET images had significant correlation (Pearson correlation coefficient, 0.9924), consistency, and excellent quantitative evaluation results with the SPET images.</p><p><strong>Conclusions: </strong>The proposed method outperformed existing state-of-the-art LPET denoising models and can be used to generate highly correlated and consistent SPET images obtained from LPET images.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660151","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":"Total brain dose estimation in single-isocenter-multiple-targets (SIMT) radiosurgery via a novel deep neural network with spherical convolutions.","authors":"Zhenyu Yang, Mercedeh Khazaieli, Eugene Vaios, Rihui Zhang, Jingtong Zhao, Trey Mullikin, Albert Yang, Fang-Fang Yin, Chunhao Wang","doi":"10.1002/mp.17748","DOIUrl":"https://doi.org/10.1002/mp.17748","url":null,"abstract":"<p><strong>Background and purpose: </strong>Accurate prediction of normal brain dosimetric parameters is crucial for the quality control of single-isocenter multi-target (SIMT) stereotactic radiosurgery (SRS) treatment planning. Reliable dose estimation of normal brain tissue is one of the great indicators to evaluate plan quality and is used as a reference in clinics to improve potentially SIMT SRS treatment planning quality consistency. This study aimed to develop a spherical coordinate-defined deep learning model to predict the dose to a normal brain for SIMT SRS treatment planning.</p><p><strong>Methods: </strong>By encapsulating the human brain within a sphere, 3D volumetric data of planning target volume (PTVs) can be projected onto this geometry as a 2D spherical representation (in azimuthal and polar angles). A novel deep learning model spherical convolutional neural network (SCNN) was developed based on spherical convolution to predict brain dosimetric evaluators from spherical representation. Utilizing 106 SIMT cases, the model was trained to predict brain V50%, V60%, and V66.7%, corresponding to V10Gy and V12Gy, as key dosimetric indicators. The model prediction performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), and mean absolute percentage error (MAPE).</p><p><strong>Results: </strong>The SCNN accurately predicted normal brain dosimetric values from the modeled spherical PTV representation, with R² scores of 0.92 ± 0.05/0.94 ± 0.10/0.93 ± 0.09 for V50%/V60%/V66.7%, respectively. MAEs values were 1.94 ± 1.61 cc/1.23 ± 0.98 cc/1.13 ± 0.99 cc, and MAPEs were 19.79 ± 20.36%/20.79 ± 21.07%/21.15 ± 22.24%, respectively.</p><p><strong>Conclusions: </strong>The deep learning model provides treatment planners with accurate prediction of dose to normal brain, enabling improved consistency in treatment planning quality. This method can be extended to other brain-related analyses as an efficient data dimension reduction method.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660171","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}