Medical physics最新文献

{"title":"Robust Fourier-based slanted-edge method to measure scatter ratio.","authors":"Lisa M Garland, Ian A Cunningham","doi":"10.1002/mp.17765","DOIUrl":"https://doi.org/10.1002/mp.17765","url":null,"abstract":"<p><strong>Background: </strong>Patient scatter incident on an x-ray detector reduces radiographic contrast and adds quantum noise, and minimizing scatter is critical in some specialized techniques such as dual-energy and energy-subtraction methods. Existing methods to measure scatter are either labor-intensive (multiple disks) or not appropriate to use in radiography where scatter often exceeds the width of the x-ray beam.</p><p><strong>Purpose: </strong>Develop a method to measure the scatter-to-primary ratio (SPR) that can be used for a wide range of radiographic and mammographic conditions, both with scatter equilibrium (scatter function does not exceed primary-beam width) and without.</p><p><strong>Methods: </strong>Fourier theory is used to show the SPR can be measured from the low-frequency drop (LFD) of the Fourier transform of the derivative of a normalized edge profile. The method was validated both experimentally and by simulation for radiography and mammography under scatter equilibrium and nonequilibrium conditions.</p><p><strong>Results: </strong>The theoretical derivation showed that by normalizing an edge profile with a profile without the edge, scatter equilibrium is not required and the method accommodates a nonuniform primary beam from beam divergence and Heel effect. The method was validated by a simulation study for a range of scatter-LSF widths, primary-beam widths, and image regions of interest used in the analysis. Experimental scatter measurements agreed with a similar edge-method published by Cooper when scatter equilibrium is achieved.</p><p><strong>Conclusions: </strong>A simple and direct method of measuring the SPR obtained with both uniform and nonuniform test phantoms is described. Validated both experimentally and theoretically, it uses the Fourier LFD obtained from a normalized slanted-edge profile and works for a wide range of practical mammographic and radiographic conditions.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701415","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":"Container applications for the development and integration of virtual imaging platforms.","authors":"Bruno Barufaldi, Miguel A Lago, Ehsan Abadi, Andrew D A Maidment","doi":"10.1002/mp.17777","DOIUrl":"https://doi.org/10.1002/mp.17777","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Virtual imaging trials (VIT) have made significant advancements through the development of realistic human anatomy models, scanner-specific simulations, and virtual image interpretation. To promote VIT widespread adoption in the medical imaging community, it is important to develop methods that unify and facilitate the use of VITs, ensuring their reliable application across various imaging studies.  PURPOSE: We developed a containerized environment to enhance collaboration and interoperability across VIT platforms. This environment integrates key components of two well-established breast imaging platforms (OpenVCT and VICTRE), enabling direct comparison between specific modules for simulating anthropomorphic phantoms, lesions, and x-ray images.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Wrappers were developed to simplify the setup and execution of OpenVCT and VICTRE platforms and ensure compatibility and interoperability across different software components. These wrappers can streamline the installation of necessary packages, data formatting, and pipeline execution. The containerized environment was built using Docker images to provide resources for cross-platform integration. The breast anatomy generated by VICTRE was augmented using a simplex-based method from OpenVCT, providing additional texture modeling of breast parenchyma. Power spectra (PS) were calculated to assess the texture complexity of the simulated breast tissue and compare the outcomes. Lesion simulations were performed using breast models with calcifications and masses, allowing for a comparison of Monte Carlo (VICTRE) and raytracing (OpenVCT) imaging techniques. Key differences in x-ray attenuation models and image reconstruction methods were analyzed to evaluate the differences in the reconstructed images and overall image quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The containerized approach simplified the setup and execution of the simulation platforms, embedding all the necessary packages and dependencies into the Docker images. These containerized environments supported the simulation of anthropomorphic breast models and x-ray images using both Monte Carlo (VICTRE) and raytracing (OpenVCT) methods. The breast images generated using the conventional VICTRE and the integrated simplex-based method from OpenVCT were visually comparable. The &lt;math&gt;&lt;semantics&gt;&lt;mi&gt;β&lt;/mi&gt; &lt;annotation&gt;$beta$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; estimates from the PS for both approaches were close to 3, as expected for mammographic images, with only minor differences observed in the high-frequency components of the spectra (a difference of 0.2). These differences were particularly evident in areas of high tissue density and the regions of interest containing lesions; variations in the acquisition geometry affected the lesion visualization, demonstrating slight differences in the MC and raytracing simulations. Despite these differences, the overall performance of both methods in simula","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694976","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":"Benchmarking of a new integrated 3D dosimetry system against Monte Carlo calculations and an established optical CT scanner.","authors":"Kawtar Lakrad, Mark Oldham, Benjamin Quinn, Justus Adamson","doi":"10.1002/mp.17773","DOIUrl":"https://doi.org/10.1002/mp.17773","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Advanced radiation therapy techniques, including intensity-modulated radiation therapy (IMRT), stereotactic radiosurgery (SRS), adaptive therapy, and proton therapy, offer high precision in delivering radiation doses to tumors while minimizing exposure to surrounding healthy tissues. These sophisticated methods necessitate stringent quality assurance (QA) measures to ensure their accuracy and safety. Three-dimensional (3D) dosimetry systems have the potential to play an important role in this context for verifying dose distributions in a comprehensive manner but have not been widely implemented partially due to a lack of streamlined systems that include dosimeter, readout, and analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;The ClearView radiochromic dosimeter, the Vista 16 Optical CT scanner, and the VistaAce analysis software have the potential as a fully integrated 3D dosimetry tool for commissioning and verifying complex radiotherapy treatment plans. We aim to benchmark this integrated 3D dosimetry system and investigate its clinical utility.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The performance of this system was benchmarked against an independent Monte Carlo dose calculation software, the Duke Large Field of View Optical CT Scanner (DLOS), and an open-source analysis software (3D Slicer v4.13). We measured two simple radiotherapy plans and a selection from the AAPM (American Association of Physicists in Medicine) Task Group 119 IMRT commissioning tests. Treatment plans were prepared within the Eclipse planning system (AAA v15.6.03) after which a Varian Truebeam linac was used to deliver the treatment plans. Vista 16 was used to reconstruct the measured 3D dose distribution which was compared to the dose distribution obtained from an independent Monte Carlo-based dose calculation algorithm, as well as the 3D dose distribution reconstructed using the well-established DLOS. Image registration, conversion from optical density to dose, and comparative analysis were done using the VistaAce software and validated against results obtained using 3D Slicer for a subset of tests.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;ClearView dosimeters exhibited a linear dose-response up to 60 Gy. For the 3-field benchmarking irradiation, the agreement (2%/2 mm 3D global gamma Index, 10% threshold) between ClearView/VistaAce versus the TPS and Monte Carlo was 97.8% and 98.8%, respectively. For the AAPM TG119 mock head and neck plan, the agreement (2%/2 mm) with the treatment planning system and Monte Carlo was 99.1% and 95.1%, respectively. For the TG119 mock prostate, the agreement was 99.7% and 98.9%, respectively. Agreement for the ClearView/ Vista 16 dose reconstruction was equivalent or superior to that of the ClearView/DLOS reconstruction for the benchmarking irradiations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The ClearView/Vista 3D dosimetry system demonstrated robust performance in measuring and verifying realistic clinical dose distributions, wit","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694972","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":"Development of a respiratory-gated computed tomography system for in-vivo murine imaging.","authors":"Qiwei Wu, Yiqun Han, Cheng Zheng, Yuxiang Wang, Zhipeng Liu, Yunwen Huang, Hui Liu, Ning Zhao, Xiaogang Yuan, Yidong Yang","doi":"10.1002/mp.17749","DOIUrl":"https://doi.org/10.1002/mp.17749","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Respiratory motion poses a critical challenge in small animal lung imaging with micro-computed tomography (µCT). Contact sensors, when utilized as respiratory gating devices, can introduce beam-hardening artifacts and degrade image quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study is to develop a respiration-gated computed tomography (CT) system utilizing a non-contact laser displacement sensor for in vivo murine imaging.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The gating system comprises an x-ray beam shutter and a non-contact laser displacement sensor. The shutter controls the beam on and off during image acquisition, while the laser sensor converts thoracic surface displacement into a respiratory signal. The system's switch latency and measurement accuracy were assessed. Then, the gating system was utilized to analyze the respiratory patterns of animals (four groups and nine mice per group) anesthetized with varying isoflurane concentrations (1.0% to 2.5%). The external respiratory signal from the laser was compared with the diaphragm motion extracted from x-ray projections to analyze the delay between the two signals. Finally, eight mice were selected for retrospective and prospective gating imaging, respectively, and a variable number of landmarks, including the diaphragm, blood vessels, and bronchioles, were used to evaluate the image blur.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The system's turn-on and turn-off latencies were 31.4 ± 4.9 ms and 32.6 ± 2.8 ms, respectively. The Pearson correlation test showed a strong correlation between the laser signal and the trajectory of the dynamic phantom (R = 0.99). In all four groups, a delay of approximately 200 ms was observed for the internal signal entering the end-expiration (EE) phase when compared with the external signal and was accounted for by a \"delayed gating\" strategy. Retrospective gating studies demonstrated that the slopes of the intensity across the diaphragm in images obtained without gating, with traditional gating, and with delayed gating were 21.5 ± 5.5, 41.5 ± 6.0, and 72.5 ± 9.5 Hounsfield units (HUs) per pixel, respectively, with significant differences among them (p &lt; 0.001). Compared to traditional gating, delayed gating reduced motion artifacts and improved the clarity of lung structures. In prospective gating studies, the intensity slope across the diaphragm for delayed gating was 72.4 ± 12.4 HU/pixel, significantly higher than in the no-gating condition, which was 20.9 ± 4.1 HU/pixel (p &lt; 0.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The analysis of mouse respiratory patterns revealed a time delay between the internal and external respiratory signals. The non-contact respiratory gating system combined with the delayed gating strategy can effectively reduce motion blur and enhance the visibility of fine structures and therefore can be applied to enhance the ability of µCT in quantitative lung imaging, such as in the early detection and precise differentia","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674914","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":"Investigation of neuro-vascular reactivity on fMRI study during visual activation in people with multiple sclerosis using EEG and hypercapnia challenge.","authors":"Wanyong Shin, Balu Krishnan, Ajay Nemani, Daniel Ontaneda, Mark J Lowe","doi":"10.1002/mp.17772","DOIUrl":"https://doi.org/10.1002/mp.17772","url":null,"abstract":"<p><strong>Background: </strong>People with multiple sclerosis (MS) exhibit a different pattern of blood oxygenation level-dependent (BOLD) activation on functional magnetic resonance imaging (fMRI) studies when compared to healthy control (HC).</p><p><strong>Purpose: </strong>The objective of this study is to determine whether observed differences in BOLD activation between people with MS (pwMS) and HC participants are due to the differences of neurovascular coupling, cerebral blood flow (CBF) or actual neuronal activity.</p><p><strong>Methods: </strong>We investigated the neuronal activation in pwMS (n = 11) and age- and sex-matched HC participants (n = 15) using simultaneous electroencephalogram (EEG) and fMRI measures during a visual task (VT) and hypercapnia condition.</p><p><strong>Results: </strong>Significant neurovascular coupling is observed in both HC and pwMS. Neuro-vascular coupling ratios are not significantly different between groups. However, we observe significantly lower CBF increase during VT and higher quantitative CBF at a rest state in pwMS than in HC (p < 0.05). From the multiple regression model, in HC group, we found that the BOLD contrast change during VT is best predicted by the EEG power change during VT (Student t-score = 2.64, p = 0.022), and the CBF change during hypercapnia (Student t-score = 2.59, p = 0.024). In pwMS, the BOLD contrast change during VT is negatively predicted by the CBF change during VT (Student t-score = -4.02, p = 0.003).</p><p><strong>Conclusion: </strong>These findings could explain that BOLD activation in pwMS is mainly determined by the blood flow change during activation rather than the direct neuronal activation measures or hemodynamic vascular reactivity during hypercapnia challenge, suggesting that altered vasodilatory effects in response to task activation in pwMS might be linked to impaired cerebral hemodynamics, possibly leading to the widely observed abnormal BOLD activation in fMRI studies of pwMS.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674934","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":"Evaluating microstructures in endometrial cancer using diffusion-relaxation correlated spectroscopic imaging: Histopathological correlations.","authors":"Yongming Dai, Gaofeng Shi, Wentao Hu, Tianshu Yang, Dongmei Wu, Zhiguo Zhuang, Mengyu Song, Yaning Wang, Xiaojia Cai, Muzi Li, Yingmin Zhai, Peng Hu","doi":"10.1002/mp.17768","DOIUrl":"https://doi.org/10.1002/mp.17768","url":null,"abstract":"<p><strong>Background: </strong>Endometrial cancer (EC) is a prevalent gynecologic malignancy where accurate grading and assessment are crucial for determining prognosis and treatment strategies. Conventional MRI techniques, including apparent diffusion coefficient (ADC) and T2-weighted imaging, often fail to capture the detailed microstructural complexities of EC.</p><p><strong>Purpose: </strong>To evaluate the efficacy of diffusion relaxation correlated spectroscopic imaging (DR-CSI) in assessing EC and to compare its diagnostic performance with conventional ADC and T2-weighted imaging.</p><p><strong>Materials and methods: </strong>Sixty-two patients with histopathologically confirmed EC were included in this prospective study. All patients underwent preoperative MRI, including DR-CSI using a multi-TE (50-90 ms) and multi-b-value (0-1600 s/mm²) echo-planar imaging sequence. The DR-CSI data were analyzed to generate a four-compartment D-T2 spectra, yielding corresponding volume fraction metrics (VF, I-IV). Voxel-wise ADC and T2 values were also obtained. The relationships between these imaging parameters and histopathologic results were evaluated using one-way ANOVA or Kruskal-Wallis tests. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.</p><p><strong>Results: </strong>VF_II and VF_III demonstrated significant differences across histological grades (p < 0.01 and p = 0.04, respectively). The combination of VF_II and VF_III provided optimal differentiation between low- and high-grade EC (Area under curve, AUC 0.801 [95% confidence interval: 0.623-0.937]). VF_IV exhibited superior performance in distinguishing lymph node metastasis (LNM) status (AUC 0.734 [0.556-0.892]). The combination of VF_IV and VF_II improved performance in predicting LNM status (AUC 0.826 [0.66-0.961]). However, no parameter alone effectively distinguished myometrial invasion (MI) statuses, but the combination of VF_I and ADC improved performance (AUC 0.706 [0.560-0.844]).</p><p><strong>Conclusion: </strong>DR-CSI offers a novel and effective method for quantifying microstructural compartments in EC, providing superior diagnostic accuracy compared to conventional ADC and T2 values. The ability to capture detailed microstructural information from DR-CSI metrics holds promise for improving EC diagnosis and grading, offering deeper insights into tumor heterogeneity.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672185","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":"Evaluating the robustness of deep learning models trained to diagnose idiopathic pulmonary fibrosis using a retrospective study.","authors":"Wenxi Yu, Michael F McNitt-Gray, Jonathan G Goldin, Jin Woo Song, Grace Hyun J Kim","doi":"10.1002/mp.17752","DOIUrl":"https://doi.org/10.1002/mp.17752","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Deep learning (DL)-based systems have not yet been broadly implemented in clinical practice, in part due to unknown robustness across multiple imaging protocols.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To this end, we aim to evaluate the performance of several previously developed DL-based models, which were trained to distinguish idiopathic pulmonary fibrosis (IPF) from non-IPF among interstitial lung disease (ILD) patients, under standardized reference CT imaging protocols. In this study, we utilized CT scans from non-IPF ILD subjects, acquired using various imaging protocols, to assess the model performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Three DL-based models, including one 2D and two 3D models, have been previously developed to classify ILD patients into IPF or non-IPF based on chest CT scans. These models were trained on CT image data from 389 IPF and 700 non-IPF ILD patients, retrospectively, obtained from five multicenter studies. For some patients, multiple CT scans were acquired (e.g., one at inhalation and one at exhalation) and/or reconstructed (e.g., thin slice and/or thick slice). Thus, for each patient, one CT image dataset was selected to be used in the construction of the classification model, so the parameters of that data set serve as the reference conditions. In one non-IPF ILD study, due to its specific study protocol, many patients had multiple CT image data sets that were acquired under both prone and supine positions and/or reconstructed under different imaging parameters. Therefore, to assess the robustness of the previously developed models under different (e.g., non-reference) imaging protocols, we identified 343 subjects from this study who had CT data from both the reference condition (used in model construction) and non-reference conditions (e.g., evaluation conditions), which we used in this model evaluation analysis. We reported the specificities from three model under the non-reference conditions. Generalized linear mixed effects model (GLMM) was utilized to identify the significant CT technical and clinical parameters that were associated with getting inconsistent diagnostic results between reference and evaluation conditions. Selected parameters include effective tube current-time product (known as \"effective mAs\"), reconstruction kernels, slice thickness, patient orientation (prone or supine), CT scanner model, and clinical diagnosis. Limitations include the retrospective nature of this study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For all three DL models, the overall specificity of the previously trained IPF diagnosis model decreased (p &lt; 0.05 for two out of three models). GLMM further suggests that for at least one out of three models, mean effective mAs across the scan is the key factor that leads to the decrease in model predictive performance (p &lt; 0.001); the difference of mean effective mAs between the reference and evaluation conditions (p = 0.03) and slice thickness (3 mm; p = 0.03) ar","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665763","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":"3D lymphoma segmentation on PET/CT images via multi-scale information fusion with cross-attention.","authors":"Huan Huang, Liheng Qiu, Shenmiao Yang, Longxi Li, Jiaofen Nan, Yanting Li, Chuang Han, Fubao Zhu, Chen Zhao, Weihua Zhou","doi":"10.1002/mp.17763","DOIUrl":"https://doi.org/10.1002/mp.17763","url":null,"abstract":"<p><strong>Background: </strong>Accurate segmentation of diffuse large B-cell lymphoma (DLBCL) lesions is challenging due to their complex patterns in medical imaging. Traditional methods often struggle to delineate these lesions accurately.</p><p><strong>Objective: </strong>This study aims to develop a precise segmentation method for DLBCL using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and computed tomography (CT) images.</p><p><strong>Methods: </strong>We propose a 3D segmentation method based on an encoder-decoder architecture. The encoder incorporates a dual-branch design based on the shifted window transformer to extract features from both PET and CT modalities. To enhance feature integration, we introduce a multi-scale information fusion (MSIF) module that performs multi-scale feature fusion using cross-attention mechanisms with a shifted window framework. A gated neural network within the MSIF module dynamically adjusts feature weights to balance the contributions from each modality. The model is optimized using the dice similarity coefficient (DSC) loss function, minimizing discrepancies between the model prediction and ground truth. Additionally, we computed the total metabolic tumor volume (TMTV) and performed statistical analyses on the results.</p><p><strong>Results: </strong>The model was trained and validated on a private dataset of 165 DLBCL patients and a publicly available dataset (autoPET) containing 145 PET/CT scans of lymphoma patients. Both datasets were analyzed using five-fold cross-validation. On the private dataset, our model achieved a DSC of 0.7512, sensitivity of 0.7548, precision of 0.7611, an average surface distance (ASD) of 3.61 mm, and a Hausdorff distance at the 95th percentile (HD95) of 15.25 mm. On the autoPET dataset, the model achieved a DSC of 0.7441, sensitivity of 0.7573, precision of 0.7427, ASD of 5.83 mm, and HD95 of 21.27 mm, outperforming state-of-the-art methods (p < 0.05, t-test). For TMTV quantification, Pearson correlation coefficients of 0.91 (private dataset) and 0.86 (autoPET) were observed, with R² values of 0.89 and 0.75, respectively. Extensive ablation studies demonstrated the MSIF module's contribution to enhanced segmentation accuracy.</p><p><strong>Conclusion: </strong>This study presents an effective automatic segmentation method for DLBCL that leverages the complementary strengths of PET and CT imaging. The method demonstrates robust performance on both private and publicly available datasets, ensuring its reliability and generalizability. Our method provides clinicians with more precise tumor delineation, which can improve the accuracy of diagnostic interpretations and assist in treatment planning for DLBCL patients. The code for the proposed method is available at https://github.com/chenzhao2023/lymphoma_seg.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665760","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":"Poisson diffusion probabilistic model for low-dose SPECT sinogram denoising.","authors":"Peng Lai, Ruifan Wu, Woliang Yuan, Haiying Li, Ying Jiang","doi":"10.1002/mp.17760","DOIUrl":"https://doi.org/10.1002/mp.17760","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Low-dose single photon emission computed tomography (SPECT) sinograms often suffer from noise due to photon attenuation during the imaging process. As a result, developing effective denoising methods for low-dose SPECT images has become an essential research topic. Traditional image denoising methods struggle to balance noise reduction with the preservation of important image details, especially in medical applications where accurate image structures are critical.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This paper proposes a diffusion probabilistic model based on Poisson noise, named the Poisson diffusion probabilistic model (PDPM), for denoising low-dose SPECT sinograms. Considering the physical principles behind the formation of low-dose SPECT sinograms, PDPM replaces the Gaussian noise traditionally used in diffusion models with Poisson noise, utilizing low-dose and normal-dose SPECT sinograms as the starting and ending points of the denoising process, respectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We present a preliminary framework for PDPM that encompasses both the forward and reverse processes. Subsequently, we refine this preliminary framework by implementing two improvements: discarding the forward process and generating the training dataset using a method based on the ideal reverse process, as well as introducing our proposed Temporal Prediction Aggregation Module (TPAM) into the reverse process to enhance the model's image denoising performance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Experiments conducted on the simulated SPECT dataset demonstrate that PDPM effectively improves the quality of sinogram images. Specifically, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the sinograms increased from 19.3156 to 35.3446 ( &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt; &lt;mo&gt;&lt;&lt;/mo&gt; &lt;mn&gt;0.0001&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$p&lt;0.0001$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ) and from 0.7531 to 0.9791 ( &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt; &lt;mo&gt;&lt;&lt;/mo&gt; &lt;mn&gt;0.0001&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$p&lt;0.0001$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ), respectively. For the reconstructed images from the sinograms, the PSNR and SSIM improved from 25.7511 to 35.1335 ( &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt; &lt;mo&gt;&lt;&lt;/mo&gt; &lt;mn&gt;0.0001&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$p&lt;0.0001$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ) and from 0.9286 to 0.9817 ( &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt; &lt;mo&gt;&lt;&lt;/mo&gt; &lt;mn&gt;0.0001&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$p&lt;0.0001$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; ), respectively. The experiments show that PDPM outperforms competitive methods in the task of low-dose SPECT sinogram denoising, including one traditional denoising algorithm and four deep learning methods. Experiments on clinical SPECT datasets further indicate that our method effectively reduces the coefficient of variation (COV) in the regions of interest (ROI) of the reconstructed images, enhancing the quality of the reconstructed images by denoising the SPECT sinograms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The","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":"143660155","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 enhanced visual state space model for myocardial pathology segmentation in multi-sequence cardiac MRI.","authors":"Shuning Li, Xiang Li, Pingping Wang, Kunmeng Liu, Benzheng Wei, Jinyu Cong","doi":"10.1002/mp.17761","DOIUrl":"https://doi.org/10.1002/mp.17761","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Myocardial pathology (scar and edema) segmentation plays a crucial role in the diagnosis, treatment, and prognosis of myocardial infarction (MI). However, the current mainstream models for myocardial pathology segmentation have the following limitations when faced with cardiac magnetic resonance(CMR) images with multiple objects and large changes in object scale: the remote modeling ability of convolutional neural networks is insufficient, and the computational complexity of transformers is high, which makes myocardial pathology segmentation challenging.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study aims to develop a novel model to address the image characteristics and algorithmic challenges faced in the myocardial pathology segmentation task and improve the accuracy and efficiency of myocardial pathology segmentation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed a novel visual state space (VSS)-based deep neural network, MPS-Mamba. In order to accurately and adequately extract CMR image features, the encoder employs a dual-branch structure to extract global and local features of the image. Among them, the VSS branch overcomes the limitations of the current mainstream models for myocardial pathology segmentation by modeling remote relationships through linear computability, while the convolutional-based branch provides complementary local information. Given the unique properties of the dual branches, we design a modular dual-branch fusion module for fusing dual branches to enhance the feature representation of the dual encoder. To improve the ability to model objects of different scales in cardiac magnetic resonance (CMR) images, a multi-scale feature fusion (MSF) module is designed to achieve effective integration and fine expression of multi-scale information. To further incorporate anatomical knowledge to optimize segmentation results, a decoder with three decoding branches is designed to output segmentation results of scar, edema, and myocardium, respectively. In addition, multiple sets of constraint functions are used to not only improve the segmentation accuracy of myocardial pathology but also effectively model the spatial position relationship between myocardium, scar, and edema.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The proposed method was comprehensively evaluated on the MyoPS 2020 dataset, and the results showed that MPS-Mamba achieved an average Dice score of 0.717 &lt;math&gt;&lt;semantics&gt;&lt;mo&gt;±&lt;/mo&gt; &lt;annotation&gt;$pm$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; 0.169 in myocardial scar segmentation, which is superior to the current mainstream methods. In addition, MPS-Mamba also performed well in the edema segmentation task, with an average Dice score of 0.735 &lt;math&gt;&lt;semantics&gt;&lt;mo&gt;±&lt;/mo&gt; &lt;annotation&gt;$pm$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; 0.073. The experimental results further demonstrate the effectiveness of MPS-Mamba in segmenting myocardial pathologies in multi-sequence CMR images, verifying its advantages in myocardial pathology","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":"143665761","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}