Physics in medicine and biology最新文献

{"title":"Real-time dose reconstruction in proton therapy from in-beam PET measurements.","authors":"V V Onecha, A Espinosa-Rodriguez, C Soneira-Landín, F Arias-Valcayo, S Gaitán-Dominguez, V Martinez-Nouvilas, M García-Díez, P Ibáñez, S España, D Sanchez-Parcerisa, F Cerrón-Campoo, J A Vera-Sánchez, A Mazal, J M Udias, L M Fraile","doi":"10.1088/1361-6560/adbfd9","DOIUrl":"10.1088/1361-6560/adbfd9","url":null,"abstract":"<p><p><i>Objective</i>. Clinical implementation of in-beam positron emission tomography (PET) monitoring in proton therapy (PT) requires the integration of an online fast and reliable dose calculation engine. This manuscript reports on the achievement of real-time reconstruction of 3D dose and activity maps with proton range verification from experimental in-beam PET measurements.<i>Approach</i>. Several cylindrical homogeneous PMMA phantoms were irradiated with a monoenergetic 70 MeV proton beam in a clinical facility. Additionally, PMMA range-shifting foils of varying thicknesses were placed at the proximal surface of the phantom to investigate range shift prediction capabilities. PET activity was measured using a state-of-the-art in-house developed six-module PET scanner equipped with online PET reconstruction capabilities. For real-time dose estimation, we integrated this system with an in-beam dose estimation algorithm, which combines a graphical processing unit-based 3D reconstruction algorithm with a dictionary-based software, capable of estimating deposited doses from the 3D PET activity images. The range shift prediction performance has been quantitatively studied in terms of the minimum dose to be delivered and the maximum acquisition time.<i>Main results</i>. With this framework, 3D dose maps were accurately reconstructed and displayed with a delay as short as one second. For a dose fraction of 8.4 Gy at the Bragg peak maximum, range shifts as small as 1 mm could be detected. The quantitative analysis shows that accumulating 20 s of statistics from the start of the irradiation, doses down to 1 Gy could be estimated online with total uncertainties smaller than 2 mm.<i>Significance</i>. The hardware and software combination employed in this work can deliver dose maps and accurately predict range shifts after short acquisition times and small doses, suggesting that real-time monitoring and dose reconstruction during PT are within reach. Future work will focus on testing the methodology in more complex clinical scenarios and on upgrading the PET prototype for increased sensitivity.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feasibility study of automating radiotherapy planning with large language model agents.","authors":"Qingxin Wang, Zhongqiu Wang, Minghua Li, Xinye Ni, Rong Tan, Wenwen Zhang, Maitudi Wubulaishan, Wei Wang, Zhiyong Yuan, Zhen Zhang, Cong Liu","doi":"10.1088/1361-6560/adbff1","DOIUrl":"10.1088/1361-6560/adbff1","url":null,"abstract":"<p><p><i>Objective.</i>Radiotherapy planning requires significant expertise to balance tumor control and organ-at-risk (OAR) sparing. Automated planning can improve both efficiency and quality. This study introduces GPT-Plan, a novel multi-agent system powered by the GPT-4 family of large language models (LLMs), for automating the iterative radiotherapy plan optimization.<i>Approach.</i>GPT-Plan uses LLM-driven agents, mimicking the collaborative clinical workflow of a dosimetrist and physicist, to iteratively generate and evaluate text-based radiotherapy plans based on predefined criteria. Supporting tools assist the agents by leveraging historical plans, mitigating LLM hallucinations, and balancing exploration and exploitation. Performance was evaluated on 12 lung (IMRT) and 5 cervical (VMAT) cancer cases, benchmarked against the ECHO auto-planning method and manual plans. The impact of historical plan retrieval on efficiency was also assessed.<i>Results.</i>For IMRT lung cancer cases, GPT-Plan generated high-quality plans, demonstrating superior target coverage and homogeneity compared to ECHO while maintaining comparable or better OAR sparing. For VMAT cervical cancer cases, plan quality was comparable to a senior physicist and consistently superior to a junior physicist, particularly for OAR sparing. Retrieving historical plans significantly reduced the number of required optimization iterations for lung cases (<i>p</i> < 0.01) and yielded iteration counts comparable to those of the senior physicist for cervical cases (<i>p</i> = 0.313). Occasional LLM hallucinations have been mitigated by self-reflection mechanisms. One limitation was the inaccuracy of vision-based LLMs in interpreting dose images.<i>Significance.</i>This pioneering study demonstrates the feasibility of automating radiotherapy planning using LLM-powered agents for complex treatment decision-making tasks. While challenges remain in addressing LLM limitations, ongoing advancements hold potential for further refining and expanding GPT-Plan's capabilities.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time 3D synthetic MRI based on kV imaging for motion monitoring of abdominal radiotherapy in a conventional LINAC.","authors":"Paulo Quintero, Can Wu, Hao Zhang, Ricardo Otazo, Laura Cerviño, Wendy Harrys","doi":"10.1088/1361-6560/adbeb5","DOIUrl":"10.1088/1361-6560/adbeb5","url":null,"abstract":"<p><p><i>Introduction.</i>Real-time 2D-kV-triggered images used to evaluate intra-fraction motion during abdominal radiotherapy only provides 2D information with poor soft-tissue contrast. The main goal of this research is to evaluate a novel method that generates synthetic 3D-MRI from single 2D-kV images for online motion monitoring in abdominal radiotherapy.<i>Methods.</i>Deformable image registration (DIR) is performed between one 4D-MRI reference phase and all other phases, and principal-component-analysis (PCA) is implemented on their respective deformation vectors. By sampling 1000 times the PCA eigenvalues and applying the new deformations over a reference CT, 1000 digital reconstructed radiographs (DRRs) were generated to train a convolutional neural network to predict their respective eigenvalues. The method was implemented and tested using a digital phantom (XCAT) and an MRI-compatible phantom (ZEUS) with five DRR angles (0°, 45°, 90°, 135°, 180°). Seven motion scenarios were tested. For model performance, mean absolute error (MAE) and root mean square error (RMSE) were reported. Image quality was evaluated with structure similarity index (SSIM) and normalized RMSE (nRMSE), and target-volume variations were evaluated with volumetric dice coefficient (VDC) and Hausdorff-distance (HD).<i>Results.</i>The model performance across the evaluated angles were MAE_{(XCAT, ZEUS)}= (0.053 ± 0.003, 0.094 ± 0.003), and RMSE_{(XCAT, ZEUS)}= (0.054 ± 0.007, 0.103 ± 0.002). Similarly, SSIM_{(XCAT, ZEUS)}= (0.994 ± 0.001, 0.96 ± 0.02), and nRMSE_{(XCAT, ZEUS)}= (0.13 ± 0.01, 0.17 ± 0.03). For all motion scenarios for XCAT and ZEUS, SSIM were 0.98 ± 0.01 and 0.84 ± 0.02, nRMSE were 0.14 ± 0.01 and 0.27 ± 0.02, VDC were 0.98 ± 0.01 and 0.90 ± 0.01, and HD were 0.24 ± 0.02 mm and 2.3 ± 0.8 mm, respectively, averaged across all angles. Finally, SSIM, nRMSE, VDC and HU values for ZEUS using the^deformedimages as ground truth, presented an improvement of 13%, 28%, 4%, and 76%, respectively.<i>Conclusions</i>. Results from a digital and physical phantom demonstrate a novel approach to generate real-time 3D synthetic MRI from onboard kV images on a conventional LINAC for intra-fraction monitoring in abdominal radiotherapy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143597638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint estimation of activity, attenuation and motion in respiratory-self-gated time-of-flight PET.","authors":"Masoud Elhamiasl, Frederic Jolivet, Ahmadreza Rezaei, Michael Fieseler, Klaus Schäfers, Johan Nuyts, Georg Schramm, Fernando Boada","doi":"10.1088/1361-6560/adbed5","DOIUrl":"10.1088/1361-6560/adbed5","url":null,"abstract":"<p><p><i>Objective</i>. Whole-body positron emission tomography (PET) imaging is often hindered by respiratory motion during acquisition, causing significant degradation in the quality of reconstructed activity images. An additional challenge in PET/CT imaging arises from the respiratory phase mismatch between CT-based attenuation correction and PET acquisition, leading to attenuation artifacts. To address these issues, we propose two new, purely data-driven methods for the joint estimation of activity, attenuation, and motion in respiratory self-gated time-of-flight PET. These methods enable the reconstruction of a single activity image free from motion and attenuation artifacts.<i>Approach</i>. The proposed methods were evaluated using data from the anthropomorphic Wilhelm phantom acquired on a Siemens mCT PET/CT system, as well as three clinical [¹⁸F]FDG PET/CT datasets acquired on a GE DMI PET/CT system. Image quality was assessed visually to identify motion and attenuation artifacts. Lesion uptake values were quantitatively compared across reconstructions without motion modeling, with motion modeling but 'static' attenuation correction, and with our proposed methods.<i>Main results</i>. For the Wilhelm phantom, the proposed methods delivered image quality closely matching the reference reconstruction from a static acquisition. The lesion-to-background contrast for a liver dome lesion improved from 2.0 (no motion correction) to 5.2 (using our proposed methods), matching the contrast from the static acquisition (5.2). In contrast, motion modeling with 'static' attenuation correction yielded a lower contrast of 3.5. In patient datasets, the proposed methods successfully reduced motion artifacts in lung and liver lesions and mitigated attenuation artifacts, demonstrating superior lesion to background separation.<i>Significance</i>. Our proposed methods enable the reconstruction of a single, high-quality activity image that is motion-corrected and free from attenuation artifacts, without the need for external hardware.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143597601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DRACO: differentiable reconstruction for arbitrary CBCT orbits.","authors":"Chengze Ye, Linda-Sophie Schneider, Yipeng Sun, Mareike Thies, Siyuan Mei, Andreas Maier","doi":"10.1088/1361-6560/adbb50","DOIUrl":"10.1088/1361-6560/adbb50","url":null,"abstract":"<p><p><i>Objective</i>. This study introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits, addressing the computational and memory challenges associated with traditional iterative reconstruction algorithms.<i>Approach</i>. The proposed method employs a differentiable shift-variant filtered backprojection neural network, optimized for arbitrary trajectories. By integrating known operators into the learning model, the approach minimizes the number of trainable parameters while enhancing model interpretability. This framework adapts seamlessly to specific orbit geometries, including non-continuous trajectories such as circular-plus-arc or sinusoidal paths, enabling faster and more accurate CBCT reconstructions.<i>Main results</i>. Experimental validation demonstrates that the method significantly accelerates reconstruction, reducing computation time by over 97% compared to conventional iterative algorithms. It achieves superior or comparable image quality with reduced noise, as evidenced by a 38.6% reduction in mean squared error, a 7.7% increase in peak signal-to-noise ratio, and a 5.0% improvement in the structural similarity index measure. The flexibility and robustness of the approach are confirmed through its ability to handle data from diverse scan geometries.<i>Significance</i>. This method represents a significant advancement in interventional medical imaging, particularly for robotic C-arm CT systems, enabling real-time, high-quality CBCT reconstructions for customized orbits. It offers a transformative solution for clinical applications requiring computational efficiency and precision in imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-channel MRI reconstruction using cascaded Swinμ transformers with overlapped attention.","authors":"Tahsin Rahman, Ali Bilgin, Sergio D Cabrera","doi":"10.1088/1361-6560/adb933","DOIUrl":"https://doi.org/10.1088/1361-6560/adb933","url":null,"abstract":"<p><p><i>Objective.</i>Deep neural networks have been shown to be very effective at artifact reduction tasks such as magnetic resonance imaging (MRI) reconstruction from undersampled k-space data. In recent years, attention-based vision transformer models have been shown to outperform purely convolutional models at a wide variety of tasks, including MRI reconstruction. Our objective is to investigate the use of different transformer architectures for multi-channel cascaded MRI reconstruction.<i>Approach.</i>In this work, we explore the effective use of cascades of small transformers in multi-channel undersampled MRI reconstruction. We introduce overlapped attention and compare it to hybrid attention in shifted-window (Swin) transformers. We also investigate the impact of the number of Swin transformer layers in each architecture. The proposed methods are compared to state-of-the-art MRI reconstruction methods for undersampled reconstruction on standard 3T and low-field (0.3T) T1-weighted MRI images at multiple acceleration rates.<i>Main results.</i>The models with overlapped attention achieve significantly higher or equivalent quantitative test metrics compared to state-of-the-art convolutional approaches. They also show more consistent reconstruction performance across different acceleration rates compared to their hybrid attention counterparts. We have also shown that transformer architectures with fewer layers can be as effective as those with more layers when used in cascaded MRI reconstruction problems.<i>Significance.</i>The feasibility and effectiveness of cascades of small transformers with overlapped attention for MRI reconstruction is demonstrated without incorporating pre-training of the transformer on ImageNet or other large-scale datasets.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"70 7","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual photoacoustic/ultrasound technologies for preclinical research: current status and future trends.","authors":"Mailyn Pérez-Liva, María Alonso de Leciñana, María Gutiérrez-Fernández, Jorge Camacho Sosa Dias, Jorge F Cruza, Jorge Rodríguez-Pardo, Iván García-Suárez, Fernando Laso-García, Joaquin L Herraiz, Luis Elvira Segura","doi":"10.1088/1361-6560/adb368","DOIUrl":"10.1088/1361-6560/adb368","url":null,"abstract":"<p><p>Photoacoustic (PA) imaging, by integrating optical and ultrasound (US) modalities, combines high spatial resolution with deep tissue penetration, making it a transformative tool in biomedical research. This review presents a comprehensive analysis of the current status of dual PA/US imaging technologies, emphasising their applications in preclinical research. It details advancements in light excitation strategies, including tomographic and microscopic modalities, innovations in pulsed laser and alternative light sources, and US instrumentation. The review further explores preclinical methodologies, encompassing dedicated instrumentation, signal processing, and data analysis techniques essential for PA/US systems. Key applications discussed include the visualisation of blood vessels, micro-circulation, and tissue perfusion; diagnosis and monitoring of inflammation; evaluation of infections, atherosclerosis, burn injuries, healing, and scar formation; assessment of liver and renal diseases; monitoring of epilepsy and neurodegenerative conditions; studies on brain disorders and preeclampsia; cell therapy monitoring; and tumour detection, staging, and recurrence monitoring. Challenges related to imaging depth, resolution, cost, and the translation of contrast agents to clinical practice are analysed, alongside advancements in high-speed acquisition, artificial intelligence-driven reconstruction, and innovative light-delivery methods. While clinical translation remains complex, this review underscores the crucial role of preclinical studies in unravelling fundamental biomedical questions and assessing novel imaging strategies. Ultimately, this review delves into the future trends of dual PA/US imaging, highlighting its potential to bridge preclinical discoveries with clinical applications and drive advances in diagnostics, therapeutic monitoring, and personalised medicine.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143365689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bowel tracking for MR-guided radiotherapy: simultaneous optimization of small bowel imaging and tracking.","authors":"S L C Damen, A L H M W van Lier, C Zachiu, B W Raaymakers","doi":"10.1088/1361-6560/adbbac","DOIUrl":"10.1088/1361-6560/adbbac","url":null,"abstract":"<p><p><i>Objective</i>. The small bowel is one of the most radiosensitive organs-at-risk during radiotherapy in the pelvis. This is further complicated due to anatomical and physiological motion. Thus, its accurate tracking becomes of particular importance during therapy delivery, to obtain better dose-toxicity relations and/or to perform safe adaptive treatments. The aim of this work is to simultaneously optimize the MR imaging sequence and motion estimation solution towards improved small bowel tracking precision during radiotherapy delivery.<i>Approach</i>. An MRI sequence was optimized, to adhere to the respiratory and peristaltic motion frequencies, by assesing the performance of an image registration algorithm on data acquired on volunteers and patients. In terms of tracking, three registration algorithms, previously-employed in the scope of image-guided radiotherapy, were investigated and optimized. The optimized scan was acquired for 7.5 min, in 18 patients and for 15 min, in 10 volunteers at a 1.5 T MRL (Unity, Elekta AB). The tracking precision was evaluated and validated by means of three different quality assurance criteria: Structural Similarity Index Measure (SSIM), Inverse Consistency (IC) and Absolute Intensity Difference.<i>Main results</i>. The optimal sequence was a balanced Fast Field Echo, which acquired a 3D volume of the abdomen, with a dynamic scan time of 1.8 s. An optical flow algorithm performed best and which was able to resolve most of the motion. This was shown by mean IC values of<1 mm and a mean SSIM>0.9for the majority of the cases. A strong positive correlation (<i>p</i> <0.001) between the registration performance and visceral fat percentage was found, where a higher visceral fat percentage gave a better registration due to the better image contrast.<i>Significance</i>. A method for simultaneous optimization of imaging and tracking was presented, which derived an imaging and registration procedure for accurate small bowel tracking on the MR-Linac.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling occult lymph node metastases in HNSCC patients with a trinary state hidden Markov model.","authors":"Yoel Samuel Pérez Haas, Roman Ludwig, Esmée Lauren Looman, Vincent Grégoire, Panagiotis Balermpas, Jan Unkelbach","doi":"10.1088/1361-6560/adc235","DOIUrl":"https://doi.org/10.1088/1361-6560/adc235","url":null,"abstract":"<p><strong>Objective: </strong>Head-and-neck squamous cell carcinoma frequently metastasize through lymphatic&#xD;system. Occult metastases are challenging for designing radiotherapy treatment volumes. Literature&#xD;values indicate that around 20% of lymph node metastases are clinically undetected. However, recent&#xD;data suggest that this value is only representative for level II whereas the rate of occult metastases&#xD;is substantially higher in levels III and IV. , while occult metastases are more common in levels III&#xD;and IV.&#xD;Approach: We propose a trinary-state Hidden Markov Model to describe ipsilateral lymphatic&#xD;tumor progression over time. Each lymph node level (LNL) can be in one of three states: healthy,&#xD;microscopically (pathologically) involved, or macroscopically (clinically) involved. In each time step,&#xD;a healthy LNL may become microscopically involved due to spread from the primary tumor or an&#xD;involved upstream LNL. In addition, a microscopically involved LNL may transition to macroscop-&#xD;ically involved. The probability of occult metastases is obtained as the conditional probability of&#xD;being in the microscopically involved state given the individual patient's macroscopic involvement.&#xD;Model parameters are learned from a dataset of 550 patients, including 263 with both pathological&#xD;and clinical LNL involvement reported.&#xD;Main Results: For oropharyngeal SCC, the model estimates an occult metastases probability&#xD;below 5% in LNL IV unless LNL III is clinically positive, suggesting potential for reducing elective&#xD;clinical target volumes. The model's estimated rate of clinically undetected metastases is 82%, 41%,&#xD;and 34% for LNL II, III, and IV, respectively, which agrees with the data.&#xD;Significance: The proposed trinary-state HMM represents a methodological extension to a previ-&#xD;ously published binary-state HMM. The binary HMM distinguished the microscopic and macroscopic&#xD;involvement via the concept of sensitivity, which may underestimate the risk of occult metastases. The&#xD;trinary HMM addresses this problem and represents a more natural description of tumor progression.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A semi-supervised prototypical network for prostate lesion segmentation from multimodality MRI.","authors":"Wen Yan, Yipeng Hu, Qianye Yang, Yunguan Fu, Tom Syer, Zhe Min, Shonit Punwani, Mark Emberton, Dean C Barratt, Carmen C M Cho, Bernard Chiu","doi":"10.1088/1361-6560/adc182","DOIUrl":"https://doi.org/10.1088/1361-6560/adc182","url":null,"abstract":"<p><strong>Objective: </strong>Prostate lesion segmentation from multiparametric MR images is particularly challenging due to the limited availability of labeled data. This scarcity of annotated images makes it difficult for supervised models to learn the complex features necessary for accurate lesion detection and segmentation. &#xD; &#xD;Approach: We proposed a novel semi-supervised algorithm that embeds prototype learning into mean-teacher training to improve the feature representation for unlabeled data. In this method, pseudo-labels generated by the teacher network simultaneously serve as supervision for unlabeled prototype-based segmentation. By enabling prototype segmentation to operate across labeled and unlabeled data, the network enriches the pool of \"lesion representative prototypes\", and allows prototypes to flow bidirectionally-from support-to-query and query-to-support paths. This intersected, bidirectional information flow strengthens the model's generalization ability. This approach is distinct from the mean-teacher algorithm as it involves few-shot training and differs from prototypical learning for adopting unlabeled data for training. &#xD;&#xD;Main results: This study evaluated multiple datasets with 767 patients from three different institutions, including the publicly available PROSTATEx/PROSTATEx2 datasets as the holdout institute for reproducibility. The experimental results showed that the proposed algorithm outperformed state-of-the-art semi-supervised methods with limited labeled data, observing an improvement in Dice similarity coefficient (DSC) with increasing labeled data, ranging from 0.04 to 0.09.&#xD;&#xD;Significance: Our method shows promise in improving segmentation outcomes with limited labeled data and potentially aiding clinicians in making informed patient treatment and management decisions.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}