Physics in medicine and biology最新文献

{"title":"Effects of count rate performance on image quality in an LAFOV PET/CT system.","authors":"Nanna Overbeck, Zuzanna Czeluśniak, Søren Holm, Julie Verne Henriksen, Flemming L Andersen, Thomas Lund Andersen","doi":"10.1088/1361-6560/aea301","DOIUrl":"https://doi.org/10.1088/1361-6560/aea301","url":null,"abstract":"<p><p>The development of Long Axial Field-of-View (LAFOV) positron emission tomography (PET) combined with computed tomography (CT) warrants reconsideration of the administered activity required for clinical imaging. This study investigated the relationship between body size, administered activity (18F), and image quality using two patient-like phantoms representing normal (78.5 kg) and obese (194.3 kg) body profiles. The phantoms were scanned in selected five-minute periods within a total duration of 27 and 21 hours, respectively, following the decay of the activity. &#xD;Analysis of the trues, scatter, and randoms demonstrated a close to linear propagation of the trues with activity up to a point where saturation was reached. The normal body profile phantom achieved higher signal-to-noise ratios (SNRs), reaching system saturation at a lower activity (680 MBq) than the large phantom, representing the obese body profile (970 MBq). The SNR2 showed a nonlinear correlation with the noise equivalent count rate (NECR), whereas SNR2 had a linear correlation with the trues. &#xD;The study demonstrated the count rate capabilities up to and beyond the saturation limit. Furthermore, we illustrated the possibilities of scanning patients with ultralow dose administration in the range of 0.03-0.07 MBq/kg body weight. However, achieving robust clinical diagnostic quality - particularly for low-contrast lesions - at these activity levels requires further clinical investigation. Utilizing these ultralow activity concentrations requires extending the scan duration to compensate for image quality, especially when examining obese patients.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892265","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":"Assessment of data-driven gating for cardiac motion extraction and LVEF estimation from routine [¹⁸F]FDG LAFOV PET.","authors":"Nanna Overbeck, Ulrich Lindberg, Esben Andreas Carlsen, Anders Bertil Rodell, Paul J Schleyer, Jorge Cabello, Philip Hasbak, Thomas Lund Andersen, Flemming L Andersen","doi":"10.1088/1361-6560/aea2eb","DOIUrl":"https://doi.org/10.1088/1361-6560/aea2eb","url":null,"abstract":"<p><strong>Objective: </strong>Chemotherapy-induced cardiotoxicity can lead to irreversible heart failure. Left ventricular ejection fraction (LVEF) is routinely monitored during treatment, but conventional assessment requires dedicated cardiac imaging and clinical resources. This study evaluated a deviceless data-driven gating (DDG) framework for extracting cardiac motion from routine [18F]FDG PET emission data and, secondary, assessed its feasibility for LVEF estimation. &#xD;Approach. The DDG framework is based on histo images and uses anatomical masking for frequency-domain filtering to obtain the cardiac signals of 169 [18F]FDG PET/CT examinations. The cardiac gating performance was evaluated, and LVEF estimates were compared with the patient's respective clinical reference method; echocardiography or multigated acquisition (MUGA).&#xD;Main results. The DDG framework successfully extracted a cardiac gating signal in 138 of 169 examinations (81.7%). Sufficient myocardial [18F]FDG uptake for software-based LVEF estimation was present in 101 patients (60%), and LVEF was successfully estimated in all cases. Compared with the reference methods, the DDG-based LVEF estimates demonstrated a mean bias of 3.4% relative to echocardiography and -2.6% relative to MUGA. Agreement was strongest with echocardiography, although the limits of agreement exceeded the threshold required for interchangeable clinical use. The extracted pulse frequencies were physiologically plausible and showed good agreement with the corresponding reference examinations, supporting the validity of the DDG-derived cardiac signal.&#xD;Significance. The deviceless DDG framework can successfully extract clinically relevant cardiac motion directly from routine [18F]FDG PET acquisitions without external hardware. The method achieved a high success rate with respect to gating the images and provided LVEF estimates that showed good agreement with established clinical reference methods, supporting the feasibility of deriving functional cardiac information from standard PET examinations. The proposed approach provides a promising foundation for retrospective functional cardiac assessment and has the potential to complement conventional LVEF evaluation while reducing additional patient burden and simplifying clinical workflows.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892263","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":"Local assessment of shear anisotropy in fiber network phantoms of the levator ani muscle using rotational shear wave elastography.","authors":"Estelle Pitti, Lorena Claeys, Zhongzheng Wang, Emilia Simone Rotstein, Lotta Herling, Gunilla Ajne, Matilda Larsson","doi":"10.1088/1361-6560/aea304","DOIUrl":"https://doi.org/10.1088/1361-6560/aea304","url":null,"abstract":"<p><strong>Objective: </strong>Childbirth is a major cause of levator ani muscle (LAM) injury, affecting over 10% of women after vaginal delivery. Progress in prevention, diagnosis, and treatment is limited by poor understanding of LAM mechanical properties particularly muscle elasticity, which is closely linked to injury mechanisms and can be measured noninvasively using shear wave elastography (SWE). Conventional SWE assumes large, isotropic tissues, making it unsuitable for the small, anisotropic, and complex LAM. This study aimed to develop fiber-network LAM phantoms and to investigate rotational SWE imaging to assess local, direction-dependent shear anisotropy properties of the LAM. &#xD;Approach: Six LAM phantoms with varying fiber type, fiber density, freeze-thaw cycles, and fiber networks (puborectalis alone or combined puborectalis-pubococcygeus) were constructed by embedding synthetic fibers within a polyvinyl alcohol matrix. A custom rotational SWE imaging setup with a Verasonics V1 system was used. Shear wave velocities were estimated from axial velocity maps using a semi-automatic Radon sum algorithm and fitted to an elliptical model. Based on the theory of shear wave propagation in transversely isotropic (TI) materials, this model enabled the estimation of shear anisotropy metrics, including fiber direction, shear anisotropy, longitudinal and transverse shear moduli, and TI profile fit quality. &#xD;Main results: Rotational SWE imaging differentiated puborectalis LAM phantoms with shear anisotropy (1.09-4.95), longitudinal shear moduli (8.67-60.41 kPa), and transverse shear moduli (3.09-12.26 kPa), reflecting distinct biomechanical and age-related LAM properties. Different muscle network configurations of the LAM were also distinguished across three probe positions, and local fiber curvature was detected. &#xD;Significance: This study demonstrates that rotational SWE imaging can characterize local shear anisotropy and fiber-network architecture in anatomically informed LAM phantoms, beyond what can be obtained from conventional SWE. These findings provide a foundation for future studies investigating whether rotational SWE imaging can improve the in vivo assessment of the LAM.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892243","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 novel preclinical system for cascade gamma photon coincidence imaging with high resolution and sensitivity.","authors":"Shuyu Xu, Kui Wang, Qingyang Wei, Hui Liu, Jing Wu, Yaqiang Liu, Zuoxiang He","doi":"10.1088/1361-6560/aea305","DOIUrl":"https://doi.org/10.1088/1361-6560/aea305","url":null,"abstract":"<p><p>We present a novel high-resolution, high-sensitivity time-space coincidence imaging system for cascade gamma photons. The system employs a LaBr 3 ring detector with hybrid pinhole-slit collimators, combining the high spatial resolution of pinhole collimation with the high detection efficiency of slit collimation. Simulations were conducted with a point source and a Derenzo phantom using 177 Lu, a theranostic radionuclide that emits cascade gamma-ray pairs at 113 keV and 208 keV. Images were reconstructed using direct back-projection (DBP) and maximum likelihood expectation maximization (MLEM), as well as two newly proposed algorithms: refined DBP (R-DBP) and multi-information joint reconstruction (MIJR).The system achieved a central coincidence efficiency of 2.98×10⁻⁵. Point source imaging with MLEM reconstruction yielded a spatial resolution of 1.7 mm full width at half maximum (FWHM) in the transaxial plane. Derenzo phantom imaging demonstrated clear resolution of hot rods as small as 1.2 mm in diameter, with a contrast-to-noise ratio (CNR) of 9.29 for the largest rods.These results demonstrate that the proposed ring detector enables high-quality cascade gamma photon coincidence imaging, with spatial resolution and sensitivity that significantly exceed those of previously reported systems. The combination of high resolution, reasonable sensitivity, and theranostic capability positions this technology as a promising platform for integrated diagnosis and therapy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892310","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":"Evaluation of neuronal activation thresholds for low-frequency electromagnetic exposure using morphologically realistic neuron models.","authors":"Joaquín Gázquez, Carolina Camacho Cadena, Wenzhe He, Eikei Yamada, Carsten Alteköster, Florian Soyka, Ilkka Laakso, Akimasa Hirata, Wout Joseph, Thomas Tarnaud, Emmeric Tanghe","doi":"10.1088/1361-6560/aea302","DOIUrl":"https://doi.org/10.1088/1361-6560/aea302","url":null,"abstract":"<p><strong>Objective: </strong>International guidelines for low‑frequency electromagnetic field exposure (LF EMF) are primarily intended to prevent substantiated adverse effects. In the frameworks, limits on internal electric fields are linked to external exposure levels through computational dosimetry. However, the relationship between internal electric fields and these adverse effects remains incompletely understood. In particular, current approaches often overlook the morphological complexity and diversity of cortical neurons, which may limit the realism of neuronal activation estimates used to support these assessments. This study aimed to evaluate LF EMF-induced neural activation using morphologically realistic neuron models representing all cortical layers.&#xD;&#xD;Approach. Twenty-five morphologically realistic neuron models were embedded within 11 detailed human head models. The internal electric fields were simulated for uniform magnetic field exposures (100 Hz-100 kHz) along the three anatomical directions, and excitation thresholds were computed using a multi‑scale framework combining voxel‑based dosimetry with biophysical neuron simulations. A real‑world exposure scenario involving a child near an acousto‑magnetic article‑surveillance deactivator was also analyzed.&#xD;&#xD;Main results. Excitation threshold varied across cell type, morphology, cortical location, subject anatomy, frequency, and exposure direction, with L2/3 pyramidal, L4 basket, and L5 thick‑tufted pyramidal cells showing the lowest thresholds. Despite this variability, all simulated thresholds were conservative with respect to the basic restrictions and dosimetric reference limits set by IEEE ICES and ICNIRP. The smallest margin occurred at 100 kHz, where the threshold remained a factor of 2.8 above the corresponding limit. &#xD;&#xD;Significance. These findings indicate that current LF EMF exposure limits remain conservative when evaluated using highly detailed, morphology‑based CNS activation models.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892246","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 deep unrolling network based on cartoon texture decomposition for low-dose CT reconstruction.","authors":"Tingyue Liu, Zhiguo Gui, Yi Liu, Jinxin Luo, Zhen Sun, Pengcheng Zhang","doi":"10.1088/1361-6560/aea2ec","DOIUrl":"https://doi.org/10.1088/1361-6560/aea2ec","url":null,"abstract":"<p><strong>Objective: </strong>Deep unrolling network, as a promising deep learning approach for low-dose computed tomography (LDCT) reconstruction, can efficiently address the issues of severe noise and artifacts in LDCT imaging. However, most existing methods predominantly unrolled the fidelity term to convolutional neural networks (CNNs) while failing to unroll regularization terms, which inevitably limits the feature capture capability of the network. To further improve the performance of deep unrolling networks, this paper proposed the CTDNet, a cartoon texture decomposition-based deep unrolling network that unrolls not only the data fidelity term but also regularization terms into CNNs.</p><p><strong>Approach: </strong>The cartoon texture decomposition model of Meyer was incorporated as regularization terms, together with the data fidelity term, to formulate the reconstruction optimization problem. This problem was solved by the Chambolle-Pock (CP) algorithm, yielding a single-loop iterative algorithm instance. This instance was then unrolled to the deep reconstruction network for LDCT, by replacing each update step of iterative process with a simple sub-network. To further enhance the quality of reconstruction results, a lightweight image&#xD;refinement and fusion module was employed to perform detail enhancement and remove residual noise of the reconstructed images.</p><p><strong>Main results: </strong>Extensive experiments were conducted on the \"Low-Dose CT Image and Projection Data\" dataset and the \"Piglet Dataset\". The experiment results demonstrated that the CTDNet effectively removes artifacts and noise from LDCT images while maximally preserving textural structures, which enables it to outperform in terms of visual effects and objective metrics.</p><p><strong>Significance: </strong>This work further unrolls the regularization terms to CNNs on the basis of unrolling the data fidelity term, providing a novel unrolling strategy for the future design of deep unrolling networks.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892293","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":"CAFDIM: A Group Convolution and Self-Attention Fusion-Based Dual-Domain Iterative Method for Sparse-View CT Reconstruction.","authors":"Jizhong Duan, Chenghong Sun, Haibo Tao, Zhenyu Huang, Yu Liu","doi":"10.1088/1361-6560/aea303","DOIUrl":"https://doi.org/10.1088/1361-6560/aea303","url":null,"abstract":"<p><strong>Objective: </strong>Sparse-view computed tomography (CT) reduces radiation dose and acquisition time by decreasing the number of projection views, but it also makes image reconstruction severely ill-posed, leading to structural distortion and severe artifacts. This study aims to develop an effective reconstruction framework for improving both projection-data fidelity and reconstructed image quality in sparse-view CT.</p><p><strong>Approach: </strong>We propose a group Convolution- and self-Attention Fusion-based Dual-domain Iterative Method (CAFDIM) for sparse-view CT reconstruction. CAFDIM follows a model-informed dual-domain iterative design. The framework consists of the Initialization Enhancement Network (IE-Net), Gradient Update Block (GUB), Projection-domain Repair Network (PR-Net), Image-domain Repair Network(IR-Net), and Momentum Update Block (MUB). The projection-domain branch employs a Deep Sparse Block (DSB) to enhance sparse projection features before full-view projection restoration, while the image-domain branch uses edge-guided residual refinement to improve anatomical structure preservation. To enhance local-global feature representation, a Convolution-Attention Fusion Block (CAFB) is embedded into both repair branches by combining group convolution with Pixel Shift Self-Attention (PSSA).</p><p><strong>Results: </strong>Experiments on simulated and real clinical projection datasets demonstrate that CAFDIM effectively suppresses sparse-view artifacts, preserves anatomical structures, and achieves superior reconstruction accuracy, visual quality, and generalization ability compared with state-of-the-art methods.</p><p><strong>Significance: </strong>CAFDIM provides an effective and efficient dual-domain reconstruction framework for sparse-view CT, showing strong potential for clinical applications in sparse-view and low-dose CT imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892277","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":"Rapid whole-brain susceptibility source separation using sub-minute three-dimensional multiple overlapping-echo detachment acquisition.","authors":"Longkun Chen, Qinqin Yang, Shiwei Huang, Nuowei Ge, Zejun Wu, Jianjun Zhou, Jianfeng Bao, Lijun Bao, Yuanhao Cheng, Jianfa Chen, Shuhui Cai, Zhong Chen, Liuhong Zhu, Congbo Cai","doi":"10.1088/1361-6560/ae9d11","DOIUrl":"10.1088/1361-6560/ae9d11","url":null,"abstract":"<p><p><i>Objective.</i>Magnetic susceptibility source separation provides profound insights into tissue pathophysiology by disentangling the underlying paramagnetic and diamagnetic sources within the human brain. However, the highly time-consuming nature of existing acquisition techniques creates a critical need for rapid and reliable separation methodologies to facilitate routine clinical applications.<i>Approach.</i>A highly accelerated framework for whole-brain magnetic susceptibility source separation was proposed by utilizing the ultra-fast 3D-MOLED sequence. This pipeline integrates the advanced deep learning architecture to accurately map the separated susceptibility components without relying on spin echo data. To ensure quantitative stability and structural fidelity, a robust three-dimensional domain adaptation strategy was incorporated during the simulation phase. The performance of the proposed method was systematically evaluated through phantom experiments, healthy volunteer assessments, and clinical examinations of patients with multiple sclerosis.<i>Main results.</i>The proposed framework achieved whole-brain susceptibility source separation at 1 mm isotropic resolution with only 50 s of data acquisition. In healthy volunteers, the paramagnetic and diamagnetic susceptibility maps were highly comparable to those obtained with the conventional multi-echo gradient-echo sequence, with structural similarity indices of 0.9244 ± 0.0181 and 0.9425 ± 0.0186, peak signal-to-noise ratios of 33.76 ± 2.37 and 37.29 ± 3.51 dB, regional coefficients of determination of 0.973 and 0.934, and Bland-Altman biases of -0.0013 and -0.0010 ppm, respectively. Furthermore, clinical evaluations in patients with multiple sclerosis demonstrated consistent characterization of complex lesion-related susceptibility features.<i>Significance.</i>Achieving whole-brain magnetic susceptibility source separation in less than one minute represents a meaningful technical advance. By substantially reducing the acquisition time required for susceptibility source separation, this accelerated framework may improve the feasibility of future clinical translation in neuroimaging workflows.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797011","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 physics-informed decomposition network for carbon ion radiotherapy dose monitoring: a proof-of-concept study.","authors":"Xin-Yu Hu, Yan Li, Wei-Guang Li, Yu-Ying Yin, Chao Yang, Cheng Chang, Ming-Qing Wang, Kai-Wen Li, Xueying Yang, Li-Sheng Geng","doi":"10.1088/1361-6560/ae9d0f","DOIUrl":"10.1088/1361-6560/ae9d0f","url":null,"abstract":"<p><p><i>Objective.</i>In-beam positron emission tomography (PET) provides a promising strategy for dose monitoring in carbon ion radiotherapy (CIRT), but accurate dose prediction remains difficult due to the complex, nonlinear relationship between positron-emitter activity and physical dose deposition. This proof-of-concept study aimed to improve activity-to-dose mapping by developing decomposition-based deep learning frameworks with auxiliary physical supervision.<i>Approach.</i>Idealized Monte Carlo (MC) simulations were conducted on computed tomography (CT) phantoms from 18 non-small cell lung cancer patients. The models were designed to predict laterally integrated one-dimensional depth-dose distributions for individual pencil-beam spots from corresponding 5 min cumulative activity and CT Hounsfield unit profiles. Two decomposition-based models, TemcoNet and NucoNet, incorporated Transformer-based decomposition modules supervised by cumulative post-irradiation activity at 10, 15, and 20 min and nuclide-specific yields of¹¹C,¹⁵O, and¹⁰C, respectively, while DirectNet served as a baseline.<i>Main results.</i>Compared with MC ground truth, all models achieved similar median range accuracy, but TemcoNet and NucoNet substantially improved dose prediction, reducing the mean relative error from 2.36% for DirectNet to below 0.4%. The mean gamma passing rate at 2 mm/2% increased from 45.31% to approximately 96% for both decomposition-based models. Ablation experiments showed that the decomposition pathway learned physically meaningful intermediate representations, and that nuclide-yield supervision provided an additional dose-prediction benefit.<i>Significance.</i>Physics-informed decomposition-based modeling improves MC-derived positron-emitter activity-to-dose mapping by combining effective representation learning with auxiliary physical supervision. The proposed framework improves dose prediction while incorporating physically meaningful priors into the learning process, offering a promising basis for PET-based dose-monitoring model development in CIRT.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148796962","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":"Diagnosis of clinically significant prostate cancer in multiparametric MRI with pseudo-localization of suspected lesion.","authors":"Xijun Liu, Rongzong Liu, Xin Zhou, Yifei Yan, Haihao He, Quan Zhou, Limin Zhang, Qi Zhang","doi":"10.1088/1361-6560/ae9e7f","DOIUrl":"10.1088/1361-6560/ae9e7f","url":null,"abstract":"<p><p><i>Objective.</i>To develop a two-stage diagnostic framework using pseudo-localization for patient-level diagnosis of clinically significant prostate cancer (csPCa) on multiparametric magnetic resonance imaging (mpMRI), enabling better utilization of examinations without manual lesion annotations.<i>Approach.</i>We included 1494 mpMRI examinations from 'the Prostate Imaging: Cancer AI' (PI-CAI) dataset, partitioned at the patient level into 217 annotated csPCa, 202 unannotated csPCa, and 1075 non-csPCa cases. First, a prostate transformer U-Net (PTUnet) was trained on the 217 annotated cases via five-fold cross-validation to generate pseudo-locations for the 202 unannotated csPCa cases. These pseudo-locations, along with expert lesion annotations and non-csPCa labels, were used to train an anisotropic UX-Net (AUX-Net). The probability-ushered lesion selection (PULSE) procedure derived patient-level diagnostic probabilities. We evaluated PTUnet, AUX-Net, PULSE, and the pseudo-localization strategy against representative models and baselines, and analyzed the link between pseudo-localization quality and diagnostic performance.<i>Main Results.</i>PTUnet achieved a dice similarity coefficient (DSC) of 64.96 ± 4.22% and an average precision of 65.69 ± 5.45%, the best overall pseudo-localization performance among evaluated models. All four diagnostic models showed higher area under the curves (AUC) with pseudo-locations. AUX-Net with PULSE achieved an AUC of 83.11%, sensitivity of 82.35%, specificity of 75.75%, and Youden's index of 58.10%, compared with the 80.96% AUC of AUX-Net without pseudo-locations. Pseudo-locations from nine different models all improved diagnostic AUC over the no-pseudo-location baseline, indicating the benefit is not model-specific. Furthermore, localization DSC was significantly and positively correlated with diagnostic AUC (Spearman's<i>ρ</i>= 0.800,<i>p</i>= 0.0096), suggesting higher-quality pseudo-localization leads to better diagnosis.<i>Significance.</i>The framework allows csPCa examinations without manual annotations to contribute to diagnostic model training. Results show pseudo-localization improves patient-level diagnosis across different architectures, and its quality is positively associated with downstream performance. The framework shows potential for non-invasive AI-assisted csPCa diagnosis and biopsy triage, pending further validation on independent larger-scale datasets and prospective clinical cohorts.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819265","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}