Magnetic resonance imaging最新文献

{"title":"Time-dependent diffusion-weighted MRI discriminates hepatocellular carcinoma from intrahepatic cholangiocarcinoma: A prospective animal model study","authors":"Yong-Mei Huang ,&nbsp;Yu-Chen Wei ,&nbsp;Xing-Qing Qin ,&nbsp;Meng-Na Lan ,&nbsp;Jin-Yuan Liao","doi":"10.1016/j.mri.2025.110601","DOIUrl":"10.1016/j.mri.2025.110601","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the diagnostic potential of microstructural parameters derived from time-dependent diffusion magnetic resonance imaging (Td-dMRI) for distinguishing hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC).</div></div><div><h3>Methods</h3><div>We established nude mouse models bearing subcutaneous xenografts of HCC (MHCC97H, HepG2 cell lines) and ICC (QBC939 cell line) (<em>n</em> = 30). All models underwent Td-dMRI scanning. Microstructural parameters, including cell diameter (<em>d</em>), extracellular diffusion coefficient (<em>D</em>_ex), intracellular volume fraction (<em>V</em>_in), and cellularity, were calculated based on the IMPULSED model. Intergroup differences were assessed using independent samples <em>t</em>-test or Mann-Whitney <em>U</em> test (significance threshold: <em>P</em> &lt; 0.05). The diagnostic performance of each parameter was evaluated by receiver operating characteristic (ROC) curve analysis. Post-operative liver tissue specimens were subjected to <em>β</em>-catenin immunohistochemical staining to validate the correlation between imaging parameters and pathological findings.</div></div><div><h3>Results</h3><div>The ICC group exhibited significantly higher <em>D</em>_ex values compared to the HCC group (<em>P</em> &lt; 0.05), whereas <em>d</em>, <em>V</em>_in, and cellularity were significantly lower in the ICC group (<em>P</em> &lt; 0.05). The areas under the ROC curve (AUCs) for differentiating HCC from ICC were 0.838 for <em>D</em>_ex, 0.779 for <em>d</em>, 0.833 for <em>V</em>_in, and 0.733 for cellularity. The <em>d</em> value measured by Td-dMRI showed a significant positive correlation with pathological results (<em>r</em> = 0.634, <em>P</em> &lt; 0.05). Notably, combining <em>V</em>_in and cellularity parameters enhanced the AUC to 0.95, outperforming any single parameter.</div><div>The ICC group exhibited a significantly higher extracellular diffusivity (<em>D</em>_ex) compared to the HCC group, whereas the cell diameter (<em>d</em>), intracellular volume fraction (<em>V</em>_in), and cellularity were significantly lower (all <em>P</em> &lt; 0.05). The area under the.</div></div><div><h3>Conclusion</h3><div>Td-dMRI enables non-invasive differentiation between HCC and ICC by quantifying distinct tumor microstructural environments. The parameters derived from this technique show promise as potential imaging biomarkers for subtyping liver cancers.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110601"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145857030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional MRI quantitative parameters as biomarkers of proliferation in synovial sarcoma xenografts: A study based on precise MR imaging-pathology correlation","authors":"Chengjiang Xu ,&nbsp;Yasmin Mushtaq ,&nbsp;Xiaoge Liu ,&nbsp;Guijiao Qin ,&nbsp;Juan Tao ,&nbsp;Yajie Liu ,&nbsp;Jinge Li ,&nbsp;Xinyu Yang ,&nbsp;Shaowu Wang","doi":"10.1016/j.mri.2025.110594","DOIUrl":"10.1016/j.mri.2025.110594","url":null,"abstract":"<div><h3>Purpose</h3><div>To determine whether quantitative parameters from diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI) are associated with proliferative activity in synovial sarcoma (SS) xenografts.</div></div><div><h3>Materials and methods</h3><div>Thirty-four synovial sarcoma (SS) xenograft models were established. All mice underwent MRI, including DWI (ADC), IVIM (D, D*, f), and DCE-MRI (K^trans, K_ep, V_e). An MR imaging–pathology correlation method was used to align imaging ROIs with pathological sampling sites.</div><div>Pearson or Spearman correlation analyses were performed to assess associations between MRI parameters and proliferation-related markers (mitotic count, Ki-67, and PTEN). Differences between high and low groups for each marker were evaluated using independent-sample <em>t</em>-tests or Wilcoxon rank-sum tests. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis, and AUCs were compared with DeLong's test.</div></div><div><h3>Results</h3><div>The D value was significantly associated with mitotic count, Ki-67 expression, and PTEN expression (<em>P</em> = 0.022, 0.004, 0.001). V_e showed a positive association with mitotic count (<em>P</em> = 0.007), while K^trans demonstrated a moderate negative association with PTEN expression (<em>P</em> &lt; 0.001). D and V_e showed moderate ability to distinguish high from low mitotic count (AUC = 0.734 and 0.786). The D value showed moderate differentiation between Ki-67 expression groups (AUC = 0.802), and K^trans provided moderate discrimination between PTEN expression groups (AUC = 0.813).</div></div><div><h3>Conclusion</h3><div>The proliferative activity of SS xenografts could be assessed using quantitative parameters derived from IVIM and DCE-MRI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110594"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145768561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive regularization weight selection for compressed sensing MRI reconstruction","authors":"Yuan Lian,&nbsp;Yuancheng Jiang,&nbsp;Hua Guo","doi":"10.1016/j.mri.2025.110579","DOIUrl":"10.1016/j.mri.2025.110579","url":null,"abstract":"<div><h3>Purpose</h3><div>Proper regularization weights are crucial for the reconstruction quality of compressed sensing (CS) MRI. This work aims to develop an automatic and adaptive regularization weights selection method for CS reconstruction</div></div><div><h3>Methods</h3><div>A statistical model based on Bayesian theory is designed, incorporating prior information about the Gaussian distribution of incoherent noise and the Laplacian distribution of wavelet coefficients in the wavelet transform domain. Using the variance of coefficients and noise, the adaptive regularization weight for achieving optimal reconstruction quality in each iteration step is obtained through a maximum a posteriori estimator. The adaptive regularization weights vary across different subjects, slices, iterations, and wavelet sub-bands</div></div><div><h3>Results</h3><div>The efficacy of the proposed method was demonstrated through retrospective and prospective studies. Compared to reconstruction results using optimal fixed regularization weights and sparsity-adaptive composite recovery method (SCoRe), the proposed method successfully reduces reconstruction errors and effectively recovers original signals from noise-like incoherent artifacts in the wavelet transform domain. It also saves weight selection time when searching for optimal fixed regularization weights</div></div><div><h3>Conclusion</h3><div>We propose an adaptive regularization weights selection method for CS-MRI reconstruction. It provides optimal regularization weights for different subjects, slices, and iterations without requiring manual intervention</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110579"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145668927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of functional neural networks of human brains with fMRI","authors":"Jie Huang","doi":"10.1016/j.mri.2025.110600","DOIUrl":"10.1016/j.mri.2025.110600","url":null,"abstract":"<div><div>The highly evolved human brain comprises numerous functional systems, ranging from essential sensory, motor, attention and memory networks to higher-order cognitive functions like reasoning and language. These neural systems and cognitive functions are functionally integrated together to perform a task. BOLD-fMRI measures the neural activity across the entire brain at large-scale systems level. We define a functional neural network (FNN) as a network in which the temporal variation of the neural activity is the same across the entire network, i.e., all voxels in the network share the same BOLD time signal. We report a novel data-driven method to objectively and automatically identify FNNs across the entire brain for both brain states measured with resting-state and task-fMRI, respectively. The identified FNNs demonstrate dominating bilateral and symmetric characteristics for both brain states at both individual and group levels. These FNNs and the quantification of the interaction between them characterize the whole-brain activity holistically for each brain state and each individual subject.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110600"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145810444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application value of prostate-specific antigen density combined with multiparametric MRI in early diagnosis of prostate cancer","authors":"Di Wu,&nbsp;Zhaobing Tang","doi":"10.1016/j.mri.2025.110593","DOIUrl":"10.1016/j.mri.2025.110593","url":null,"abstract":"<div><h3>Background</h3><div>Diagnosis of prostate cancer in the PSA gray zone (4–10 ng/mL) and PI-RADS 3 cases remains challenging. Although multiparametric MRI (mpMRI) is widely used, its diagnostic accuracy is limited by inter-reader variability and the lack of integration with clinical indicators. Prostate-specific antigen density (PSAD) is a valuable risk stratifier, but its optimal combination with mpMRI remains unclear.</div></div><div><h3>Methods</h3><div>We developed a deep-learning model that integrates PSAD with mpMRI—including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps derived from DWI. A cross-modal attention-guided (CM-AG) fusion module weights the PSAD and mpMRI feature branches. Performance was assessed in the PSA gray zone and the PI-RADS 3 subgroup. Ablation experiments quantified the incremental contributions of PSAD and CM-AG.</div></div><div><h3>Results</h3><div>The model achieved AUC = 0.89 in the PSA gray-zone cohort and AUC = 0.83 in PI-RADS 3, outperforming single-modality MRI baselines and PI-RADS–based assessment alone (DeLong <em>p</em> &lt; 0.01). In patients with larger prostate volumes, specificity increased by 10.2 %. Ablation results confirmed that both PSAD and CM-AG contributed materially to performance gains.</div></div><div><h3>Conclusion</h3><div>Fusing PSAD with mpMRI via cross-modal attention improves diagnostic performance, particularly in challenging subgroups (PSA gray zone, PI-RADS 3). This approach may support more consistent risk stratification and earlier detection.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110593"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145751972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dense recurrent unrolling network leveraging spatio-temporal priors for highly-accelerated dynamic MRI","authors":"Bin Wang ,&nbsp;Yusheng Lian ,&nbsp;Wan Zhang ,&nbsp;Zilong Liu ,&nbsp;Xiaojie Hu ,&nbsp;Beiqing Huang ,&nbsp;Yuanyuan Wang","doi":"10.1016/j.mri.2025.110595","DOIUrl":"10.1016/j.mri.2025.110595","url":null,"abstract":"<div><div>Dynamic magnetic resonance imaging (MRI) requires accurate reconstruction from undersampled k-space data to achieve high temporal resolution within clinically acceptable scan times. Deep unrolling architectures have recently emerged as effective solutions by integrating physics-based data consistency with learned priors. However, their ability to exploit temporal relationships remains limited, as many approaches rely on independent stage-wise processing with only final-stage outputs propagated across iterations, which restricts feature interaction and often leads to performance degradation when acceleration factors increase. To enhance temporal prior learning, we introduce a bidirectional recurrent convolutional unit within the sparse prior update module. Our approach strengthens temporal dependency modeling by recurrently aggregating contextual information from both past and future frames, thereby improving stability and representation capacity under highly undersampled conditions. Furthermore, we incorporate inter-stage feature transmission that forwards intermediate representations instead of only single-stage outputs. This design substantially improves multi-stage collaboration, enabling more effective refinement across iterations. Experimental results on accelerated dynamic MRI datasets (6×, 12×, and 24×) demonstrate that the proposed method consistently outperforms state-of-the-art unrolling and deep learning strategies in reconstruction accuracy and temporal fidelity. Ablation studies further validate the contributions of recurrent temporal learning and inter-stage feature transmission.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110595"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145763081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NLMap-ATVR: A novel combination of nonlinear mapping network and adaptive total variation regularization for MRI denoising","authors":"Yu Weng ,&nbsp;Jufeng Zhao ,&nbsp;Christakis Damianou ,&nbsp;Kun Luo ,&nbsp;Guangmang Cui","doi":"10.1016/j.mri.2025.110608","DOIUrl":"10.1016/j.mri.2025.110608","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) is an advanced imaging technique that is used to aid in medical diagnosis. However, common noises such as Gaussian and Rician noise can blur details and structures, affect contrast and reduce signal-to-noise ratio (SNR), so MRI denoising technique becomes an critical step to get noise-free MRI images. Traditional methods still have limitations in effectively balancing noise removal and the preservation of image details and structural information. To address the challenge, this paper proposes an MRI image denoising model that combines Nonlinear Mapping Network (NLMap) and Attention Mechanism-guided Adaptive Total Variation Regularization (ATVR). The model includes a NLMap-ATVR network, a crafted joint loss function and a Bayesian optimization framework. Firstly, the network uses an encoder-decoder architecture, combined with ATVR to ensure noise removal. Secondly, the joint loss function includes mean square error (MSE) loss, perceptual loss and ATVR loss, which are used to consider pixel-level and feature-level spatial structural errors to preserve details and structures. Thirdly, a Bayesian optimization framework is applied to automatically tune the hyperparameters to obtain optimal parameters. Compared with State-of-the-art methods, both subjective and objective evaluations based on experimental results demonstrate that the proposed method not only effectively removes noise but also significantly preserves details and structural information, which greatly improves SNR.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110608"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative assessment of early intervertebral disc degeneration with MR diffusion kurtosis imaging: A radiologic correlation with Pfirrmann grade","authors":"Neil Abraham Barnes ,&nbsp;Winniecia Dkhar ,&nbsp;Rajagopal Kadavigere ,&nbsp;Suresh Sukumar ,&nbsp;K. Vaishali ,&nbsp;Abhimanyu Pradhan ,&nbsp;P.S. Priya ,&nbsp;Ashwin Prabhu ,&nbsp;Nikhil Raj","doi":"10.1016/j.mri.2025.110592","DOIUrl":"10.1016/j.mri.2025.110592","url":null,"abstract":"<div><h3>Background</h3><div>Degenerative spinal disease is a leading cause of lower back pain worldwide, impairing mobility and quality of life. Early degeneration is increasingly seen in younger adults due to sedentary lifestyles and occupational stress. Conventional MRI, particularly T2-weighted imaging, is limited in detecting early microstructural changes. Diffusion kurtosis imaging (DKI), by quantifying non-Gaussian water diffusion, offers enhanced sensitivity for identifying subtle alterations preceding overt degeneration<strong>.</strong></div></div><div><h3>Objective</h3><div>To evaluate the ability of quantitative DKI parameters to detect early intervertebral disc degeneration by correlating them with the Pfirrmann grading system in the thoracolumbar spine.</div></div><div><h3>Methods</h3><div>This prospective study included 76 participants, 54 with degenerative spine disease and 22 healthy controls. MRI was performed on a 3 T scanner using sagittal T1-, T2-, and DKI sequences. Parameter maps included mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA), and diffusivity indices (MD, AD, RD, KA). Regions of interest were placed in the nucleus pulposus and annulus fibrosus, with subregional analysis of the anterior and posterior annulus. Grade V discs were excluded, yielding (1,287) discs (Grades I–IV). Statistical analyses included group comparisons and Spearman's correlation.</div></div><div><h3>Results</h3><div>MK, AK, and RK were significantly higher in the degenerative group (<em>p</em> &lt; 0.001), while MD, AD, and RD were substantially lower (p &lt; 0.001). Correlations were strongest in the mid-to-lower lumbar levels, particularly within the nucleus pulposus and anterior annulus<strong>.</strong></div></div><div><h3>Conclusion</h3><div>DKI enables quantitative characterisation of disc microstructure and demonstrates diagnostic potential for differentiating degenerative from non-degenerative discs, supporting its role as an emerging imaging biomarker for early degeneration assessment.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110592"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145751912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application value of delayed contrast enhancement-T2-fluid-attenuated inversion recovery in traumatic brain injury","authors":"Dandan Sun ,&nbsp;Kuntao Chen ,&nbsp;Di Liang ,&nbsp;Yuan Gui ,&nbsp;Jing Zhang","doi":"10.1016/j.mri.2025.110562","DOIUrl":"10.1016/j.mri.2025.110562","url":null,"abstract":"<div><h3>Objective</h3><div>Exploring the diagnostic value of the contrast-enhanced delayed enhancement-T2-fluid attenuation inversion recovery sequence (CE-T2-FLAIR) in traumatic brain injury (TBI).</div></div><div><h3>Methods</h3><div>Clinical and Magnetic resonance imaging (MRI) data of 30 patients with brain trauma who met the inclusion criteria were retrospectively collected, and the MRI images were classified into three groups: the MRI plain scan group, the contrast-enhanced T1-weighted imaging (CE-T1WI) group, and the delayed CE-T2-FLAIR group. The chi-square test was employed to analyze the differences among the three groups of images in terms of MRI-detected cerebral contusion and laceration, intracranial hematoma, and the number of positive imaging findings. The chi-square test was also used to analyze the differences among the three groups of images in terms of the presentation of cerebral contusion and laceration, intracranial hematoma, and the number of positive imaging results. The paired-samples <em>t</em>-test was utilized to analyze whether there were statistical differences in the meningeal enhancement scores, meningeal Enhancement Index (EI), and meningeal Normalized Signal Intensity (NSI) between delayed CE-T2-FLAIR and CE-T1WI. Spearman's rank correlation analysis was conducted to explore the correlations between the meningeal enhancement scores, meningeal EI, meningeal NSI from the two enhanced examinations and clinical symptoms, cerebral contusion and laceration, as well as intracranial hematoma.</div></div><div><h3>Results</h3><div>The number of positive imaging findings in the delayed CE-T2-FLAIR group was higher than that in the MRI plain scan group and the CE-T1WI group (<em>p</em> &lt; 0.05). The meningeal enhancement score, meningeal EI, and meningeal NSI in the delayed CE-T2-FLAIR group were higher than those in the CE-T1WI group (<em>p</em> &lt; 0.05). The meningeal enhancement scores of both delayed CE-T2-FLAIR and CE-T1WI were positively correlated with headache, cerebral contusion and laceration, and subdural hemorrhage (<em>p</em> &lt; 0.05). The meningeal EI of delayed CE-T2-FLAIR was positively correlated with transient loss of consciousness and subdural hemorrhage (<em>p</em> &lt; 0.05), while there was no correlation between the meningeal NSI of delayed CE-T2-FLAIR and transient loss of consciousness or subdural hemorrhage (<em>p</em> &gt; 0.05). There was no significant correlation between the meningeal EI, meningeal NSI of CE-T1WI and clinical manifestations, cerebral contusion and laceration, or intracranial hematoma (<em>p</em> &gt; 0.05).</div></div><div><h3>Conclusion</h3><div>Compared with MRI plain scan and CE-T1WI, delayed CE-T2-FLAIR has more advantages in TBI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110562"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145518938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arterial spin labelling as a contrast-free alternative to dynamic contrast enhancement for the evaluation of peripheral zone prostatic lesions on MRI","authors":"Valentina Corato ,&nbsp;Vincenzo Vingiani ,&nbsp;Bernardo Proner ,&nbsp;Petros Martirosian ,&nbsp;Armin Pycha ,&nbsp;Emanuela Trenti ,&nbsp;Alessandro Lanaro ,&nbsp;Giovanna Nordio ,&nbsp;Riccardo Valletta ,&nbsp;Matteo Bonatti","doi":"10.1016/j.mri.2025.110575","DOIUrl":"10.1016/j.mri.2025.110575","url":null,"abstract":"<div><h3>Purpose</h3><div>To assess the correlation between ASL-derived perfusion parameters and enhancement pattern on DCE in patients with histologically uncharacterized lesions of the peripheral prostatic zone.</div></div><div><h3>Materials and methods</h3><div>Prospective study: informed consent was obtained. We included 52 patients with peripheral zone lesions ≥4 mm, PI-RADS ≥3. All patients underwent PI-RADS-recommended multiparametric MRI and ASL acquisition using a Flow-sensitive Alternating Inversion Recovery (FAIR) with True Fast Imaging with Steady-State Precession (True-FISP) pulse sequence at 3 T. DCE images were assessed for early focal enhancement (DCE+). Two radiologists independently measured prostatic blood flow (PBF) in the lesion and adjacent normal PZ tissue; the mean of the values was used for further analysis. The PBF ratio (lesion/healthy tissue) was calculated. Statistical analysis included Mann-Whitney U, Wilcoxon tests, and ROC curve analysis.</div></div><div><h3>Results</h3><div>Fifty-two lesions with median diameter of 8 mm (IQR: 6–12 mm) were included. On DCE, 31/52 (60 %) were DCE+, and 21/52 (40 %) were DCE−. Interobserver agreement for PBF measurements was excellent (ICC 0.980–0.994). Median PBF was significantly higher in target lesions than in healthy tissue (28.03 vs. 15.76 ml/100 g/min, <em>p</em> = 0.0003). DCE+ lesions showed significantly higher PBF than DCE– ones (39.17 vs. 19.04 ml/100 g/min; <em>p</em> = 0.0001), as well as a significantly higher PBF ratio (2.6 vs. 1.09; <em>p</em> &lt; 0.0001). On ROC analysis, PBF ratio showed an AUC of 0.97 (95 % CI: 0.87–0.99; p &lt; 0.0001) in discriminating between DCE+ and DCE− lesions; a cut-off value of &gt;1.66 provided 86 % sensitivity and 100 % specificity in identifying DCE+ lesions.</div></div><div><h3>Conclusions</h3><div>ASL may serve as a safe, contrast-free alternative for evaluating vascularization of peripheral prostatic zone lesions.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110575"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}