Pub Date : 2026-04-01Epub Date: 2025-12-29DOI: 10.1016/j.mri.2025.110608
Yu Weng , Jufeng Zhao , Christakis Damianou , Kun Luo , Guangmang Cui
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.
{"title":"NLMap-ATVR: A novel combination of nonlinear mapping network and adaptive total variation regularization for MRI denoising","authors":"Yu Weng , Jufeng Zhao , Christakis Damianou , Kun Luo , 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}
Pub Date : 2026-04-01Epub Date: 2025-12-11DOI: 10.1016/j.mri.2025.110593
Di Wu, Zhaobing Tang
Background
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.
Methods
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.
Results
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 p < 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.
Conclusion
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.
{"title":"Application value of prostate-specific antigen density combined with multiparametric MRI in early diagnosis of prostate cancer","authors":"Di Wu, 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> < 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}
Pub Date : 2026-04-01Epub Date: 2025-12-13DOI: 10.1016/j.mri.2025.110595
Bin Wang , Yusheng Lian , Wan Zhang , Zilong Liu , Xiaojie Hu , Beiqing Huang , Yuanyuan Wang
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.
{"title":"A dense recurrent unrolling network leveraging spatio-temporal priors for highly-accelerated dynamic MRI","authors":"Bin Wang , Yusheng Lian , Wan Zhang , Zilong Liu , Xiaojie Hu , Beiqing Huang , 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}
Pub Date : 2026-04-01Epub Date: 2025-12-11DOI: 10.1016/j.mri.2025.110592
Neil Abraham Barnes , Winniecia Dkhar , Rajagopal Kadavigere , Suresh Sukumar , K. Vaishali , Abhimanyu Pradhan , P.S. Priya , Ashwin Prabhu , Nikhil Raj
Background
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.
Objective
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.
Methods
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.
Results
MK, AK, and RK were significantly higher in the degenerative group (p < 0.001), while MD, AD, and RD were substantially lower (p < 0.001). Correlations were strongest in the mid-to-lower lumbar levels, particularly within the nucleus pulposus and anterior annulus.
Conclusion
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.
{"title":"Quantitative assessment of early intervertebral disc degeneration with MR diffusion kurtosis imaging: A radiologic correlation with Pfirrmann grade","authors":"Neil Abraham Barnes , Winniecia Dkhar , Rajagopal Kadavigere , Suresh Sukumar , K. Vaishali , Abhimanyu Pradhan , P.S. Priya , Ashwin Prabhu , 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> < 0.001), while MD, AD, and RD were substantially lower (p < 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}
Pub Date : 2026-03-21DOI: 10.1016/j.mri.2026.110669
Yupeng Wu, Siyuan Fang, Siyuan Wang, Caixia Fu, Jianqi Li
Purpose: To develop and validate a framework for rapid, accurate, and repeatable whole-brain, multi-pool chemical exchange saturation transfer (CEST) imaging at 3 T, addressing challenges of long acquisition times and confounding factors.
Methods: A single-shot 3D true fast imaging with steady-state precession (True FISP) sequence was optimized for whole-brain multi-pool CEST. Rapid B0, B1, and T1 mapping was performed using a dual-echo modified four-angle method. A feed-forward neural network was developed for rapid B1 correction, trained against the conventional multi-power method. The apparent exchange-dependent relaxation (AREX) metric was used to correct for T1 and magnetization transfer (MT) effects. The framework was validated in phantoms and 50 healthy subjects, including a different-day test-retest repeatability assessment.
Results: The True FISP sequence yielded high-quality, whole-brain images with minimal artifacts and distortion in a clinically feasible scan time (~9 min). Phantom studies confirmed the effectiveness of B1 correction (coefficient of variation [CV] for the magnetization transfer ratio based on Lorentzian difference (MTRLD) of the MT pool decreased from 22.49% to 4.61%) and AREX-based confounder correction (CV for APT_AREX reduced from 33.6% to 6.9%). The neural network B1 correction showed excellent agreement with the conventional multi-power method in vivo (ICC > 0.95). High different-day test-retest repeatability was demonstrated across 96 brain regions, with the average CV for APT_AREX under 10% for 95 of 96 analyzed regions.
Conclusion: A rapid and robust framework for whole-brain quantitative multi-pool CEST imaging was successfully developed and validated. By integrating an efficient acquisition sequence with a streamlined correction pipeline, this approach overcomes key barriers to clinical translation, enabling reliable metabolic imaging for widespread brain pathologies.
{"title":"A framework for rapid, repeatable, and high-fidelity whole-brain multi-pool CEST imaging at 3 T.","authors":"Yupeng Wu, Siyuan Fang, Siyuan Wang, Caixia Fu, Jianqi Li","doi":"10.1016/j.mri.2026.110669","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110669","url":null,"abstract":"<p><strong>Purpose: </strong>To develop and validate a framework for rapid, accurate, and repeatable whole-brain, multi-pool chemical exchange saturation transfer (CEST) imaging at 3 T, addressing challenges of long acquisition times and confounding factors.</p><p><strong>Methods: </strong>A single-shot 3D true fast imaging with steady-state precession (True FISP) sequence was optimized for whole-brain multi-pool CEST. Rapid B<sub>0</sub>, B<sub>1</sub>, and T<sub>1</sub> mapping was performed using a dual-echo modified four-angle method. A feed-forward neural network was developed for rapid B<sub>1</sub> correction, trained against the conventional multi-power method. The apparent exchange-dependent relaxation (AREX) metric was used to correct for T<sub>1</sub> and magnetization transfer (MT) effects. The framework was validated in phantoms and 50 healthy subjects, including a different-day test-retest repeatability assessment.</p><p><strong>Results: </strong>The True FISP sequence yielded high-quality, whole-brain images with minimal artifacts and distortion in a clinically feasible scan time (~9 min). Phantom studies confirmed the effectiveness of B<sub>1</sub> correction (coefficient of variation [CV] for the magnetization transfer ratio based on Lorentzian difference (MTR<sub>LD</sub>) of the MT pool decreased from 22.49% to 4.61%) and AREX-based confounder correction (CV for APT_AREX reduced from 33.6% to 6.9%). The neural network B<sub>1</sub> correction showed excellent agreement with the conventional multi-power method in vivo (ICC > 0.95). High different-day test-retest repeatability was demonstrated across 96 brain regions, with the average CV for APT_AREX under 10% for 95 of 96 analyzed regions.</p><p><strong>Conclusion: </strong>A rapid and robust framework for whole-brain quantitative multi-pool CEST imaging was successfully developed and validated. By integrating an efficient acquisition sequence with a streamlined correction pipeline, this approach overcomes key barriers to clinical translation, enabling reliable metabolic imaging for widespread brain pathologies.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110669"},"PeriodicalIF":2.0,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147504238","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}
Pub Date : 2026-03-20DOI: 10.1016/j.mri.2026.110668
Ozra Dehkordi, Stephen Lin, Safia Mohamud, Martha Davila-Garcia, Richard M Millis, Paul C Wang
Background: The rostral ventromedial medulla (RVM) is a brainstem structure that integrates descending modulatory signaling and contains neurons highly responsive to opioid receptor activation. Despite the well-established effects of opioids in the RVM, the neurochemical adaptations following sustained morphine exposure remain poorly understood. In particular, the contribution of G-protein-coupled inwardly rectifying potassium type 2 (GIRK2) channels, key mediators of opioid receptor-dependent antinociception has not been fully characterized. We hypothesized that GIRK2 channels are essential for morphine-induced metabolic alterations in the RVM.
Methods: In vivo proton nuclear magnetic resonance spectroscopy (1H NMR) was used to examine metabolite responses to prolonged morphine exposure. Metabolite profiles were compared between wild-type and GIRK2 heterozygous mutant (GIRK2+/-) mice before and after four days of subcutaneous implantation with placebo or morphine pellets.
Results: In wild-type mice, morphine exposure significantly increased levels of phosphocreatine, total creatine, glutamine, glutathione, taurine, and glycerophosphocholine plus phosphocholine (GPC + PCh), while decreasing N-acetylaspartate (NAA). These changes suggest enhanced energy storage, activation of antioxidant pathways, increased membrane turnover, and alterations in neuronal integrity and excitatory neurotransmission. In contrast, GIRK2+/- mice exhibited attenuated or opposite responses to morphine, characterized by elevated glutamate and reductions in glutamine, GPC + PCh, and total creatine, with no change in NAA. These differential responses indicate that GIRK2 channels influence neurochemical adaptations to morphine in the RVM.
Conclusion: These findings identify the GIRK2 channel as an important modulator of morphine-induced metabolic changes in the RVM. The observed neurochemical alterations likely reflect adaptive responses to sustained opioid exposure.
{"title":"Role of GIRK2 channels in morphine-induced metabolite changes in the rostral ventromedial medulla.","authors":"Ozra Dehkordi, Stephen Lin, Safia Mohamud, Martha Davila-Garcia, Richard M Millis, Paul C Wang","doi":"10.1016/j.mri.2026.110668","DOIUrl":"https://doi.org/10.1016/j.mri.2026.110668","url":null,"abstract":"<p><strong>Background: </strong>The rostral ventromedial medulla (RVM) is a brainstem structure that integrates descending modulatory signaling and contains neurons highly responsive to opioid receptor activation. Despite the well-established effects of opioids in the RVM, the neurochemical adaptations following sustained morphine exposure remain poorly understood. In particular, the contribution of G-protein-coupled inwardly rectifying potassium type 2 (GIRK2) channels, key mediators of opioid receptor-dependent antinociception has not been fully characterized. We hypothesized that GIRK2 channels are essential for morphine-induced metabolic alterations in the RVM.</p><p><strong>Methods: </strong>In vivo proton nuclear magnetic resonance spectroscopy (<sup>1</sup>H NMR) was used to examine metabolite responses to prolonged morphine exposure. Metabolite profiles were compared between wild-type and GIRK2 heterozygous mutant (GIRK2<sup>+</sup>/<sup>-</sup>) mice before and after four days of subcutaneous implantation with placebo or morphine pellets.</p><p><strong>Results: </strong>In wild-type mice, morphine exposure significantly increased levels of phosphocreatine, total creatine, glutamine, glutathione, taurine, and glycerophosphocholine plus phosphocholine (GPC + PCh), while decreasing N-acetylaspartate (NAA). These changes suggest enhanced energy storage, activation of antioxidant pathways, increased membrane turnover, and alterations in neuronal integrity and excitatory neurotransmission. In contrast, GIRK2<sup>+</sup>/<sup>-</sup> mice exhibited attenuated or opposite responses to morphine, characterized by elevated glutamate and reductions in glutamine, GPC + PCh, and total creatine, with no change in NAA. These differential responses indicate that GIRK2 channels influence neurochemical adaptations to morphine in the RVM.</p><p><strong>Conclusion: </strong>These findings identify the GIRK2 channel as an important modulator of morphine-induced metabolic changes in the RVM. The observed neurochemical alterations likely reflect adaptive responses to sustained opioid exposure.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110668"},"PeriodicalIF":2.0,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147499382","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}
Purpose: To investigate the diagnostic value of the 3D multi-echo Dixon (ME-DIXON) and High-Speed T2-Corrected Multiecho Acquisition proton magnetic resonance spectroscopy (1H MRS, HISTO) sequences in MRI for lumbar spine osteoporosis.
Methods: This study enrolled 138 eligible participants (aged 41-70 years), all of whom underwent both lumbar spine MRI and quantitative computed tomography (QCT). Demographic data, proton density fat fraction (PDFF) values from L1 to L4 vertebrae, and QCT-derived bone mineral density (BMD) were systematically collected. Participants were stratified into three groups based on BMD: osteoporosis, osteopenia, and normal. Differences in vertebral PDFF parameters among these three BMD cohorts were compared using one-way analysis of variance (ANOVA). Receiver operating characteristic (ROC) analysis and correlation analysis were performed to evaluate diagnostic performance and assess the correlation between PDFF and BMD, respectively.
Results: The mean lumbar ME-DIXON-PDFF and HISTO-PDFF values showed negative correlations with BMD (r = -0.68, r = -0.56), age-adjusted(rp = -0.51, rp = -0.33) (all p < 0.05); Both ME-DIXON-PDFF (AUC = 0.833) and HISTO-PDFF (AUC = 0.819) demonstrated good diagnostic performance for osteoporosis. Performance improved after age adjustment (AUC = 0.868 for both models), with no significant difference between the two sequences (all p > 0.05).
Conclusion: This study confirms a strong inverse correlation between MRI-derived PDFF and QCT-based BMD, and demonstrates diagnostic equivalence between the fully automated HISTO sequence and the established ME-DIXON sequence for osteoporosis identification. These findings support PDFF as a promising imaging biomarker for bone density assessment and validate the technical feasibility of automated spinal fat quantification.
{"title":"Evaluation of primary lumbar vertebral osteoporosis by MRI fat quantification technique.","authors":"Chumin Huang, Wenzhao Yuan, Chenhui Li, Zide Zhang, Zisan Zeng","doi":"10.1016/j.mri.2026.110667","DOIUrl":"10.1016/j.mri.2026.110667","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the diagnostic value of the 3D multi-echo Dixon (ME-DIXON) and High-Speed T2-Corrected Multiecho Acquisition proton magnetic resonance spectroscopy (1H MRS, HISTO) sequences in MRI for lumbar spine osteoporosis.</p><p><strong>Methods: </strong>This study enrolled 138 eligible participants (aged 41-70 years), all of whom underwent both lumbar spine MRI and quantitative computed tomography (QCT). Demographic data, proton density fat fraction (PDFF) values from L1 to L4 vertebrae, and QCT-derived bone mineral density (BMD) were systematically collected. Participants were stratified into three groups based on BMD: osteoporosis, osteopenia, and normal. Differences in vertebral PDFF parameters among these three BMD cohorts were compared using one-way analysis of variance (ANOVA). Receiver operating characteristic (ROC) analysis and correlation analysis were performed to evaluate diagnostic performance and assess the correlation between PDFF and BMD, respectively.</p><p><strong>Results: </strong>The mean lumbar ME-DIXON-PDFF and HISTO-PDFF values showed negative correlations with BMD (r = -0.68, r = -0.56), age-adjusted(r<sub>p</sub> = -0.51, r<sub>p</sub> = -0.33) (all p < 0.05); Both ME-DIXON-PDFF (AUC = 0.833) and HISTO-PDFF (AUC = 0.819) demonstrated good diagnostic performance for osteoporosis. Performance improved after age adjustment (AUC = 0.868 for both models), with no significant difference between the two sequences (all p > 0.05).</p><p><strong>Conclusion: </strong>This study confirms a strong inverse correlation between MRI-derived PDFF and QCT-based BMD, and demonstrates diagnostic equivalence between the fully automated HISTO sequence and the established ME-DIXON sequence for osteoporosis identification. These findings support PDFF as a promising imaging biomarker for bone density assessment and validate the technical feasibility of automated spinal fat quantification.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110667"},"PeriodicalIF":2.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147481369","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}
Pub Date : 2026-02-01Epub Date: 2025-11-12DOI: 10.1016/j.mri.2025.110562
Dandan Sun , Kuntao Chen , Di Liang , Yuan Gui , Jing Zhang
Objective
Exploring the diagnostic value of the contrast-enhanced delayed enhancement-T2-fluid attenuation inversion recovery sequence (CE-T2-FLAIR) in traumatic brain injury (TBI).
Methods
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 t-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.
Results
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 (p < 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 (p < 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 (p < 0.05). The meningeal EI of delayed CE-T2-FLAIR was positively correlated with transient loss of consciousness and subdural hemorrhage (p < 0.05), while there was no correlation between the meningeal NSI of delayed CE-T2-FLAIR and transient loss of consciousness or subdural hemorrhage (p > 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 (p > 0.05).
Conclusion
Compared with MRI plain scan and CE-T1WI, delayed CE-T2-FLAIR has more advantages in TBI.
{"title":"Application value of delayed contrast enhancement-T2-fluid-attenuated inversion recovery in traumatic brain injury","authors":"Dandan Sun , Kuntao Chen , Di Liang , Yuan Gui , 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> < 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> < 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> < 0.05). The meningeal EI of delayed CE-T2-FLAIR was positively correlated with transient loss of consciousness and subdural hemorrhage (<em>p</em> < 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> > 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> > 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}
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.
Materials and methods
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.
Results
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, p = 0.0003). DCE+ lesions showed significantly higher PBF than DCE– ones (39.17 vs. 19.04 ml/100 g/min; p = 0.0001), as well as a significantly higher PBF ratio (2.6 vs. 1.09; p < 0.0001). On ROC analysis, PBF ratio showed an AUC of 0.97 (95 % CI: 0.87–0.99; p < 0.0001) in discriminating between DCE+ and DCE− lesions; a cut-off value of >1.66 provided 86 % sensitivity and 100 % specificity in identifying DCE+ lesions.
Conclusions
ASL may serve as a safe, contrast-free alternative for evaluating vascularization of peripheral prostatic zone lesions.
目的探讨前列腺周围区组织学特征不明显病变患者asl灌注参数与DCE增强模式的相关性。材料与方法前瞻性研究：获得知情同意。我们纳入52例外周区病变≥4mm， PI-RADS≥3的患者。所有患者都接受了pi - rads推荐的多参数MRI和ASL采集，使用流量敏感交替反转恢复（FAIR）和真正的快速成像与稳态进动（True- fisp）脉冲序列在3 t时进行DCE图像的早期病灶增强（DCE+）评估。两名放射科医生独立测量病变和邻近正常PZ组织的前列腺血流量（PBF）；取平均值作进一步分析。计算PBF比（病变/健康组织）。统计分析采用Mann-Whitney U检验、Wilcoxon检验和ROC曲线分析。结果共纳入52个中位直径为8 mm （IQR: 6 ~ 12 mm）的病变。在DCE中，31/52（60%）为DCE+， 21/52（40%）为DCE−。观察者间对PBF测量的一致性非常好（ICC 0.980-0.994）。靶病变的中位PBF显著高于健康组织（28.03 vs. 15.76 ml/100 g/min, p = 0.0003）。DCE+病变PBF显著高于DCE -病变（39.17 vs. 19.04 ml/100 g/min, p = 0.0001）， PBF比值显著高于DCE -病变（2.6 vs. 1.09, p < 0.0001）。在ROC分析中，PBF比值显示区分DCE+和DCE -病变的AUC为0.97 (95% CI: 0.87-0.99; p < 0.0001)；截断值>；1.66为鉴别DCE+病变提供了86%的敏感性和100%的特异性。结论sasl可作为一种安全、无对比的评价前列腺周围区病变血管化的方法。
{"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 , Vincenzo Vingiani , Bernardo Proner , Petros Martirosian , Armin Pycha , Emanuela Trenti , Alessandro Lanaro , Giovanna Nordio , Riccardo Valletta , 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> < 0.0001). On ROC analysis, PBF ratio showed an AUC of 0.97 (95 % CI: 0.87–0.99; p < 0.0001) in discriminating between DCE+ and DCE− lesions; a cut-off value of >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}
Pub Date : 2026-02-01Epub Date: 2025-11-17DOI: 10.1016/j.mri.2025.110573
Jiaying Zhang , Xiangwei Kong , Xi Lin , Yanbin Li , Jeff L. Zhang , Xiaopeng Zong
The multi-TI flow-sensitive alternating inversion recovery sequence is a common ASL technique for probing renal perfusion. However, traditional method for quantifying perfusion, bolus arrival time (BAT) and bolus length (BL) from the images faces challenges due to low signal-to-noise ratio, large vessel contamination, and the absence of magnetization direction information in magnitude images. We proposed a BiLSTM-based deep learning (DL) approach for quantifying perfusion, BAT, and BL, and excluding large vessels. The network was trained on simulated pixel-wise multi-TI signals and tested using simulated and in vivo data. For comparison, the traditional quantification based on Buxton's model fitting was carried out, and manual cortex, medulla, and large vessel masks were drawn on fully relaxed magnitude images. For in vivo data, the quantification results from averages over all repetitions served as reference. In simulation, the DL approach had smaller quantification errors for perfusion and BAT but larger errors for BL than the traditional method. All in vivo parameters derived from the traditional method deviated more from references as number of averages decreased than those derived from DL. The DL masks excluded more high-perfusion pixels than the manual masks. Significant differences between the traditional and DL methods' quantification of in vivo perfusion, BAT, and BL cannot be explained by their differences observed in simulation, suggesting differences between simulated and in vivo data characteristics. The proposed network may serve as a useful tool for quantification in ASL, which is more accurate and more robust against noise than the traditional method.
{"title":"Deep learning-based perfusion quantification and large vessel exclusion for renal multi-TI arterial spin labelling MRI","authors":"Jiaying Zhang , Xiangwei Kong , Xi Lin , Yanbin Li , Jeff L. Zhang , Xiaopeng Zong","doi":"10.1016/j.mri.2025.110573","DOIUrl":"10.1016/j.mri.2025.110573","url":null,"abstract":"<div><div>The multi-TI flow-sensitive alternating inversion recovery sequence is a common ASL technique for probing renal perfusion. However, traditional method for quantifying perfusion, bolus arrival time (BAT) and bolus length (BL) from the images faces challenges due to low signal-to-noise ratio, large vessel contamination, and the absence of magnetization direction information in magnitude images. We proposed a BiLSTM-based deep learning (DL) approach for quantifying perfusion, BAT, and BL, and excluding large vessels. The network was trained on simulated pixel-wise multi-TI signals and tested using simulated and <em>in vivo</em> data. For comparison, the traditional quantification based on Buxton's model fitting was carried out, and manual cortex, medulla, and large vessel masks were drawn on fully relaxed magnitude images. For <em>in vivo</em> data, the quantification results from averages over all repetitions served as reference. In simulation, the DL approach had smaller quantification errors for perfusion and BAT but larger errors for BL than the traditional method. All <em>in vivo</em> parameters derived from the traditional method deviated more from references as number of averages decreased than those derived from DL. The DL masks excluded more high-perfusion pixels than the manual masks. Significant differences between the traditional and DL methods' quantification of <em>in vivo</em> perfusion, BAT, and BL cannot be explained by their differences observed in simulation, suggesting differences between simulated and <em>in vivo</em> data characteristics. The proposed network may serve as a useful tool for quantification in ASL, which is more accurate and more robust against noise than the traditional method.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"126 ","pages":"Article 110573"},"PeriodicalIF":2.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145557172","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}
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