Magnetic resonance imaging最新文献

{"title":"Combined model based on multimodal imaging and clinical pathology: A new pathway to assess hypoxia status and HIF-1α expression in breast cancer","authors":"Zhiyuan He ,&nbsp;Yiwen Wang ,&nbsp;Yunxi Li ,&nbsp;Laimin Zhu ,&nbsp;Yueqin Chen ,&nbsp;Ning Mao ,&nbsp;Wenwen Zhao ,&nbsp;Xiuzheng Yue ,&nbsp;Yufei Xue ,&nbsp;Shuzhen Wang ,&nbsp;Weiwei Wang ,&nbsp;Zhanguo Sun","doi":"10.1016/j.mri.2025.110537","DOIUrl":"10.1016/j.mri.2025.110537","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop and validate a combined model integrating multimodal magnetic resonance imaging (MRI) and clinical-pathological parameters to assess hypoxia status and hypoxia-inducible factor-1α (HIF-1α) expression in mass-like breast cancer.</div></div><div><h3>Methods</h3><div>This retrospective cohort study included 197 patients with mass-like breast cancer from two medical centers, who were divided into a training set (<em>n</em> = 104), an internal validation set (<em>n</em> = 45), and an external validation set (<em>n</em> = 48). Clinical-pathological and multimodal MRI parameters were analyzed using histopathology as the reference. The combined model was developed through logistic and least absolute shrinkage and selection operator (LASSO) regression analysis to identify features, and visualized using nomograms.</div></div><div><h3>Results</h3><div>Axillary lymph node (ALN) metastasis, time-intensity curve (TIC) type, mean diffusivity (MD), volumetric transfer constant (K^trans), and rate constant (Kep) were selected to construct the combined model. The diagnostic performance of the combined model [area under the curve (AUC) = 0.967, 95 % CI: 0.913–0.992] (training), was significantly better than that of other individual models (<em>P</em> &lt; 0.001). This performance was replicated in internal (AUC= 0.907) and external (AUC = 0.928) validation sets. The nomogram of the combined model showed excellent calibration (Hosmer-Lemeshow <em>P</em> = 0.653) and the highest net benefit across threshold probabilities in the decision curve analysis (DCA). Tumors with high HIF-1α expression exhibited higher ALN metastasis, histological grade, unclear margins, Type 3 TIC, and elevated K^trans, Kep, and MK, but reduced MD values.</div></div><div><h3>Conclusions</h3><div>The combined model (ALN + TIC + MD + K^trans + Kep) shows potential as a reliable tool for predicting HIF-1α expression levels in mass-like breast cancer.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110537"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145232921","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":"Correction of orientation dependence in magnetization transfer measures in the context of tractometry: Challenges, pitfalls and solutions","authors":"Philippe Karan ,&nbsp;Manon Edde ,&nbsp;Guillaume Gilbert ,&nbsp;Muhamed Barakovic ,&nbsp;Stefano Magon ,&nbsp;Maxime Descoteaux","doi":"10.1016/j.mri.2025.110522","DOIUrl":"10.1016/j.mri.2025.110522","url":null,"abstract":"<div><div>In this work we investigate the feasibility of a correction method for removing the orientation dependence of magnetization transfer (MT) measures in the context of tractometry. Following previous work on the track-based characterization of such orientation dependence using diffusion MRI, a correction method was developed. It uses polynomial fits to extrapolate the single-fiber characterizations and allows the MT measures across all white matter tracks to be shifted towards a chosen reference value, effectively removing the bias of fiber orientation with respect to the main magnetic field. Three different references were tested on a dataset of one hundred acquisitions and the performance was accessed by evaluating the removal of the orientation dependence and the reduction of variance between acquisitions, while also exploring the effects on tractometry results. Throughout these experiments, various challenges and pitfalls of an empirical correction method were laid out, like the absence of ground truth or the lack of knowledge about the complex behavior of the phenomenon in crossing-fiber voxels. Nonetheless, a solution was presented, paving the way towards a fully validated correction method for MT measures.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110522"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145065287","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":"Pure steady-state CEST","authors":"Johannes Hammacher,&nbsp;Christoph Kolbitsch,&nbsp;Patrick Schuenke","doi":"10.1016/j.mri.2025.110506","DOIUrl":"10.1016/j.mri.2025.110506","url":null,"abstract":"<div><h3>Summary:</h3><div>A novel steady-state CEST sequence design, based on the underlying physical model of longitudinal magnetization development during CEST saturation and data acquisition is presented and validated <em>in-silico</em>, <em>in vitro</em> and <em>in vivo</em>. This design ensures consistent data acquisition in the pure CEST steady-state, leading to high MTR_asym scores and image quality, both <em>in vitro</em> and <em>in vivo</em>, when compared to contemporary sequential and steady-state CEST sequences.</div></div><div><h3>Purpose:</h3><div>The aim of this study was to enhance CEST sequences by utilizing the pure CEST steady-state in order to deliver higher CEST effects and better sensitivity.</div></div><div><h3>Methods:</h3><div>A novel CEST saturation/readout scheme was designed, tested in numerical simulations and subsequently validated <em>in vitro</em> and <em>in vivo</em>.</div></div><div><h3>Results:</h3><div>The novel Multi-2D Spiral pure steady-state CEST sequence showed to deliver advantageous sensitivity and efficacy.</div></div><div><h3>Conclusion:</h3><div>Constraining image acquisition to the pure CEST steady-state showed promising results in first <em>in vitro</em> and <em>in vivo</em> experiments.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110506"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993026","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":"Reply to “Unveiling cartilage nanostructures via anisotropic relaxometry of water protons: feasibility or fallacy?”","authors":"Theodore Aptekarev ,&nbsp;Gregory Furman ,&nbsp;Vladimir Sokolovsky ,&nbsp;Yang Xia","doi":"10.1016/j.mri.2025.110523","DOIUrl":"10.1016/j.mri.2025.110523","url":null,"abstract":"<div><div>This Reply addresses the issues raised in the Letter, entitled “Unveiling Cartilage Nanostructures via Anisotropic Relaxometry of Water Protons: Feasibility or Fallacy?” by Y. Pang, K. Momot, and V. Mlynárik (<em>Magnetic Resonance Imaging,</em> 123, 2025, 110508). The authors of the previous letter claimed that our model and theoretical results presented in our paper “Depth-dependent characterization of cartilage nanostructures using MRI signal decays” (<em>Magnetic Resonance Imaging</em>, 120, 2025, 110395) are incorrect. Here, we demonstrate that our model is based on theoretical and experimental data obtained using various methods, and that our results are consistent with previously published findings. Our developed approach allows analyzing the tissue structure: estimating the volumes of ellipsoidal water-filled nanocavities, their angular distributions, and the variations of these characteristics with depth. The principal difference of the new method from the early published studies is that these characteristics were obtained using a single NMR/MRI signal (echo decay) without a sample rotation.</div><div>We believe our results contribute to a better understanding of the complex issues related to MRI signal and tissue nanostructures.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110523"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145040677","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":"DF-TransUNet: A novel TransUNet model of pixel level classification for cardiac MR image segmentation","authors":"Yunhui Zheng,&nbsp;Zhiyong Wu,&nbsp;Fengna Ji,&nbsp;Lei Du,&nbsp;Zhenyu Yang","doi":"10.1016/j.mri.2025.110502","DOIUrl":"10.1016/j.mri.2025.110502","url":null,"abstract":"<div><div>A crucial step in the intelligent healthcare system is the automatic analysis of medical images by machines and processing them accordingly, particularly in disease diagnosis, as it provides accurate anatomical structure information for subsequent treatment. This process provides precise anatomical data vital for the subsequent treatments. There is a problem of uneven intensity distribution and fuzzy boundaries etc. in medical images, which creates a great problem in the segmentation task. To cope with this problem, an improved TransUNet structure is introduced in this paper. This method is based on the TransUNet framework, and adds a pixel level classification module on the basis of TransUNet segmentation. This module can further classify the boundary parts of the mask to be segmented at the pixel level to achieve more accurate segmentation results. The method in this paper effectively reduces the classification errors associated with pixels near the boundaries of various masks within the MR image. In particular, The pixel classification module aims to learn the category of pixels near the mask boundary, and then enhance the original segmentation results through pixel level adjustments. To validate the effectiveness of this model, a series of experiments were conducted using the 2017 MICCAI Automated Cardiac Diagnostic Challenge (ACDC) dataset. The results show that with an average Dice coefficient of 90.55% and a Hausdorff distance of up to 2.23 mm, the proposed approach achieves a commendable segmentation performance. Code and models are available at <span><span>https://github.com/laodeyip/DF-TransUNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110502"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102708","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":"Coherent flow effects on IVIM-based perfusion measurements: A phantom study in 3 T","authors":"Kamil Lipiński ,&nbsp;Grzegorz Domański ,&nbsp;Piotr Bogorodzki","doi":"10.1016/j.mri.2025.110533","DOIUrl":"10.1016/j.mri.2025.110533","url":null,"abstract":"<div><div>Quantitative, non-invasive measurement of perfusion remains a challenge in MRI. The Intravoxel Incoherent Motion (IVIM) technique, based on Diffusion-Weighted Imaging (DWI), offers a method to estimate perfusion at the sub-voxel level by modeling blood flow as a pseudo-diffusive process within a microvascular network. Key IVIM-derived parameters include the perfusion fraction (f) and the pseudo-diffusion coefficient (D*). While under specific structural assumptions, IVIM can be used to estimate Cerebral Blood Flow (CBF), direct conversions from IVIM metrics to CBF are rarely validated. This study investigates the accuracy of the IVIM-based perfusion measures, exploring coherent flow influence on signal from voxel. To assess the relation between flow (ground truth CBF), IVIM parameters, and calculated CBF, a phantom, composed of agarose gel and tubes mimicking arterioles with regulated flow velocity, was constructed. Thus, a signal fraction from the “fast” compartment, described by D* in the IVIM model, is represented by flow in tubes occupying a fixed fraction of the imaged voxel, simulating desired Cerebral Blood Volume (CBV) and CBF values. The phantom was scanned using IVIM-optimized DWI protocol, and data were processed for the IVIM measures. Besides IVIM, we also propose a model based on extended Bloch-Torrey equations with coherent flow term. Results demonstrate that IVIM can yield reasonable estimates of CBF and CBV within physiologically relevant flow ranges (0.5–2 mm/s) observed in the brain vasculature. However, a consistent overestimation of flow (up to 200 %) was obscerved at higher velocities, especially in arterial or vein-like flow conditions.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110533"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149806","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":"Histogram analysis of quantitative susceptibility mapping and diffusion kurtosis imaging for the grading prediction of 2021 WHO adult-type diffuse gliomas","authors":"Pengxin Shen ,&nbsp;Yangyang Li ,&nbsp;Wenji Xu ,&nbsp;Zhiyi Zhang ,&nbsp;Xiaochun Wang ,&nbsp;Guoqiang Yang ,&nbsp;Jiangfeng Du ,&nbsp;Hui Zhang ,&nbsp;Yan Tan","doi":"10.1016/j.mri.2025.110528","DOIUrl":"10.1016/j.mri.2025.110528","url":null,"abstract":"<div><h3>Objectives</h3><div>To evaluate the role of quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI) histogram features in improving the 2021 World Health Organization Classification of Central Nervous System Tumors (WHO CNS 5) grading accuracy for adult-type diffuse gliomas when combined with conventional imaging sequences.</div></div><div><h3>Methods</h3><div>A total of 62 patients were retrospectively collected. Histogram features of QSM, DKI, CE-T1WI, T2FLAIR were extracted from tumor parenchyma. Independent <em>t</em>-tests and Mann-Whitney <em>U</em> tests were used to compare differences between grade 2/3 and grade 4 gliomas. The evaluation of the model included receiver operating characteristic (ROC) curves, 5-fold cross-validation, nomogram construction, and calibration curve analysis. Prognostic differences between two groups were assessed using Kaplan-Meier survival analysis.</div></div><div><h3>Results</h3><div>The functional imaging model (AUC = 0.892) was constructed using QSM mean absolute deviation (MAD) and relative mean kurtosis 90th percentile (rMK P90), while the conventional imaging model (AUC = 0.776) was built using CE-T1WI robust mean absolute deviation (RMAD) and T2FLAIR maximum. The imaging combined model, incorporating CE-T1WI RMAD, QSM MAD, and rMK P90, achieved an AUC of 0.936. Among the clinical factors, age showed a statistically significant difference between the two groups, with an AUC of 0.769. The integrated model combining the imaging model and age achieved the highest AUC of 0.949. The 5-fold internal cross validation showed that the average AUC was 0.944. Survival analysis revealed a significant difference between grade 2/3 and grade 4 gliomas.</div></div><div><h3>Conclusion</h3><div>Histogram features of QSM and DKI can complement conventional sequences, enhancing the diagnostic performance for adult-type diffuse gliomas grading.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110528"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145086431","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":"Sensitivity of quantitative diffusion MRI tractography and microstructure to anisotropic spatial sampling","authors":"Elyssa M. McMaster ,&nbsp;Nancy R. Newlin ,&nbsp;Chloe Cho ,&nbsp;Gaurav Rudravaram ,&nbsp;Adam M. Saunders ,&nbsp;Aravind R. Krishnan ,&nbsp;Lucas W. Remedios ,&nbsp;Michael E. Kim ,&nbsp;Hanliang Xu ,&nbsp;Kurt G. Schilling ,&nbsp;François Rheault ,&nbsp;Laurie E. Cutting ,&nbsp;Bennett A. Landman","doi":"10.1016/j.mri.2025.110539","DOIUrl":"10.1016/j.mri.2025.110539","url":null,"abstract":"<div><h3>Purpose</h3><div>Diffusion weighted MRI (dMRI) and its models of neural structure provide insight into human brain organization and variations in white matter. A recent study by McMaster, et al. showed that complex graph measures of the connectome, the graphical representation of a tractogram, vary with spatial sampling changes, but biases introduced by anisotropic voxels in the process have not been well characterized. This study uses microstructural measures (fractional anisotropy and mean diffusivity) and white matter bundle properties (bundle volume, length, and surface area) to further understand the effect of anisotropic voxels on microstructure and tractography.</div></div><div><h3>Methods</h3><div>The statistical significance of the selected measures derived from dMRI data were assessed by comparing three white matter bundles at different spatial resolutions with 44 subjects from the Human Connectome Project – Young Adult dataset scan/rescan data using the Wilcoxon Signed-Rank test. The original isotropic resolution (1.25 mm isotropic) was explored with 6 anisotropic resolutions with 0.25 mm incremental steps in the <em>z</em> dimension. Then, all generated resolutions were upsampled to 1.25 mm isotropic and 1 mm isotropic.</div></div><div><h3>Results</h3><div>There were statistically significant differences between at least one microstructural and one bundle measure at every resolution (<span><math><mi>p</mi><mo>≤</mo><mn>0.05</mn></math></span>, corrected for multiple comparisons). Cohen's <span><math><mi>d</mi></math></span> coefficient evaluated the effect size of anisotropic voxels on microstructure and tractography.</div></div><div><h3>Conclusion</h3><div>Fractional anisotropy and mean diffusivity cannot be recovered with basic up-sampling from low quality data with gold-standard data with the methods selected for this study. However, the bundle measures across our selected regions of interest become more repeatable when voxels are resampled to 1 mm isotropic.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110539"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244670","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":"MCA-GAN: A lightweight Multi-scale Context-Aware Generative Adversarial Network for MRI reconstruction","authors":"Baohua Hou,&nbsp;Hongwei Du","doi":"10.1016/j.mri.2025.110465","DOIUrl":"10.1016/j.mri.2025.110465","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) is widely utilized in medical imaging due to its high resolution and non-invasive nature. However, the prolonged acquisition time significantly limits its clinical applicability. Although traditional compressed sensing (CS) techniques can accelerate MRI acquisition, they often lead to degraded reconstruction quality under high undersampling rates. Deep learning-based methods, including CNN- and GAN-based approaches, have improved reconstruction performance, yet are limited by their local receptive fields, making it challenging to effectively capture long-range dependencies. Moreover, these models typically exhibit high computational complexity, which hinders their efficient deployment in practical scenarios. To address these challenges, we propose a lightweight Multi-scale Context-Aware Generative Adversarial Network (MCA-GAN), which enhances MRI reconstruction through dual-domain generators that collaboratively optimize both k-space and image-domain representations. MCA-GAN integrates several lightweight modules, including Depthwise Separable Local Attention (DWLA) for efficient local feature extraction, Adaptive Group Rearrangement Block (AGRB) for dynamic inter-group feature optimization, Multi-Scale Spatial Context Modulation Bridge (MSCMB) for multi-scale feature fusion in skip connections, and Channel-Spatial Multi-Scale Self-Attention (CSMS) for improved global context modeling. Extensive experiments conducted on the IXI, MICCAI 2013, and MRNet knee datasets demonstrate that MCA-GAN consistently outperforms existing methods in terms of PSNR and SSIM. Compared to SepGAN, the latest lightweight model, MCA-GAN achieves a 27.3% reduction in parameter size and a 19.6% reduction in computational complexity, while attaining the shortest reconstruction time among all compared methods. Furthermore, MCA-GAN exhibits robust performance across various undersampling masks and acceleration rates. Cross-dataset generalization experiments further confirm its ability to maintain competitive reconstruction quality, underscoring its strong generalization potential. Overall, MCA-GAN improves MRI reconstruction quality while significantly reducing computational cost through a lightweight architecture and multi-scale feature fusion, offering an efficient and accurate solution for accelerated MRI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110465"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144804409","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":"On the transverse relaxation enhancement effect in 1H-MRI of the lung","authors":"Flavio Carinci ,&nbsp;Felix A. Breuer ,&nbsp;Peter M. Jakob","doi":"10.1016/j.mri.2025.110514","DOIUrl":"10.1016/j.mri.2025.110514","url":null,"abstract":"<div><h3>Purpose</h3><div>Presenting a technique to quantify the transverse relaxation time <em>T</em>_2,diff, which is associated with the diffusion of water molecules through the internal magnetic field gradients of the lung <em>in-vivo</em>.</div></div><div><h3>Methods</h3><div>A Half-Fourier-Acquired Single-shot Turbo spin-Echo (HASTE) pulse sequence with Hahn-echo preparation was implemented and used for image acquisition. Quantification of <em>T</em>_2,diff was performed by acquiring multiple images with identical TE, but with a different number of refocusing pulses between excitation and signal acquisition. <em>T</em>_2,diff was quantified on a voxel-by-voxel basis from the signal attenuation in the different acquisitions.</div><div>Phantom experiments were performed to evaluate the ability of the proposed technique to discriminate signals with different <em>T</em>_2,diff. Six samples containing a mixture of water and glass microspheres of different nominal diameters were used. The dependence of <em>T</em>_2,diff on the sphere diameter was compared with that obtained from the conventional Hahn-echo experiment. <em>In-vivo</em> experiments were performed to investigate the dependence of <em>T</em>_2,diff on both lung inflation and perfusion. For this, data were acquired in eleven healthy volunteers in different breathing states and different cardiac phases.</div></div><div><h3>Results</h3><div>Phantom experiments showed a monotonic increase of <em>T</em>_2,diff with the sphere diameter in agreement with the results of the Hahn-echo experiment, demonstrating an excellent discrimination between signals with different <em>T</em>_2,diff. <em>In-vivo</em> experiments showed a rather homogeneous distribution of <em>T</em>_2,diff throughout the lung with a slight dependence on inflation. Mean values obtained in the diastolic cardiac phase resulted in 29 ms at Functional Residual Capacity (FRC) and in 24 ms at Total Lung Capacity (TLC). In the systolic phase the mean value at FRC was 14 ms, indicating a strong dependence of <em>T</em>_2,diff on perfusion.</div></div><div><h3>Conclusion</h3><div>The proposed technique allows to quantify <em>T</em>_2,diff of the lung in a single breath-hold of approximately 10s duration and could help in detecting functional and microstructural injuries of the lung.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"124 ","pages":"Article 110514"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993008","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}