Pub Date : 2025-11-01Epub Date: 2025-08-05DOI: 10.1016/j.mri.2025.110481
Xiangzhen Meng
Background: Magnetic resonance diffusion tensor imaging (DTI) is recognized for its diagnostic capabilities in severe craniocerebral trauma but is less explored for its prognostic utility. This study assesses the prognostic value of DTI in predicting outcomes for patients with severe craniocerebral trauma.
Methods: We conducted a retrospective analysis of 125 patients who sustained severe craniocerebral injuries between March 2021 and September 2022. Patients were evaluated 90 days post-injury using the Glasgow Outcome Scale (GOS) and categorized into good (GOS 4-5, n = 74) and poor (GOS 1-3, n = 51) prognosis groups. DTI parameters were analyzed using logistic regression and Receiver Operating Characteristic (ROC) curves to identify prognostic indicators.
Results: No significant demographic differences were observed (P > 0.05); however, significant variances were noted in DTI parameters like ADC and FA, correlating with patient outcomes. Multifactorial analysis highlighted GCS ≤ 4, midline shift ≥5 mm, and ADC ≤ 2.7 × 10-3 mm2/s as key predictors of poor prognosis.
Discussion: DTI provides valuable insights into the structural impacts of severe craniocerebral trauma, with ADC and FA serving as reliable indicators of prognosis. Identifying these parameters early can guide clinical interventions and potentially improve outcomes, underscoring the need for integrating DTI into routine prognostic assessments.
{"title":"A study of magnetic resonance diffusion tensor imaging in the prognostic assessment of severe craniocerebral trauma.","authors":"Xiangzhen Meng","doi":"10.1016/j.mri.2025.110481","DOIUrl":"10.1016/j.mri.2025.110481","url":null,"abstract":"<p><strong>Background: </strong>Magnetic resonance diffusion tensor imaging (DTI) is recognized for its diagnostic capabilities in severe craniocerebral trauma but is less explored for its prognostic utility. This study assesses the prognostic value of DTI in predicting outcomes for patients with severe craniocerebral trauma.</p><p><strong>Methods: </strong>We conducted a retrospective analysis of 125 patients who sustained severe craniocerebral injuries between March 2021 and September 2022. Patients were evaluated 90 days post-injury using the Glasgow Outcome Scale (GOS) and categorized into good (GOS 4-5, n = 74) and poor (GOS 1-3, n = 51) prognosis groups. DTI parameters were analyzed using logistic regression and Receiver Operating Characteristic (ROC) curves to identify prognostic indicators.</p><p><strong>Results: </strong>No significant demographic differences were observed (P > 0.05); however, significant variances were noted in DTI parameters like ADC and FA, correlating with patient outcomes. Multifactorial analysis highlighted GCS ≤ 4, midline shift ≥5 mm, and ADC ≤ 2.7 × 10<sup>-3</sup> mm<sup>2</sup>/s as key predictors of poor prognosis.</p><p><strong>Discussion: </strong>DTI provides valuable insights into the structural impacts of severe craniocerebral trauma, with ADC and FA serving as reliable indicators of prognosis. Identifying these parameters early can guide clinical interventions and potentially improve outcomes, underscoring the need for integrating DTI into routine prognostic assessments.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110481"},"PeriodicalIF":2.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144794871","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 : 2025-11-01Epub Date: 2025-08-06DOI: 10.1016/j.mri.2025.110482
Gyu-Han Lee, Hojin Ha, Kyung Jin Park, Hyun Jung Koo, June-Goo Lee, Hyun Seo Lee, Jong Eun Lee, Dong Hyun Yang, Dae-Hee Kim
Purpose: Four-dimensional (4D) flow MRI enables comprehensive assessment of aortic hemodynamics, but normative values remain limited, especially for Asian populations. This study aimed to establish age- and sex-specific normative values for flow-related parameters in healthy Korean adults.
Methods: Seventy-seven healthy volunteers (aged 20-79 years) underwent 4D flow MRI. Hemodynamic parameters-velocity, viscous energy loss, normalized vorticity, and helicity-were quantified in the ascending aorta (AAo), aortic arch (AoA), and descending aorta (DAo).
Results: Significant sex differences were found for velocity in the AAo (p < 0.001) and viscous energy loss across all segments (p < 0.05), while normalized vorticity and helicity showed no sex-related differences. In viscous energy loss, males had higher values than females in the AAo (2.84 ± 1.05 vs 2.22 ± 0.60 mW, p = 0.017), AoA (0.74 ± 0.37 vs 0.42 ± 0.19 mW, p = 0.001), and DAo (4.78 ± 1.82 vs 3.32 ± 1.55 mW, p = 0.002). Most parameters demonstrated age-related declines, especially in the DAo. Strongest correlations with age were seen for velocity (r = -0.725, p < 0.001) and viscous energy loss (r = -0.745, p < 0.001). Positive helicity showed sex-specific aging trends, with the strongest correlations in the DAo for men and the AoA for women. Negative helicity showed the strongest correlation in the DAo in both sexes.
Conclusions: This study established age- and sex-specific normative values for aortic hemodynamic parameters in healthy Korean adults using 4D flow MRI. While sex differences were limited, most flow parameters declined with age.
{"title":"4D flow MRI of aortic blood flow parameters in healthy volunteers: Sex- and age-specific analysis.","authors":"Gyu-Han Lee, Hojin Ha, Kyung Jin Park, Hyun Jung Koo, June-Goo Lee, Hyun Seo Lee, Jong Eun Lee, Dong Hyun Yang, Dae-Hee Kim","doi":"10.1016/j.mri.2025.110482","DOIUrl":"10.1016/j.mri.2025.110482","url":null,"abstract":"<p><strong>Purpose: </strong>Four-dimensional (4D) flow MRI enables comprehensive assessment of aortic hemodynamics, but normative values remain limited, especially for Asian populations. This study aimed to establish age- and sex-specific normative values for flow-related parameters in healthy Korean adults.</p><p><strong>Methods: </strong>Seventy-seven healthy volunteers (aged 20-79 years) underwent 4D flow MRI. Hemodynamic parameters-velocity, viscous energy loss, normalized vorticity, and helicity-were quantified in the ascending aorta (AAo), aortic arch (AoA), and descending aorta (DAo).</p><p><strong>Results: </strong>Significant sex differences were found for velocity in the AAo (p < 0.001) and viscous energy loss across all segments (p < 0.05), while normalized vorticity and helicity showed no sex-related differences. In viscous energy loss, males had higher values than females in the AAo (2.84 ± 1.05 vs 2.22 ± 0.60 mW, p = 0.017), AoA (0.74 ± 0.37 vs 0.42 ± 0.19 mW, p = 0.001), and DAo (4.78 ± 1.82 vs 3.32 ± 1.55 mW, p = 0.002). Most parameters demonstrated age-related declines, especially in the DAo. Strongest correlations with age were seen for velocity (r = -0.725, p < 0.001) and viscous energy loss (r = -0.745, p < 0.001). Positive helicity showed sex-specific aging trends, with the strongest correlations in the DAo for men and the AoA for women. Negative helicity showed the strongest correlation in the DAo in both sexes.</p><p><strong>Conclusions: </strong>This study established age- and sex-specific normative values for aortic hemodynamic parameters in healthy Korean adults using 4D flow MRI. While sex differences were limited, most flow parameters declined with age.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110482"},"PeriodicalIF":2.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144799546","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 : 2025-10-31DOI: 10.1016/j.mri.2025.110551
Guijiao Zhao , Chen Zhou , Jianxing Liu , Yue Hu , Peng Li
Accelerated magnetic resonance imaging (MRI) reconstruction from undersampled -space data is a challenging inverse problem that has attracted significant attention in the MRI community. Diffusion models have recently emerged as a promising solution for MRI reconstruction, as they can generate high-quality samples while maintaining sample diversity. However, the inference process of diffusion models is computationally expensive, requiring thousands of steps to ensure the quality of the generated samples, which can take tens of minutes to complete. To address this issue, we propose a novel fast diffusion model for MRI reconstruction, termed FDMR, which aims to accelerate the inference process and improve reconstruction quality. The FDMR framework consists of two main components: the adversarial training of the denoising diffusion GAN and the three-stage inference framework. The adversarial training process is used to train the denoising diffusion GAN with large steps, learning an unconditional diffusion prior and embedding a deep generative prior. The proposed three-stage inference framework includes fast diffusion generation, early stopped deep generative prior adaptation, and diffusion refinement, aiming to accelerate the inference process and improve the reconstruction quality. Extensive experiments demonstrate that FDMR can achieve superior reconstruction accuracy compared to state-of-the-art diffusion methods, yet it operates 4-10 times faster, enabling the reconstruction within just 8 s.
{"title":"Fast unconditional diffusion model for accelerated MRI reconstruction","authors":"Guijiao Zhao , Chen Zhou , Jianxing Liu , Yue Hu , Peng Li","doi":"10.1016/j.mri.2025.110551","DOIUrl":"10.1016/j.mri.2025.110551","url":null,"abstract":"<div><div>Accelerated magnetic resonance imaging (MRI) reconstruction from undersampled <span><math><mi>k</mi></math></span>-space data is a challenging inverse problem that has attracted significant attention in the MRI community. Diffusion models have recently emerged as a promising solution for MRI reconstruction, as they can generate high-quality samples while maintaining sample diversity. However, the inference process of diffusion models is computationally expensive, requiring thousands of steps to ensure the quality of the generated samples, which can take tens of minutes to complete. To address this issue, we propose a novel fast diffusion model for MRI reconstruction, termed FDMR, which aims to accelerate the inference process and improve reconstruction quality. The FDMR framework consists of two main components: the adversarial training of the denoising diffusion GAN and the three-stage inference framework. The adversarial training process is used to train the denoising diffusion GAN with large steps, learning an unconditional diffusion prior and embedding a deep generative prior. The proposed three-stage inference framework includes fast diffusion generation, early stopped deep generative prior adaptation, and diffusion refinement, aiming to accelerate the inference process and improve the reconstruction quality. Extensive experiments demonstrate that FDMR can achieve superior reconstruction accuracy compared to state-of-the-art diffusion methods, yet it operates 4-10 times faster, enabling the reconstruction within just 8 s.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110551"},"PeriodicalIF":2.0,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145431523","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 : 2025-10-30DOI: 10.1016/j.mri.2025.110554
Steven Winata , Daniel Christopher Hoinkiss , Graeme Alexander Keith , Salim al-Wasity , David Andrew Porter
Magnetic resonance imaging (MRI) at ultra-high field strengths such as 7 T unlocks new opportunities. Functional MRI (fMRI) is especially able to benefit due to the increase in the inherent blood‑oxygen-level-dependant (BOLD) signal. In order to utilise this, the higher motion sensitivity at 7 T and various motion sources in fMRI protocols, especially task-based ones, need to be mitigated. This motivated the development of a 7 T implementation of the real-time, prospective Multislice Prospective Acquisition Correction (MS-PACE) technique. MS-PACE allows for a sub-TR, higher temporal resolution motion correction without the need for external tracking equipment. We present an echo-planar imaging (EPI) implementation, evaluated in a 7 T task-based fMRI study. The results show that the technique led to significant, consistent reduction in residual motion across the scanned cohort. An analysis of the temporal SNR of the resting-state scans indicated a general increase in this metric when prospective motion correction was activated. Functional analysis of the data showed an apparent reduction of artefactual activations compared to a standard retrospective motion correction algorithm.
{"title":"Real-time multislice-to-volume motion correction for task-based EPI-fMRI at 7 T","authors":"Steven Winata , Daniel Christopher Hoinkiss , Graeme Alexander Keith , Salim al-Wasity , David Andrew Porter","doi":"10.1016/j.mri.2025.110554","DOIUrl":"10.1016/j.mri.2025.110554","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) at ultra-high field strengths such as 7 T unlocks new opportunities. Functional MRI (fMRI) is especially able to benefit due to the increase in the inherent blood‑oxygen-level-dependant (BOLD) signal. In order to utilise this, the higher motion sensitivity at 7 T and various motion sources in fMRI protocols, especially task-based ones, need to be mitigated. This motivated the development of a 7 T implementation of the real-time, prospective Multislice Prospective Acquisition Correction (MS-PACE) technique. MS-PACE allows for a sub-TR, higher temporal resolution motion correction without the need for external tracking equipment. We present an echo-planar imaging (EPI) implementation, evaluated in a 7 T task-based fMRI study. The results show that the technique led to significant, consistent reduction in residual motion across the scanned cohort. An analysis of the temporal SNR of the resting-state scans indicated a general increase in this metric when prospective motion correction was activated. Functional analysis of the data showed an apparent reduction of artefactual activations compared to a standard retrospective motion correction algorithm.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110554"},"PeriodicalIF":2.0,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145422017","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 : 2025-10-30DOI: 10.1016/j.mri.2025.110542
Yuemei Cui , Ya Li , Jing Na , Junling Lu , Xinyou Wang , Shichao Han , Jun Wang
Objectives
To assess the diagnostic performance of radiomics, habitat imaging, and 2.5D deep learning models for MRI-based prediction of parametrial invasion in cervical cancer, and to evaluate the clinical utility of a multimodal integrated model.
Methods
This dual-center retrospective study included 290 patients with FIGO stage IB1–IIB cervical cancer who underwent preoperative MRI. Patients from Center A (n = 227) were divided into training and validation cohorts, while patients from Center B (n = 63) comprised the external test cohort. Radiomic features were extracted, habitat imaging was performed using k-means clustering, and a 2.5D deep learning model incorporated adjacent slices. Feature selection was conducted using Pearson correlation and LASSO regression. Machine learning models were developed, and an integrated model was constructed. Model performance was evaluated using AUC and accuracy. AUCs were compared with DeLong tests, calibration was assessed with the Hosmer–Lemeshow test, and clinical utility was evaluated with decision curve analysis.
Results
The integrated model outperformed all individual models, achieving AUCs of 0.973, 0.901, and 0.906 in the training, validation, and external test cohorts, respectively. Among individual models, the deep-learning model showed the highest AUCs (0.954, 0.803, 0.833), followed by habitat imaging (0.860, 0.811, 0.843). In the external test cohort, the peritumoral radiomics model outperformed the intratumoral model (0.843 vs. 0.719). The clinical model showed the lowest performance. Hosmer–Lemeshow tests indicated good calibration, and decision curve analysis confirmed superior clinical utility of the integrated model.
Conclusion
The multimodal integrated model, combining radiomics, habitat imaging, 2.5D deep learning, and clinical features, demonstrated superior predictive performance for parametrial invasion in cervical cancer compared with individual models. This approach may enhance preoperative assessment, guide clinical decision-making, and optimize treatment strategies.
{"title":"Integration of radiomics, habitat imaging, and deep learning for MRI-based prediction of parametrial invasion in cervical cancer: A dual-center study","authors":"Yuemei Cui , Ya Li , Jing Na , Junling Lu , Xinyou Wang , Shichao Han , Jun Wang","doi":"10.1016/j.mri.2025.110542","DOIUrl":"10.1016/j.mri.2025.110542","url":null,"abstract":"<div><h3>Objectives</h3><div>To assess the diagnostic performance of radiomics, habitat imaging, and 2.5D deep learning models for MRI-based prediction of parametrial invasion in cervical cancer, and to evaluate the clinical utility of a multimodal integrated model.</div></div><div><h3>Methods</h3><div>This dual-center retrospective study included 290 patients with FIGO stage IB1–IIB cervical cancer who underwent preoperative MRI. Patients from Center A (<em>n</em> = 227) were divided into training and validation cohorts, while patients from Center B (<em>n</em> = 63) comprised the external test cohort. Radiomic features were extracted, habitat imaging was performed using k-means clustering, and a 2.5D deep learning model incorporated adjacent slices. Feature selection was conducted using Pearson correlation and LASSO regression. Machine learning models were developed, and an integrated model was constructed. Model performance was evaluated using AUC and accuracy. AUCs were compared with DeLong tests, calibration was assessed with the Hosmer–Lemeshow test, and clinical utility was evaluated with decision curve analysis.</div></div><div><h3>Results</h3><div>The integrated model outperformed all individual models, achieving AUCs of 0.973, 0.901, and 0.906 in the training, validation, and external test cohorts, respectively. Among individual models, the deep-learning model showed the highest AUCs (0.954, 0.803, 0.833), followed by habitat imaging (0.860, 0.811, 0.843). In the external test cohort, the peritumoral radiomics model outperformed the intratumoral model (0.843 vs. 0.719). The clinical model showed the lowest performance. Hosmer–Lemeshow tests indicated good calibration, and decision curve analysis confirmed superior clinical utility of the integrated model.</div></div><div><h3>Conclusion</h3><div>The multimodal integrated model, combining radiomics, habitat imaging, 2.5D deep learning, and clinical features, demonstrated superior predictive performance for parametrial invasion in cervical cancer compared with individual models. This approach may enhance preoperative assessment, guide clinical decision-making, and optimize treatment strategies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110542"},"PeriodicalIF":2.0,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145422019","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 : 2025-10-26DOI: 10.1016/j.mri.2025.110555
Constantin von Deuster , Georg Wilhelm Kajdi , Shila Pazahr , Nikolai Pfender , Markus Hupp , Armin Curt , Reto Sutter , Daniel Nanz
Background
The aim of this study was to investigate the feasibility of MR-tagging for direct visualization and quantification of pathologically altered spinal-cord motion in patients with cervical spinal stenosis and compare it to the standard approach of phase contrast (PC) imaging.
Methods
In this prospective study, sagittal sections (22 cm field of view) of the cervical spine of nine patients with mono-segmental spinal-canal stenosis were imaged in different heart phases after selective pre-saturation of tissue magnetization in an axially oriented tag-stripe pattern (MR-tagging). Video loops of images acquired in different heart phases were viewed to directly observe and compare head-feet (HF) displacement at the level of the stenosis. The maximum HF displacement of MR-tags in the cord was quantitatively assessed and compared to that derived from integration of PC velocity data.
Results
Regional MR-tag displacement in the spinal cord could successfully be observed in all patients (4 females, 5 males, mean age 57 ± 7 years). Maximum displacement data derived from tagging at the stenosis level correlated excellently (R2 = 0.84) with matched measurements from PC imaging.
Conclusion
Without complex post-processing, MR-tag imaging provides an intuitive direct visualization and quantification of pathologically altered spinal-cord motion at the level of cervical stenosis offering a faster alternative to PC imaging in clinical routine.
{"title":"Feasibility of MR-tagging to quantify spinal cord motion in degenerative cervical myelopathy","authors":"Constantin von Deuster , Georg Wilhelm Kajdi , Shila Pazahr , Nikolai Pfender , Markus Hupp , Armin Curt , Reto Sutter , Daniel Nanz","doi":"10.1016/j.mri.2025.110555","DOIUrl":"10.1016/j.mri.2025.110555","url":null,"abstract":"<div><h3>Background</h3><div>The aim of this study was to investigate the feasibility of MR-tagging for direct visualization and quantification of pathologically altered spinal-cord motion in patients with cervical spinal stenosis and compare it to the standard approach of phase contrast (PC) imaging.</div></div><div><h3>Methods</h3><div>In this prospective study, sagittal sections (22 cm field of view) of the cervical spine of nine patients with mono-segmental spinal-canal stenosis were imaged in different heart phases after selective pre-saturation of tissue magnetization in an axially oriented tag-stripe pattern (MR-tagging). Video loops of images acquired in different heart phases were viewed to directly observe and compare head-feet (HF) displacement at the level of the stenosis. The maximum HF displacement of MR-tags in the cord was quantitatively assessed and compared to that derived from integration of PC velocity data.</div></div><div><h3>Results</h3><div>Regional MR-tag displacement in the spinal cord could successfully be observed in all patients (4 females, 5 males, mean age 57 ± 7 years). Maximum displacement data derived from tagging at the stenosis level correlated excellently (R<sup>2</sup> = 0.84) with matched measurements from PC imaging.</div></div><div><h3>Conclusion</h3><div>Without complex post-processing, MR-tag imaging provides an intuitive direct visualization and quantification of pathologically altered spinal-cord motion at the level of cervical stenosis offering a faster alternative to PC imaging in clinical routine.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110555"},"PeriodicalIF":2.0,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145390825","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 : 2025-10-24DOI: 10.1016/j.mri.2025.110550
Radim Kořínek, Lucie Krátká, Zenon Starčuk Jr
Purpose
Quantifying proton density fat fraction (PDFF) in small abdominal organs is challenging due to low T1/T2 contrast and susceptibility artifacts. We develop a hybrid 7-echo CSE-MRI sequence with arbitrary echo spacing, inspired by GRASE-type imaging, aiming for distortion-free PDFF mapping in small animals. The method is designed to be comparable to established conventional methods, with potential for increased robustness.
Methods
We developed a Fast Spin Echo Asymmetric Bipolar Multi-Gradient Echo (FSE-AbMGE) sequence by integrating a fast spin-echo readout with an asymmetrically placed bipolar multi-echo gradient-echo train. The sequence was implemented at 9.4 T and combined with robust phase unwrapping and water-fat reconstruction algorithms using full fat spectral modeling. Validation was performed using phantoms with known PDFF values (0–22 %) and in vivo experiments on several female mice (n = 2). Reference PDFF values were obtained using single-voxel 1H-MRS.
Results
The proposed method enabled high-resolution PDFF mapping with minimal chemical shift and susceptibility artifacts. Phantom experiments showed strong agreement with both spectroscopic and ground truth values (R2 > 0.98, p < 0.001). The method was also tested in vivo, demonstrating robust water-fat separation and quantification.
Conclusion
The FSE-AbMGE sequence is well-suited for accurate abdominal fat quantification in small animals. While additional validation is needed, especially in reproducibility and broader biological settings, the method shows promise for high-field fat quantification and may offer a framework adaptable to lower-field pre-clinical applications.
{"title":"Quantitative proton density fat-fraction at 9.4 T using fast spin echo and asymmetric multi-echo gradient-echo pulse sequences","authors":"Radim Kořínek, Lucie Krátká, Zenon Starčuk Jr","doi":"10.1016/j.mri.2025.110550","DOIUrl":"10.1016/j.mri.2025.110550","url":null,"abstract":"<div><h3><strong>Purpose</strong></h3><div>Quantifying proton density fat fraction (PDFF) in small abdominal organs is challenging due to low <em>T</em><sub>1</sub>/<em>T</em><sub>2</sub> contrast and susceptibility artifacts. We develop a hybrid 7-echo CSE-MRI sequence with arbitrary echo spacing, inspired by GRASE-type imaging, aiming for distortion-free PDFF mapping in small animals. The method is designed to be comparable to established conventional methods, with potential for increased robustness.</div></div><div><h3><strong>Methods</strong></h3><div>We developed a Fast Spin Echo Asymmetric Bipolar Multi-Gradient Echo (FSE-AbMGE) sequence by integrating a fast spin-echo readout with an asymmetrically placed bipolar multi-echo gradient-echo train. The sequence was implemented at 9.4 T and combined with robust phase unwrapping and water-fat reconstruction algorithms using full fat spectral modeling. Validation was performed using phantoms with known PDFF values (0–22 %) and in vivo experiments on several female mice (<em>n</em> = 2). Reference PDFF values were obtained using single-voxel <sup>1</sup>H-MRS.</div></div><div><h3><strong>Results</strong></h3><div>The proposed method enabled high-resolution PDFF mapping with minimal chemical shift and susceptibility artifacts. Phantom experiments showed strong agreement with both spectroscopic and ground truth values (R<sup>2</sup> > 0.98, <em>p</em> < 0.001). The method was also tested in vivo, demonstrating robust water-fat separation and quantification.</div></div><div><h3><strong>Conclusion</strong></h3><div>The FSE-AbMGE sequence is well-suited for accurate abdominal fat quantification in small animals. While additional validation is needed, especially in reproducibility and broader biological settings, the method shows promise for high-field fat quantification and may offer a framework adaptable to lower-field pre-clinical applications.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110550"},"PeriodicalIF":2.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145425788","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 : 2025-10-24DOI: 10.1016/j.mri.2025.110552
Jie Huang
It is imperative to study individual brain functioning for understanding the neural bases of individual behavioral and clinical traits. BOLD-fMRI measures the four-dimensional (3 spatial and 1 temporal) neural activity across the entire brain at large-scale systems level. All local activities across the entire brain constitute the whole brain activity and each local activity is a part of that whole brain activity. Unlike a local activity that is characterized by its temporal neural activity, the whole brain activity is characterized by its spatial variation across the entire brain. We present a novel data-driven method to analyze the whole brain activity when performing tasks. The method enabled us to analyze the whole brain activity for each task trial and each individual subject with no requirement of a priori knowledge of task-evoked BOLD response. Our study revealed a quantitative spatiotemporal relationship of the whole brain activity with the local activities. The whole brain activity demonstrated a remarkable dynamic activity that varied from trial to trial when performing the same task repeatedly, showing the importance of analyzing the whole brain activity for investigating the neural bases of personal traits.
{"title":"The quantitative spatiotemporal relationship of whole brain activity of human brains revealed by fMRI","authors":"Jie Huang","doi":"10.1016/j.mri.2025.110552","DOIUrl":"10.1016/j.mri.2025.110552","url":null,"abstract":"<div><div>It is imperative to study individual brain functioning for understanding the neural bases of individual behavioral and clinical traits. BOLD-fMRI measures the four-dimensional (3 spatial and 1 temporal) neural activity across the entire brain at large-scale systems level. All local activities across the entire brain constitute the whole brain activity and each local activity is a part of that whole brain activity. Unlike a local activity that is characterized by its temporal neural activity, the whole brain activity is characterized by its spatial variation across the entire brain. We present a novel data-driven method to analyze the whole brain activity when performing tasks. The method enabled us to analyze the whole brain activity for each task trial and each individual subject with no requirement of a priori knowledge of task-evoked BOLD response. Our study revealed a quantitative spatiotemporal relationship of the whole brain activity with the local activities. The whole brain activity demonstrated a remarkable dynamic activity that varied from trial to trial when performing the same task repeatedly, showing the importance of analyzing the whole brain activity for investigating the neural bases of personal traits.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110552"},"PeriodicalIF":2.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145425789","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 : 2025-10-24DOI: 10.1016/j.mri.2025.110553
Lauritz Klünder , Bastian Maus , María Belén Rivas Aiello , Athina Drakonaki , Michael Holtkamp , Uwe Karst , Christos Gatsogiannis , Cristian Strassert , Cornelius Faber
Superparamagnetic iron oxide nanoparticles are used in MRI as or contrast agents and, in combination with relaxometry, enable quantitative analysis of physiological and pathological processes. However, the induced changes in relaxation times are influenced by a complex interplay of the contrast agents' physicochemical properties. Here, an open-source Monte Carlo simulation pipeline was implemented, which enables the characterization of these relaxation time changes caused by MRI contrast agents. The simulation tool was validated by showing that simulated relaxation times for iron oxide particles matched the solutions of analytical models of the respective diffusion regimes. For comparison with relaxometry measurements, and of four MRI contrast agents Ferucarbotran, FeraSpin XL, magnetite nanohexagons (MNH@OH) and magnetite nanocubes (MNC@OH) were simulated, using three approaches for modeling contrast agent size and composition: 1) uniform particle sizes using the median hydrodynamic radii; 2) distributed radii corresponding to measured hydrodynamic radius distributions; 3) size-distributed magnetite cores in a coating layer of uniform radius. The simulation accurately reproduced measured relaxation times when appropriate modeling strategies for contrast agent size and composition were used. For FeraSpin XL and MNH@OH, using uniform radii provided good estimates of relaxation times, which was further improved by using the size distributions. For MNC@OH, discrepancies in simulated and measured for all approaches were attributed to particle aggregation. For Ferucarbotran, coating and size distribution of the core had to be considered to match experimental data.
{"title":"Monte Carlo simulations of transverse relaxation for characterization of physicochemical properties of superparamagnetic iron oxide nanoparticles","authors":"Lauritz Klünder , Bastian Maus , María Belén Rivas Aiello , Athina Drakonaki , Michael Holtkamp , Uwe Karst , Christos Gatsogiannis , Cristian Strassert , Cornelius Faber","doi":"10.1016/j.mri.2025.110553","DOIUrl":"10.1016/j.mri.2025.110553","url":null,"abstract":"<div><div>Superparamagnetic iron oxide nanoparticles are used in MRI as <span><math><msub><mi>T</mi><mn>2</mn></msub></math></span> or <span><math><msubsup><mi>T</mi><mn>2</mn><mo>∗</mo></msubsup></math></span> contrast agents and, in combination with relaxometry, enable quantitative analysis of physiological and pathological processes. However, the induced changes in relaxation times are influenced by a complex interplay of the contrast agents' physicochemical properties. Here, an open-source Monte Carlo simulation pipeline was implemented, which enables the characterization of these relaxation time changes caused by MRI contrast agents. The simulation tool was validated by showing that simulated relaxation times for iron oxide particles matched the solutions of analytical models of the respective diffusion regimes. For comparison with relaxometry measurements, <span><math><msub><mi>T</mi><mn>2</mn></msub></math></span> and <span><math><msubsup><mi>T</mi><mn>2</mn><mo>∗</mo></msubsup></math></span> of four MRI contrast agents Ferucarbotran, FeraSpin XL, magnetite nanohexagons (MNH@OH) and magnetite nanocubes (MNC@OH) were simulated, using three approaches for modeling contrast agent size and composition: 1) uniform particle sizes using the median hydrodynamic radii; 2) distributed radii corresponding to measured hydrodynamic radius distributions; 3) size-distributed magnetite cores in a coating layer of uniform radius. The simulation accurately reproduced measured relaxation times when appropriate modeling strategies for contrast agent size and composition were used. For FeraSpin XL and MNH@OH, using uniform radii provided good estimates of relaxation times, which was further improved by using the size distributions. For MNC@OH, discrepancies in simulated and measured <span><math><msub><mi>T</mi><mn>2</mn></msub></math></span> for all approaches were attributed to particle aggregation. For Ferucarbotran, coating and size distribution of the core had to be considered to match experimental data.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110553"},"PeriodicalIF":2.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145425790","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 : 2025-10-17DOI: 10.1016/j.mri.2025.110541
Zalán Petneházy , Dávid Bognár , Péter Laar , Tamás Dóczi , Attila Schwarcz , Bálint S. Környei , Arnold Tóth
Objectives
This study aimed to determine whether focal MRI lesions such as microbleeds (MBs) and focal white matter hyperintensities (FWMHs) serve as reliable and specific markers for tract-level white matter injury in traumatic brain injury (TBI).
Materials & methods
Twenty-two patients with moderate-to-severe TBI and 22 age-matched healthy controls underwent MRI on a 3 T Siemens Prisma scanner. Imaging included susceptibility-weighted imaging (SWI), fluid-attenuated inversion recovery (FLAIR), and diffusion tensor imaging (DTI). Focal lesions were manually identified on SWI and FLAIR and mapped onto tractography reconstructions. Diffusion metrics—fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared between lesion-affected tracts, contralateral normal-appearing white matter (NAWM), and corresponding control tracts. Statistical analyses were performed using repeated measures ANOVA with Greenhouse-Geisser correction and Bonferroni-adjusted post hoc tests for FA. Friedman tests were conducted for MD, AD, and RD, followed by Bonferroni-corrected Wilcoxon post hoc comparisons.
Results
In this study, we identified 27 MBs and 66 FWMHs intersecting white matter tracts. We observed notable differences in diffusion metrics when comparing lesion-affected tracts to healthy controls. In MB-affected tracts, fractional anisotropy (FA) differed significantly (p = 0.002), while mean diffusivity (MD) also showed a significant alteration (p = 0.002), along with radial diffusivity (RD) (p < 0.001). Similarly, in FWMH-affected tracts, significant differences were observed in FA (p < 0.001), MD (p < 0.001), axial diffusivity (AD) (p < 0.001), and RD (p < 0.001). However, we did not find any significant differences between lesion-affected tracts and the contralateral normal-appearing white matter (NAWM).
Conclusion
MBs and FWMHs do not co-localize with axonal injury at the tract level but indicate a global white matter damage.
{"title":"Investigating microbleeds and white matter hyperintensities in TBI at a tract-level: A DTI study","authors":"Zalán Petneházy , Dávid Bognár , Péter Laar , Tamás Dóczi , Attila Schwarcz , Bálint S. Környei , Arnold Tóth","doi":"10.1016/j.mri.2025.110541","DOIUrl":"10.1016/j.mri.2025.110541","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to determine whether focal MRI lesions such as microbleeds (MBs) and focal white matter hyperintensities (FWMHs) serve as reliable and specific markers for tract-level white matter injury in traumatic brain injury (TBI).</div></div><div><h3>Materials & methods</h3><div>Twenty-two patients with moderate-to-severe TBI and 22 age-matched healthy controls underwent MRI on a 3 T Siemens Prisma scanner. Imaging included susceptibility-weighted imaging (SWI), fluid-attenuated inversion recovery (FLAIR), and diffusion tensor imaging (DTI). Focal lesions were manually identified on SWI and FLAIR and mapped onto tractography reconstructions. Diffusion metrics—fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared between lesion-affected tracts, contralateral normal-appearing white matter (NAWM), and corresponding control tracts. Statistical analyses were performed using repeated measures ANOVA with Greenhouse-Geisser correction and Bonferroni-adjusted post hoc tests for FA. Friedman tests were conducted for MD, AD, and RD, followed by Bonferroni-corrected Wilcoxon post hoc comparisons.</div></div><div><h3>Results</h3><div>In this study, we identified 27 MBs and 66 FWMHs intersecting white matter tracts. We observed notable differences in diffusion metrics when comparing lesion-affected tracts to healthy controls. In MB-affected tracts, fractional anisotropy (FA) differed significantly (<em>p</em> = 0.002), while mean diffusivity (MD) also showed a significant alteration (p = 0.002), along with radial diffusivity (RD) (<em>p</em> < 0.001). Similarly, in FWMH-affected tracts, significant differences were observed in FA (<em>p</em> < 0.001), MD (p < 0.001), axial diffusivity (AD) (p < 0.001), and RD (p < 0.001). However, we did not find any significant differences between lesion-affected tracts and the contralateral normal-appearing white matter (NAWM).</div></div><div><h3>Conclusion</h3><div>MBs and FWMHs do not co-localize with axonal injury at the tract level but indicate a global white matter damage.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"125 ","pages":"Article 110541"},"PeriodicalIF":2.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145329540","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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