Magnetic Resonance Imaging (MRI) as a representative of noninvasive medical imaging, it has excellent soft tissue resolution, multi-parameter imaging capability and no radiation, thus it has been widely used in the fields of disease diagnosis, treatment and drug development. However, MRI technology still has problems such as long time consuming, sensitive to motion artefacts, and difficult to balance the imaging effect and speed. In this paper, a Nesterov accelerated spectral conjugate gradient algorithm (TV_NASCG) for MRI with TV regularisation is established from optimising reconstruction algorithms based on Total Variation (TV) regularised MRI model. The algorithm is based on a set of conjugate and spectral parameters proposed in this paper, and inspired by Nesterov acceleration technique, a set of Nesterov acceleration extrapolation step and acceleration parameter are proposed. In this paper, we also use step size acceleration technique and Powell restart strategy to further improve performance of the algorithm. In addition, we give a convergence analysis of TV_NASCG algorithm, which proves that it is sufficiently descent and globally convergent. Finally, in order to verify MRI performance of TV_NASCG algorithm, it was tested in MRI experiment and compared with other algorithms, and experimental results show that TV_NASCG algorithm can improve MRI quality and shorten imaging time.
{"title":"Nesterov accelerated spectral conjugate gradient algorithm for magnetic resonance imaging with TV regularisation","authors":"YueHong Ding , ZhiBin Zhu , Shuo Wang , BenXin Zhang","doi":"10.1016/j.mri.2025.110604","DOIUrl":"10.1016/j.mri.2025.110604","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) as a representative of noninvasive medical imaging, it has excellent soft tissue resolution, multi-parameter imaging capability and no radiation, thus it has been widely used in the fields of disease diagnosis, treatment and drug development. However, MRI technology still has problems such as long time consuming, sensitive to motion artefacts, and difficult to balance the imaging effect and speed. In this paper, a Nesterov accelerated spectral conjugate gradient algorithm (TV_NASCG) for MRI with TV regularisation is established from optimising reconstruction algorithms based on Total Variation (TV) regularised MRI model. The algorithm is based on a set of conjugate and spectral parameters proposed in this paper, and inspired by Nesterov acceleration technique, a set of Nesterov acceleration extrapolation step and acceleration parameter are proposed. In this paper, we also use step size acceleration technique and Powell restart strategy to further improve performance of the algorithm. In addition, we give a convergence analysis of TV_NASCG algorithm, which proves that it is sufficiently descent and globally convergent. Finally, in order to verify MRI performance of TV_NASCG algorithm, it was tested in MRI experiment and compared with other algorithms, and experimental results show that TV_NASCG algorithm can improve MRI quality and shorten imaging time.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110604"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850274","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}
This study aimed to evaluate, qualitatively and quantitatively, cross-sectional black-blood images obtained using T2-prepared phase-sensitive inversion-recovery steady-state free precession (T2PSIR-SSFP), in comparison with conventional double inversion recovery turbo spin-echo (DIR-TSE), in patients with Kawasaki disease (KD), and to assess the feasibility of T2PSIR-SSFP imaging.
Materials and methods
Nine patients (three female and six male; median age, 6.2 years; range, 8 months–14 years) were enrolled. Black-blood imaging was separately analyzed in aneurysmal and regressed aneurysmal regions. Lumen and outer wall boundary image quality was visually graded using a four-point scale. Lumen area (LA) reproducibility measurements were determined using intraclass correlation coefficients (ICCs) between T2PSIR-SSFP and coronary magnetic resonance angiography (MRA) images, as well as between DIR-TSE and MRA. Agreement between T2PSIR-SSFP and MRA was further examined using Bland–Altman analysis.
Results
A total of 22 coronary regions (11 aneurysmal and 11 regressed aneurysmal) were assessed. T2PSIR-SSFP exhibited excellent reproducibility with MRA in both aneurysmal and regressed aneurysmal regions (ICCs = 0.99 and 1.00, respectively). DIR-TSE showed high reproducibility in regressed aneurysmal regions (ICC = 0.93) but poor agreement in aneurysmal regions (ICC = 0.43). Bland–Altman analysis revealed strong agreement between T2PSIR-SSFP and MRA, with no fixed or proportional bias in either region (P > 0.1).
Conclusions
Flow-independent coronary black-blood imaging using T2PSIR-SSFP provided values within the expected range in patients with KD. T2PSIR-SSFP imaging appears suitable for KD follow-up because it can provide accurate cross-sectional images and reproducibility of LA measurements.
{"title":"Coronary artery black-blood imaging via T2-prepared phase-sensitive inversion-recovery steady-state free precession in Kawasaki disease","authors":"Koji Matsumoto , Hajime Yokota , Hiroki Mukai , Ryota Ebata , Kentaro Okunushi , Hiromichi Hamada , Hiroyuki Takaoka , Masami Yoneyama , Takashi Namiki , Takashi Iimori , Takashi Uno","doi":"10.1016/j.mri.2025.110589","DOIUrl":"10.1016/j.mri.2025.110589","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to evaluate, qualitatively and quantitatively, cross-sectional black-blood images obtained using T<sub>2</sub>-prepared phase-sensitive inversion-recovery steady-state free precession (T<sub>2</sub>PSIR-SSFP), in comparison with conventional double inversion recovery turbo spin-echo (DIR-TSE), in patients with Kawasaki disease (KD), and to assess the feasibility of T<sub>2</sub>PSIR-SSFP imaging.</div></div><div><h3>Materials and methods</h3><div>Nine patients (three female and six male; median age, 6.2 years; range, 8 months–14 years) were enrolled. Black-blood imaging was separately analyzed in aneurysmal and regressed aneurysmal regions. Lumen and outer wall boundary image quality was visually graded using a four-point scale. Lumen area (LA) reproducibility measurements were determined using intraclass correlation coefficients (ICCs) between T<sub>2</sub>PSIR-SSFP and coronary magnetic resonance angiography (MRA) images, as well as between DIR-TSE and MRA. Agreement between T<sub>2</sub>PSIR-SSFP and MRA was further examined using Bland–Altman analysis.</div></div><div><h3>Results</h3><div>A total of 22 coronary regions (11 aneurysmal and 11 regressed aneurysmal) were assessed. T<sub>2</sub>PSIR-SSFP exhibited excellent reproducibility with MRA in both aneurysmal and regressed aneurysmal regions (ICCs = 0.99 and 1.00, respectively). DIR-TSE showed high reproducibility in regressed aneurysmal regions (ICC = 0.93) but poor agreement in aneurysmal regions (ICC = 0.43). Bland–Altman analysis revealed strong agreement between T<sub>2</sub>PSIR-SSFP and MRA, with no fixed or proportional bias in either region (<em>P</em> > 0.1).</div></div><div><h3>Conclusions</h3><div>Flow-independent coronary black-blood imaging using T<sub>2</sub>PSIR-SSFP provided values within the expected range in patients with KD. T<sub>2</sub>PSIR-SSFP imaging appears suitable for KD follow-up because it can provide accurate cross-sectional images and reproducibility of LA measurements.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110589"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145708504","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: 2025-11-27DOI: 10.1016/j.mri.2025.110577
Baihe Luo , Aoran Yang , Jialin Li , Chen Pan, Chunli Li, Minghui Zhou, Zhiying Wang, Chengli Gu, Xiaoli Yin, Yun Zhao, Yu Shi
Objective
Tyrosine kinase inhibitors (TKIs), such as sorafenib, are standard therapies for advanced hepatocellular carcinoma (HCC), but their biomechanical impact and the role of magnetic resonance elastography (MRE) in treatment evaluation remain unclear. This study explored whether TKIs reduce tumor stiffness by inhibiting malignant behavior and whether MRE can detect such changes early.
Methods
A prospective animal study was performed using subcutaneous SK-HEP-1 HCC xenografts in 50 nude rats. Forty tumor-bearing rats were randomized to control or sorafenib-treated groups (n = 20 each). Multiparametric 3.0 T MRI included T1- and T2-weighted imaging, T1/T2/T2* mapping, and MRE at 200 Hz and 100 Hz. Imaging was conducted at baseline (∼2 cm3 tumor volume) and on days 1, 2, and 3 post-intervention. Histology involved H&E and immunohistochemistry for VEGFR-1, BRAF, Ki67, and TUNEL. Ex vivo stiffness was measured by atomic force microscopy. Cell behavior was assessed by EdU, Transwell, CCK-8, and Western blot. Statistical analysis included ICC, Bland–Altman, Mann–Whitney U, repeated measures ANOVA, Spearman correlation, and multivariate regression.
Results
TKIs reduced tumor stiffness at cellular (P = 0.02) and tissue (P = 0.004) levels. Stiffness decreased by day 2 at 200 Hz and day 3 at both frequencies. Treated tumors showed reduced cellularity, lower Ki67, and increased apoptosis. Stiffness correlated with cellularity (r = 0.527) and Ki67 (r = 0.623), both predicting MRE stiffness (R2 = 0.537).
Conclusion
TKIs reduce stiffness and malignancy in HCC. MRE is a promising tool for early treatment response evaluation.
{"title":"Tumor stiffness as an imaging biomarker of tyrosine kinase inhibitor response: A preclinical study","authors":"Baihe Luo , Aoran Yang , Jialin Li , Chen Pan, Chunli Li, Minghui Zhou, Zhiying Wang, Chengli Gu, Xiaoli Yin, Yun Zhao, Yu Shi","doi":"10.1016/j.mri.2025.110577","DOIUrl":"10.1016/j.mri.2025.110577","url":null,"abstract":"<div><h3>Objective</h3><div>Tyrosine kinase inhibitors (TKIs), such as sorafenib, are standard therapies for advanced hepatocellular carcinoma (HCC), but their biomechanical impact and the role of magnetic resonance elastography (MRE) in treatment evaluation remain unclear. This study explored whether TKIs reduce tumor stiffness by inhibiting malignant behavior and whether MRE can detect such changes early.</div></div><div><h3>Methods</h3><div>A prospective animal study was performed using subcutaneous SK-HEP-1 HCC xenografts in 50 nude rats. Forty tumor-bearing rats were randomized to control or sorafenib-treated groups (<em>n</em> = 20 each). Multiparametric 3.0 T MRI included T1- and T2-weighted imaging, T1/T2/T2* mapping, and MRE at 200 Hz and 100 Hz. Imaging was conducted at baseline (∼2 cm<sup>3</sup> tumor volume) and on days 1, 2, and 3 post-intervention. Histology involved H&E and immunohistochemistry for VEGFR-1, BRAF, Ki67, and TUNEL. Ex vivo stiffness was measured by atomic force microscopy. Cell behavior was assessed by EdU, Transwell, CCK-8, and Western blot. Statistical analysis included ICC, Bland–Altman, Mann–Whitney U, repeated measures ANOVA, Spearman correlation, and multivariate regression.</div></div><div><h3>Results</h3><div>TKIs reduced tumor stiffness at cellular (<em>P</em> = 0.02) and tissue (<em>P</em> = 0.004) levels. Stiffness decreased by day 2 at 200 Hz and day 3 at both frequencies. Treated tumors showed reduced cellularity, lower Ki67, and increased apoptosis. Stiffness correlated with cellularity (<em>r</em> = 0.527) and Ki67 (<em>r</em> = 0.623), both predicting MRE stiffness (R<sup>2</sup> = 0.537).</div></div><div><h3>Conclusion</h3><div>TKIs reduce stiffness and malignancy in HCC. MRE is a promising tool for early treatment response evaluation.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110577"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145634504","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 explore the value of time-dependent diffusion MRI(Td-dMRI) in predicting the expression status of cytokeratin 19(CK19) in hepatocellular carcinoma(HCC) before surgery.
Materials and methods
Prospective inclusion of 72 HCC patients confirmed by surgical pathology (43 CK19-negative and 29 CK19-positive). All patients underwent time-dependent diffusion MRI (Td-dMRI) using a 3.0 T MR scanner before surgery, and quantitative parameters were calculated. Clinical data and MRI features of the patients were collected. Using univariate and multivariate logistic regression analysis to identify the risk factors for CK19 positive expression and establish a predictive model. The diagnostic performance of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration analysis.
Results
CK19-positive HCC exhibited significantly lower d and higher cellularity compared to CK19-negative HCC. CK19-positive HCC demonstrated significantly higher proportions of AFP (>200 ng/ml), CEA (>5 ng/ml), arterial phase rim enhancement, peritumoral hypointensity on hepatobiliary phase, peritumoral arterial hyperenhancement, and intratumoral necrosis/hemorrhage than CK19-negative HCC. Univariate and multivariate logistic regression analyses identified cellularity, AFP (>200 ng/ml), arterial phase rim enhancement, and peritumoral hypointensity on hepatobiliary phase as independent predictors of CK19 positivity. The combined model incorporating these four factors achieved an AUC of 0.889 (95 % CI: 0.809–0.968), with a sensitivity of 82.8 % and specificity of 86.0 %.
Conclusions
The cellularity value based on Td-dMRI was a potential quantitative biomarker for predicting CK19-positive HCC.
{"title":"Time-dependent diffusion MRI combined with enhanced MRI and clinical indicators for preoperative prediction of CK19 expression status in hepatocellular carcinoma: a prospective study","authors":"Yu-chen Wei , Xing-Qing Qin , Jian-sun Li , Yuan-fang Tao , Chongze Yang , Qing ling Huang , Yan-yan Yu , Huiting Zhang , Haodong Qin , Thorsten Feiweier , Jin-yuan Liao","doi":"10.1016/j.mri.2025.110602","DOIUrl":"10.1016/j.mri.2025.110602","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the value of time-dependent diffusion MRI(Td-dMRI) in predicting the expression status of cytokeratin 19(CK19) in hepatocellular carcinoma(HCC) before surgery.</div></div><div><h3>Materials and methods</h3><div>Prospective inclusion of 72 HCC patients confirmed by surgical pathology (43 CK19-negative and 29 CK19-positive). All patients underwent time-dependent diffusion MRI (Td-dMRI) using a 3.0 T MR scanner before surgery, and quantitative parameters were calculated. Clinical data and MRI features of the patients were collected. Using univariate and multivariate logistic regression analysis to identify the risk factors for CK19 positive expression and establish a predictive model. The diagnostic performance of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration analysis.</div></div><div><h3>Results</h3><div>CK19-positive HCC exhibited significantly lower d and higher cellularity compared to CK19-negative HCC. CK19-positive HCC demonstrated significantly higher proportions of AFP (>200 ng/ml), CEA (>5 ng/ml), arterial phase rim enhancement, peritumoral hypointensity on hepatobiliary phase, peritumoral arterial hyperenhancement, and intratumoral necrosis/hemorrhage than CK19-negative HCC. Univariate and multivariate logistic regression analyses identified cellularity, AFP (>200 ng/ml), arterial phase rim enhancement, and peritumoral hypointensity on hepatobiliary phase as independent predictors of CK19 positivity. The combined model incorporating these four factors achieved an AUC of 0.889 (95 % CI: 0.809–0.968), with a sensitivity of 82.8 % and specificity of 86.0 %.</div></div><div><h3>Conclusions</h3><div>The cellularity value based on Td-dMRI was a potential quantitative biomarker for predicting CK19-positive HCC.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110602"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850324","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 model and predict the dynamics of conductive nonmagnetic objects moved within the MRI room under the influence of Lenz effect. High frequency motions, like vibrations induced by gradient eddy currents are not taken into account.
Methods:
The dynamics are described by an ordinary differential equation and the Lenz effect approximated under the assumption of negligible skin effect. This allows to separate the Lenz effect dependency on the object position and velocity, leading to a simple numerical procedure for objects of any shape.
Results:
The proposed model and numerical procedure were validated with experimental data recording the rotation of an aluminium plate falling inside a 1.5 T MRI scanner. The model was also applied for studying the translation of an aluminium plate pushed with constant force towards the MRI bore through the fringe field.
Conclusion:
The collected results showed that it is possible to obtain accurate predictions of motion in the presence of Lenz effect by neglecting the skin effect while determining the motional eddy currents induced in the metallic object.
目的：模拟和预测在伦兹效应的影响下，在核磁共振室内移动的导电非磁性物体的动力学。高频运动，如由梯度涡流引起的振动没有被考虑在内。方法：用常微分方程描述动力学，并在可忽略集肤效应的假设下近似地描述Lenz效应。这允许分离依赖于物体位置和速度的伦茨效应，导致任何形状的物体的一个简单的数值过程。结果：所提出的模型和数值过程通过记录铝板落在1.5 T MRI扫描仪内的旋转的实验数据得到验证。该模型还应用于研究铝板在恒力作用下通过边缘场向核磁共振成像孔的平移。结论：收集到的结果表明，在确定金属物体中产生的运动涡流时，忽略集肤效应可以获得存在Lenz效应时的准确运动预测。
{"title":"Lenz effect in conductive nonmagnetic objects moved in MRI environments","authors":"Alessandro Arduino , Oriano Bottauscio , Michael Steckner , Umberto Zanovello , Luca Zilberti","doi":"10.1016/j.mri.2025.110605","DOIUrl":"10.1016/j.mri.2025.110605","url":null,"abstract":"<div><h3>Purpose:</h3><div>To model and predict the dynamics of conductive nonmagnetic objects moved within the MRI room under the influence of Lenz effect. High frequency motions, like vibrations induced by gradient eddy currents are not taken into account.</div></div><div><h3>Methods:</h3><div>The dynamics are described by an ordinary differential equation and the Lenz effect approximated under the assumption of negligible skin effect. This allows to separate the Lenz effect dependency on the object position and velocity, leading to a simple numerical procedure for objects of any shape.</div></div><div><h3>Results:</h3><div>The proposed model and numerical procedure were validated with experimental data recording the rotation of an aluminium plate falling inside a 1.5 T MRI scanner. The model was also applied for studying the translation of an aluminium plate pushed with constant force towards the MRI bore through the fringe field.</div></div><div><h3>Conclusion:</h3><div>The collected results showed that it is possible to obtain accurate predictions of motion in the presence of Lenz effect by neglecting the skin effect while determining the motional eddy currents induced in the metallic object.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110605"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880744","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: 2025-12-29DOI: 10.1016/j.mri.2025.110606
Shuluan Chen , Shunan Che , Mengying Yang , Yufei Chen , Yuan Tian , Kun Ma , Sicong Wang , Jing Li
Objectives
This study aimed to develop and validate a prognostic nomogram integrating baseline MRI and clinicopathological features to predict disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).
Materials and methods
A retrospective cohort of 402 invasive breast cancer patients who underwent pre-treatment MRI, NAC, and surgery between January 2014 and December 2021 was analyzed. Patients were randomly assigned to a training group (n = 280) and a validation group (n = 122). Variables were selected via univariate Cox regression and Lasso-Cox analyses, with significant factors used to construct nomogram models. The clinicopathological, baseline MRI and combined models were constructed. Model performance was assessed using the area under the curve (AUC), concordance index (C-index), and calibration curves. A risk score derived from the combined model facilitated stratification into high- and low-risk groups, with log-rank test used for survival comparison.
Results
Key predictors in the clinicopathological model included clinical T stage, pathological complete response (pCR) in primary tumor, pCR in axillary lymph nodes, and lymphovascular invasion. MRI-based predictors included multifocal or multicentric lesions, subcutaneous edema, and ipsilateral suspicious internal mammary lymph nodes. The combined model outperformed the clinicopathological (training C-index = 0.67, validation C-index = 0.754) and baseline MRI models (training C-index = 0.665, validation C-index = 0.605), achieving C-indices of 0.706 and 0.719 in the training and validation groups, respectively. A risk score cut-off of −0.35 effectively stratified patients into high- and low-risk groups.
Conclusion
This combined nomogram integrating clinicopathological and MRI features offers improved predictive accuracy for DFS in breast cancer patients after NAC, enabling enhanced risk stratification and individualized follow-up strategies.
{"title":"Development and validation of a prognostic prediction nomogram incorporating MRI and clinicopathological features in breast cancer patients after neoadjuvant chemotherapy","authors":"Shuluan Chen , Shunan Che , Mengying Yang , Yufei Chen , Yuan Tian , Kun Ma , Sicong Wang , Jing Li","doi":"10.1016/j.mri.2025.110606","DOIUrl":"10.1016/j.mri.2025.110606","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to develop and validate a prognostic nomogram integrating baseline MRI and clinicopathological features to predict disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).</div></div><div><h3>Materials and methods</h3><div>A retrospective cohort of 402 invasive breast cancer patients who underwent pre-treatment MRI, NAC, and surgery between January 2014 and December 2021 was analyzed. Patients were randomly assigned to a training group (<em>n</em> = 280) and a validation group (<em>n</em> = 122). Variables were selected via univariate Cox regression and Lasso-Cox analyses, with significant factors used to construct nomogram models. The clinicopathological, baseline MRI and combined models were constructed. Model performance was assessed using the area under the curve (AUC), concordance index (C-index), and calibration curves. A risk score derived from the combined model facilitated stratification into high- and low-risk groups, with log-rank test used for survival comparison.</div></div><div><h3>Results</h3><div>Key predictors in the clinicopathological model included clinical T stage, pathological complete response (pCR) in primary tumor, pCR in axillary lymph nodes, and lymphovascular invasion. MRI-based predictors included multifocal or multicentric lesions, subcutaneous edema, and ipsilateral suspicious internal mammary lymph nodes. The combined model outperformed the clinicopathological (training C-index = 0.67, validation C-index = 0.754) and baseline MRI models (training C-index = 0.665, validation C-index = 0.605), achieving C-indices of 0.706 and 0.719 in the training and validation groups, respectively. A risk score cut-off of −0.35 effectively stratified patients into high- and low-risk groups.</div></div><div><h3>Conclusion</h3><div>This combined nomogram integrating clinicopathological and MRI features offers improved predictive accuracy for DFS in breast cancer patients after NAC, enabling enhanced risk stratification and individualized follow-up strategies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110606"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878664","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: 2025-11-27DOI: 10.1016/j.mri.2025.110578
Kevin Sun Zhang , Philip Alexander Glemser , Christian Jan Oliver Neelsen , Markus Wennmann , Lukas Thomas Rotkopf , Nils Netzer , Clara Meinzer , Thomas Hielscher , Vivienn Weru , Magdalena Görtz , Albrecht Stenzinger , Markus Hohenfellner , Heinz-Peter Schlemmer , David Bonekamp
Objectives
To assess variability of maximum diameter measurements of prostate lesions in MRI assessing patient repositioning, rater and sequence effects.
Methods
Forty-two patients were included retrospectively, who received a clinical bi−/multiparametric prostate MRI examination and agreed to have the T2-weighted (T2WI) and diffusion weighted-imaging (DWI) sequences scanned twice. Maximum diameter measurements of prostate lesions mentioned in the clinical radiologist reports were performed by four readers in multiple reading sessions for determination of inter-sequence (between two DWI sequences), inter-scan (between clinical and additional scan), intra-rater and inter-rater variability. The primary calculated metrics were the repeatability and reproducibility coefficient (RC/RDC), including pooled RC/RDC.
Results
Variability measured by RCs/RDCs was lowest for measurements obtained within the same reading session, with inter-scan RCs up to 5.6 mm/6.5 mm for T2WI/DWI, pooled RCs of 4.8 mm/5.8 mm, respectively, and inter-sequence RDCs of 5.4 mm–5.9 mm, pooled RDC 5.8 mm. Measurements performed in separate reading sessions demonstrated significantly higher variability for both settings in the majority of cases (RCs: up to 10.9 mm/11.7 mm/10.2 mm for T2WI/DWI/inter-sequence, p ≤ 0.002), pooled RCs/RDCs 9.2 mm–9.9 mm. Measurements necessarily generated in different reading sessions, i.e., intra-rater or inter-rater, demonstrated high variability (RCs/RDCs up to 11.4 mm/11.5 mm for T2WI/DWI).
Conclusions
Prostate lesion measurements demonstrate considerable variability. When measured in one reading session by one rater, lesion diameter differences below the pooled RCs of 4.8 mm, 95 %-CI [3.9, 5.6] for T2WI and 5.8 mm, 95 %-CI [4.7, 7.1] for DWI should not necessarily assumed to be true biological change, as these differences may result from measurement- or repositioning-based variability alone. Caution needs to be taken assessing size changes.
目的：评估磁共振成像中前列腺病变最大直径测量的可变性，以评估患者重新定位、排序和序列效应。方法：回顾性分析42例接受临床双参数/多参数前列腺MRI检查的患者，并同意进行2次t2加权（T2WI）和弥散加权成像（DWI）序列扫描。临床放射科医生报告中提到的前列腺病变的最大直径测量由四名读取器在多次读取会话中完成，以确定序列间（两个DWI序列之间）、扫描间（临床和附加扫描之间）、分级内和分级间的变异性。主要计算指标为重复性和再现性系数（RC/RDC），包括合并RC/RDC。结果：在相同的读数过程中，RCs/RDC测量的变变性最低，T2WI/DWI的扫描间RCs高达5.6 mm/6.5 mm，合并RCs分别为4.8 mm/5.8 mm，序列间RDC为5.4 mm-5.9 mm，合并RDC为5.8 mm。在单独的读数过程中进行的测量显示，在大多数情况下，这两种设置的变异性显著更高（T2WI/DWI/序列间的RCs：高达10.9 mm/11.7 mm/10.2 mm, p ≤ 0.002），合并的RCs/RDCs为9.2 mm-9.9 mm。在不同的阅读过程中产生的测量结果，即内部或内部的测量结果，显示出很高的可变性（T2WI/DWI的RCs/ rdc高达11.4 mm/11.5 mm）。结论：前列腺病变测量显示出相当大的可变性。当由一名评估者在一次读数中测量时，T2WI的病变直径差异低于4.8 mm, 95% %- ci [3.9, 5.6]， DWI的病变直径差异低于5.8 mm, 95% %- ci[4.7, 7.1]，这并不一定被认为是真正的生物学变化，因为这些差异可能仅仅是由测量或重新定位的可变性造成的。评估大小变化时需要谨慎。
{"title":"Repeatability and reproducibility of maximum diameter measurements of prostate lesions on MRI with repositioning and variation of imaging sequences: A test-retest study","authors":"Kevin Sun Zhang , Philip Alexander Glemser , Christian Jan Oliver Neelsen , Markus Wennmann , Lukas Thomas Rotkopf , Nils Netzer , Clara Meinzer , Thomas Hielscher , Vivienn Weru , Magdalena Görtz , Albrecht Stenzinger , Markus Hohenfellner , Heinz-Peter Schlemmer , David Bonekamp","doi":"10.1016/j.mri.2025.110578","DOIUrl":"10.1016/j.mri.2025.110578","url":null,"abstract":"<div><h3>Objectives</h3><div>To assess variability of maximum diameter measurements of prostate lesions in MRI assessing patient repositioning, rater and sequence effects.</div></div><div><h3>Methods</h3><div>Forty-two patients were included retrospectively, who received a clinical bi−/multiparametric prostate MRI examination and agreed to have the T2-weighted (T2WI) and diffusion weighted-imaging (DWI) sequences scanned twice. Maximum diameter measurements of prostate lesions mentioned in the clinical radiologist reports were performed by four readers in multiple reading sessions for determination of inter-sequence (between two DWI sequences), inter-scan (between clinical and additional scan), intra-rater and inter-rater variability. The primary calculated metrics were the repeatability and reproducibility coefficient (RC/RDC), including pooled RC/RDC.</div></div><div><h3>Results</h3><div>Variability measured by RCs/RDCs was lowest for measurements obtained within the same reading session, with inter-scan RCs up to 5.6 mm/6.5 mm for T2WI/DWI, pooled RCs of 4.8 mm/5.8 mm, respectively, and inter-sequence RDCs of 5.4 mm–5.9 mm, pooled RDC 5.8 mm. Measurements performed in separate reading sessions demonstrated significantly higher variability for both settings in the majority of cases (RCs: up to 10.9 mm/11.7 mm/10.2 mm for T2WI/DWI/inter-sequence, <em>p</em> ≤ 0.002), pooled RCs/RDCs 9.2 mm–9.9 mm. Measurements necessarily generated in different reading sessions, i.e., intra-rater or inter-rater, demonstrated high variability (RCs/RDCs up to 11.4 mm/11.5 mm for T2WI/DWI).</div></div><div><h3>Conclusions</h3><div>Prostate lesion measurements demonstrate considerable variability. When measured in one reading session by one rater, lesion diameter differences below the pooled RCs of 4.8 mm, 95 %-CI [3.9, 5.6] for T2WI and 5.8 mm, 95 %-CI [4.7, 7.1] for DWI should not necessarily assumed to be true biological change, as these differences may result from measurement- or repositioning-based variability alone. Caution needs to be taken assessing size changes.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110578"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145634519","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 evaluate the diagnostic potential of microstructural parameters derived from time-dependent diffusion magnetic resonance imaging (Td-dMRI) for distinguishing hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC).
Methods
We established nude mouse models bearing subcutaneous xenografts of HCC (MHCC97H, HepG2 cell lines) and ICC (QBC939 cell line) (n = 30). All models underwent Td-dMRI scanning. Microstructural parameters, including cell diameter (d), extracellular diffusion coefficient (Dex), intracellular volume fraction (Vin), and cellularity, were calculated based on the IMPULSED model. Intergroup differences were assessed using independent samples t-test or Mann-Whitney U test (significance threshold: P < 0.05). The diagnostic performance of each parameter was evaluated by receiver operating characteristic (ROC) curve analysis. Post-operative liver tissue specimens were subjected to β-catenin immunohistochemical staining to validate the correlation between imaging parameters and pathological findings.
Results
The ICC group exhibited significantly higher Dex values compared to the HCC group (P < 0.05), whereas d, Vin, and cellularity were significantly lower in the ICC group (P < 0.05). The areas under the ROC curve (AUCs) for differentiating HCC from ICC were 0.838 for Dex, 0.779 for d, 0.833 for Vin, and 0.733 for cellularity. The d value measured by Td-dMRI showed a significant positive correlation with pathological results (r = 0.634, P < 0.05). Notably, combining Vin and cellularity parameters enhanced the AUC to 0.95, outperforming any single parameter.
The ICC group exhibited a significantly higher extracellular diffusivity (Dex) compared to the HCC group, whereas the cell diameter (d), intracellular volume fraction (Vin), and cellularity were significantly lower (all P < 0.05). The area under the.
Conclusion
Td-dMRI enables non-invasive differentiation between HCC and ICC by quantifying distinct tumor microstructural environments. The parameters derived from this technique show promise as potential imaging biomarkers for subtyping liver cancers.
{"title":"Time-dependent diffusion-weighted MRI discriminates hepatocellular carcinoma from intrahepatic cholangiocarcinoma: A prospective animal model study","authors":"Yong-Mei Huang , Yu-Chen Wei , Xing-Qing Qin , Meng-Na Lan , Jin-Yuan Liao","doi":"10.1016/j.mri.2025.110601","DOIUrl":"10.1016/j.mri.2025.110601","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the diagnostic potential of microstructural parameters derived from time-dependent diffusion magnetic resonance imaging (Td-dMRI) for distinguishing hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC).</div></div><div><h3>Methods</h3><div>We established nude mouse models bearing subcutaneous xenografts of HCC (MHCC97H, HepG2 cell lines) and ICC (QBC939 cell line) (<em>n</em> = 30). All models underwent Td-dMRI scanning. Microstructural parameters, including cell diameter (<em>d</em>), extracellular diffusion coefficient (<em>D</em><sub>ex</sub>), intracellular volume fraction (<em>V</em><sub>in</sub>), and cellularity, were calculated based on the IMPULSED model. Intergroup differences were assessed using independent samples <em>t</em>-test or Mann-Whitney <em>U</em> test (significance threshold: <em>P</em> < 0.05). The diagnostic performance of each parameter was evaluated by receiver operating characteristic (ROC) curve analysis. Post-operative liver tissue specimens were subjected to <em>β</em>-catenin immunohistochemical staining to validate the correlation between imaging parameters and pathological findings.</div></div><div><h3>Results</h3><div>The ICC group exhibited significantly higher <em>D</em><sub>ex</sub> values compared to the HCC group (<em>P</em> < 0.05), whereas <em>d</em>, <em>V</em><sub>in</sub>, and cellularity were significantly lower in the ICC group (<em>P</em> < 0.05). The areas under the ROC curve (AUCs) for differentiating HCC from ICC were 0.838 for <em>D</em><sub>ex</sub>, 0.779 for <em>d</em>, 0.833 for <em>V</em><sub>in</sub>, and 0.733 for cellularity. The <em>d</em> value measured by Td-dMRI showed a significant positive correlation with pathological results (<em>r</em> = 0.634, <em>P</em> < 0.05). Notably, combining <em>V</em><sub>in</sub> and cellularity parameters enhanced the AUC to 0.95, outperforming any single parameter.</div><div>The ICC group exhibited a significantly higher extracellular diffusivity (<em>D</em><sub>ex</sub>) compared to the HCC group, whereas the cell diameter (<em>d</em>), intracellular volume fraction (<em>V</em><sub>in</sub>), and cellularity were significantly lower (all <em>P</em> < 0.05). The area under the.</div></div><div><h3>Conclusion</h3><div>Td-dMRI enables non-invasive differentiation between HCC and ICC by quantifying distinct tumor microstructural environments. The parameters derived from this technique show promise as potential imaging biomarkers for subtyping liver cancers.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110601"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145857030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2025-12-14DOI: 10.1016/j.mri.2025.110594
Chengjiang Xu , Yasmin Mushtaq , Xiaoge Liu , Guijiao Qin , Juan Tao , Yajie Liu , Jinge Li , Xinyu Yang , Shaowu Wang
Purpose
To determine whether quantitative parameters from diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI) are associated with proliferative activity in synovial sarcoma (SS) xenografts.
Materials and methods
Thirty-four synovial sarcoma (SS) xenograft models were established. All mice underwent MRI, including DWI (ADC), IVIM (D, D*, f), and DCE-MRI (Ktrans, Kep, Ve). An MR imaging–pathology correlation method was used to align imaging ROIs with pathological sampling sites.
Pearson or Spearman correlation analyses were performed to assess associations between MRI parameters and proliferation-related markers (mitotic count, Ki-67, and PTEN). Differences between high and low groups for each marker were evaluated using independent-sample t-tests or Wilcoxon rank-sum tests. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis, and AUCs were compared with DeLong's test.
Results
The D value was significantly associated with mitotic count, Ki-67 expression, and PTEN expression (P = 0.022, 0.004, 0.001). Ve showed a positive association with mitotic count (P = 0.007), while Ktrans demonstrated a moderate negative association with PTEN expression (P < 0.001). D and Ve showed moderate ability to distinguish high from low mitotic count (AUC = 0.734 and 0.786). The D value showed moderate differentiation between Ki-67 expression groups (AUC = 0.802), and Ktrans provided moderate discrimination between PTEN expression groups (AUC = 0.813).
Conclusion
The proliferative activity of SS xenografts could be assessed using quantitative parameters derived from IVIM and DCE-MRI.
{"title":"Functional MRI quantitative parameters as biomarkers of proliferation in synovial sarcoma xenografts: A study based on precise MR imaging-pathology correlation","authors":"Chengjiang Xu , Yasmin Mushtaq , Xiaoge Liu , Guijiao Qin , Juan Tao , Yajie Liu , Jinge Li , Xinyu Yang , Shaowu Wang","doi":"10.1016/j.mri.2025.110594","DOIUrl":"10.1016/j.mri.2025.110594","url":null,"abstract":"<div><h3>Purpose</h3><div>To determine whether quantitative parameters from diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI) are associated with proliferative activity in synovial sarcoma (SS) xenografts.</div></div><div><h3>Materials and methods</h3><div>Thirty-four synovial sarcoma (SS) xenograft models were established. All mice underwent MRI, including DWI (ADC), IVIM (D, D*, f), and DCE-MRI (K<sup>trans</sup>, K<sub>ep</sub>, V<sub>e</sub>). An MR imaging–pathology correlation method was used to align imaging ROIs with pathological sampling sites.</div><div>Pearson or Spearman correlation analyses were performed to assess associations between MRI parameters and proliferation-related markers (mitotic count, Ki-67, and PTEN). Differences between high and low groups for each marker were evaluated using independent-sample <em>t</em>-tests or Wilcoxon rank-sum tests. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis, and AUCs were compared with DeLong's test.</div></div><div><h3>Results</h3><div>The D value was significantly associated with mitotic count, Ki-67 expression, and PTEN expression (<em>P</em> = 0.022, 0.004, 0.001). V<sub>e</sub> showed a positive association with mitotic count (<em>P</em> = 0.007), while K<sup>trans</sup> demonstrated a moderate negative association with PTEN expression (<em>P</em> < 0.001). D and V<sub>e</sub> showed moderate ability to distinguish high from low mitotic count (AUC = 0.734 and 0.786). The D value showed moderate differentiation between Ki-67 expression groups (AUC = 0.802), and K<sup>trans</sup> provided moderate discrimination between PTEN expression groups (AUC = 0.813).</div></div><div><h3>Conclusion</h3><div>The proliferative activity of SS xenografts could be assessed using quantitative parameters derived from IVIM and DCE-MRI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110594"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145768561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2025-12-01DOI: 10.1016/j.mri.2025.110579
Yuan Lian, Yuancheng Jiang, Hua Guo
Purpose
Proper regularization weights are crucial for the reconstruction quality of compressed sensing (CS) MRI. This work aims to develop an automatic and adaptive regularization weights selection method for CS reconstruction
Methods
A statistical model based on Bayesian theory is designed, incorporating prior information about the Gaussian distribution of incoherent noise and the Laplacian distribution of wavelet coefficients in the wavelet transform domain. Using the variance of coefficients and noise, the adaptive regularization weight for achieving optimal reconstruction quality in each iteration step is obtained through a maximum a posteriori estimator. The adaptive regularization weights vary across different subjects, slices, iterations, and wavelet sub-bands
Results
The efficacy of the proposed method was demonstrated through retrospective and prospective studies. Compared to reconstruction results using optimal fixed regularization weights and sparsity-adaptive composite recovery method (SCoRe), the proposed method successfully reduces reconstruction errors and effectively recovers original signals from noise-like incoherent artifacts in the wavelet transform domain. It also saves weight selection time when searching for optimal fixed regularization weights
Conclusion
We propose an adaptive regularization weights selection method for CS-MRI reconstruction. It provides optimal regularization weights for different subjects, slices, and iterations without requiring manual intervention
{"title":"Adaptive regularization weight selection for compressed sensing MRI reconstruction","authors":"Yuan Lian, Yuancheng Jiang, Hua Guo","doi":"10.1016/j.mri.2025.110579","DOIUrl":"10.1016/j.mri.2025.110579","url":null,"abstract":"<div><h3>Purpose</h3><div>Proper regularization weights are crucial for the reconstruction quality of compressed sensing (CS) MRI. This work aims to develop an automatic and adaptive regularization weights selection method for CS reconstruction</div></div><div><h3>Methods</h3><div>A statistical model based on Bayesian theory is designed, incorporating prior information about the Gaussian distribution of incoherent noise and the Laplacian distribution of wavelet coefficients in the wavelet transform domain. Using the variance of coefficients and noise, the adaptive regularization weight for achieving optimal reconstruction quality in each iteration step is obtained through a maximum a posteriori estimator. The adaptive regularization weights vary across different subjects, slices, iterations, and wavelet sub-bands</div></div><div><h3>Results</h3><div>The efficacy of the proposed method was demonstrated through retrospective and prospective studies. Compared to reconstruction results using optimal fixed regularization weights and sparsity-adaptive composite recovery method (SCoRe), the proposed method successfully reduces reconstruction errors and effectively recovers original signals from noise-like incoherent artifacts in the wavelet transform domain. It also saves weight selection time when searching for optimal fixed regularization weights</div></div><div><h3>Conclusion</h3><div>We propose an adaptive regularization weights selection method for CS-MRI reconstruction. It provides optimal regularization weights for different subjects, slices, and iterations without requiring manual intervention</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110579"},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145668927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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