Magnetic resonance imaging最新文献

{"title":"Noise reduction in magnitude diffusion-weighted images using spatial similarity and diffusion redundancy","authors":"Liming Yang,&nbsp;Yuanjun Wang","doi":"10.1016/j.mri.2025.110344","DOIUrl":"10.1016/j.mri.2025.110344","url":null,"abstract":"<div><h3>Purpose</h3><div>Diffusion-weighted imaging (DWI) has significant value in clinical application, which however suffers from a serious low signal-to-noise ratio (SNR) problem, especially at high spatial resolution and/or high diffusion sensitivity factor.</div></div><div><h3>Methods</h3><div>Here, we propose a denoising method for magnitude DWI. The method consists of two modules: pre-denoising and post-filtering, the former mines the diffusion redundancy by local kernel principal component analysis, and the latter fully mines the non-local self-similarity using patch-based non-local mean.</div></div><div><h3>Results</h3><div>Validated by simulation and in vivo datasets, the experiment results show that the proposed method is capable of improving the SNR of the whole brain, thus enhancing the performance for diffusion metrics estimation, crossing fiber discrimination, and human brain fiber tractography tracking compared with the different three state-of-the-art comparison methods. More importantly, the proposed method consistently exhibits superior performance to comparison methods when used for denoising diffusion data acquired with sensitivity encoding (SENSE).</div></div><div><h3>Conclusion</h3><div>The proposed denoising method is expected to show significant practicability in acquiring high-quality whole-brain diffusion data, which is crucial for many neuroscience studies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110344"},"PeriodicalIF":2.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074824","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":"JotlasNet: Joint tensor low-rank and attention-based sparse unrolling network for accelerating dynamic MRI","authors":"Yinghao Zhang ,&nbsp;Haiyan Gui ,&nbsp;Ningdi Yang ,&nbsp;Yue Hu","doi":"10.1016/j.mri.2025.110337","DOIUrl":"10.1016/j.mri.2025.110337","url":null,"abstract":"<div><div>Joint low-rank and sparse unrolling networks have shown superior performance in dynamic MRI reconstruction. However, existing works mainly utilized matrix low-rank priors, neglecting the tensor characteristics of dynamic MRI images, and only a global threshold is applied for the sparse constraint to the multi-channel data, limiting the flexibility of the network. Additionally, most of them have inherently complex network structure, with intricate interactions among variables. In this paper, we propose a novel deep unrolling network, JotlasNet, for dynamic MRI reconstruction by jointly utilizing tensor low-rank and attention-based sparse priors. Specifically, we utilize tensor low-rank prior to exploit the structural correlations in high-dimensional data. Convolutional neural networks are used to adaptively learn the low-rank and sparse transform domains. A novel attention-based soft thresholding operator is proposed to assign a unique learnable threshold to each channel of the data in the CNN-learned sparse domain. The network is unrolled from the elaborately designed composite splitting algorithm and thus features a simple yet efficient parallel structure. Extensive experiments on two datasets (OCMR, CMRxRecon) demonstrate the superior performance of JotlasNet in dynamic MRI reconstruction.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110337"},"PeriodicalIF":2.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074611","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":"Association of preoperative MRI with breast cancer treatment and survival: A single institution observational study","authors":"B. Bersu Ozcan ,&nbsp;Ann R. Mootz ,&nbsp;Dogan S. Polat ,&nbsp;Yin Xi ,&nbsp;Asal Rahimi ,&nbsp;Başak E. Dogan","doi":"10.1016/j.mri.2025.110343","DOIUrl":"10.1016/j.mri.2025.110343","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Purpose&lt;/h3&gt;&lt;div&gt;To evaluate the association between preoperative breast MRI with surgery type, contralateral cancer, recurrence-free (RFS) and overall survival (OS) in women with early-stage breast cancer.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Materials and methods&lt;/h3&gt;&lt;div&gt;In this dual-affiliated single institution, retrospective study, we identified women with Stage I-III breast cancer diagnosed between 03/01/2013–03/31/2016 with available follow-up. Patient and tumor characteristics were recorded. Two cohorts were created based on the use of preoperative MRI(PMRI) versus no preoperative MRI(no-MRI) with Wilcoxon signed-rank and χ2 tests utilized for cross-group comparisons. Kaplan-Meier, log-rank and cox proportional hazards model analysis were used to compare RFS and OS in women with and without MRI.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;593 eligible patients were included [322(54.3 %) with PMRI, 271(45.7 %) no-MRI]. Mean patient age was younger (53.8 ± 11.8vs59.3 ± 12.6 years, &lt;em&gt;p&lt;/em&gt; &lt; 0.001) and dense breasts more common (51.6 %vs22.5 %, p &lt; 0.001) in PMRI group. Seventeen bilateral cancers (5.3 %) were in PMRI [14/17(82.4 %) detected only on MRI] vs 10 (3.7 %) in no-MRI (&lt;em&gt;p&lt;/em&gt; = 0.34). Molecular subtype distribution(luminal A:27.2 % vs 31.1 %; luminal B:51.8 %vs44.2 %; HER2:5.4 %vs4.2 %; triple negative:15.6 %vs20.5 %, &lt;em&gt;p&lt;/em&gt; = 0.28) were similar in PMRI vs no-MRI groups. PMRI group had higher rates of cT2–4(45.0 %vs28.8 %, &lt;em&gt;p&lt;/em&gt; &lt; 0.001), cN+(27.3 % vs 18.1 %, &lt;em&gt;p&lt;/em&gt; &lt; 0.01), and neoadjuvant therapy (NAC, 41.3 % vs 18.8 %, p &lt; 0.001). Total mastectomy(57.8 %vs51.3 %, &lt;em&gt;p&lt;/em&gt; = 0.12), margin positivity(6.2 %vs7.4 %, &lt;em&gt;p&lt;/em&gt; = 0.63), recurrence(10.2 %vs7.0 %, &lt;em&gt;p&lt;/em&gt; = 0.20) and death rates(8.1 %vs7.7 %, &lt;em&gt;p&lt;/em&gt; = 0.88) were similar in PMRI vs no-MRI. Mastectomy rates remained comparable after adjusting for age and breast density (&lt;em&gt;p&lt;/em&gt; = 0.28). At median follow-up of 70 months(IQR, 64–70), time to recurrence was [PMRI:30(IQR, 19–47)vs no-MRI:23(IQR, 9–31) months, &lt;em&gt;p&lt;/em&gt; = 0.04]. Contralateral cancers were identified sooner and more frequently in the no-MRI group [4(2.1 %)vs2(0.9 %) cancers, &lt;em&gt;p&lt;/em&gt; = 0.32, 21 ± 20vs48 ± 13 months, &lt;em&gt;p&lt;/em&gt; = 0.27]. There was no significant difference in 5-year RFS[hazard ratio(HR) 1.05, 95 %CI 0.67–1.67, &lt;em&gt;p&lt;/em&gt; = 0.84] and OS[HR 0.94, 95 %CI: 0.51–1.74, &lt;em&gt;p&lt;/em&gt; = 0.85] between PMRI and no-MRI groups even after adjusting for age, cancer type, breast density, cN stage, and NAC. which were different between two groups (RFS, HR 0.87, 95 %CI: 0.53–1.43, &lt;em&gt;p&lt;/em&gt; = 0.57; OS, HR 0.78, 95 %CI: 0.40–1.52, &lt;em&gt;p&lt;/em&gt; = 0.46). NHW patients had higher RFS compared to Black patients in PMRI group (HR 0.45, 95 % CI: 0.21–0.96, &lt;em&gt;p&lt;/em&gt; = 0.04) in adjusted analysis.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;Preoperative MRI utilization is not associated with improved surgical margin, 5-year RFS or OS in our cohort. This effect persisted after adjusting for p","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110343"},"PeriodicalIF":2.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074181","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":"Advanced diffusion-weighted imaging-derived quantitative parameters as biomarkers of fibrosarcoma-cell proliferation in nude mice: A study based on precise imaging-pathology correlation","authors":"Jie Huang ,&nbsp;Zhiqing Duan ,&nbsp;Yu Cheng ,&nbsp;Juan Tao ,&nbsp;Siyu Dai ,&nbsp;Jianwen Zhou ,&nbsp;Shaowu Wang","doi":"10.1016/j.mri.2025.110345","DOIUrl":"10.1016/j.mri.2025.110345","url":null,"abstract":"<div><h3>Purpose</h3><div>To determine whether quantitative parameters derived using diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) imaging reflect pathological changes in fibrosarcoma.</div></div><div><h3>Methods</h3><div>Thirty nude mouse models of fibrosarcoma underwent T1/T2-weighted imaging, DKI, and IVIM imaging on a 3.0-T scanner. Immunohistochemistry was utilized for the hematoxylin and eosin, aquaporin 1 (AQP1), aquaporin 4 (AQP4), and Ki-67 staining of fibrosarcoma tissue, and AQP1 and AQP4 staining of normal muscle tissue (NMT). The independent-sample <em>t</em>-test was used to compare AQP1 and AQP4 expression in fibrosarcoma and NMT. Pearson and Spearman correlation analyses were conducted to evaluate the correlation between imaging parameters and pathological indicators. Multiple linear regression analysis was employed to identify the pathological indicators independently associated with quantitative DKI and IVIM parameters.</div></div><div><h3>Results</h3><div>Apparent diffusion coefficient (ADC), D, f, and mean kurtosis (MK) indicated cell density and Ki-67 and AQP1 expression intensity. D values reflected AQP4 expression intensity, while MD reflected cell density and AQP1 expression intensity. Cell density (CD) independently influenced ADC and f values, while CD and AQP1 independently influenced D values.</div></div><div><h3>Conclusion</h3><div>CD and Ki-67 independently influenced MK. DKI- and IVIM imaging-derived ADC, D, f, MD, and MK were correlated with AQP1, AQP4, Ki-67, and CD in nude mice with fibrosarcoma.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110345"},"PeriodicalIF":2.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073649","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":"Prediction of metachronous liver metastasis in mid-low rectal cancer using quantitative perirectal fat content from high-resolution MRI","authors":"Jiaming Qin ,&nbsp;Wenjin Dong ,&nbsp;Fengshu Zhao ,&nbsp;Tianqi Liu ,&nbsp;Mengxin Chen ,&nbsp;Rui Zhang ,&nbsp;Yumeng Zhao ,&nbsp;Cheng Zhang ,&nbsp;Wenhong Wang","doi":"10.1016/j.mri.2025.110338","DOIUrl":"10.1016/j.mri.2025.110338","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the relationship between perirectal fat content and metachronous liver metastasis (MLM) in patients with Mid-low rectal cancer (MLRC).</div></div><div><h3>Materials and methods</h3><div>A retrospective analysis was conducted on 254 patients who underwent curative surgery for MLRC between December 2016 and December 2021. Preoperative MRI measurements of the rectal mesenteric fat area (MFA), rectal posterior mesorectal thickness (PMT), and rectal mesenteric fascia envelopment volume (MFEV) were performed, along with collection of relevant clinical, pathological, and imaging data. Patients were categorized into the MLM group (Group A), other recurrence or metastasis group (Group B), and no recurrence and metastasis group (Group C). Analyze the differences between Group A and the other groups, and independent risk factors for MLM were explored. Kaplan-Meier analysis and log-rank test were used to validate independent predictive biomarkers for MLM.</div></div><div><h3>Results</h3><div>Patients with MLM from MLRC had later pathological and imaging T stages and lower perirectal fat content (all <em>P</em> &lt; 0.05). Compared to patients with other types of recurrent metastasis, male gender, poorly differentiated tumors, and advanced tumor N stage were more likely to develop MLM (all <em>P</em> &lt; 0.05). In Cox univariate and multivariate regression analysis, smaller rectal PMT (hazard ratio (HR) 0.361 [0.154–0.846], <em>P</em> = 0.019) and MFEV (HR 0.983 [0.968–0.998], <em>P</em> = 0.022) were independently associated with MLM in MLRC (HR 0.361;0.983). Kaplan-Meier analysis showed that patients with rectal PMT &lt;1.43 cm and rectal MFEV &lt;137.46 cm³ had a significantly higher risk of MLM compared to patients with rectal PMT ≥1.43 cm and rectal MFEV ≥137.46 cm³ (all <em>P</em> &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>Rectal PMT and rectal MFEV can serve as novel parameters for predicting MLM in patients with MLRC.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110338"},"PeriodicalIF":2.1,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074892","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":"Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach","authors":"Guihai Pan ,&nbsp;Zejun Pan ,&nbsp;Wubiao Chen ,&nbsp;Yongjun Wu ,&nbsp;Xiaoqing Di ,&nbsp;Fei Zhou ,&nbsp;Yuting Liao","doi":"10.1016/j.mri.2025.110339","DOIUrl":"10.1016/j.mri.2025.110339","url":null,"abstract":"<div><h3>Background</h3><div>Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectiveness of integrating MRI radiomic features with clinical data using a deep learning approach to predict vascular invasion in breast cancer patients.</div></div><div><h3>Methods</h3><div>A retrospective analysis was conducted on 102 patients with invasive breast cancer confirmed by surgical pathology. Using the MR750 3.0 T as the examination device, the subject underwent the examination in standard breast positions and sequences. Diffusion-weighted imaging (DWI) was performed with two selected b-values, specifically 0 and 1000 s/mm². Following the injection of the contrast agent, dynamic scans were conducted across six phases, and delayed phase sagittal images were acquired using the VIBRANT sequence. Texture features were extracted from MRI images, and key radiomic and clinical features were selected using variance thresholding, correlation filtering, and logistic regression. A predictive model was developed combining these features, and its performance was evaluated through sensitivity, specificity, and area under the curve (AUC) metrics.</div></div><div><h3>Results</h3><div>The univariate models based on individual MRI sequences or clinical data demonstrated variable diagnostic performance. In contrast, the multifactorial model that combined radiomic features with clinical data achieved significantly higher accuracy, with an AUC of 0.829, sensitivity of 76.9 %, and specificity of 83.3 %.</div></div><div><h3>Conclusion</h3><div>Integrating MRI radiomics and clinical data enhances the preoperative prediction of vascular invasion in breast cancer. This approach can improve diagnostic accuracy, providing valuable insights for clinical decision-making and personalized treatment strategies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110339"},"PeriodicalIF":2.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066616","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":"Diffusion-weighted arterial spin labeling MRI to investigate mannitol-induced blood brain barrier disruption","authors":"Yash Vardhan Tiwari ,&nbsp;Eric R. Muir ,&nbsp;Zhao Jiang ,&nbsp;Tim Q. Duong","doi":"10.1016/j.mri.2025.110335","DOIUrl":"10.1016/j.mri.2025.110335","url":null,"abstract":"<div><h3>Purpose</h3><div>Diffusion-weighted arterial spin labeling (DW-ASL) MRI has been proposed to determine the rate of water exchange (K_w) across the blood brain barrier (BBB). This study aims to further evaluate K_w MRI by comparing it with standard dynamic contrast-enhanced (DCE) MRI and histology in association with mannitol-induced disruption of the BBB.</div></div><div><h3>Methods</h3><div>DW-ASL was measured using a multiple b-value MRI protocol in normal rats at three post-labeling delays (<em>N</em> = 19), before and after intra-carotid injection of mannitol to disrupt BBB in one hemisphere (<em>N</em> = 13). An approach using only two b-values to detect mannitol-induced changes was also tested. DCE MRI and Evans blue histology were performed on the same animals. Quantitative analysis and pixel-by-pixel correlation were performed amongst K_w, DCE MRI and Evans blue histology.</div></div><div><h3>Results</h3><div>K_w in the grey matter in the normal rat brain was 252 ± 38 min⁻¹ (±standard error of the mean). The two b-value approach provided reasonable approximation of multiple-b DW-ASL parameters, reducing acquisition time. K_w is sensitive to mannitol-induced changes in BBB permeability and was reduced to 89 ± 17 min⁻¹ in the affected hemisphere compared to 191 ± 22 min⁻¹ in the unaffected hemisphere (<em>P</em> &lt; 0.05). Regions with abnormality in K_w maps were in general agreement with DCE and Evans blue maps, although there are some distinct differences in location and the change in values.</div></div><div><h3>Conclusion</h3><div>K_w is sensitive to mannitol-induced changes in the BBB, with BBB disruption confirmed by DCE MRI and Evans blue histology.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110335"},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046580","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":"FDuDoCLNet: Fully dual-domain contrastive learning network for parallel MRI reconstruction","authors":"Huiyao Zhang ,&nbsp;Tiejun Yang ,&nbsp;Heng Wang ,&nbsp;Jiacheng Fan ,&nbsp;Wenjie Zhang ,&nbsp;Mingzhu Ji","doi":"10.1016/j.mri.2025.110336","DOIUrl":"10.1016/j.mri.2025.110336","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that is widely used for high-resolution imaging of soft tissues and organs. However, the slow speed of MRI imaging, especially in high-resolution or dynamic scans, makes MRI reconstruction an important research topic. Currently, MRI reconstruction methods based on deep learning (DL) have garnered significant attention, and they improve the reconstruction quality by learning complex image features. However, DL-based MR image reconstruction methods exhibit certain limitations. First, the existing reconstruction networks seldom account for the diverse frequency features in the wavelet domain. Second, existing dual-domain reconstruction methods may pay too much attention to the features of a single domain (such as the global information in the image domain or the local details in the wavelet domain), resulting in the loss of either critical global structures or fine details in certain regions of the reconstructed image. In this work, inspired by the lifting scheme in wavelet theory, we propose a novel Fully Dual-Domain Contrastive Learning Network (FDuDoCLNet) based on variational networks (VarNet) for accelerating PI in both the image and wavelet domains. It is composed of several cascaded dual-domain regularization units and data consistency (DC) layers, in which a novel dual-domain contrastive loss is introduced to optimize the reconstruction performance effectively. The proposed FDuDoCLNet was evaluated on the publicly available fastMRI multi-coil knee dataset under a 6× acceleration factor, achieving a PSNR of 34.439 dB and a SSIM of 0.895.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110336"},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046780","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":"Spatial-frequency aware zero-centric residual unfolding network for MRI reconstruction","authors":"Yupeng Lian ,&nbsp;Zhiwei Liu ,&nbsp;Jin Wang ,&nbsp;Shuai Lu","doi":"10.1016/j.mri.2025.110334","DOIUrl":"10.1016/j.mri.2025.110334","url":null,"abstract":"<div><div>Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality. Compressed Sensing (CS) theory has enabled the acquisition of partial k-space data, which can then be effectively reconstructed to recover the original image using advanced reconstruction algorithms. Recently, deep learning has been widely applied to MRI reconstruction, aiming to reduce the artifacts in the image domain caused by undersampling in k-space and enhance image quality. As deep learning continues to evolve, the undersampling factors in k-space have gradually increased in recent years. However, these layers are limited in compensating for reconstruction errors in the unsampled areas, impeding further performance improvements. To address this, we propose a learnable spatial-frequency difference-aware module that complements the learnable data consistency layer, mapping k-space domain differences to the spatial image domain for perceptual compensation. Additionally, inspired by wavelet decomposition, we introduce explicit priors by decomposing images into mean and residual components, enforcing a refined zero-mean constraint on the residuals while maintaining computational efficiency. Comparative experiments on the FastMRI and Calgary-Campinas datasets demonstrate that our method achieves superior reconstruction performance against seven state-of-the-art techniques. Ablation studies further confirm the efficacy of our model's architecture, establishing a new pathway for enhanced MRI reconstruction.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110334"},"PeriodicalIF":2.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039641","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":"Improving MRI turbulence quantification by addressing the measurement errors caused by the derivatives of the turbulent velocity field – Sequence development and in-vitro validation","authors":"Swantje Romig ,&nbsp;Kristine John ,&nbsp;Simon Schmidt ,&nbsp;Sebastian Schmitter ,&nbsp;Sven Grundmann ,&nbsp;Martin Bruschewski","doi":"10.1016/j.mri.2025.110333","DOIUrl":"10.1016/j.mri.2025.110333","url":null,"abstract":"<div><h3>Purpose</h3><div>To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.</div></div><div><h3>Methods</h3><div>We developed a Cartesian phase contrast sequence with FAST velocity encoding and two separately measured partial echoes with opposite readout directions. This design aims to reduce the high-order gradient moments that are responsible for the described measurement error. Velocity encoding directions follow the ICOSA6 scheme to capture the full RST. Turbulence data is reconstructed using the intra-voxel phase dispersion (IVPD) technique. We validated this sequence in vitro using a periodic hill flow benchmark with highly anisotropic turbulence. MRI data underwent extensive averaging, with multiple velocity encoding values employed to reduce noise and isolate systematic effects.</div></div><div><h3>Results</h3><div>The RST data obtained from the new sequence agree well with the ground truth. Compared to a state-of-the-art sequence, the maximum errors were reduced by factor five.</div></div><div><h3>Conclusion</h3><div>Simple adjustments to current MRI protocols can greatly enhance turbulence measurement accuracy through the reduction of high-order gradient moments. The proposed measures include applying FAST velocity encoding, high readout bandwidth, and a highly asymmetric readout. Ringing artifacts due to the asymmetric readout can be removed via a second, inverted readout.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110333"},"PeriodicalIF":2.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}