Magnetic resonance imaging最新文献

{"title":"Cerebrospinal fluid flow dynamics and volume changes are related with sigmoid sinus wall dehiscence-pulsatile tinnitus with idiopathic intracranial hypertension","authors":"Lanyue Chen ,&nbsp;Wei Li ,&nbsp;Xiaobo Ma ,&nbsp;Xiaoxia Qu ,&nbsp;Dandan Zheng ,&nbsp;Zhaohui Liu","doi":"10.1016/j.mri.2024.110315","DOIUrl":"10.1016/j.mri.2024.110315","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate cerebrospinal fluid (CSF) flow dynamics and volume changes of pulsatile tinnitus (PT) patients induced by sigmoid sinus wall dehiscence (SSWD) with intracranial hypertension.</div></div><div><h3>Methods</h3><div>Thirty-five SSWD-PT patients coexisted with intracranial hypertension and 35, age-, gender-, and handedness-matched healthy volunteers were prospectively enrolled and performed MRI. Clinical data were collected. CSF flow dynamics were evaluated by phase-contrast magnetic resonance imaging (PC-MRI) and CSF volume was measured using ITK-SNAP software.</div></div><div><h3>Results</h3><div>Compared with controls, the body mass index (BMI) of PT patients increased significantly (<em>P</em> = 0.046). Among CSF flow dynamics, PT patients presented significantly decreased mean flux (MF) (<em>P</em> = 0.017), mean velocity (MV) (<em>P</em> = 0.038), peak velocity (PV) (<em>P</em> = 0.023), and significantly increased regurgitant fraction (RF) (<em>P</em> = 0.010) than controls. There were no significant differences in other CSF flow dynamics parameters between the groups. CSF volume of PT patients was significantly increased than controls (<em>P</em> = 0.024). RF and CSF volume had potential diagnostic value. The AUC, sensitivity, specificity and accuracy of RF and CSF volume were 0.678, 68.6 %, 60.0 %, 61.4 % and 0.656, 68.6 %, 57.1 %, 55.7 %, respectively. The combined diagnostic efficacy of RF and CSF volume was highest, and the AUC, sensitivity, specificity and accuracy were 0.733, 74.3 %, 62.9 %, 67.1 % respectively.</div></div><div><h3>Conclusion</h3><div>SSWD-PT patients present CSF flow dynamics and volume changes, which may be related to the occurrence of PT. In addition to structural abnormalities, the combination of RF and CSF volume can be innovative as a complementary index to identify SSWD as the accurate etiology of PT.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110315"},"PeriodicalIF":2.1,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142882410","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":"Effect of carotid artery stenting on cognitive function in patients with asymptomatic carotid artery stenosis, a multimodal magnetic resonance study","authors":"Dangzhen Wang ,&nbsp;Juan Xia ,&nbsp;Liang Li ,&nbsp;Tao Wang","doi":"10.1016/j.mri.2024.110296","DOIUrl":"10.1016/j.mri.2024.110296","url":null,"abstract":"<div><h3>Introduction</h3><div>More and more evidence suggesting that internal carotid artery stenosis is not only a risk factor for ischemic stroke but also for cognitive impairments. Hypoperfusion and silent micro emboli have been reported as the pathophysiological mechanisms causing cognitive impairment. The effect of carotid artery stenting (CAS) on cognitive function varied from study to study. This study aims to explore the effect of CAS on cognition and exam the changes in cerebral perfusion and brain connectivity with pulsed arterial spin labeling (pASL) and resting-state functional MRI (R-fMRI).</div></div><div><h3>Methods</h3><div>We conducted a controlled trial to assess alterations in cognitive performance among patients with “asymptomatic” carotid artery stenosis prior to and 3 months post-CAS intervention. Cognitive function including the Montreal Cognitive Assessment (MoCA) Beijing Version, the Minimum Mental State Examination (MMSE), the Digit Symbol Test, the Rey Auditory Verbal Learning Test (RAVLT), and the Verbal Memory Test. pASL perfusion MRI and R-fMRI were also performed prior to and 3 months post-CAS intervention.</div></div><div><h3>Results</h3><div>13 patients completed all the follow-up. We observed increased perfusion in the right parietal lobe and right occipital lobe, increased amplitude of low-frequency fluctuation (ALFF) in the right precentral gyrus, increased connectivity to the posterior cingulate cortex (PCC) in the right frontal gyrus and right precuneus, and increased voxel-wise mirrored homotopic connectivity (VMHC) in the right precuneus 3 months after CAS when compared with prior to CAS. Cognitive test results showed significant improvement in the scores on the MMSE, the Verbal Memory test, and the delayed recall.</div></div><div><h3>Conclusion</h3><div>CAS can partly improve the cognitive function in patients with “asymptomatic” carotid artery stenosis, and the improvement may be attributable to the increased perfusion in the right parietal lobe and right occipital lobe, increased ALFF in the right precentral gyrus, increased connectivity to the PCC in the right frontal gyrus and right precuneus, and increased VMHC in the right precuneus.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110296"},"PeriodicalIF":2.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142877425","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":"Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI","authors":"Juntong Jing ,&nbsp;Anthony Mekhanik ,&nbsp;Melanie Schellenberg ,&nbsp;Victor Murray ,&nbsp;Ouri Cohen ,&nbsp;Ricardo Otazo","doi":"10.1016/j.mri.2024.110310","DOIUrl":"10.1016/j.mri.2024.110310","url":null,"abstract":"<div><div>Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI. DCE-Movienet offers rapid reconstruction of high spatiotemporal resolution 4D MRI data, reducing reconstruction time of the full acquisition to only 0.66 s, which is significantly shorter than compressed sensing's order of 10 min-long reconstructions, without affecting image quality. DCE-Qnet can then perform comprehensive quantification of perfusion parameter maps (K^trans, v_p, v_e), and other parameters affecting quantification (T1, B1, and BAT) from a single contrast-enhanced acquisition. The end-to-end deep learning pipeline was implemented to process data acquired with a golden-angle stack-of-stars k-space trajectory and validated on healthy volunteers and a cervical cancer patient against a compressed sensing reconstruction. The end-to-end deep learning DCE-MRI technique addresses key limitations in DCE-MRI in terms of speed and quantification robustness, which is expected to improve the performance of DCE-MRI in a clinical setting.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110310"},"PeriodicalIF":2.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142877514","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":"Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function","authors":"Makoto Orii ,&nbsp;Momoko Sugawara ,&nbsp;Tsuyoshi Sugawara ,&nbsp;Kunihiro Yoshioka","doi":"10.1016/j.mri.2024.110313","DOIUrl":"10.1016/j.mri.2024.110313","url":null,"abstract":"<div><h3>Objective</h3><div>The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency of the pre-and post-contrast performance of 2D deep learning-based highly accelerated cardiac cine (DL cine) imaging by evaluating the image quality and the quantification of biventricular volumes and function in the clinical setting.</div></div><div><h3>Material and methods</h3><div>Thirty patients (20 men, mean age 53.7 ± 17.8 years) underwent cardiac magnetic resonance on a 1.5 T scanner for clinical indications, and pre- and post-contrast DL cine images were acquired with a short-axis view. Image-quality was scored according to three main criteria: the blood-to-myocardial contrast, endocardial edge delineation, and presence of motion artifacts throughout the cardiac cycle.</div><div>Biventricular end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and left ventricular mass (LVM) were analyzed and compared between the pre- and post-contrast DL cine images.</div></div><div><h3>Results</h3><div>The actual median time of 2D DL cine acquisition was 38.4 ± 9.1 s. There were no significant differences in the image quality scores between pre- and post-contrast DL cine images (<em>p</em> &gt; 0.05). In the volume and functional analysis, there was no significant difference in terms of biventricular EDV, ESV, SV, EF, and LVM (<em>p</em> &gt; 0.05).</div></div><div><h3>Conclusions</h3><div>The performance of 2D DL cine is equivalent before and after contrast injection for the assessment of image quality and ventricular function in the clinical setting.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110313"},"PeriodicalIF":2.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872533","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":"Deep learning radiomics nomograms predict Isocitrate dehydrogenase (IDH) genotypes in brain glioma: A multicenter study","authors":"Darui Li ,&nbsp;Wanjun Hu ,&nbsp;Laiyang Ma ,&nbsp;Wenxia Yang ,&nbsp;Yang Liu ,&nbsp;Jie Zou ,&nbsp;Xin Ge ,&nbsp;Yuping Han ,&nbsp;Tiejun Gan ,&nbsp;Dan Cheng ,&nbsp;Kai Ai ,&nbsp;Guangyao Liu ,&nbsp;Jing Zhang","doi":"10.1016/j.mri.2024.110314","DOIUrl":"10.1016/j.mri.2024.110314","url":null,"abstract":"<div><h3>Purpose</h3><div>To explore the feasibility of Deep learning radiomics nomograms (DLRN) in predicting IDH genotype.</div></div><div><h3>Methods</h3><div>A total of 402 glioma patients from two independent centers were retrospectively included, and the data from center I was randomly divided into a training cohort (<em>n</em> = 239) and an internal validation cohort (<em>n</em> = 103) on a 7:3 basis. Center II served as an independent external validation cohort (<em>n</em> = 60). We developed a DLRN for IDH classification of gliomas based on T2 images. This hybrid model integrates deep learning features, radiomics features, and clinical features most relevant to IDH genotypes and finally classifies them using multivariate logistic regression analysis. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the performance of the model and applied the DLRN score to the survival analysis of some of the follow-up glioma patients.</div></div><div><h3>Results</h3><div>The proposed model had an area under the curve (AUC) of 0.98 in an externally validated cohort, and DLRN scores were significantly associated with the overall survival of glioma patients.</div></div><div><h3>Conclusions</h3><div>Deep learning radiomics nomograms performed well in non-invasively predicting IDH mutation status in gliomas, assisting stratified management and targeted therapy for glioma patients.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110314"},"PeriodicalIF":2.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872530","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":"Multiple b value diffusion-weighted MRI of liver: A novel respiratory frequency-modulated continuous-wave radar-trigger technique and comparison with free-breathing technique","authors":"Kai Liu ,&nbsp;Caizhong Chen ,&nbsp;Tingting Shen ,&nbsp;Xixi Wen ,&nbsp;Mengsu Zeng ,&nbsp;Pengju Xu","doi":"10.1016/j.mri.2024.110312","DOIUrl":"10.1016/j.mri.2024.110312","url":null,"abstract":"<div><h3>Objective</h3><div>The aim of this study was to evaluate a novel respiratory frequency-modulated continuous-wave radar-trigger (FT) technique for multiple -b-value diffusion-weighted imaging (DWI) of liver and compare it with conventional free breathing (FB) DWI technique.</div></div><div><h3>Material and methods</h3><div>39 patients with focal liver lesions underwent both frequency-modulated continuous-wave radar-trigger (FT) and conventional free-breathing (FB) multi-b-value diffusion-weighted imaging (DWI,b = 0,50,400,800 s/mm²⁾. Two abdominal radiologists independently assessed the quality of liver DWI images obtained using both techniques, measured and compared liver signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) at different b-values, as well as apparent diffusion coefficient (ADC) values calculated from all b-values.</div></div><div><h3>Results</h3><div>In terms of image quality, the FT technique is superior to the conventional FB technique, with overall image quality scores (Reader 1, 3.56 ± 0.50 and Reader 2, 3.59 ± 0.55)vs (Reader 1, 2.90 ± 0.75 and Reader 2, 2.97 ± 0.71), respectively. The liver SNR (at b-values of 50,400,and 800 s/mm²) obtained by FT was (138.5 ± 43.48, 96.67 ± 31.95, 71.54 ± 22.03), respectively, which was significantly higher than that obtained by conventional FB (110.90 ± 39.28, 80.86 ± 29.13, 60.43 ± 18.61, <em>P</em> &lt; 0.05). The lesion CNR with FT was significantly higher than that with conventional FB (258.99 ± 151.38 vs 174.60 ± 99.90; 164.56 ± 87.25 vs 111.12 ± 42.43; 118.83 ± 68.76 vs 76.01 ± 35.48, <em>P</em> &lt; 0.001). There was no significant difference in ADC values of liver and lesions between the two techniques: ADCliver-L and ADCliver-R: (FT 1479.3 ± 270.0 vs FB 1529.3 ± 275.5 and FT 1219.6 ± 127.4 vs FB 1248.7 ± 168.2, <em>P</em> &gt; 0.05); ADC lesion:FT(969.0 ± 261.3) vs FB (1017.5 ± 240.4, <em>P</em> &gt; 0.05).</div></div><div><h3>Conclusion</h3><div>For multi-b-value liver diffusion-weighted imaging, FT technique has higher image quality and better lesion visibility than conventional FB technique and there is no significant difference in ADC values of liver and lesions between the two techniques.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110312"},"PeriodicalIF":2.1,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854345","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":"Segmental myocardial tissue remodeling and atrial arrhythmias in hypertrophic cardiomyopathy: Findings from T1-mapping MRI","authors":"Danqing Liu ,&nbsp;Hong Luo ,&nbsp;Changjing Feng ,&nbsp;Yufei Lian ,&nbsp;Zhenyu Pan ,&nbsp;Xiaojuan Guo ,&nbsp;Qi Yang","doi":"10.1016/j.mri.2024.110311","DOIUrl":"10.1016/j.mri.2024.110311","url":null,"abstract":"<div><h3>Background</h3><div>Myocardial fibrosis of the left ventricle (LV) has been associated with atrial fibrillation and other arrhythmias in individuals with hypertrophic cardiomyopathy (HCM). However, few studies have quantitatively examined the segmental relationship between diffuse LV fibrosis and atrial arrhythmias in HCM using T1 mapping and extracellular volume fraction (ECV). The aim of this study is to explore this relationship through T1 mapping, offering imaging insights into the pathophysiology of HCM with atrial arrhythmia.</div></div><div><h3>Methods</h3><div>A total of 38 patients with HCM were classified into two groups—those with atrial arrhythmia and those without—based on electrocardiographic and Holter monitor recordings. A covariance analysis was conducted to compare T1 mapping parameters between the two groups, adjusting for wall thickness (WT) as a covariate. Analysis was performed collectively for all 16 myocardial segments, as well as for each segment individually.</div></div><div><h3>Results</h3><div>Native T1 values were elevated in the entire LV myocardium and in segments S1–3 in patients with HCM with atrial arrhythmias compared to those without (<em>P</em> &lt; 0.001; <em>P</em> &lt; 0.05, 1316.0 ms ± 15.9 vs 1263.1 ms ± 13.6, 1350.5 ms ± 14.2 vs 1311.9 ms ± 11.7, 1305.7 ms ± 2.5 vs 1271.5 ms ± 10.6, respectively). Notably, the basal anterior segment (S1) and basal inferotseptal segment (S3) exhibited prolonged ECV and elevated native T1 values in patients with HCM and atrial arrhythmia (<em>P</em> &lt; 0.05). Multivariable binary logistic regression analysis identified myocardial native T1 values in the basal anteroseptal segment (S2) as a predictor of atrial arrhythmia presence in HCM, with values exceeding 1350 ms correlating with an increased likelihood of arrhythmia development. No significant difference in WT was observed between the groups in hypertrophic myocardial regions (<em>P</em> &gt; 0.05), while non-hypertrophic myocardium in individuals with HCM and atrial arrhythmias exhibited reduced wall thickness (7.7 mm ± 3.0 vs 9 mm ± 3.0, <em>P</em> &lt; 0.001) compared to those without arrhythmias.</div></div><div><h3>Conclusion</h3><div>Fibrosis in the basal septal and anterior regions of the left ventricle plays a crucial role in myocardial tissue remodeling, contributing to the development of atrial arrhythmia in HCM. Elevated native T1 values in the basal anteroseptal segment may may serve as a significant marker for the concurrent occurrence of atrial arrhythmias in individuals with HCM.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110311"},"PeriodicalIF":2.1,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142847024","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":"Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method","authors":"Wanqing Ren ,&nbsp;Xiaoming Xi ,&nbsp;Xiaodong Zhang ,&nbsp;Kesong Wang ,&nbsp;Menghan Liu ,&nbsp;Dawei Wang ,&nbsp;Yanan Du ,&nbsp;Jingxiang Sun ,&nbsp;Guang Zhang","doi":"10.1016/j.mri.2024.110305","DOIUrl":"10.1016/j.mri.2024.110305","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.</div></div><div><h3>Methods</h3><div>In this study, we retrospectively analyzed clinical data and five types of MRI images (FS-T1WI, T2WI, Contrast-enhanced T1-weighted imaging (T1-C), DWI, and ADC) from 325 patients with pathologically confirmed breast cancer. Using the five types of MRI images as inputs to the ResNeXt50 model respectively, five base models were constructed, and then the outputs of the five base models were fused using an ensemble learning approach to develop a multi-parametric MRI model. Breast cancer was classified into four molecular subtypes based on immunohistochemical results: luminal A, luminal B, human epidermal growth factor receptor 2-positive (HER2-positive), and triple-negative (TN). The whole dataset was randomly divided into a training set (<em>n</em> = 260; 76 luminal A, 80 luminal B, 50 HER2-positive, 54 TN) and a testing set (<em>n</em> = 65; 20 luminal A, 20 luminal B, 12 HER2-positive, 13 TN). Accuracy, sensitivity, specificity, receiver operating characteristic curve (ROC) and area under the curve (AUC) were calculated to assess the predictive performance of the models.</div></div><div><h3>Results</h3><div>In the testing set, for the assessment of the four molecular subtypes of breast cancer, the multi-parametric MRI model yielded an AUC of 0.859–0.912; the AUCs based on the FS-T1WI, T2WI, T1-C, DWI, and ADC models achieved respectively 0.632–0. 814, 0.641–0.788, 0.621–0.709, 0.620–0.701and 0.611–0.785.</div></div><div><h3>Conclusion</h3><div>The multi-parametric MRI model we developed outperformed the base models in predicting breast cancer molecular subtypes. Our study also showed the potential of FS-T1WI base model in predicting breast cancer molecular subtypes.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110305"},"PeriodicalIF":2.1,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142837200","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":"Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging","authors":"Phillip Martin ,&nbsp;Maria Altbach ,&nbsp;Ali Bilgin","doi":"10.1016/j.mri.2024.110309","DOIUrl":"10.1016/j.mri.2024.110309","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted images (DWIs). This model addresses the challenge of prolonged data acquisition times in diffusion MRI while preserving metric accuracy.</div></div><div><h3>Methods</h3><div>DiffDL was trained using data from the Human Connectome Project, including 300 training/validation subjects and 50 testing subjects. High-quality DTI and DKI metrics were generated using many DWIs and combined with subsets of DWIs to form training pairs. A UNet architecture was used for denoising, trained over 500 epochs with a linear noise schedule. Performance was evaluated against conventional DTI/DKI modeling and a reference UNet model using normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), and Pearson correlation coefficient (PCC).</div></div><div><h3>Results</h3><div>DiffDL showed significant improvements in the quality and accuracy of fractional anisotropy (FA) and mean diffusivity (MD) maps compared to conventional methods and the baseline UNet model. For DKI metrics, DiffDL outperformed conventional DKI modeling and the UNet model across various acceleration scenarios. Quantitative analysis demonstrated superior NMAE, PSNR, and PCC values for DiffDL, capturing the full dynamic range of DTI and DKI metrics. The generative nature of DiffDL allowed for multiple predictions, enabling uncertainty quantification and enhancing performance.</div></div><div><h3>Conclusion</h3><div>The DiffDL framework demonstrated the potential to significantly reduce data acquisition times in diffusion MRI while maintaining high metric quality. Future research should focus on optimizing computational demands and validating the model with clinical cohorts and standard MRI scanners.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110309"},"PeriodicalIF":2.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142828896","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":"Longitudinal DTI analysis of microstructural changes in lumbar nerve roots following Interspinous process device placement","authors":"L. Monti ,&nbsp;M. Bellini MD ,&nbsp;M. Alberti ,&nbsp;E. Piane ,&nbsp;T. Casseri ,&nbsp;G. Sadotti ,&nbsp;S. Marcia ,&nbsp;J.A. Hirsc ,&nbsp;F. Ginanneschi ,&nbsp;A. Rossi","doi":"10.1016/j.mri.2024.110306","DOIUrl":"10.1016/j.mri.2024.110306","url":null,"abstract":"<div><div>Diffusion tensor imaging (DTI) and its parameters such as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) are increasingly being used to assess peripheral nerve integrity alongside nerve conduction studies. This pilot study aims to compare DTI values of lumbar spinal nerve roots before (T0) and after (T1) treatment with an interspinous process device (IPD). Seven patients (5 females, 2 males; mean age: 68) suffering from neurogenic claudication and lumbar spinal canal and foraminal stenosis were evaluated. Visual Analog Scale (VAS) for perceived pain, Oswestry Disability Index (ODI), and DTI parameters were assessed between T0 and T1. No significant difference in FA was found in treated roots, while MD (<em>p</em> = 0.0015), RD (<em>p</em> = 0.0032), and AD (<em>p</em> = 0.0221) were significantly altered. At untreated levels, all DTI parameters showed highly significant differences (<em>p</em> &lt; 0.0001) between T0 and T1. In treated roots, FA values significantly increased in the intraforaminal segment(<em>p</em> = 0.0229), while MD(<em>p</em> = 0.0124), AD(<em>p</em> = 0.0128), and RD (<em>p</em> = 0.0143) values decreased in the pre-foraminal segment. In untreated roots, FA significantly increased in pre(<em>p</em> = 0.0039)and intraforaminal(<em>p</em> = 0.0003) segments, and MD, AD, and RD decreased in all segments (p &lt; 0.0001). VAS (p &lt; 0.0001) also decreased between T0 and T1. This pilot study aims to clarify the biomechanical impact of interspinous spacers through microstructural analysis of both treated and adjacent untreated nerve roots. To our knowledge, no studies have examined the short- to medium-term changes in DTI values of lumbar nerve roots before and after IPD placement, or compared changes between treated and untreated roots.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110306"},"PeriodicalIF":2.1,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822227","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}