Magnetic resonance imaging最新文献

{"title":"Brain network fingerprints of Alzheimer's disease risk factors in mouse models with humanized APOE alleles","authors":"Steven Winter ,&nbsp;Ali Mahzarnia ,&nbsp;Robert J. Anderson ,&nbsp;Zay Yar Han ,&nbsp;Jessica Tremblay ,&nbsp;Jacques A. Stout ,&nbsp;Hae Sol Moon ,&nbsp;Daniel Marcellino ,&nbsp;David B. Dunson ,&nbsp;Alexandra Badea","doi":"10.1016/j.mri.2024.110251","DOIUrl":"10.1016/j.mri.2024.110251","url":null,"abstract":"<div><div>Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic and modifiable risk factors influence disease susceptibility are under intense investigation, with APOE being the major genetic risk factor for late onset AD. Yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear.</div><div>To model multiple risk factors, including APOE genotype, age, sex, diet, and immunity we used a cross sectional design, leveraging mice expressing human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. We used network topological and GraphClass analyses of brain connectomes derived from accelerated diffusion-weighted MRI to assess the global and local impact of risk factors, in the absence of AD pathology.</div><div>Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles. Sparse Canonical Correlation Analysis (CCA) including spatial memory as a risk factor resulted in a network comprising 80 edges, showing significant overlap with risk-associated networks from GraphClass. The largest overlaps were observed with networks impacted by diet (47 edges), immunity (39 edges), APOE3 vs 4 (26 edges), sex (23 edges), and age (19 edges), the resulting networks supporting the use of sensory cues in spatial memory retrieval.</div><div>These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110251"},"PeriodicalIF":2.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142372248","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":"Use of multiparametric MRI to noninvasively assess iodinated contrast-induced acute kidney injury","authors":"Yongfang Wang ,&nbsp;Bin Wang ,&nbsp;Jiangbo Qin ,&nbsp;Haili Yan ,&nbsp;Haoyuan Chen ,&nbsp;Jinxia Guo ,&nbsp;Pu-Yeh Wu ,&nbsp;Xiaochun Wang","doi":"10.1016/j.mri.2024.110248","DOIUrl":"10.1016/j.mri.2024.110248","url":null,"abstract":"<div><h3>Purpose</h3><div>To gauge the utility of multiparametric MRI in characterizing pathologic changes after iodinated contrast-induced acute kidney injury (CI-AKI) in rats.</div></div><div><h3>Methods</h3><div>We randomly grouped 24 rats injected with 8 g iodine/kg of body weight (<em>n</em> = 6 each) and 6 rats injected with saline as controls. All rats underwent T1, T2 mapping and diffusion kurtosis imaging (DKI) after contrast injection at 0 (control), 1, 3, 7, 13 days. T1, T2, and mean kurtosis (MK) values were performed in renal outer/inner stripes of outer medulla (OSOM and ISOM) and cortex (CO), and their diagnosis performance for CI-AKI also been evaluated. Serum creatinine (SCr), insulin-like growth factor-binding protein 7 (IGFBP7), tissue inhibitor metalloproteinase 2 (TIMP-2), aquaporin-1 (AQP1), α-smooth muscle actin (α-SMA), and histologic indices were examined.</div></div><div><h3>Results</h3><div>Compared with controls, urinary concentrations of both TIMP-2 and IGFBP7 were obviously elevated from Day 1 to Day 13 (all <em>p</em> &lt; 0.05). T2 values were significantly higher than control group for Days 1 and 3, and T1 and MK increased were more remarkable at all time points (Days 1–13) in CI-AKI (all <em>p</em> &lt; 0.05) than control group. Changes in T1 and MK strongly correlated with renal injury scores of all anatomical compartments and with expression levels of AQP1 and moderately correlated with α-SMA. Changes in T2 values correlating moderately with renal scores of CO, ISOM and OSOM and AQP1. The MK obtained the highest area under the receiver operating characteristic (ROC) curve of 0.846 with a sensitivity of 70.8 % and specificity of 88.9 %.</div></div><div><h3>Conclusions</h3><div>Combined use of multiparametric MRI could be a valid noninvasive method for comprehensive monitoring of CI-AKI. Among these parameters, MK may achieve the best diagnostic performance for CI-AKI.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110248"},"PeriodicalIF":2.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142365769","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":"Gd-EOB-DTPA-enhanced MR imaging features of hepatocellular carcinoma in non-cirrhotic liver","authors":"Mingyue Song ,&nbsp;Yuhao Tao ,&nbsp;Hanjun Zhang ,&nbsp;Mingzhan Du ,&nbsp;Lingchuan Guo ,&nbsp;Chunhong Hu ,&nbsp;Weiguo Zhang","doi":"10.1016/j.mri.2024.110241","DOIUrl":"10.1016/j.mri.2024.110241","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate clinical, pathological and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) findings of hepatocellular carcinoma (HCC) in non-cirrhotic livers and compare with HCC in cirrhotic livers.</div></div><div><h3>Methods</h3><div>This retrospective study included patients with pathologically confirmed HCC who underwent preoperative Gd-EOB-DTPA-enhanced MRI between January 2015 and October 2021. Propensity scores were utilized to match non-cirrhotic HCCs (NCHCCs) patients with cirrhotic HCCs (CHCCs) patients. The clinical, pathological and MR imaging features of NCHCCs were compared with CHCCs. Correlation between these features and the presence of NCHCCs were analyzed by logistic regression analysis. The predictive efficacy was evaluated using receiver operating characteristic (ROC) analysis. The area under the receiver operating characteristic curve (AUC) was used to compare performance, and the Delong test was used to compare AUCs.</div></div><div><h3>Results</h3><div>After propensity score matching (1:3), a total of 144 patients with HCCs (36 NCHCCs and 108 CHCCs) were included. NCHCCs were larger in tumor size than CHCCs (<em>P</em> &lt; 0.001, Cohen's d = 0.737). NCHCCs were more common in patients who have hepatitis C (5.6 % vs 1.9 %, <em>P</em> &gt; 0.05) or have no known liver disease (11.1 % vs 0.9 %, <em>P</em> = 0.004), while hepatitis B was more common in CHCC patients (83.3 % vs 97.2 %, <em>P</em> = 0.003). Compared with CHCCs, NCHCCs more frequently demonstrated non-smooth tumor margin (<em>P</em> = 0.001, Cramer's V = 0.273), peri-tumoral hyperintensity (<em>P</em> &lt; 0.05, Cramer's V = 0.185), hyperintense and heterogeneous signals in hepatobiliary phase (HBP) (<em>P</em> &lt; 0.05). CHCCs were more likely to have satellite nodules compared to NCHCCs (33.3 % vs 57.4 %, <em>P</em> &lt; 0.05, Cramer's V = 0.209). Based on the univariate and multivariate logistic regression analysis, the tumor size, non-smooth tumor margin, heterogeneous intensity in HBP and satellite nodule were significantly correlated to NCHCCs (<em>P</em> all &lt;0.05). ROC curve analysis demonstrated that tumor size and non-smooth tumor margin were potential imaging predictors for the diagnosis of NCHCC, with AUC values of 0.715 and 0.639, respectively. The combination of the two imaging features for identifying NCHCC achieved an AUC value of 0.761, with a sensitivity of 0.889 and a specificity of 0.630.</div></div><div><h3>Conclusion</h3><div>NCHCCs were more likely to show larger tumor size, non-smooth tumor margin, peri-tumoral hyperintensity, as well as hyperintense and heterogeneous signals in HBP at Gd-EOB-DTPA-enhanced MR imaging compared with NCHCCs. Tumor size and non-smooth tumor margin in HBP may help to discriminate NCHCCs.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110241"},"PeriodicalIF":2.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142372149","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":"Proton Density Fat Fraction Quantification (PD-FFQ): Capability for hematopoietic ability assessment and aplastic anemaia diagnosis of adults","authors":"Yoshiharu Ohno ,&nbsp;Takahiro Ueda ,&nbsp;Masahiko Nomura ,&nbsp;Yuichiro Sano ,&nbsp;Kaori Yamamoto ,&nbsp;Maiko Shinohara ,&nbsp;Masato Ikedo ,&nbsp;Masao Yui ,&nbsp;Akiyoshi Iwase ,&nbsp;Hiroyuki Nagata ,&nbsp;Takeshi Yoshikawa ,&nbsp;Daisuke Takenaka ,&nbsp;Akihiro Tomita ,&nbsp;Nobuyuki Fujita ,&nbsp;Yoshiyuki Ozawa","doi":"10.1016/j.mri.2024.110240","DOIUrl":"10.1016/j.mri.2024.110240","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to determine the capability of proton density with fat fraction (PD-FFQ) imaging to help assess hematopoietic ability and diagnose aplastic anemia in adults.</div></div><div><h3>Methods</h3><div>Between January 2021 and March 2023, patients diagnosed with aplastic anemia (AA: <em>n</em> = 14) or myelodysplastic syndrome (MDS: n = 14) were examined by whole-body PD-FFQ imaging, and 14 of 126 age and gender matched patients who had undergone the same PD-FFQ imaging were selected as control group. All proton density fat fraction (PDFF) index evaluations were then performed by using regions of interest (ROIs). Pearson's correlation was used to determine the relationship between blood test results and each quantitative index, and ROC-based positive test and discrimination analyses to compare capability to differentiate the AA from the non-AA group. Finally, sensitivity, specificity and accuracy of all quantitative indexes were compared by means of McNemar's test.</div></div><div><h3>Results</h3><div>Mean PDFF, standard deviation (SD) and percentage of coefficient of variation (%CV) for vertebrae showed significant correlation with blood test results (−0.52 ≤ <em>r</em> ≤ −0.34, <em>p</em> &lt; 0.05). Specificity (SP) and accuracy (AC) of %CV of PDFF in vertebrae were significantly higher than those of mean PDFF in vertebrae and the posterior superior iliac spine (SP: <em>p</em> = 0.0002, AC: <em>p</em> = 0.0001) and SD of PDFF in vertebrae (SP: <em>p</em> = 0.008, AC: p = 0.008). Moreover, AC of SD of PDFF in vertebrae was significantly higher than that of mean PDFF in vertebrae and the posterior superior iliac spine (<em>p</em> = 0.03).</div></div><div><h3>Conclusion</h3><div>Whole-body PD-FFQ imaging is useful for hematopoietic ability assessment and diagnosis of aplastic anemia in adults.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110240"},"PeriodicalIF":2.1,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142365768","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":"Neurite Orientation Dispersion and Density Imaging (NODDI) reveals white matter microstructural changes in Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) patients with cognitive impairment","authors":"Ke Ning ,&nbsp;Dechao Fan ,&nbsp;Yuzhu Liu ,&nbsp;Yubing Sun ,&nbsp;Yajie Liu ,&nbsp;Yongzhong Lin","doi":"10.1016/j.mri.2024.110234","DOIUrl":"10.1016/j.mri.2024.110234","url":null,"abstract":"<div><h3>Purpose</h3><p>This study aimed to assess changes in white matter microstructure among patients undergoing obstructive sleep apnea hypopnea syndrome (OSAHS) complicated by cognitive impairment through neurite orientation dispersion and density imaging (NODDI), and evaluate the relationship to cognitive impairment as well as the diagnostic performance in early intervention.</p></div><div><h3>Methods</h3><p>Totally 23 OSAHS patients, 43 OSAHS patients complicated by cognitive impairment, and 15 healthy controls were enrolled in OSA, OSACI and HC groups of this work. NODDI toolbox and FMRIB's Software Library (FSL) were used to calculate neurite density index (NDI), Fractional anisotropy (FA), volume fraction of isotropic water molecules (Viso), and orientation dispersion index (ODI). Tract-based spatial statistics (TBSS) were carried out to examine the above metrics with one-way ANOVA. This study explored the correlations of the above metrics with mini-mental state examination (MMSE), and montreal cognitive assessment (MoCA) scores. Furthermore, receiver operating characteristic (ROC) curves were plotted. Meanwhile, area under curve (AUC) values were calculated to evaluate the diagnostic performance of the above metrics.</p></div><div><h3>Results</h3><p>NDI, ODI, Viso, and FA were significantly different among different brain white matter regions, among which, difference in NDI showed the greatest statistical significance. In comparison with HC group, OSA group had reduced NDI and ODI, whereas elevated Viso levels. Conversely, compared to the OSA group, the OSACI group displayed a slight increase in NDI and ODI values, which remained lower than HC group, viso values continued to rise. Post-hoc analysis highlighted significant differences in these metrics, except for FA, which showed no notable changes or correlations with neuropsychological tests. ROC analysis confirmed the diagnostic efficacy of NDI, ODI, and Viso with AUCs of 0.6908, 0.6626, and 0.6363, respectively, whereas FA's AUC of 0.5042, indicating insufficient diagnostic efficacy.</p></div><div><h3>Conclusions</h3><p>This study confirmed that NODDI effectively reveals microstructural changes in white matter of OSAHS patients with cognitive impairment, providing neuroimaging evidence for early clinical diagnosis and intervention.</p></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110234"},"PeriodicalIF":2.1,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239989","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":"Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks","authors":"Rira Masumoto ,&nbsp;Yawara Eguchi ,&nbsp;Hidenari Takeuchi ,&nbsp;Kazuhide Inage ,&nbsp;Miyako Narita ,&nbsp;Yasuhiro Shiga ,&nbsp;Masahiro Inoue ,&nbsp;Noriyasu Toshi ,&nbsp;Soichiro Tokeshi ,&nbsp;Kohei Okuyama ,&nbsp;Shuhei Ohyama ,&nbsp;Noritaka Suzuki ,&nbsp;Satoshi Maki ,&nbsp;Takeo Furuya ,&nbsp;Seiji Ohtori ,&nbsp;Sumihisa Orita","doi":"10.1016/j.mri.2024.110237","DOIUrl":"10.1016/j.mri.2024.110237","url":null,"abstract":"<div><h3>【Purpose】</h3><p>Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed manually on hospital workstations. The purpose of this study is to construct a system that extracts the lumbar nerve and generates tractography automatically using deep learning semantic segmentation.</p></div><div><h3>【Methods】</h3><p>We acquired 839 axial diffusion weighted images (DWI) from the DTI data of 90 patients with degenerative lumbar disorders, and segmented the lumbar nerve roots using U-Net, a semantic segmentation model. Using five architectural models, the accuracy of the lumbar nerve root segmentation was evaluated using a Dice coefficient. We also created automatic scripts from three commercially available software tools, including MRICronGL for medical image viewing, Diffusion Toolkit for reconstruction of the DWI data, and Trackvis for the creation of the tractography, and compared the time required to create the tractography, and evaluated the quality of the automated tractography was evaluated.</p></div><div><h3>【Results】</h3><p>Among the five models, the architectural model Resnet34 performed the best with a Dice = 0.780. The creation time for the automatic lumbar nerve tractography was 191 s, which was significantly shorter by 235 s than the manual time of 426 s (<em>p</em> &lt; 0.05). Furthermore, the agreement between manual and automated tractography was 3.67 ± 1.53 (satisfactory).</p></div><div><h3>【Conclusions】</h3><p>Using deep learning semantic segmentation, we were able to construct a system that automatically extracted the lumbar nerve and generated lumbar nerve tractography. This technology makes it possible to analyze lumbar nerve DTI and create tractography automatically, and is expected to advance the clinical applications of DTI for the assessment of the lumbar nerve.</p></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110237"},"PeriodicalIF":2.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239932","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":"Prospective head motion correction at 3 Tesla with wireless NMR markers and ultrashort echo navigators","authors":"Saikat Sengupta ,&nbsp;Antonio Glenn ,&nbsp;Baxter P. Rogers","doi":"10.1016/j.mri.2024.110238","DOIUrl":"10.1016/j.mri.2024.110238","url":null,"abstract":"<div><h3>Purpose</h3><div>Prospective motion correction (PMC) with inductively-coupled wireless NMR markers has been shown to be an effective plug-and-play method for dealing with head motion at 7 Tesla [<span><span>29</span></span>,<span><span>30</span></span>]. However, technical challenges such as one-to-one identification of three wireless markers, generation of hyper-intense marker artifacts and low marker peak SNR in the navigators has limited the adoption of this technique. The goal of this work is to introduce solutions to overcome these issues and extend this technique to PMC for brain imaging at 3 Tesla.</div></div><div><h3>Methods</h3><div>PMC with 6 degrees of freedom (DOF) was implemented using a novel ∼8 ms, ultrashort echo time (UTE) navigator in concert with optimally chosen MnCl₂ marker samples to minimize marker artifacts. Distinct head coil sensitivities were leveraged to enable identification and tracking of individual markers and a variable flip angle (VFA) scheme and real time filtering were used to boost marker SNR. PMC was performed in 3D T₁ weighted brain imaging at 3 Tesla with voluntary head motions in adult volunteers.</div></div><div><h3>Results</h3><div>PMC with wireless markers improved image quality in 3D T₁ weighted images in all subjects compared to non-motion corrected images for similar motions with no noticeable marker artifacts. Precision of motion tracking was found to be in the range of 0.01–0.06 mm/degrees. Navigator execution had minimal impact on sequence duration.</div></div><div><h3>Conclusions</h3><div>Wireless NMR markers provide an accurate, calibration-free and economical option for 6 DOF PMC in brain imaging across field strengths. Challenges in this technique can be addressed by combining navigator design, sample selection and real time data processing strategies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110238"},"PeriodicalIF":2.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142290445","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":"POSE: POSition Encoding for accelerated quantitative MRI","authors":"Albert Jang ,&nbsp;Fang Liu","doi":"10.1016/j.mri.2024.110239","DOIUrl":"10.1016/j.mri.2024.110239","url":null,"abstract":"<div><div>Quantitative MRI utilizes multiple acquisitions with varying sequence parameters to sufficiently characterize a biophysical model of interest, resulting in undesirable scan times. Here we propose, validate and demonstrate a new general strategy for accelerating MRI using subvoxel shifting as a source of encoding called POSition Encoding (POSE). The POSE framework applies unique subvoxel shifts along the acquisition parameter dimension, thereby creating an extra source of encoding. Combining with a biophysical signal model of interest, accelerated and enhanced resolution maps of biophysical parameters are obtained. This has been validated and demonstrated through numerical Bloch equation simulations, phantom experiments and in vivo experiments using the variable flip angle signal model in 3D acquisitions as an application example. Monte Carlo simulations were performed using in vivo data to investigate our method's noise performance. POSE quantification results from numerical Bloch equation simulations of both a numerical phantom and realistic digital brain phantom concur well with the reference method, validating our method both theoretically and for realistic situations. NIST phantom experiment results show excellent overall agreement with the reference method, confirming our method's applicability for a wide range of <span><math><msub><mi>T</mi><mn>1</mn></msub></math></span> values. In vivo results not only exhibit good agreement with the reference method, but also show <em>g</em>-factors that significantly outperforms conventional parallel imaging methods with identical acceleration. Furthermore, our results show that POSE can be combined with parallel imaging to further accelerate while maintaining superior noise performance over parallel imaging that uses lower acceleration factors.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110239"},"PeriodicalIF":2.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142290444","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":"The impact of benign tissue within cancerous regions in the prostate: Characterizing sparse and dense prostate cancers on whole-mount histopathology and on multiparametric MRI","authors":"Olga Starobinets ,&nbsp;Jeffry P. Simko ,&nbsp;Matthew Gibbons ,&nbsp;John Kurhanewicz ,&nbsp;Peter R. Carroll ,&nbsp;Susan M. Noworolski","doi":"10.1016/j.mri.2024.110233","DOIUrl":"10.1016/j.mri.2024.110233","url":null,"abstract":"<div><h3>Purpose</h3><p>To establish the incidence, size, zonal location and Gleason Score(GS)/Gleason Grade Group(GG) of sparse versus dense prostate cancer (PCa) lesions and to identify the imaging characteristics of sparse versus dense cancers on multiparametric MRI (mpMRI).</p></div><div><h3>Methods</h3><p>Seventy-six men with untreated PCa were scanned prior to prostatectomy with endorectal-coil 3 T MRI including T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced MRI. Cancerous regions were outlined and graded on the whole-mount, processed specimens, with tissue compositions estimated. Regions with cancer comprising &lt;50 % and ≥ 50 % of the tissue were considered sparse and dense respectively. Regions of interest (ROI) were manually drawn on T2-weighted MRI. Within each patient, area-weighted ROI averages were calculated for each imaging measure for each tissue type, GS/GG, and sparse/dense composition.</p></div><div><h3>Results</h3><p>A large number of cancer regions were identified on histopathology (<em>n</em> = 1193: 939 (peripheral zone (PZ)) and 254 (transition zone (TZ))). Thirty-seven percent of these lesions were sparse. Sparse lesions were primarily low-grade with the majority of PZ and 100 % of TZ sparse lesions ≤GS3 + 3/GG1. Dense lesions were significantly larger than sparse lesions in both PZ and TZ, <em>p</em> &lt; 0.0001. On imaging, 246/45 PZ and 109/8 TZ dense/sparse 2D cancerous ROIs were drawn. Sparse GS3 + 3 and sparse ≥GS3 + 4 cancers did not have significantly different MRI intensities to dense GS3 + 3 cancers, while sparse GS3 + 3/GG1 cancers differed from benign, <em>p</em> &lt; 0.05.</p></div><div><h3>Conclusion</h3><p>Histopathologically identified prostate cancer lesions were sparse in 37 % of cases. Sparse cancers were entirely low grade in TZ and predominantly low-grade in PZ and generally small, thus likely posing lower risk for spread and progression than dense lesions. Sparse lesions were not distinguishable from dense lesions on mpMRI, but could be distinguished from benign tissues.</p></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"114 ","pages":"Article 110233"},"PeriodicalIF":2.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0730725X24002145/pdfft?md5=03ba7f48c3f2360fbd09af87173f00b9&pid=1-s2.0-S0730725X24002145-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239947","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}

{"title":"Introducing article numbering to Magnetic Resonance Imaging","authors":"","doi":"10.1016/S0730-725X(24)00210-8","DOIUrl":"10.1016/S0730-725X(24)00210-8","url":null,"abstract":"","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"113 ","pages":"Article 110229"},"PeriodicalIF":2.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0730725X24002108/pdfft?md5=0cf2a017ca410494ce54e08efbf66342&pid=1-s2.0-S0730725X24002108-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158230","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}