NMR in Biomedicine最新文献

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Multiparametric Free-Breathing 3D Whole-Heart Cardiac MR for Anatomical Bright- and Black-Blood Imaging With Co-Registered <ns0:math> <ns0:semantics> <ns0:mrow> <ns0:msub><ns0:mrow><ns0:mi>T</ns0:mi></ns0:mrow> <ns0:mrow><ns0:mn>1</ns0:mn></ns0:mrow> </ns0:msub> <ns0:mo>/</ns0:mo> <ns0:msub><ns0:mrow><ns0:mi>T</ns0:mi></ns0:mrow> <ns0:mrow><ns0:mn>2</ns0:mn></ns0:mrow> </ns0:msub> </ns0:mrow> <ns0:annotation>$$ {T}_1/{T}_2 $$</ns0:annotation></ns0:semantics> </ns0:math> Myocardial Tissue Mapping at <ns0:math> <ns0:semantics><ns0:mrow><ns0:mn>0</ns0:mn> <ns0:mo>.</ns0:mo> <ns0:mn>55</ns0:mn></ns0:mrow> <ns0:annotation>$$ 0.55 $$</ns0:annotation></ns0:semantics> </ns0:math> T.","authors":"Ivan Kokhanovskyi, Carlos Castillo-Passi, Michael G Crabb, Diego Pedraza, Simon J Littlewood, Rafael De la Sotta, Carl Ganter, Karl P Kunze, Dimitrios C Karampinos, Marcus R Makowski, Daniel Rueckert, Claudia Prieto, Rene M Botnar","doi":"10.1002/nbm.70317","DOIUrl":"10.1002/nbm.70317","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Low-field MRI has recently gained interest due to its potential for increased accessibility, reduced cost, and improved safety. However, high-quality anatomical imaging and robust tissue characterization remains an active area of research, particularly when aiming for a simple, one-click scan that captures all relevant information in a single acquisition. Bright-blood imaging is widely used for visualizing cardiac structures and coronary arteries, whereas black-blood is optimal for delineating the myocardium, atrial and vessel walls. High-resolution imaging is required for the accurate detection and segmentation of small anatomical structures, such as the coronary arteries, to enable assessment of narrowing or blockages. Co-registered &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_1/{T}_2 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; mapping enables quantitative myocardial tissue characterization, offering valuable clinical information for the detection of myocardial abnormalities. In this study, we sought to develop a novel free-breathing, motion-compensated 3D multi-contrast high-resolution cardiac MR sequence for simultaneous assessment of whole-heart cardiovascular anatomy via bright- and black-blood imaging and myocardial tissue quantification by joint &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_1 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; and &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_2 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; mapping at 0.55 T in a single scan. Data were acquired over six interleaved contrasts with various preparation modules using a variable flip angle bSSFP spiral-like readout with 2D image-based navigation for translational motion correction, resulting in a predictable acquisition time of &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mo&gt;≈&lt;/mo&gt; &lt;mn&gt;12&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ approx 12 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; min. Images were reconstructed using non-rigid motion corrected iterative sensitivity encoding followed by high-dimensional patch-based low-rank denoising, resulting in the acquisition, reconstruction and quantitative mapping time of &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mo&gt;≈&lt;/mo&gt; &lt;mn&gt;31&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ approx 31 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; min. In the phantom study, sequence performance was evaluated using correlation and Bland-Altman analysis against reference gold-standard and clinical mapping methods. In vivo, 3D bright- and black-blood volumes were assessed in multiple views, and vessel sharpness was quantified from multiplanar images. For joint &lt;math&gt; &lt;semantics&gt; &lt;mrow&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_1/{T}_2 $$&lt;/annotat","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 7","pages":"e70317"},"PeriodicalIF":2.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148056013","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":"SELFIE: Self-Supervised Learning for Fast Dynamic Golden-Angle Radial MRI.","authors":"Melanie Schellenberg, Anthony Mekhanik, Victor Murray, Pamela Argiriadi, Yulia Lakhman, Galina Levin, Oguz Akin, Ricardo Otazo","doi":"10.1002/nbm.70309","DOIUrl":"10.1002/nbm.70309","url":null,"abstract":"<p><p>Accelerated golden-angle radial acquisitions are widely used for dynamic MRI, but compressed sensing (CS)-based reconstruction presents residual artifacts at high acceleration and long computation times. Supervised deep learning (DL) enables fast reconstruction with improved image quality but usually relies on CS because fully-sampled references are unavailable. The proposed SELFIE (SElf-supervised Learning for Fast dynamIc golden-anglE radial MRI) technique enables self-supervised reconstruction that neither requires fully-sampled nor CS training references. SELFIE operates slice-by-slice in the image domain and thus avoids computationally expensive k-space data-consistency operations, allowing for a single forward-pass inference. Self-supervision is achieved by leveraging two properties of dynamic imaging with golden-angle radial sampling: (i) variable temporal resolution to form data-derived self-references at multiple temporal resolutions and (ii) temporal sparsity. SELFIE was evaluated on dynamic contrast-enhanced (DCE)-MRI in patients with gynecologic cancer and on motion-resolved free-breathing abdominal MRI in patients with liver cancer. Reconstructions were compared against CS and supervised DL using quantitative image-quality metrics and qualitative assessment from radiologists. Across both applications, SELFIE achieved image quality and dynamic fidelity (contrast enhancement and motion depiction) comparable to CS and supervised DL, while reconstructing 3D time series in seconds per case, substantially faster than CS. Ranking analysis and reader study favored SELFIE over CS and were comparable to supervised DL, with no statistically significant differences. Overall, SELFIE provides a fast, reference-free reconstruction framework for dynamic golden-angle radial MRI with competitive performance, representing a viable alternative to existing methods.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 7","pages":"e70309"},"PeriodicalIF":2.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147982273","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":"Metabolic Signatures of Vertigo: A qNMR Exploration Into the Biochemical Landscape.","authors":"Muhammed Parlak, Şahabettin Selek, Yasin Arifoğlu, Hüseyin Metin, Tuğçe Yıldız, Metin Demirel, Sedat Meydan","doi":"10.1002/nbm.70319","DOIUrl":"10.1002/nbm.70319","url":null,"abstract":"<p><p>Vertigo is a type of dizziness that typically occurs without movement and may arise independently or as a symptom of an underlying condition. It is classified as either central or peripheral, with central vertigo often caused by brain tumors, hemorrhages, or vascular aneurysms. This study aims to identify functional metabolites associated with vertigo and explore their related metabolic pathways. Metabolic profiling of serum samples from 33 vertigo patients and 34 healthy controls was conducted using ¹H-QNMR spectroscopy. Following log₁₀ transformation and normalization, univariate and multivariate analyses (FC, p-value, FDR, and VIP) were applied to identify significant metabolites. KEGG-based pathway and disease enrichment analyses were performed to explore biological relevance and clinical associations. ROC analysis was performed to evaluate the ability of selected metabolites to discriminate between vertigo patients and healthy controls within this exploratory dataset, and inter-metabolite relationships were assessed using Pearson correlation. Vertigo patients showed significantly different metabolomic profiles compared to healthy controls. Serum levels of capric acid, homocysteine, O-phosphoethanolamine, methylmalonate, N-carbamoylaspartate, and proline were significantly elevated, while glycylproline levels were decreased. These findings suggest a potential link between vertigo and these functionally relevant metabolites. Vertigo patients showed lower glycylproline and higher homocysteine and capric acid levels than healthy controls. These metabolites discriminated between groups in this exploratory cohort, but they should not be interpreted as clinically validated diagnostic markers. Further independent, longitudinal, and clinically well-characterized studies are required.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 7","pages":"e70319"},"PeriodicalIF":2.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148016636","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":"Robust Magnetic Field Homogeneity Enhancement via Local Passive Shimming in a Superconducting Magnet for Animal MRI.","authors":"Haoran Chen, Yaohui Wang, Wenchen Wang, Hui Wang, Wenhui Yang, Shufeng Wei, Ce Xu, Shunzhong Chen, Jianhua Liu, Jinshui Sun, Liang Li, Feng Liu","doi":"10.1002/nbm.70298","DOIUrl":"10.1002/nbm.70298","url":null,"abstract":"<p><p>In ultrahigh-field magnetic resonance imaging (MRI) systems, achieving subppm static magnetic field homogeneity is critical. However, engineering tolerances and assembly errors often introduce complex field inhomogeneities that global shimming alone cannot always fully correct. In this study, a novel local passive shimming method is developed for small-bore MRI superconducting magnets. By establishing a linear programming model for local shimming, the approach enables region-specific correction of high-order field variations. Numerical simulations and experimental verification on a 7 T/300-mm superconducting magnet demonstrate significant improvements in homogeneity, reducing root mean square error (RMSE) and peak-to-peak variation across the diameter of spherical volume (DSV). The optimized shim configuration achieved a peak-to-peak inhomogeneity of 4.23 ppm within the DSV, indicating substantial enhancement over baseline performance. These results indicate that the proposed technique serves as a practical and efficient complement to global shimming strategies, offering a robust solution for field correction in small-bore MRI systems.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 6","pages":"e70298"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147777385","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":"FENCE: Flexible Electric Noise Reduction Endo-Shield for the Suppression of Electromagnetic Interference in Low-Field MRI.","authors":"Julia Pfitzer, Martin Uecker, Hermann Scharfetter","doi":"10.1002/nbm.70287","DOIUrl":"10.1002/nbm.70287","url":null,"abstract":"<p><p>Electromagnetic interference (EMI) is a significant challenge for low-field MRI systems operating without conventional Faraday-shielded rooms. This interference degrades image quality and limits deployment in space-constrained or electromagnetically noisy environments. Traditional EMI mitigation approaches include external shields, subject grounding via electrodes, or active noise cancellation requiring synchronized receive channels. These methods either limit portability, introduce patient discomfort, or demand advanced hardware. In this work, we start from the hypothesis that EMI primarily couples capacitively from the body to the RF coil. We investigated two methods of blocking capacitive coupling while preserving inductive MRI signal detection: First, we employed capacitive segmentation of the RF coil and studied its effect on EMI coupling. Second, we present FENCE(Flexible Electromagnetic Noise reduCtion Endo-shield), a novel approach blocking capacitive coupling using flexible PCB shields placed inside the RF coil. FENCE can be retrofitted to existing RF coils without significant mechanical modifications. Finite element (FE) simulations were used to estimate the expected shielding performance and the impact on RF coil losses prior to practical implementation. Testing in various scenarios then demonstrated that the combination of FENCE with segmented solenoid coils is effective against both environmental noise sources and controlled EMI. In phantom experiments, FENCE significantly improved imaging performance and reduced EMI levels to near-baseline levels with  9% reduction in coil quality factor (Q factor), showing good agreement with the predictions from the FE simulations. In vivo head imaging confirmed these results across diverse electromagnetic environments significantly improving imaging performance while showing an  18% decrease in Q factor. FENCE provides a simple method for EMI mitigation in low-field MRI, enhancing image quality while maintaining system portability and accessibility. This approach could help to expand the deployment of low-field MRI systems in low-cost point-of-care applications where conventional shielding is impractical.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 6","pages":"e70287"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13111743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147777431","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":"Input Layer Regularization and Automated Regularization Hyperparameter Tuning for Myelin Water Estimation Using Deep Learning.","authors":"Mirage Modi, Shashank Sule, Jonathan Palumbo, Michael Rozowski, Griffin S Hampton, Mustapha Bouhrara, Wojciech Czaja, Richard G Spencer","doi":"10.1002/nbm.70276","DOIUrl":"10.1002/nbm.70276","url":null,"abstract":"<p><p>We present a deep learning framework that combines classical regularization and data preprocessing to improve estimation of the myelin water fraction (MWF) in the brain from magnetic resonance relaxometry data. The proposed method is developed within the context of biexponential signal modeling, a standard approach for quantifying MWF. Building on prior work on input layer regularization (ILR), we introduce several key extensions. First, we incorporate optimal regularization hyperparameter selection using either a dedicated neural network or generalized cross-validation (GCV), applied on a signal-by-signal (or pixel-by-pixel) basis to generate concatenated input features. Second, we extend the framework to directly estimate MWF in addition to exponential time constants. On synthetic data, the proposed architecture outperforms both conventional regularized fitting methods and standard multilayer perceptrons. When applied to in vivo brain data, it again yields superior accuracy, with GCV-based parameter selection performing slightly better than the neural network alternative. These findings demonstrate that ILR enhances MWF estimation within the biexponential model and that classical regularization techniques, when integrated with deep learning, can substantially improve quantitative estimation of myelin content.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 6","pages":"e70276"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100866/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147777380","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":"Unique Peritumoral Patterns Unveiled by Quantitative Multiparametric MRI and Their Diagnostic and Prognostic Value in Treated Malignant Gliomas.","authors":"Keyi Chai, Puyang Wang, Austin Carmichael, Jingpu Wu, Zongpai Zhang, Qianqi Huang, Karisa C Schreck, Jordina Rincon-Torroella, Matthias Holdhoff, David O Kamson, Lindsay Blair, Haris I Sair, Solmaz Sahebjam, Jaishri O Blakeley, John Laterra, Chetan Bettegowda, Jinyuan Zhou, Shanshan Jiang","doi":"10.1002/nbm.70290","DOIUrl":"10.1002/nbm.70290","url":null,"abstract":"<p><p>Malignant gliomas remain among the most challenging brain tumors to manage, with high recurrence rates and difficulty in distinguishing progression from treatment-related changes. While the peritumoral region offers valuable insights into tumor behavior, its quantitative MRI characteristics remain largely underexplored. This study investigated the diagnostic and prognostic value of multiparametric MRI biomarkers from noncontrast-enhancing peritumoral regions, particularly the surrounding nonenhancing FLAIR hyperintensity (SNFH). Thirty-eight patients with treated malignant gliomas underwent amide proton transfer-weighted (APTw) imaging, T₁ and T₂ mapping, and diffusion-weighted imaging. Histogram analysis of signal intensities from the enhancing tumor, immediate SNFH (≤ 10 mm), and distant SNFH regions was performed. Progression cases typically showed APTw hyperintensity in enhancing tumor cores and surrounding SNFH, while response cases showed minimal hyperintensity to isointensity in these regions. Repeated measures ANOVA revealed significant parameter variation across the tumor and SNFH regions, with APTw showing the largest dynamic range. Logistic regression models trained on histogram features from APTw and T₁ maps achieved a maximum test area under the curve (AUC) of 0.79 in differentiating progression from response. In survival analysis, parameters including higher APTw skewness was associated with better overall survival (hazard ratio = 0.32; p = 0.006). These findings demonstrate that APTw and T₁ imaging biomarkers from peritumoral edema regions reflect key features of tumor invasion and metabolic activity. Their combined application has potential to improve diagnostic accuracy and inform prognosis in posttreatment high-grade gliomas.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 6","pages":"e70290"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13191571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723417","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":"Improving the Identification of Muscular Dystrophies From Their Mimickers Based on MRI Muscle Texture Features Combined With Clinical Biomarkers.","authors":"Xinyue Niu, Qi Bing, Ruiqing Shi, Jing Hu, Jing Wang, Haoyue Xiang, Xiaohui Tong, Ling Wang, Jian Zhao","doi":"10.1002/nbm.70312","DOIUrl":"10.1002/nbm.70312","url":null,"abstract":"<p><p>MRI can detect the most significant pathological changes of muscle-fat replacement and muscle edema in muscular dystrophies. MRI-derived texture analysis is superior to conventional MRI for identifying pathological changes in muscular dystrophies. Furthermore, the combination of selected radiomics features and clinical biomarkers can enhance the diagnostic accuracy for muscular dystrophies. Muscular dystrophies are difficult to discriminate from their mimickers, so further research is warranted to identify the optimal feature combination and validate the performance of the combined model in myopathies prone to misdiagnosis. This study evaluated the diagnostic accuracy of radiomics features and clinical biomarkers in differentiating muscular dystrophies from their mimickers in a cohort of 161 myopathy patients using machine learning techniques. Multiple machine learning algorithms were jointly applied to screen robust features. The results showed that the combined nomogram exhibited better performance than the individual model using clinical or radiomic features, achieving an AUC of 0.955 in the training set and 0.923 in the validation set. Decision curve analysis confirmed the clinical utility of the nomogram. This multiparametric approach, combining texture features from MRI T1-weighted sequences and STIR sequences with clinical biomarkers (age, gender, and creatine kinase), significantly enhanced the discriminative power. The better performance of the nomogram compared with expert evaluations demonstrated its potential application in distinguishing muscular dystrophies from their mimickers.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 6","pages":"e70312"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147931879","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":"Simultaneous Water-Specific and Fat-Specific T1 and Fat Fraction Mapping of the Abdomen in Free Breathing Conditions With a Radially Encoded MP2RAGE.","authors":"Nadège Corbin, François Maingault, Aurélien J Trotier, Emile Kadalie, Laurence Dallet, Marc Biran, Sylvain Miraux, Eric Thiaudière, William Lefrançois, Emeline J Ribot","doi":"10.1002/nbm.70297","DOIUrl":"10.1002/nbm.70297","url":null,"abstract":"<p><p>The purpose of this study is to enable 3D abdominal imaging and quantitative parameter mapping in free breathing conditions and simultaneously provide proton-density fat fraction (PDFF), water-specific and fat-specific T1 maps. A radially encoded MP2RAGE with alternated fat and water selective pulses was implemented and validated on a phantom containing gadolinium (Gd) and pork fat at different concentrations. Comparison with MR spectroscopy and imaging techniques of reference was performed in vitro. Multiple experiments were carried out on healthy volunteers and individuals with a record of liver disease and benign bone marrow lesions to evaluate the method's repeatability, accuracy and potential for clinical application. In vitro water or fat specific T1 was in agreement with estimates provided by reference methods over a wide range of mixture ratios. PDFF was strongly correlated with spectroscopy (Pearson coefficient of 0.98) and other imaging techniques although underestimated due to the imperfect pulse selectivity profile. Images free of ghosting artefacts were acquired in vivo on seven volunteers. The whole abdomen was imaged, as well as a large part of the spine (from T11 to L5). Parametric maps provided repeatable estimates in the liver, the bone marrow and the subcutaneous fat that were consistent with values reported in literature and other imaging techniques. The acquisition time could be halved without significantly affecting the quantitative values. Overall, high-contrast MP2RAGE abdominal images, water- and fat- specific T1 maps and PDFF maps were achieved in a single 3D acquisition under free breathing.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 6","pages":"e70297"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13156445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856715","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":"Radiation Effects on Normal Brain in Subjects With Recurrent Glioblastoma by Spectroscopic MRI.","authors":"Abram Abdou, Sulaiman Sheriff, Kelly Rojas, Miah Nicholls, Eric A Mellon","doi":"10.1002/nbm.70303","DOIUrl":"10.1002/nbm.70303","url":null,"abstract":"<p><p>Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor in adults and almost always recurs. In recurrent GBM, conventional MRI has limited ability to detect microscopic tumor infiltration and distinguish progression from treatment-related change. Spectroscopic MRI (sMRI) provides quantitative metabolic maps across much of the brain, including choline (Cho) and N-acetylaspartate (NAA), a marker of healthy tissue. Because Cho/NAA is commonly used to define metabolically abnormal tissue, and prior radiation therapy (RT) can alter metabolite levels, we evaluated whether prior RT shifts baseline Cho/NAA in recurrent disease. We retrospectively studied 20 patients with recurrent GBM previously treated with maximal safe resection, RT, and temozolomide who underwent whole-brain sMRI at recurrence. The median interval from RT completion to sMRI was 8.99 months. T1-weighted images were co-registered to sMRI in MIDAS and aligned with planning CT in MIM to generate radiation dose maps. MIDAS generated tissue-water-referenced Cho, NAA, and creatine (Cr) maps, and Cho/NAA was normalized to contralateral normal-appearing white matter. Voxel-wise linear regression was performed between prior radiation dose and metabolite values at recurrence. Higher prior dose was associated with reduced NAA (-0.26%/Gy, r² = 0.011, p < 0.001) and Cr (-0.18%/Gy, r² = 0.004, p < 0.001), while Cho changed minimally (-0.023%/Gy, r² = 0.001, p < 0.001). Accordingly, normalized Cho/NAA increased with dose, with a mean slope of 0.0018/Gy (p < 0.001). A standard Cho/NAA threshold of 2 corresponded to a median of 2.39 (range, 1.91-2.72) in tissue previously receiving 60 Gy. These findings suggest prior RT modestly elevates baseline Cho/NAA, primarily through NAA reduction, and that dose-corrected Cho/NAA maps may improve tumor delineation in recurrent GBM.</p>","PeriodicalId":19309,"journal":{"name":"NMR in Biomedicine","volume":"39 6","pages":"e70303"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840707","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}