Magnetic Resonance in Medicine最新文献

{"title":"Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.","authors":"Brian Toner, Simon Arberet, Shu Zhang, Fei Han, Eze Ahanonu, Ute Goerke, Kevin Johnson, Zeyad Abouelfetouh, Ion Codreanu, Sajeev Sridhar, Hina Arif-Tiwari, Vibhas Deshpande, Diego R Martin, Mariappan Nadar, Maria I Altbach, Ali Bilgin","doi":"10.1002/mrm.70017","DOIUrl":"https://doi.org/10.1002/mrm.70017","url":null,"abstract":"<p><strong>Purpose: </strong>To accelerate respiratory triggered free-breathing T2 mapping of the abdomen while maintaining high-quality anatomical images, accurate T2 maps, and fast reconstruction times.</p><p><strong>Methods: </strong>We developed a flexible deep learning framework that can be trained in a fully supervised manner to improve T2-weighted images or in a self-supervised manner to reconstruct T2 maps.</p><p><strong>Results: </strong>For retrospectively undersampled data, anatomical images and T2 maps reconstructed by the proposed deep learning method demonstrated reduced voxel-wise error compared to existing traditional and compressed sensing techniques. Reconstruction times were approximately 1 s per slice, significantly faster than existing compressed sensing techniques. Prospectively undersampled data were also acquired to assess the model.</p><p><strong>Conclusion: </strong>The proposed deep-learning framework reconstructed high-quality anatomical images and accurate T2 maps from datasets undersampled to only 160 total radial views (5 views per echo time), enabling full liver coverage in under three minutes on average with per-slice reconstruction times of approximately one second.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative macromolecular proton fraction imaging using pulsed spin-lock.","authors":"Qianxue Shan, Ziqiang Yu, Baiyan Jiang, Jian Hou, Qiuyi Shen, Winnie Chiu Wing Chu, Vincent Wai Sun Wong, Weitian Chen","doi":"10.1002/mrm.70021","DOIUrl":"https://doi.org/10.1002/mrm.70021","url":null,"abstract":"<p><strong>Purpose: </strong>Recent studies have shown that spin-lock MRI can simplify quantitative magnetization transfer (MT) by eliminating its dependency on water pool parameters, removing the need for a T1 map in macromolecular proton fraction (MPF) quantification. However, its application is often limited by the requirement for long radiofrequency (RF) pulse durations, which are constrained by RF hardware capabilities despite remaining within specific absorption rate (SAR) safety limits.</p><p><strong>Methods: </strong>To address this challenge, we propose a novel method, MPF mapping using pulsed spin-lock (MPF-PSL). MPF-PSL employs a pulsed spin-lock train with intermittent free precession periods, enabling extended total spin-lock durations without exceeding hardware and specific absorption rate limits. A comprehensive analytical framework was developed to model the magnetization dynamics of the two-pool MT system under pulsed spin-lock, demonstrating that MPF-PSL achieves MT-specific quantification while minimizing confounding effects from the water pool. The proposed method is validated with Bloch-McConnell simulations, phantoms, and in vivo studies at 3T.</p><p><strong>Results: </strong>Both Bloch-McConnell simulations and phantom validation demonstrated that MPF-PSL exhibits insensitivity to water pool parameters while enabling robust MPF quantification. In vivo validation studies confirmed the method's clinical utility in detecting collagen deposition in patients with liver fibrosis.</p><p><strong>Conclusion: </strong>MPF-PSL presents a practical solution for quantitative MT imaging, with strong potential for clinical applications.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A size-adaptive RF coil for MRI of the pediatric human brain at 7 T.","authors":"Pedram Yazdanbakhsh, Marcus J Couch, Christian Sprang, Kyle M Gilbert, Sajjad Feizollah, Christine L Tardif, David A Rudko","doi":"10.1002/mrm.70011","DOIUrl":"https://doi.org/10.1002/mrm.70011","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this work was to design and build a size-adaptive pediatric RF head coil for 7 T neuroimaging. The coil can be safely applied for imaging children 4-9 years old.</p><p><strong>Methods: </strong>The pediatric head coil incorporates eight, transmit dipole elements for operation in parallel transmit (pTx) mode. The receive architecture is comprised of a 32-channel conformal, size-adaptive receive array. Receive elements were arranged into five sections of a mechanically adjustable 3D printed head former, allowing adjustment of the receive array according to child head size. The transmit coil was carefully simulated to calculate specific absorption rate (SAR) and B₁ ⁺ efficiency. Coil performance was then evaluated with a pediatric head phantom at both the largest and smallest dimensions of the receive former. In vivo imaging was carried out in 3 pediatric subjects (aged 5, 6, and 9 years old) to acquire B₁ ⁺ field maps and anatomical MP2RAGE images.</p><p><strong>Results: </strong>A comparison of simulated and experimental B₁ ⁺ performance in the pediatric head phantom was used to validate SAR models and to demonstrate that the coil was safe for pediatric imaging. The SNR performance in the pediatric phantom was improved by adjusting the position of the receive array to the smallest possible position. The in vivo B₁ ⁺ efficiency agreed with expectations, and the coil provided precise anatomical images of the brain.</p><p><strong>Conclusions: </strong>The proposed size-adaptive coil enables safe, high-quality imaging of children at 7 T, with a range of ages and head sizes. Accurate SAR modeling enabled imaging using both combined circularly polarized and dynamic pTx modes.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Utilizing 3D fast spin echo anatomical imaging to reduce the number of contrast preparations in <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:mi>ρ</ns0:mi></ns0:mrow> </ns0:msub> </ns0:mrow> <ns0:annotation>$$ {T}_{1rho } $$</ns0:annotation></ns0:semantics> </ns0:math> quantification of knee cartilage using learning-based methods.","authors":"Junru Zhong, Chaoxing Huang, Ziqiang Yu, Fan Xiao, Thierry Blu, Siyue Li, Tim-Yun Michael Ong, Ki-Wai Kevin Ho, Queenie Chan, James F Griffith, Weitian Chen","doi":"10.1002/mrm.70022","DOIUrl":"https://doi.org/10.1002/mrm.70022","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To propose and evaluate an accelerated &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;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; quantification method that combines &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;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomical FSE images, leveraging deep learning models for &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;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; mapping. The goal is to reduce scan time and facilitate integration into routine clinical workflows for osteoarthritis (OA) assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This retrospective study utilized MRI data from 40 participants (30 OA patients and 10 healthy volunteers). A volume of PD-weighted anatomical FSE images and a volume of &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;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -weighted images acquired at a non-zero spin-lock time were used as input to train deep learning models, including a 2D U-Net and a multi-layer perceptron (MLP). &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;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; maps generated by these models were compared with ground truth maps derived from a traditional non-linear least squares (NLLS) fitting method using four &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;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -weighted images. Evaluation metrics included mean absolute error (MAE), mean absolute percentage error (MAPE), regional error (RE), and regional percentage error (RPE).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The best-performed deep learning models achieved RPEs below 5% across all evaluated scenarios. This performance was consistent even in reduced acquisition settings that included only one PD-weighted image and one &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;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -weighted image, where NLLS methods cannot be applied. Furthermore, the results were comparable to those obtained with NLLS when longer acquisitions with four &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;mi&gt;ρ&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ {T}_{1rho } $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; -weighted images were used.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The proposed approach enables efficient &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;mi","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image space formalism of convolutional neural networks for k-space interpolation.","authors":"P Dawood, F Breuer, M Gram, I Homolya, P M Jakob, M Zaiss, M Blaimer","doi":"10.1002/mrm.70002","DOIUrl":"https://doi.org/10.1002/mrm.70002","url":null,"abstract":"<p><strong>Purpose: </strong>Noise resilience in image reconstructions by scan-specific robust artificial neural networks for k-space interpolation (RAKI) is linked to nonlinear activations in k-space. To gain a deeper understanding of this relationship, an image space formalism of RAKI is introduced for analyzing noise propagation analytically, identifying and characterizing image reconstruction features and to describe the role of nonlinear activations in a human-readable manner.</p><p><strong>Theory and methods: </strong>The image space formalism for RAKI inference is employed by expressing nonlinear activations in k-space as element-wise multiplications with activation masks, which transform into convolutions in image space. Jacobians of the de-aliased, coil-combined image relative to the aliased coil images can be expressed algebraically; thus, the noise amplification is quantified analytically (g-factor maps). We analyze the role of nonlinearity for noise resilience by controlling the degree of nonlinearity in the reconstruction model via the negative slope parameter in leaky ReLU.</p><p><strong>Results: </strong>The analytical g-factor maps correspond with those obtained from Monte Carlo simulations and from an auto differentiation approach for in vivo brain images. Apparent blurring and contrast loss artifacts are identified as implications of enhanced noise resilience. These residual artifacts can be traded against noise resilience by adjusting the degree of nonlinearity in the model (Tikhonov-like regularization) in case of limited training data. The inspection of image space activations reveals an autocorrelation pattern leading to a potential center artifact.</p><p><strong>Conclusion: </strong>The image space formalism of RAKI provides the means for analytical quantitative noise-propagation analysis and human-readable visualization of the effects of the nonlinear activation functions in k-space.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of deep learning-based denoising for arterial spin labeling: Effects of averaging and training strategies.","authors":"Jia Guo, Arun Sharma, Greg Zaharchuk, Hossein Rahimzadeh, Naveed Ilyas","doi":"10.1002/mrm.70013","DOIUrl":"https://doi.org/10.1002/mrm.70013","url":null,"abstract":"<p><strong>Purpose: </strong>Systematic study of the effects of averaging and other relevant training strategies in deep learning (DL)-based denoising is required to optimize such processing pipelines for improving the quality of arterial spin labeling (ASL) images.</p><p><strong>Methods: </strong>Different averaging strategies, including windowed and interleaved averaging methods, and different levels of averaging before and after convolutional neural network-based and transformer-based denoising were studied. The experiments were performed on 152 single-delay ASL scans from 152 subjects, including pulsed and pseudo-continuous ASL acquisitions. Four-fold cross-validation was implemented in all experiments. The effect of including calibration scans (M₀) was studied and compared across images of different levels of signal-to-noise ratio (SNR). The generalizability of DL denoising was examined in experiments using low-SNR ground truth in training. The results were assessed using image-quality metrics including structural similarity, peak SNR, and normalized mean absolute error.</p><p><strong>Results: </strong>Including M₀ was almost always beneficial, with a dependence on the SNR of the input ASL images. Windowed averaging outperformed interleaved averaging, supporting the practice of reducing scan time. Averaging of ASL images before DL denoising was more advantageous than averaging after. Matching the SNR levels of the images in training and inferencing was important for optimal performance. These findings were consistent across convolutional neural network-based and transformer-based models. The generalizability of DL-based denoising was confirmed, and its capability to reduce artifacts was observed.</p><p><strong>Conclusion: </strong>This study supports the use of DL-based denoising in improving the image quality of ASL and reducing scan time and provides insights to help optimize DL-denoising pipelines.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144784631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nuclear spin hyperpolarization of pyruvate enables longitudinal monitoring of treatment response in intestinal tumor organoids.","authors":"Josh P Peters, Hang Xiang, Charbel D Assaf, Farhad Haj Mohamad, Philip Rosenstiel, Stefan Schreiber, Jan-Bernd Hövener, Konrad Aden, Andrey N Pravdivtsev","doi":"10.1002/mrm.70008","DOIUrl":"https://doi.org/10.1002/mrm.70008","url":null,"abstract":"<p><strong>Purpose: </strong>Colorectal cancer, a leading cause of death in the Western world, is increasingly affecting younger populations. The Warburg effect, characterized by enhanced lactate production, is a hallmark of this cancer type. Although ¹⁸F-FDG PET-CT is commonly used for diagnosis, MRI offers higher spatial and chemical resolution without the drawbacks of radiation. However, MRI's low sensitivity has been a barrier to real-time metabolic imaging, and hence its implementation in clinical practice. Hyperpolarization has significantly boosted NMR sensitivity, enabling detailed metabolic studies in vivo.</p><p><strong>Methods: </strong>This study uses hyperpolarized [1-¹³C]pyruvate with dissolution dynamic nuclear polarization to noninvasively monitor metabolic changes in intestinal organoids from a genetically defined mouse model of spontaneous carcinogenesis (Rnaseh2b/Xbp1^ΔIEC) with a previously established targeted therapeutic intervention (mTOR inhibition by rapamycin).</p><p><strong>Results: </strong>Hyperpolarized NMR revealed a 6.6-fold reduction (p < 0.05) in lactate production in rapamycin-treated organoids, indicating suppressed metabolic activity. This method also detected alanine and bicarbonate metabolism, highlighting its sensitivity. Unlike traditional methods that destroy cellular integrity, hyperpolarization enabled repetitive, noninvasive metabolic assessments.</p><p><strong>Conclusion: </strong>Hyperpolarized [1-13C]pyruvate combined with NMR enables noninvasive, longitudinal monitoring of tumor metabolism in intestinal organoids while preserving cell viability and recultivation potential, bridging preclinical and clinical applications and affirming the method's potential for targeted metabolic imaging as a novel diagnostic and treatment control approach in cancer medicine.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144753733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigator-free multi-shot diffusion MRI via non-local low-rank reconstruction","authors":"","doi":"10.1002/mrm.70023","DOIUrl":"https://doi.org/10.1002/mrm.70023","url":null,"abstract":"","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"94 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to \"Quantification of whole-organ individual and bilateral renal metabolic rate of oxygen\".","authors":"","doi":"10.1002/mrm.30639","DOIUrl":"https://doi.org/10.1002/mrm.30639","url":null,"abstract":"","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144753732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced spectral response in frequency-dependent diffusion measurements using a linear encoding model.","authors":"Eric Seth Michael, Franciszek Hennel, Klaas Paul Pruessmann","doi":"10.1002/mrm.70006","DOIUrl":"https://doi.org/10.1002/mrm.70006","url":null,"abstract":"<p><strong>Purpose: </strong>To devise a more comprehensive quantitative representation for spectral encodings in frequency-dependent diffusion measurements for improved estimation of D(ω).</p><p><strong>Theory and methods: </strong>Whereas a spectral diffusion measurement is typically represented by a Dirac delta function at a single attributed frequency, spectral response is represented here by the encoding power in |Q(ω)|² over a set of contiguous frequency intervals. Using this representation paradigm, a linear encoding model is formulated wherein diffusivity over each interval can be estimated by inverting the encoding process from a set of measurements. This strategy was validated in in vivo human brain imaging experiments evaluating D(ω) up to 50 Hz over 10-Hz intervals using high-performance gradients. The employed spectral encodings were selected using an accompanying framework devised to ensure robust encoding performance given the chosen frequency intervals. Additionally, simulated measurements were carried out to compare the performance in estimating D(ω) using the proposed encoding model versus using single-frequency attribution in relation to the form of D(ω) and the width of frequency intervals.</p><p><strong>Results: </strong>In vivo D(ω) determined using the proposed encoding strategy were found to increase with increasing frequency and could be mapped to spectral responses more spectrally selective than those characteristic of single-frequency attribution. In turn, simulated measurements demonstrated that the linear encoding model permitted D(ω) estimation with improved accuracy, especially for more nonlinear D(ω), at the expense of reduced precision, particularly for narrower frequency intervals.</p><p><strong>Conclusion: </strong>By devising a more holistic representation paradigm for frequency-dependent diffusion measurements, D(ω) can be recovered with higher fidelity.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144731978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}