Magnetic Resonance in Medicine最新文献

{"title":"Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging.","authors":"Sam Coveney, Maryam Afzali, Lars Mueller, Irvin Teh, Filip Szczepankiewicz, Derek K Jones, Jürgen E Schneider","doi":"10.1002/mrm.70037","DOIUrl":"https://doi.org/10.1002/mrm.70037","url":null,"abstract":"<p><strong>Purpose: </strong>Cardiac diffusion tensor imaging (cDTI) can investigate the microstructure of heart tissue. At sufficiently high b-values, additional information on microstructure can be observed, but the data require a representation such as diffusion kurtosis imaging (DKI). cDTI is prone to image corruption, which is usually treated with shot rejection but which can be handled more generally with robust estimation. Unconstrained fitting allows DKI parameters to violate necessary constraints on signal behavior, causing errors in diffusion and kurtosis measures.</p><p><strong>Methods: </strong>We developed robust constrained weighted least squares (RCWLS) specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom scanner up to b-value <math> <semantics><mrow><mn>1350</mn> <mspace></mspace> <mi>s</mi> <mo>/</mo> <mi>m</mi> <msup><mrow><mi>m</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> <annotation>$$ 1350kern0.3em mathrm{s}/mathrm{m}{mathrm{m}}^2 $$</annotation></semantics> </math> , we compared fitting techniques with/without robustness and with/without constraints.</p><p><strong>Results: </strong>Constraints, but not robustness, made a significant difference on all measures. Robust fitting corrected large errors for some subjects. RCWLS was the only technique that showed radial kurtosis to be larger than axial kurtosis for all subjects, which is expected in myocardium due to increased restrictions to diffusion perpendicular to the primary myocyte direction. For <math> <semantics><mrow><mi>b</mi> <mo>=</mo> <mn>1350</mn> <mspace></mspace> <mi>s</mi> <mo>/</mo> <mi>m</mi> <msup><mrow><mi>m</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> <annotation>$$ b=1350kern0.3em mathrm{s}/mathrm{m}{mathrm{m}}^2 $$</annotation></semantics> </math> , RCWLS gave the following measures across subjects: mean diffusivity (MD) <math> <semantics><mrow><mn>1</mn> <mo>.</mo> <mn>68</mn> <mo>±</mo> <mn>0</mn> <mo>.</mo> <mn>050</mn> <mspace></mspace> <mo>×</mo> <mn>1</mn> <msup><mrow><mn>0</mn></mrow> <mrow><mo>-</mo> <mn>3</mn></mrow> </msup> <msup><mrow><mtext>mm</mtext></mrow> <mrow><mn>2</mn></mrow> </msup> <mo>/</mo> <mi>s</mi></mrow> <annotation>$$ 1.68pm 0.050kern3.0235pt times 1{0}^{-3}{mathrm{mm}}^2/mathrm{s} $$</annotation></semantics> </math> , fractional anisotropy (FA) <math> <semantics><mrow><mn>0</mn> <mo>.</mo> <mn>30</mn> <mo>±</mo> <mn>0</mn> <mo>.</mo> <mn>013</mn></mrow> <annotation>$$ 0.30pm 0.013 $$</annotation></semantics> </math> , mean kurtosis (MK) <math> <semantics><mrow><mn>0</mn> <mo>.</mo> <mn>36</mn> <mo>±</mo> <mn>0</mn> <mo>.</mo> <mn>027</mn></mrow> <annotation>$$ 0.36pm 0.027 $$</annotation></semantics> </math> , axial kurtosis (AK) <math> <semantics><mrow><mn>0</mn> <mo>.</mo> <mn>26</mn> <mo>±</mo> <mn>0</mn> <mo>.</mo> <mn>027</mn></mrow> <annotation>$$ 0.26pm 0.027 $$</annotation></semantics> </math> , radial kurtosis (RK) <math> <semantics><mrow><mn>0</mn> <mo>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144959403","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":"Mimicking focused ultrasound with a loop coil in acoustic radiation force imaging","authors":"Kristen Zarcone,&nbsp;Anuj Sharma,&nbsp;William A. Grissom","doi":"10.1002/mrm.70014","DOIUrl":"10.1002/mrm.70014","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To enable development of MR-acoustic radiation force imaging (MR-ARFI) methods for targeting ultrasound in human subjects without the regulatory, acoustic, or hardware challenges associated with actual transcranial ultrasound setups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>MR-ARFI is a phase-contrast imaging method that measures focal tissue displacement produced by an ultrasound transducer, when the transducer is pulsed simultaneously with a motion encoding gradient. The ultrasound-induced focal phase shift can be mimicked with a small loop coil that is driven by a DC pulse to produce a resonance frequency offset at the same time as the ultrasound pulse in an MR-ARFI pulse sequence. A coil was designed and built for use in MR-ARFI. Its focus size was characterized, its field map was measured, and volunteer experiments were performed to demonstrate its function in transcranial phase-contrast and magnetization-prepared MR-ARFI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Off-resonance field maps measured with the constructed loop coil were within 0.87% of simulations in a slice 15 mm from the coil's surface. Its “focus” further had a full-width-at-half-maximum of 22.9 mm in simulation versus 22.7 mm in the field map. In vivo results showed that the same coil driven with 13.7 mA current produced a phase shift corresponding to a realistic effective displacement of 3.5 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>μ</mi>\u0000 </mrow>\u0000 <annotation>$$ upmu $$</annotation>\u0000 </semantics></math>m in a slice 19 mm from the coil in MR-ARFI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A pulsed DC loop coil can mimic ARF-induced displacements in vivo, facilitating development of MR-ARFI methods in vivo.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"94 6","pages":"2529-2536"},"PeriodicalIF":3.0,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144959340","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":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Motion-robust <ns0:math> <ns0:semantics> <ns0:mrow> <ns0:msubsup><ns0:mrow><ns0:mi>T</ns0:mi></ns0:mrow> <ns0:mrow><ns0:mn>2</ns0:mn></ns0:mrow> <ns0:mrow><ns0:mo>∗</ns0:mo></ns0:mrow> </ns0:msubsup> </ns0:mrow> <ns0:annotation>$$ {mathrm{T}}_2^{ast } $$</ns0:annotation></ns0:semantics> </ns0:math> quantification from low-resolution gradient echo brain MRI with physics-informed deep learning.","authors":"Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Lina Felsner, Kilian Weiss, Christine Preibisch, Julia A Schnabel","doi":"10.1002/mrm.70050","DOIUrl":"https://doi.org/10.1002/mrm.70050","url":null,"abstract":"<p><strong>Purpose: </strong><math> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {mathrm{T}}_2^{ast } $$</annotation></semantics> </math> quantification from gradient echo magnetic resonance imaging is particularly affected by subject motion due to its high sensitivity to magnetic field inhomogeneities, which are influenced by motion and might cause signal loss. Thus, motion correction is crucial to obtain high-quality <math> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {mathrm{T}}_2^{ast } $$</annotation></semantics> </math> maps.</p><p><strong>Methods: </strong>We extend PHIMO, our previously introduced learning-based physics-informed motion correction method for low-resolution <math> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {mathrm{T}}_2^{ast } $$</annotation></semantics> </math> mapping. Our extended version, PHIMO+, utilizes acquisition knowledge to enhance the reconstruction performance for challenging motion patterns and increase PHIMO's robustness to varying strengths of magnetic field inhomogeneities across the brain. We perform comprehensive evaluations regarding motion detection accuracy and image quality for data with simulated and real motion.</p><p><strong>Results: </strong>PHIMO+ outperforms the learning-based baseline methods both qualitatively and quantitatively with respect to line detection and image quality. Moreover, PHIMO+ performs on par with a conventional state-of-the-art motion correction method for <math> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {mathrm{T}}_2^{ast } $$</annotation></semantics> </math> quantification from gradient echo MRI, which relies on redundant data acquisition.</p><p><strong>Conclusion: </strong>PHIMO+'s competitive motion correction performance, combined with a reduction in acquisition time by over 40% compared to the state-of-the-art method, makes it a promising solution for motion-robust <math> <semantics> <mrow> <msubsup><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow> <mrow><mo>∗</mo></mrow> </msubsup> </mrow> <annotation>$$ {mathrm{T}}_2^{ast } $$</annotation></semantics> </math> quantification in research settings and clinical routine.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144959483","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":"Modeling the MRI gradient system with a temporal convolutional network: Improved reconstruction by prediction of readout gradient errors.","authors":"Jonathan B Martin, Hannah E Alderson, John C Gore, Mark D Does, Kevin D Harkins","doi":"10.1002/mrm.70044","DOIUrl":"10.1002/mrm.70044","url":null,"abstract":"<p><strong>Purpose: </strong>Our objective is to develop a general, nonlinear gradient system model that can accurately predict gradient distortions using convolutional networks.</p><p><strong>Methods: </strong>A set of training gradient waveforms were measured on a small animal imaging system and used to train a temporal convolutional network to predict the gradient waveforms produced by the imaging system.</p><p><strong>Results: </strong>The trained network was able to accurately predict nonlinear distortions produced by the gradient system. Network prediction of gradient waveforms was incorporated into the image reconstruction pipeline and provided improvements in image quality and diffusion parameter mapping compared to both the nominal gradient waveform and the gradient impulse response function.</p><p><strong>Conclusion: </strong>Temporal convolutional networks can more accurately model gradient system behavior than existing linear methods and may be used to retrospectively correct gradient errors.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144873825","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 data-driven algorithm to determine ¹H-MRS basis set composition.","authors":"Christopher W Davies-Jenkins, Helge J Zöllner, Dunja Simicic, Seyma Alcicek, Richard A E Edden, Georg Oeltzschner","doi":"10.1002/mrm.70030","DOIUrl":"10.1002/mrm.70030","url":null,"abstract":"<p><strong>Purpose: </strong>Metabolite amplitude estimates derived from linear combination modeling of MR spectra depend on the precise list of constituent metabolite basis functions used (the \"basis set\"). The absence of clear consensus on the \"ideal\" composition or objective criteria to determine the suitability of a particular basis set contributes to the poor reproducibility of MRS. In this proof-of-concept study, we demonstrate a novel, data-driven approach for deciding the basis-set composition using Akaike information criteria (AIC).</p><p><strong>Methods: </strong>We have developed an algorithm that iteratively adds metabolites to the basis set using iterative modeling, informed by AIC scores. We investigated two quantitative \"stopping conditions,\" referred to as max-AIC and zero-amplitude, and whether to optimize the selection of basis set on a per-spectrum basis or at the group level. The algorithm was tested using two groups of synthetic in vivo-like spectra representing healthy brain and tumor spectra, respectively, and the derived basis sets (and metabolite amplitude estimates) were compared to the ground truth.</p><p><strong>Results: </strong>All derived basis sets correctly identified high-concentration metabolites and provided reasonable fits of the spectra. At the single-spectrum level, the two stopping conditions derived the underlying basis set with 84% to 88% accuracy. When optimizing across a group, basis set determination accuracy improved to 89% to 92%.</p><p><strong>Conclusion: </strong>Data-driven determination of the basis set composition is feasible. With refinement, this approach could provide a valuable data-driven way to derive or refine basis sets, reducing the operator bias of MRS analyses, enhancing the objectivity of quantitative analyses, and increasing the clinical viability of MRS.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144862321","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":"Importance of R₂ accuracy in susceptibility source separation.","authors":"Tereza Beatriz Oliveira Assunção, Nashwan Naji, Jeff Snyder, Peter Seres, Gregg Blevins, Penelope Smyth, Alan H Wilman","doi":"10.1002/mrm.70034","DOIUrl":"https://doi.org/10.1002/mrm.70034","url":null,"abstract":"<p><strong>Purpose: </strong>To examine the importance of R₂ accuracy on independent paramagnetic and diamagnetic outputs from susceptibility source separation in the brain from two publicly available methods.</p><p><strong>Methods: </strong>The effects of R₂ errors, which translate into <math> <semantics> <mrow><msubsup><mi>R</mi> <mn>2</mn> <mo>'</mo></msubsup> </mrow> <annotation>$$ {mathrm{R}}_2^{prime } $$</annotation></semantics> </math> errors, on output maps from χ-separation and χ-sepnet were examined using data from 11 healthy volunteers. Baseline R₂ values were determined by Bloch modeling a dual-echo turbo spin echo decay with measured flip angles. R₂ errors were introduced from either simple exponential fitting, R₂ multiplication factors, or R₂ approximation using only <math> <semantics> <mrow><msubsup><mi>R</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {mathrm{R}}_2^{ast } $$</annotation></semantics> </math> . Altered R₂ maps were then used as input for the susceptibility source separation models using either default or calculated relaxometric constant. Difference maps and mean percentage errors within regions of interest (ROIs) were measured.</p><p><strong>Results: </strong>Errors in R₂, and hence <math> <semantics> <mrow><msubsup><mi>R</mi> <mn>2</mn> <mo>'</mo></msubsup> </mrow> <annotation>$$ {mathrm{R}}_2^{prime } $$</annotation></semantics> </math> , directly affected paramagnetic and diamagnetic components. χ-sepnet was less sensitive to R₂ errors than χ-separation and had reduced variance among subjects. χ-sepnet susceptibility component errors did not reach more than ±20% in most ROIs for all alteration approaches. In contrast, χ-separation, with default relaxometric constant, reached 56% susceptibility component error with -25% R₂ error input. Exponential fitting R₂ error exceeded -25%, thus, even larger component errors occurred. <math> <semantics> <mrow><msubsup><mi>R</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {mathrm{R}}_2^{ast } $$</annotation></semantics> </math> -based approximation had -25% R₂ mean error across ROIs (-18% across whole brain), yielding 57% mean susceptibility component error across ROIs.</p><p><strong>Conclusion: </strong>Paramagnetic and diamagnetic outputs of susceptibility source separation methods have variable responses to R₂ error, that may occur with simple R₂ fitting or R₂ approximation, and can be strongly biased by it.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144862322","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":"Comparative evaluation of supervised and unsupervised deep learning strategies for denoising hyperpolarized ¹²⁹Xe lung MRI.","authors":"Abdullah S Bdaiwi, Matthew M Willmering, Riaz Hussain, Erik Hysinger, Jason C Woods, Laura L Walkup, Zackary I Cleveland","doi":"10.1002/mrm.70033","DOIUrl":"10.1002/mrm.70033","url":null,"abstract":"<p><strong>Purpose: </strong>Reduced signal-to-noise ratio (SNR) in hyperpolarized ¹²⁹Xe MR images can affect accurate quantification for research and diagnostic evaluations. Thus, this study explores the application of supervised deep learning (DL) denoising, traditional (Trad) and Noise2Noise (N2N) and unsupervised Noise2void (N2V) approaches for ¹²⁹Xe MR imaging.</p><p><strong>Methods: </strong>The DL denoising frameworks were trained and tested on 952 ¹²⁹Xe MRI data sets (421 ventilation, 125 diffusion-weighted, and 406 gas-exchange acquisitions) from healthy subjects and participants with cardiopulmonary conditions and compared with the block matching 3D denoising technique. Evaluation involved mean signal, noise standard deviation (SD), SNR, and sharpness. Ventilation defect percentage (VDP), apparent diffusion coefficient (ADC), membrane uptake, red blood cell (RBC) transfer, and RBC:Membrane were also evaluated for ventilation, diffusion, and gas-exchange images, respectively.</p><p><strong>Results: </strong>Denoising methods significantly reduced noise SDs and enhanced SNR (p < 0.05) across all imaging types. Traditional ventilation model (Trad_vent) improved sharpness in ventilation images but underestimated VDP (bias = -1.37%) relative to raw images, whereas N2N_vent overestimated VDP (bias = +1.88%). Block matching 3D and N2V_vent showed minimal VDP bias (≤ 0.35%). Denoising significantly reduced ADC mean and SD (p < 0.05, bias ≤ - 0.63 × 10^-2). The values of Trad_vent and N2N_vent increased mean membrane and RBC (p < 0.001) with no change in RBC:Membrane. Denoising also reduced SDs of all gas-exchange metrics (p < 0.01).</p><p><strong>Conclusions: </strong>Low SNR may impair the potential of ¹²⁹Xe MRI for clinical diagnosis and lung function assessment. The evaluation of supervised and unsupervised DL denoising methods enhanced ¹²⁹Xe imaging quality, offering promise for improved clinical interpretation and diagnosis.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855717","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":"Enhancing cardiac MRI reliability at 3 T using motion-adaptive B₀ shimming.","authors":"Yuheng Huang, Archana Vadiraj Malagi, Xinqi Li, Xingmin Guan, Chia-Chi Yang, Li-Ting Huang, Ziyang Long, Jeremy Zepeda, Xinheng Zhang, Ghazal Yoosefian, Xioaming Bi, Chang Gao, Yun Shang, Nader Binesh, Hsu-Lei Lee, Debiao Li, Rohan Dharmakumar, Hui Han, Hsin-Jung R Yang","doi":"10.1002/mrm.70026","DOIUrl":"https://doi.org/10.1002/mrm.70026","url":null,"abstract":"<p><strong>Purpose: </strong>Magnetic susceptibility differences at the heart-lung interface introduce B₀-field inhomogeneities that challenge cardiac MRI at high field strengths (≥ 3 T). Although hardware-based shimming has advanced, conventional approaches often neglect dynamic variations in thoracic anatomy caused by cardiac and respiratory motion, leading to residual off-resonance artifacts. This study aims to characterize motion-induced B₀-field fluctuations in the heart and evaluate a deep learning-enabled motion-adaptive B₀ shimming pipeline to mitigate them.</p><p><strong>Methods: </strong>A motion-resolved B₀ mapping sequence was implemented at 3 T to quantify cardiac and respiratory-induced B₀ variations. A motion-adaptive shimming framework was then developed and validated through numerical simulations and human imaging studies. B₀-field homogeneity and T₂* mapping accuracy were assessed in multiple breath-hold positions using standard and motion-adaptive shimming.</p><p><strong>Results: </strong>Respiratory motion significantly altered myocardial B₀ fields (p < 0.01), whereas cardiac motion had minimal impact (p = 0.49). Compared with conventional scanner shimming, motion-adaptive B₀ shimming yielded significantly improved field uniformity across both inspiratory (post-shim SD_ratio: 0.68 ± 0.10 vs. 0.89 ± 0.11; p < 0.05) and expiratory (0.65 ± 0.16 vs. 0.84 ± 0.20; p < 0.05) breath-hold states. Corresponding improvements in myocardial T₂* map homogeneity were observed, with reduced coefficient of variation (0.44 ± 0.19 vs. 0.39 ± 0.22; 0.59 ± 0.30 vs. 0.46 ± 0.21; both p < 0.01).</p><p><strong>Conclusion: </strong>The proposed motion-adaptive B₀ shimming approach effectively compensates for respiration-induced B₀ fluctuations, enhancing field homogeneity and reducing off-resonance artifacts. This strategy improves the robustness and reproducibility of T₂* mapping, enabling more reliable high-field cardiac MRI.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855718","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":"Dynamic contrast enhanced-magnetic resonance fingerprinting (DCE-MRF): A new quantitative MRI method to reliably assess tumor vascular perfusion","authors":"Christina J. MacAskill,&nbsp;Yuran Zhu,&nbsp;Guanhua Wang,&nbsp;Bernadette O. Erokwu,&nbsp;Chetan B. Dhakan,&nbsp;Andrew Dupuis,&nbsp;Barbara J. Schiemann,&nbsp;Michael Kavran,&nbsp;Chunying Wu,&nbsp;William P. Schiemann,&nbsp;Mark A. Griswold,&nbsp;Xin Yu,&nbsp;Mark D. Pagel,&nbsp;Chris A. Flask","doi":"10.1002/mrm.70019","DOIUrl":"10.1002/mrm.70019","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The clinical utility of conventional DCE-MRI methods is limited by the use of conventional qualitative dynamic T₁-weighted images, resulting in poor reproducibility. This study presents the initial implementation of a new DCE-magnetic resonance fingerprinting (DCE-MRF) methodology to provide reproducible, quantitative assessments of tumor vascular perfusion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The DCE-MRF acquisition combines multiple T₁ preparations, highly undersampled spiral trajectories (<i>R</i> = 48), a low-rank reconstruction method, and low tip angles on a 9.4 T preclinical MRI scanner to rapidly generate dynamic T₁ maps (23-s temporal resolution). In vitro validation experiments were conducted across a range of Gadovist concentrations to assess accuracy and temporal precision in comparison to conventional methods. The DCE-MRF method was also evaluated in vivo in an orthotopic 4T1 mouse model of breast cancer (<i>n</i> = 25). Pharmacokinetic modeling of the in vivo data was performed using a linear reference region model (LRRM).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In vitro DCE-MRF studies demonstrated good agreement with conventional MRI methods for T₁ measurements (<i>R</i>² <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≥</mo>\u0000 </mrow>\u0000 <annotation>$$ ge $$</annotation>\u0000 </semantics></math> 0.99). The iterative low-rank reconstruction method also reduced artifacts compared to conventional reconstruction methods. DCE-MRF demonstrated a 2- to 3-fold reduction in temporal variability compared to conventional DCE-MRI, and enabled effective in vivo pharmacokinetic modeling using the LRRM by generating voxelwise maps of <i>RK</i>^trans and <i>k</i>_ep,T as measures of tumor vascular perfusion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>DCE-MRF represents a new inherently quantitative approach to measuring tumor vascular perfusion that can be used in animal models and eventually in patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":"94 6","pages":"2578-2592"},"PeriodicalIF":3.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144847272","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":"Absolute quantification of cerebral metabolites using two-dimensional proton MR spectroscopic imaging with quantitative MRI-based water reference.","authors":"Dennis C Thomas, Seyma Alcicek, Andrei Manzhurtsev, Elke Hattingen, Katharina J Wenger, Ulrich Pilatus","doi":"10.1002/mrm.70027","DOIUrl":"https://doi.org/10.1002/mrm.70027","url":null,"abstract":"<p><strong>Purpose: </strong>Metabolite concentrations are valuable biomarkers in brain tumors (BTs). However, absolute quantification of metabolites using MR spectroscopy requires a correction of water relaxation using time-consuming quantitative MRI (qMRI) sequences in addition to a lengthy two-dimensional spectroscopic water-reference acquisition. The goal of this work was to develop and validate a fast quantification method where a two-dimensional spectroscopic water reference is obtained using qMRI and a single-voxel stimulated-echo acquisition mode (STEAM) sequence.</p><p><strong>Methods: </strong>The semi-adiabatic localization by adiabatic selective refocusing (sLASER) sequence was used for MR spectroscopy imaging (MRSI) acquisition. A single-voxel unsuppressed water signal was acquired using a STEAM sequence. A qMRI protocol was also acquired, and the H₂O map was calibrated based on the STEAM signal to obtain the spectroscopic water reference (proposed method). Five healthy volunteers and one BT patient were scanned at 3 T. Concentrations obtained using the proposed and two reference methods-one where water-relaxation effects were corrected using literature values (reference method) and one where they were corrected using qMRI-derived values (reference method with qMRI)-were compared.</p><p><strong>Results: </strong>In healthy subjects, white-matter metabolite concentrations obtained using water relaxation using literature values (reference method) significantly differed from those using individual-specific corrections (reference method with qMRI and proposed method). Bland-Altman analyses revealed a very low bias and standard deviation of the differences between the reference method with qMRI and the proposed method (bias < 0.5% and standard deviation < 10%). The BT regions showed an approximate 35% underestimation of metabolite concentrations using the reference method.</p><p><strong>Conclusion: </strong>For metabolite quantification, accurate water referencing with individual-specific corrections for water relaxation times was obtained in 8 min using the proposed method.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144847270","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}