Magnetic Resonance in Medicine最新文献

{"title":"Advanced microstructure imaging at high b-values and high resolution combining ultra-high performance gradient diffusion imaging and model-based deep learning demonstrated using 3D multi-slab acquisition.","authors":"Chu-Yu Lee, Reza Ghorbani, Mahsa Rajabi, Merry Mani","doi":"10.1002/mrm.70046","DOIUrl":"10.1002/mrm.70046","url":null,"abstract":"<p><strong>Purpose: </strong>To demonstrate the extended capabilities of 3D multi-slab diffusion-weighted acquisition (3D-msDWI) on high-performance gradients (HPG) to support advanced microstructure modeling for in-vivo human studies at high resolutions.</p><p><strong>Methods: </strong>Despite optimal SNR-efficiency, the application of 3D-msDWI has been limited by the long volume acquisition times (VAT) required for encoding the 3D k-space using multi-shot approaches. Substantial reduction of VAT is possible by employing optimized 3D k-space under-sampling methods. We demonstrate that with reduced VAT, 3D-msDWI can be successfully utilized for advanced brain microstructure modeling at high resolution. HPG systems (e.g., <math> <semantics><mrow><mo>></mo> <mn>200</mn></mrow> <annotation>$$ >200 $$</annotation></semantics> </math>  mT/m, <math> <semantics><mrow><mo>></mo> <mn>300</mn></mrow> <annotation>$$ >300 $$</annotation></semantics> </math>  T/m/s) enable further optimization through shorter echo times at high b-values. We evaluated the accelerated 3D-msDWI method's ability to support diffusion studies at 1mm isotropic resolution using data collected across three shells, with b-values extended up to 6000  <math> <semantics> <mrow> <msup><mrow><mtext>s/mm</mtext></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> <annotation>$$ mathrm{s}/{mathrm{mm}}^2 $$</annotation></semantics> </math> , and employing compartment models. The reconstruction employed a navigator-based, motion-compensated approach using a regularized, iterative model-based algorithm.</p><p><strong>Results: </strong>The accelerated 3D-msDWI framework enabled the generation of whole-brain parametric maps of a three-compartment model, at 1mm isotropic resolution, using a 3-shell, 66-direction acquisition completed in <math> <semantics><mrow><mo><</mo></mrow> <annotation>$$ < $$</annotation></semantics> </math> 15 min. The intra-axonal diffusivities (in <math> <semantics><mrow><mi>μ</mi> <msup><mrow><mi>m</mi></mrow> <mrow><mn>2</mn></mrow> </msup> <mo>/</mo> <mi>m</mi> <mi>s</mi></mrow> <annotation>$$ mu {m}^2/ ms $$</annotation></semantics> </math> ) and volume fractions reported from the method are as follows: 2.27 <math> <semantics><mrow><mo>±</mo></mrow> <annotation>$$ pm $$</annotation></semantics> </math> 0.14; 0.6 <math> <semantics><mrow><mo>±</mo></mrow> <annotation>$$ pm $$</annotation></semantics> </math> 0.04 in corpus-callosum, 2.17 <math> <semantics><mrow><mo>±</mo></mrow> <annotation>$$ pm $$</annotation></semantics> </math> 0.09; 0.66 <math> <semantics><mrow><mo>±</mo></mrow> <annotation>$$ pm $$</annotation></semantics> </math> 0.03 in anterior limb of internal capsule, 2.18 <math> <semantics><mrow><mo>±</mo></mrow> <annotation>$$ pm $$</annotation></semantics> </math> 0.08; 0.68 <math> <semantics><mrow><mo>±</mo></mrow> <annotation>$$ pm $$</annotation></semantics> </math> 0.04 in posterior limb of internal capsule, 2.07 <math> <semantics><mrow><mo>±</mo></mrow> <ann","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":"144959589","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":"Interpreting amide proton transfer-weighted imaging contrast between normal and tumor brain tissues using the asymmetry analysis method at 4.7 T.","authors":"Malvika Viswanathan, Yashwant Kurmi, Xiaoyu Jiang, Junzhong Xu, Zhongliang Zu","doi":"10.1002/mrm.70041","DOIUrl":"https://doi.org/10.1002/mrm.70041","url":null,"abstract":"<p><strong>Purpose: </strong>To provide a comprehensive analysis of the contributors to the amide proton transfer-weighted (APTw) imaging signal using an asymmetry analysis method, as well as its contrast between tumors and the contralateral normal tissues at 4.7 T.</p><p><strong>Methods: </strong>First, a signal model was developed to demonstrate the dependence of APTw signal on various contributors, including water T₁, reference signal containing direct water saturation (DS) and magnetization transfer (MT), as well as APT, amine CEST, and nuclear Overhauser enhancement (NOE) effects. Second, these effects were measured in rat brains bearing 9 L tumors, with saturation field strengths (B₁) of 2 and 3 μT, at 4.7 T to assess their relative contributions. Specifically, the reference signal was determined using an extrapolated MT reference approach. The amine CEST effect was isolated using an auxiliary asymmetry analysis method, while the APT and NOE effects were quantified through a multiple-pool Lorentzian fit of CEST Z-spectra acquired at 15.2 T.</p><p><strong>Results: </strong>Our findings reveal that at 2 μT, the APT effect is comparable to the NOE/asymmetric MT effects in tumors. Whereas at 3 μT, the APT effect becomes greater than the NOE/asymmetric MT effects in tumors. At these two B₁ levels, the contribution from the amine CEST effect cannot be ignored. APTw contrast between tumors and normal tissues primarily arises from decreased NOE/asymmetric MT effects, with an additional spillover-dilution effect from the reduced MT effect in tumors.</p><p><strong>Conclusion: </strong>This study provides insights into the contributors to APTw signal and its contrast between tumors and normal tissues, thereby enhancing our understanding of APTw imaging.</p>","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":"144959291","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":"Motion corrected 3D whole-heart SAVA T₁ mapping at 0.55 T.","authors":"Rafael I De la Sotta, Michael G Crabb, Karl P Kunze, René M Botnar, Claudia Prieto","doi":"10.1002/mrm.70038","DOIUrl":"https://doi.org/10.1002/mrm.70038","url":null,"abstract":"<p><strong>Purpose: </strong>To propose a novel highly efficient isotropic-resolution 3D whole-heart saturation-recovery and variable-flip-angle (SAVA) T₁ mapping sequence at 0.55 T, incorporating image navigator (iNAV)-based non-rigid motion correction and dictionary matching.</p><p><strong>Methods: </strong>The proposed iNAV-based isotropic-resolution 3D whole-heart SAVA T₁ mapping sequence at 0.55 T acquires three gradient echo T₁-weighted volumes sequentially: an equilibrium contrast with 4° flip angle, and two saturation recovery T₁-weighted contrasts with 10° flip angles and different saturation delays. Sequence parameters were optimized for the lower field strength by simulations and phantom experiments. Two-dimensional iNAVs are acquired at each heartbeat to enable respiratory motion estimation and correction and 100% respiratory scan efficiency. The T₁ mapping is computed by dictionary matching, using subject-specific dictionaries based on Bloch equations simulations. Non-rigid motion correction is implemented based on respiratory bins reconstructed by iterative-SENSE and subsequent patch-based low-rank denoising, for each contrast separately. The proposed approach was evaluated in a standardized T₁ phantom and 10 healthy subjects, in comparison to spin-echo reference and 2D MOLLI, respectively.</p><p><strong>Results: </strong>Excellent agreement is observed between iNAV-based SAVA T₁ mapping at 0.55 T and spin echo reference in phantom, with a <math> <semantics> <mrow><msup><mi>R</mi> <mn>2</mn></msup> <mo>=</mo> <mn>0.998</mn></mrow> <annotation>$$ {R}^2=0.998 $$</annotation></semantics> </math> for all phantom vials. Good image quality was obtained in vivo for the contrast images and corresponding T₁ maps in a scan time of 6:30 min ±40 s. Average and SD of myocardial T₁ values across subjects and segments was 706 ± 41 ms, which is comparable to acquired 2D MOLLI values of 681 ± 26 ms, and previously reported 2D MOLLI values of 701 ± 24 ms. Coefficient of variation values (12%) are higher than those previously reported for diaphragmatic navigator-based non-isotropic SAVA T₁ mapping at 3 T (7.4%).</p><p><strong>Conclusion: </strong>The proposed iNAV-based SAVA approach achieves free-breathing motion-corrected 3D whole-heart T₁ mapping at 0.55 T in approximately 7 min scan time for an isotropic resolution of 2 mm. In vivo experiments showed that the proposed sequence achieves good map quality, with comparable T₁ values and spatial variability compared to 2D MOLLI T₁ mapping. Further evaluation is warranted in patients with cardiovascular disease.</p>","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":"144959370","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":"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":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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 &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;1350&lt;/mn&gt; &lt;mspace&gt;&lt;/mspace&gt; &lt;mi&gt;s&lt;/mi&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;mi&gt;m&lt;/mi&gt; &lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt; &lt;/msup&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ 1350kern0.3em mathrm{s}/mathrm{m}{mathrm{m}}^2 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , we compared fitting techniques with/without robustness and with/without constraints.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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 &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;1350&lt;/mn&gt; &lt;mspace&gt;&lt;/mspace&gt; &lt;mi&gt;s&lt;/mi&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;mi&gt;m&lt;/mi&gt; &lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt; &lt;/msup&gt; &lt;/mrow&gt; &lt;annotation&gt;$$ b=1350kern0.3em mathrm{s}/mathrm{m}{mathrm{m}}^2 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , RCWLS gave the following measures across subjects: mean diffusivity (MD) &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt; &lt;mo&gt;.&lt;/mo&gt; &lt;mn&gt;68&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;.&lt;/mo&gt; &lt;mn&gt;050&lt;/mn&gt; &lt;mspace&gt;&lt;/mspace&gt; &lt;mo&gt;×&lt;/mo&gt; &lt;mn&gt;1&lt;/mn&gt; &lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mo&gt;-&lt;/mo&gt; &lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt; &lt;/msup&gt; &lt;msup&gt;&lt;mrow&gt;&lt;mtext&gt;mm&lt;/mtext&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt; &lt;/msup&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;mi&gt;s&lt;/mi&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ 1.68pm 0.050kern3.0235pt times 1{0}^{-3}{mathrm{mm}}^2/mathrm{s} $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , fractional anisotropy (FA) &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;.&lt;/mo&gt; &lt;mn&gt;30&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;.&lt;/mo&gt; &lt;mn&gt;013&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ 0.30pm 0.013 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , mean kurtosis (MK) &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;.&lt;/mo&gt; &lt;mn&gt;36&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;.&lt;/mo&gt; &lt;mn&gt;027&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ 0.36pm 0.027 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , axial kurtosis (AK) &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;.&lt;/mo&gt; &lt;mn&gt;26&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;.&lt;/mo&gt; &lt;mn&gt;027&lt;/mn&gt;&lt;/mrow&gt; &lt;annotation&gt;$$ 0.26pm 0.027 $$&lt;/annotation&gt;&lt;/semantics&gt; &lt;/math&gt; , radial kurtosis (RK) &lt;math&gt; &lt;semantics&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt; &lt;mo&gt;","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}