Magnetic Resonance Materials in Physics, Biology and Medicine最新文献

{"title":"Myelin water fraction mapping with joint inversion of gradient-echo and spin-echo data.","authors":"Ségolène Dega, Mónica Ferreira, Marten Veldmann, Rüdiger Stirnberg, Hendrik Paasche, Tony Stöcker","doi":"10.1007/s10334-025-01235-5","DOIUrl":"10.1007/s10334-025-01235-5","url":null,"abstract":"<p><strong>Objective: </strong>Accurate estimation of brain myelin-water content from multi-echo data is challenging due to the inherent ill-posedness of the inversion problem. In this study, we propose a novel method for myelin-water imaging that jointly utilizes gradient-echo and spin-echo imaging data to enhance the accuracy of myelin-water estimation.</p><p><strong>Material and methods: </strong>Multi-echo gradient-echo and spin-echo data were simulated and acquired in vivo. The simulations are based on a parameterized myelin and free water signal model, which is also used for the inversion by means of nonlinear local-search optimization. Single inversions of the individual datasets as well as joint inversion of the combined datasets were performed on simulated and real data. While single inversions estimate either the <math><msub><mi>T</mi> <mn>2</mn></msub> </math> or <math><mmultiscripts><mi>T</mi> <mrow><mn>2</mn></mrow> <mrow><mrow></mrow> <mo>∗</mo></mrow> </mmultiscripts> </math> relaxation spectrum, the joint inversion estimates both spectra simultaneously.</p><p><strong>Results: </strong>Simulation results show that the accuracy of myelin-water imaging improves when jointly inverting gradient-echo and spin-echo synthetic data. In vivo experiments show that the joint inversion of both datasets leads to sharper and more distinct myelin-water images as compared to the individual inversions.</p><p><strong>Discussion: </strong>Our method addresses the ill-posed nature of the myelin-water inversion problem by leveraging complementary information from multi-echo gradient-echo and multi-echo spin-echo imaging sequences, thus improving the reliability of myelin-water quantification.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"317-332"},"PeriodicalIF":2.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573408","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":"Whole pancreas water T₁ mapping at 3 Tesla.","authors":"Elizabeth Huaroc Moquillaza, Kilian Weiss, Lisa Steinhelfer, Jonathan Stelter, Marcus R Makowski, Rickmer Braren, Mariya Doneva, Dimitrios C Karampinos","doi":"10.1007/s10334-025-01224-8","DOIUrl":"10.1007/s10334-025-01224-8","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;A fast T&lt;sub&gt;1&lt;/sub&gt; mapping method of the whole pancreas remains a challenge, due to the complex anatomy of the organ. In addition, a technique for pancreas water T&lt;sub&gt;1&lt;/sub&gt; mapping is needed, since the T&lt;sub&gt;1&lt;/sub&gt; is biased in the presence of fat. The purpose of this work is to accelerate the acquisition of water selective T&lt;sub&gt;1&lt;/sub&gt; (wT&lt;sub&gt;1&lt;/sub&gt;) mapping for the whole pancreas at 3 T.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The proposed method combines a continuous inversion-recovery Look-Locker acquisition with a single-shot gradient echo spiral readout, water-fat separation and dictionary matching for wT&lt;sub&gt;1&lt;/sub&gt; mapping of the whole pancreas at 3 T. The bias of T&lt;sub&gt;1&lt;/sub&gt; in the presence of fat was evaluated in a phantom by comparing the modified Look-Locker inversion-recovery (MOLLI) and the proposed method to MRS measurements. The present method was validated in 11 volunteers by evaluating its pancreas coverage and repeatability and by comparing it to MOLLI. Four pancreatitis patients were evaluated using the proposed method and clinical scans.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The phantom wT&lt;sub&gt;1&lt;/sub&gt; results are in better agreement to MRS ( &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mtext&gt;wT&lt;/mtext&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;1.02&lt;/mn&gt; &lt;msub&gt;&lt;mtext&gt;*wT&lt;/mtext&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt; &lt;mtext&gt;MRS&lt;/mtext&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;-&lt;/mo&gt; &lt;mn&gt;47.81&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mrow&gt;&lt;/mrow&gt; &lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;mrow&gt;&lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.99&lt;/mn&gt; &lt;mo&gt;)&lt;/mo&gt; &lt;mrow&gt;&lt;/mrow&gt;&lt;/mrow&gt; &lt;/mrow&gt; &lt;/math&gt; than MOLLI ( &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mtext&gt;T&lt;/mtext&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt; &lt;mtext&gt;MOLLI&lt;/mtext&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;1.13&lt;/mn&gt; &lt;mrow&gt;&lt;/mrow&gt; &lt;mo&gt;∗&lt;/mo&gt; &lt;msub&gt;&lt;mtext&gt;wT&lt;/mtext&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt; &lt;mtext&gt;MRS&lt;/mtext&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;-&lt;/mo&gt; &lt;mn&gt;74.65&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;mrow&gt;&lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.98&lt;/mn&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;/mrow&gt; &lt;/math&gt; . The volunteer wT&lt;sub&gt;1&lt;/sub&gt; results demonstrate the whole pancreas coverage capability for different fat fractions, good repeatability ( &lt;math&gt; &lt;mrow&gt;&lt;mmultiscripts&gt;&lt;mtext&gt;wT&lt;/mtext&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mrow&gt;&lt;/mrow&gt; &lt;msup&gt;&lt;mn&gt;2&lt;/mn&gt; &lt;mo&gt;∘&lt;/mo&gt;&lt;/msup&gt; &lt;/mrow&gt; &lt;mrow&gt;&lt;/mrow&gt;&lt;/mmultiscripts&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.98&lt;/mn&gt; &lt;mrow&gt;&lt;/mrow&gt; &lt;mo&gt;∗&lt;/mo&gt; &lt;mmultiscripts&gt;&lt;mtext&gt;wT&lt;/mtext&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;mrow&gt;&lt;/mrow&gt; &lt;msup&gt;&lt;mn&gt;1&lt;/mn&gt; &lt;mo&gt;∘&lt;/mo&gt;&lt;/msup&gt; &lt;/mrow&gt; &lt;mrow&gt;&lt;/mrow&gt;&lt;/mmultiscripts&gt; &lt;mo&gt;+&lt;/mo&gt; &lt;mn&gt;17.40&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;mrow&gt;&lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.69&lt;/mn&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;/mrow&gt; &lt;/math&gt; and lower T&lt;sub&gt;1&lt;/sub&gt; values than MOLLI ( &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mtext&gt;wT&lt;/mtext&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.34&lt;/mn&gt; &lt;msub&gt;&lt;mtext&gt;*T&lt;/mtext&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt; &lt;mtext&gt;MOLLI&lt;/mtext&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;+&lt;/mo&gt; &lt;mn&gt;383.65&lt;/mn&gt; &lt;mo&gt;,&lt;/mo&gt; &lt;msup&gt;&lt;mrow&gt;&lt;mspace&gt;&lt;/mspace&gt; &lt;mtext&gt;R&lt;/mtext&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;mrow&gt;&lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.26&lt;/mn&gt; &lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt; &lt;/mrow&gt; &lt;/math&gt; . The wT&lt;sub&gt;1&lt;/sub&gt; maps in patients captured diverse pancreatitis regions with higher values &lt;","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"271-283"},"PeriodicalIF":2.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143567581","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":"Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.","authors":"S Sophie Schauman, Siddharth S Iyer, Christopher M Sandino, Mahmut Yurt, Xiaozhi Cao, Congyu Liao, Natthanan Ruengchaijatuporn, Itthi Chatnuntawech, Elizabeth Tong, Kawin Setsompop","doi":"10.1007/s10334-024-01222-2","DOIUrl":"10.1007/s10334-024-01222-2","url":null,"abstract":"<p><strong>Object: </strong>Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction time using deep learning.</p><p><strong>Materials and methods: </strong>This study focuses on accelerating the reconstruction of volumetric multi-axis spiral projection MRF, aiming for whole-brain T1 and T2 mapping, while ensuring a streamlined approach compatible with clinical requirements. To optimize reconstruction time, the traditional method is first revamped with a memory-efficient GPU implementation. Deep Learning Initialized Compressed Sensing (Deli-CS) is then introduced, which initiates iterative reconstruction with a DL-generated seed point, reducing the number of iterations needed for convergence.</p><p><strong>Results: </strong>The full reconstruction process for volumetric multi-axis spiral projection MRF is completed in just 20 min compared to over 2 h for the previously published implementation. Comparative analysis demonstrates Deli-CS's efficiency in expediting iterative reconstruction while maintaining high-quality results.</p><p><strong>Discussion: </strong>By offering a rapid warm start to the iterative reconstruction algorithm, this method substantially reduces processing time while preserving reconstruction quality. Its successful implementation paves the way for advanced spatio-temporal MRI techniques, addressing the challenge of extensive reconstruction times and ensuring efficient, high-quality imaging in a streamlined manner.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"221-237"},"PeriodicalIF":2.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074990","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":"Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.","authors":"Lei Li, Qingyuan He, Shufeng Wei, Huixian Wang, Zheng Wang, Wenhui Yang","doi":"10.1007/s10334-025-01234-6","DOIUrl":"10.1007/s10334-025-01234-6","url":null,"abstract":"<p><strong>Objective: </strong>Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.</p><p><strong>Methods: </strong>First, fast and high-quality unshielded imaging is achieved using active electromagnetic shielding and basic super-resolution. Then, the speed of basic super-resolution imaging is further improved by reducing the number of excitations. Next, the feasibility of using cross-field super-resolution to map low-field low-resolution images to high-field ultra-high-resolution images is analyzed. Finally, by cascading basic and cross-field super-resolution, the quality of the low-field low-resolution image is improved to the level of the high-field ultra-high-resolution image.</p><p><strong>Results: </strong>Under unshielded conditions, our 0.2 T scanner can achieve image quality comparable to that of a 1.5 T scanner (acquisition resolution of 512 × 512, spatial resolution of 0.45 mm²), and a single-orientation imaging time of less than 3.3 min.</p><p><strong>Discussion: </strong>The proposed strategy overcomes the physical limitations of the hardware and rapidly acquires images close to the high-field level on a low-field unshielded MRI scanner. These findings have significant practical implications for the advances in MRI technology, supporting the shift from conventional scanners to point-of-care imaging systems.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"253-269"},"PeriodicalIF":2.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440875","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":"Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.","authors":"Sandra Martin, Rémi André, Amira Trabelsi, Constance P Michel, Etienne Fortanier, Shahram Attarian, Maxime Guye, Marc Dubois, Redha Abdeddaim, David Bendahan","doi":"10.1007/s10334-024-01221-3","DOIUrl":"10.1007/s10334-024-01221-3","url":null,"abstract":"<p><strong>Objective: </strong>Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.</p><p><strong>Material and methods: </strong>U-Net architectures with different complexities have been compared for the quantification of the fat fraction in each muscle group selected in the central part of the thigh region. The corresponding performance has been assessed in terms of Dice score (DSC) and FF quantification error. The database contained 1450 thigh images from 59 patients and 14 healthy subjects (age: 47 ± 17 years, sex: 36F, 37M). Ten individual muscles were segmented in each image. The performance of each model was compared to nnU-Net, a complex architecture with 4.35 <math><mo>×</mo></math> 10⁷ parameters, 12.8 Gigabytes of peak memory usage and 167 h of training time.</p><p><strong>Results: </strong>As expected, nnU-Net achieved the highest DSC (94.77 ± 0.13%). A simpler U-Net (5.81 <math><mo>×</mo></math> 10⁵ parameters, 2.37 Gigabytes, 14 h of training time) achieved a lower DSC but still above 90%. Surprisingly, both models achieved a comparable FF estimation.</p><p><strong>Discussion: </strong>The poor correlation between observed DSC and FF indicates that less complex architectures, reducing GPU memory utilization and training time, can still accurately quantify FF.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"175-189"},"PeriodicalIF":2.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142965498","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":"Improved phosphorus MRSI acquisition through compressed sensing acceleration combined with low-rank reconstruction.","authors":"Julien Songeon, François Lazeyras, Thomas Agius, Oscar Dabrowski, Raphael Ruttimann, Christian Toso, Alban Longchamp, Antoine Klauser, Sebastien Courvoisier","doi":"10.1007/s10334-024-01218-y","DOIUrl":"10.1007/s10334-024-01218-y","url":null,"abstract":"<p><strong>Objectives: </strong>Phosphorus-31 magnetic resonance spectroscopic imaging (³¹P-MRSI) is a non-invasive tool for assessing cellular high-energy metabolism in-vivo. However, its acquisition suffers from a low sensitivity, which necessitates large voxel sizes or multiple averages to achieve an acceptable signal-to-noise ratio (SNR), resulting in long scan times.</p><p><strong>Materials and methods: </strong>To overcome these limitations, we propose an acquisition and reconstruction scheme for FID-MRSI sequences. Specifically, we employed Compressed Sensing (CS) and Low-Rank (LR) with Total Generalized Variation (TGV) regularization in a combined CS-LR framework. Additionally, we used a novel approach to k-space undersampling that utilizes distinct pseudo-random patterns for each average. To evaluate the proposed method's performance, we performed a retrospective analysis on healthy volunteers' brains and ex-vivo perfused kidneys.</p><p><strong>Results: </strong>The presented method effectively improves the SNR two-to-threefold while preserving spectral and spatial quality even with threefold acceleration. We were able to recover signal attenuation of anatomical information, and the SNR improvement was obtained while maintaining the metabolites peaks linewidth.</p><p><strong>Conclusions: </strong>We presented a novel combined CS-LR acceleration and reconstruction method for FID-MRSI sequences, utilizing a unique approach to k-space undersampling. Our proposed method has demonstrated promising results in enhancing the SNR making it applicable for reducing acquisition time.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"161-173"},"PeriodicalIF":2.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895854","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":"Brain tumor detection and segmentation using deep learning.","authors":"Rafia Ahsan, Iram Shahzadi, Faisal Najeeb, Hammad Omer","doi":"10.1007/s10334-024-01203-5","DOIUrl":"10.1007/s10334-024-01203-5","url":null,"abstract":"<p><strong>Objectives: </strong>Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep learning-based algorithms are available for object detection; however, the performance of detection algorithms on brain tumor data has not been widely explored. Therefore, we aim to compare different object detection algorithms (Faster R-CNN, YOLO & SSD) for brain tumor detection on MRI data. Furthermore, the best-performing detection network is paired with a 2D U-Net for pixel-wise segmentation of abnormal tumor cells.</p><p><strong>Materials and methods: </strong>The proposed model was evaluated on the Brain Tumor Figshare (BTF) dataset, and the best-performing detection network cascaded with 2D U-Net for pixel-wise segmentation of tumors. The best-performing detection network was also fine-tuned on BRATS 2018 data to detect and classify the glioma tumor.</p><p><strong>Results: </strong>For the detection of three tumor types, YOLOv5 achieved the highest mAP of 89.5% on test data compared to other networks. For segmentation, YOLOv5 combined with 2D U-Net achieved a higher DSC compared to the 2D U-Net alone (DSC: YOLOv5 + 2D U-Net = 88.1%; 2D U-Net = 80.5%). The proposed method was compared with the existing detection and segmentation network i.e. Mask R-CNN and achieved a higher mAP (YOLOv5 + 2D U-Net = 89.5%; Mask R-CNN = 67%) and DSC (YOLOv5 + 2D U-Net = 88.1%; Mask R-CNN = 44.2%).</p><p><strong>Conclusion: </strong>In this work, we propose a deep-learning-based method for multi-class tumor detection, classification and segmentation that combines YOLOv5 with 2D U-Net. The results show that the proposed method not only detects different types of brain tumors accurately but also delineates the tumor region precisely within the detected bounding box.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"13-22"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142133157","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":"Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.","authors":"Luka C Liebrand, Dimitrios Karkalousos, Émilie Poirion, Bart J Emmer, Stefan D Roosendaal, Henk A Marquering, Charles B L M Majoie, Julien Savatovsky, Matthan W A Caan","doi":"10.1007/s10334-024-01200-8","DOIUrl":"10.1007/s10334-024-01200-8","url":null,"abstract":"<p><strong>Objective: </strong>To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reconstructed.</p><p><strong>Materials and methods: </strong>Twelve-fold accelerated 3D T2-FLAIR images were obtained from a cohort of 62 patients with neurological deficits on 3 T MRI. Images were reconstructed offline via CS and the CIRIM. Image quality was assessed in a blinded and randomized manner by two experienced interventional neuroradiologists and one experienced pediatric neuroradiologist on imaging artifacts, perceived spatial resolution (sharpness), anatomic conspicuity, diagnostic confidence, and contrast. The methods were also compared in terms of self-referenced quality metrics, image resolution, patient groups and reconstruction time. In ten scans, the contrast ratio (CR) was determined between lesions and white matter. The effect of acceleration factor was assessed in a publicly available fully sampled dataset, since ground truth data are not available in prospectively accelerated clinical scans. Specifically, 451 FLAIR scans, including scans with white matter lesions, were adopted from the FastMRI database to evaluate structural similarity (SSIM) and the CR of lesions and white matter on ranging acceleration factors from four-fold up to 12-fold.</p><p><strong>Results: </strong>Interventional neuroradiologists significantly preferred the CIRIM for imaging artifacts, anatomic conspicuity, and contrast. One rater significantly preferred the CIRIM in terms of sharpness and diagnostic confidence. The pediatric neuroradiologist preferred CS for imaging artifacts and sharpness. Compared to CS, the CIRIM reconstructions significantly improved in terms of imaging artifacts and anatomic conspicuity (p < 0.01) for higher resolution scans while yielding a 28% higher SNR (p = 0.001) and a 5.8% lower CR (p = 0.04). There were no differences between patient groups. Additionally, CIRIM was five times faster than CS was. An increasing acceleration factor did not lead to changes in CR (p = 0.92), but led to lower SSIM (p = 0.002).</p><p><strong>Discussion: </strong>Patients with neurological deficits can undergo MRI at a range of moderate to high acceleration. DL reconstruction outperforms CS in terms of image resolution, efficient denoising with a modest reduction in contrast and reduced reconstruction times.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142108914","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":"Morphology of the human inner ear and vestibulocochlear nerve assessed using 7 T MRI.","authors":"Kingkarn Aphiwatthanasumet, Ketan Jethwa, Paul Glover, Gerard O'Donoghue, Dorothee Auer, Penny Gowland","doi":"10.1007/s10334-024-01213-3","DOIUrl":"10.1007/s10334-024-01213-3","url":null,"abstract":"<p><strong>Objective: </strong>To optimize high-resolution 7 T MRI of the cochlea and measure normal cochlea and the cochlear nerve morphometry in vivo.</p><p><strong>Materials and methods: </strong>Eight volunteers with normal hearing were scanned at 7 T using an optimized protocol. Two neuroradiologists independently scored image quality. The basal turn lumen diameter (BTLD), height, width, length and volume of the cochlear, long (LD) and short (SD) diameter the calculated cross-sectional area (CSA) of the cochlear nerve were measured. Intra and inter-observer reliability was assessed using intraclass correlation (ICC).</p><p><strong>Results: </strong>3D T2W DRIVE combined with dielectric pads, allowed acquisition of high-resolution images showing detailed structures, such as the crista ampullaris in the semicircular canals. The overall grading scores from neuroradiologists were excellent. In the left ear, averaging over all subjects gave BTLD of 2.6 ± 0.05 mm, height of 4.9 ± 0.1 mm, width of 4.4 ± 0.2 mm, length of 36.5 ± 0.4 mm, volume of 0.16 ± 0.02 ml, LD of 1.31 ± 0.1 mm, SD of 1.06 ± 0.1 mm, and CSA of 1.1 ± 0.1 mm². The right ear gave BTLD of 2.6 ± 0.04 mm, height of 4.9 ± 0.1 mm, width of 4.4 ± 0.3 mm, length of 35.5 ± 0.4 mm, volume of 0.16 ± 0.02 ml, LD of 1.29 ± 0.1 mm, SD of 1.07 ± 0.1 mm, and CSA of 1.10 ± 0.2 mm². No statistically significant difference was found between the sides of the head (p-value > 0.05). The intra-observer reliability was high (0.77-0.94), while the inter-observer reliability varied from moderate to high (0.55-0.81).</p><p><strong>Conclusion: </strong>7 T MRI can provide excellent visualization of the internal structure of the cochlear and of the vestibulocochlear nerve in vivo.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"121-130"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142623021","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":"Free-breathing qRF-MRF with pilot tone respiratory motion navigator for T₁, T₂, T₂*, and off-resonance mapping of the human body at 3 T.","authors":"Madison E Kretzler, Sherry S Huang, Jessie E P Sun, Leonardo K Bittencourt, Yong Chen, Mark A Griswold, Rasim Boyacioglu","doi":"10.1007/s10334-024-01209-z","DOIUrl":"10.1007/s10334-024-01209-z","url":null,"abstract":"<p><p>Standard quantitative abdominal MRI techniques are time consuming, require breath-holds, and are susceptible to patient motion artifacts. Magnetic resonance fingerprinting (MRF) is naturally multi-parametric and quantifies multiple tissue properties, including T₁ and T₂. This work includes T₂* and off-resonance mapping into a free-breathing MRF framework utilizing a pilot tone navigator. The new acquisition and reconstruction are compared to current clinical standards. Prospective. Ten volunteers. 3 T scanner, Quadratic-RF MRF, Balanced SSFP, Inversion recovery spin-echo, LiverLab. MRI ROIs were evaluated in the liver, spleen, pancreas, kidney (cortex and medulla), and paravertebral muscle by two abdominal imaging investigators for ten healthy adult volunteers for clinical standard, breath-Hold (BH) qRF-MRF, and free-breathing qRF-MRF with pilot-tone (PT) acquisitions. Bland-Altman analysis as well as Student's T tests were used to evaluate and compare the respective ROI analyses. Quantitative values between breath-Hold (BH) and free-breathing qRF-MRF with pilot-tone (PT) results show good agreement with clinical standard T1 and T2 quantitative mapping, and Dixon q-VIBE (acquired using the Siemens LiverLAB). In this work, we show free-breathing abdominal MRF (T₁, T₂) with T₂* results that are quantitatively comparable to current breath-hold MRF and clinical techniques.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"85-95"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11973999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142469070","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}