Magnetic Resonance in Medical Sciences最新文献

{"title":"Vessel Wall Imaging of Intracranial Arteries: Fundamentals and Clinical Applications.","authors":"Miho Gomyo,&nbsp;Kazuhiro Tsuchiya,&nbsp;Kenichi Yokoyama","doi":"10.2463/mrms.rev.2021-0140","DOIUrl":"10.2463/mrms.rev.2021-0140","url":null,"abstract":"<p><p>With the increasing use of 3-tesla MRI scanners and the development of applicable sequences, it has become possible to achieve high-resolution, good contrast imaging, which has enabled the imaging of the walls of small-diameter intracranial arteries. In recent years, the usefulness of vessel wall imaging has been reported for numerous intracranial arterial diseases, such as for the detection of vulnerable plaque in atherosclerosis, diagnosis of cerebral arterial dissection, prediction of the rupture of cerebral aneurysms, and status of moyamoya disease and cerebral vasculitis. In this review, we introduce the histological characteristics of the intracranial artery, discuss intracranial vessel wall imaging methods, and review the findings of vessel wall imaging for various major intracranial arterial diseases.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b7/37/mrms-22-447.PMC10552670.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40678874","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":"Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo.","authors":"Yo Taniguchi, Suguru Yokosawa, Toru Shirai, Ryota Sato, Tomoki Amemiya, Yoshihisa Soutome, Yoshitaka Bito, Hisaaki Ochi","doi":"10.2463/mrms.mp.2021-0045","DOIUrl":"10.2463/mrms.mp.2021-0045","url":null,"abstract":"<p><strong>Purpose: </strong>MR parameter mapping is a technique that obtains distributions of parameters such as relaxation time and proton density (PD) and is starting to be used for disease quantification in clinical diagnoses. Quantitative susceptibility mapping is also promising for the early diagnosis of brain disorders such as degenerative neurological disorders. Therefore, we developed an MR quantitative parameter mapping (QPM) method to map four tissue-related parameters (T₁, T₂*, PD, and susceptibility) and B₁ simultaneously by using a 3D partially RF-spoiled gradient echo (pRSGE). We verified the accuracy and repeatability of QPM in phantom and volunteer experiments.</p><p><strong>Methods: </strong>Tissue-related parameters are estimated by varying four scan parameters of the 3D pRSGE: flip angle, RF-pulse phase increment, TR and TE, performing multiple image scans, and finding a least-squares fit for an intensity function (which expresses the relationship between the scan parameters and intensity values). The intensity function is analytically complex, but by using a Bloch simulation to create it numerically, the least-squares fitting can be used to estimate the quantitative values. This has the advantage of shortening the image-reconstruction processing time needed to estimate the quantitative values than with methods using pattern matching.</p><p><strong>Results: </strong>A 1.1-mm isotropic resolution scan covering the whole brain was completed with a scan time of approximately 12 minutes, and the reconstruction time using a GPU was approximately 1 minute. The phantom experiments confirmed that both the accuracy and repeatability of the quantitative values were high. The volunteer scans also confirmed that the accuracy of the quantitative values was comparable to that of conventional methods.</p><p><strong>Conclusion: </strong>The proposed QPM method can map T₁, T₂*, PD, susceptibility, and B₁ simultaneously within a scan time that can be applied to human subjects.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ed/41/mrms-22-459.PMC10552665.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40657587","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":"Region Expansion of Background Field Removal with Local Spherical Harmonics Approximation for Whole-brain Quantitative Susceptibility Mapping.","authors":"Toru Shirai,&nbsp;Ryota Sato,&nbsp;Yasuo Kawata,&nbsp;Yoshitaka Bito,&nbsp;Hisaaki Ochi","doi":"10.2463/mrms.mp.2021-0043","DOIUrl":"10.2463/mrms.mp.2021-0043","url":null,"abstract":"<p><strong>Purpose: </strong>Quantitative susceptibility mapping (QSM) is useful for obtaining biological information. To calculate susceptibility distribution, it is necessary to calculate the local field caused by the differences of susceptibility between the tissues. The local field can be obtained by removing a background field from a total field acquired by MR phase image. Conventional approaches based on spherical mean value (SMV) filtering, which are widely used for background field calculations, fail to calculate the background field of the brain surface region corresponding to the radius of the SMV kernel, and consequently cannot calculate the QSM of the brain surface region. Accordingly, a new method calculating the local field by expansively removing the background field is proposed for whole brain QSM.</p><p><strong>Methods: </strong>The proposed method consists of two steps. First, the background field of the brain surface is calculated from the total field using a locally polynomial approximation of spherical harmonics. Second, the whole brain local field is calculated by SMV filtering with a constraint term of the background field of the brain surface. The parameters of the approximation were optimized to reduce calculation errors through simulations using both a numerical phantom and a measured human brain. Performance of the proposed method with the optimized parameters was quantitatively and visually compared with conventional methods in an experiment of five healthy volunteers.</p><p><strong>Results: </strong>The proposed method showed the accurate local field over the expanded brain region in the simulation studies. It also showed consistent QSM with conventional methods inside of the brain surface and showed clear vein structures on the brain surface.</p><p><strong>Conclusion: </strong>The proposed method enables accurate calculation of whole brain QSM without eroding the brain surface region while maintaining same values inside of the brain surface as the conventional methods.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e5/11/mrms-22-497.PMC10552664.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40683975","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":"Optimal Temporal Resolution to Achieve Good Image Quality and Perform Pharmacokinetic Analysis in Free-breathing Dynamic Contrast-enhanced MR Imaging of the Pancreas.","authors":"Kazuki Oyama,&nbsp;Fumihito Ichinohe,&nbsp;Akira Yamada,&nbsp;Yoshihiro Kitoh,&nbsp;Yasuo Adachi,&nbsp;Hayato Hayashihara,&nbsp;Marcel D Nickel,&nbsp;Katsuya Maruyama,&nbsp;Yasunari Fujinaga","doi":"10.2463/mrms.mp.2022-0035","DOIUrl":"10.2463/mrms.mp.2022-0035","url":null,"abstract":"<p><strong>Purpose: </strong>The optimal temporal resolution for free-breathing dynamic contrast-enhanced MRI (FBDCE-MRI) of the pancreas has not been determined. This study aimed to evaluate the appropriate temporal resolution to achieve good image quality and to perform pharmacokinetic analysis in FBDCE-MRI of the pancreas using golden-angle radial sparse parallel (GRASP).</p><p><strong>Methods: </strong>Sixteen participants (53 ± 15 years, eight females) undergoing FBDCE-MRI were included in this prospective study. Images were retrospectively reconstructed at four temporal resolutions (1.8, 3.0, 4.8, and 7.8s). Two radiologists (5 years of experience) evaluated the image quality of each reconstructed image by assessing the visualization of the celiac artery (CEA), the common hepatic artery, the splenic artery, each area of the pancreas, and artifacts using a 5-point scale. Using Tissue-4D, pharmacokinetic parameters were calculated for each area in the reconstructed images at each temporal resolution for 16 examinations, excluding two with errors in the pharmacokinetic modeling analysis. Friedman and Bonferroni tests were used for analysis. A P value < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>During vascular assessment, only scores for the CEA at 7.8s were significantly lower than the other temporal resolutions. Scores of all pancreatic regions and artifacts were significantly lower at 1.8s than at 4.8s and 7.8s. In the pharmacokinetic analysis, all volume transfer coefficients (K_trans), rate constants (K_ep), and the initial area under the concentration curve (iAUC) in the pancreatic head and tail were significantly lower at 4.8s and 7.8s than at 1.8s. iAUC in the pancreatic body and extracellular extravascular volume fraction (V_e) in the pancreatic head were significantly lower at 7.8s than at 1.8s.</p><p><strong>Conclusion: </strong>A temporal resolution of 3.0s is appropriate to achieve image quality and perform pharmacokinetic analysis in FBDCE-MRI of the pancreas using GRASP.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a1/9f/mrms-22-477.PMC10552666.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40636198","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":"Nonlinear Gradient Field Mapping Using a Spherical Grid Phantom for 3 and 7 Tesla MR Imaging Systems Equipped with High-performance Gradient Coils.","authors":"Ryoichi Kose,&nbsp;Katsumi Kose,&nbsp;Koji Fujimoto,&nbsp;Tomohisa Okada,&nbsp;Daiki Tamada,&nbsp;Utaroh Motosugi","doi":"10.2463/mrms.tn.2023-0063","DOIUrl":"https://doi.org/10.2463/mrms.tn.2023-0063","url":null,"abstract":"<p><p>Recent high-performance gradient coils are fabricated mainly at the expense of spatial linearity. In this study, we measured the spatial nonlinearity of the magnetic field generated by the gradient coils of two MRI systems with high-performance gradient coils. The nonlinearity of the gradient fields was measured using 3D gradient echo sequences and a spherical phantom with a built-in lattice structure. The spatial variation of the gradient field was approximated to the 3rd order polynomials. The coefficients of the polynomials were calculated using the steepest descent method. The geometric distortion of the acquired 3D MR images was corrected using the polynomials and compared with the 3D images corrected using the harmonic functions provided by the MRI venders. As a result, it was found that the nonlinearity correction formulae provided by the vendors were insufficient and needed to be verified or corrected using a geometric phantom such as used in this study.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10553809","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":"Evaluating T1, T2 Relaxation, and Proton Density in Normal Brain Using Synthetic MRI with Fast Imaging Protocol.","authors":"Zuofeng Zheng,&nbsp;Yawen Liu,&nbsp;Hongxia Yin,&nbsp;Pengling Ren,&nbsp;Tingting Zhang,&nbsp;Jiafei Yang,&nbsp;Zhenchang Wang","doi":"10.2463/mrms.tn.2022-0161","DOIUrl":"https://doi.org/10.2463/mrms.tn.2022-0161","url":null,"abstract":"<p><p>Synthetic MRI is being increasingly used for the quantification of brain longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) values. However, the effect of fast imaging protocols on these quantitative values has not been fully estimated. The purpose of this study was to investigate the effect of fast scan parameters on T1, T2, and PD measured with a multi-dynamic multi-echo (MDME) sequence of normal brain at 3.0T. Thirty-four volunteers were scanned using 3 MDME sequences with different scan times (named Fast, 2 min, 29 sec; Routine, 4 min, 07 sec; and Research, 7 min, 46 sec, respectively). The measured T1, T2, and PD in 18 volumes of interest (VOI) of brain were compared between the 3 sequences using rank sum test, t test, coefficients of variation (CVs) analysis, correlation analysis, and Bland-Altman analysis. We found that even though T1, T2, and PD were significantly different between the 3 sequences in most of the brain regions, the intersequence CVs were relatively low and linear correlation were high. Bland-Altman plots showed that most of the values fall within the 95% prediction limits. We concluded that fast imaging protocols of MDME sequence used in our study can potentially be used for quantitative evaluation of brain tissues. Since changing scan parameters can affect the measured T1, T2, and PD values, it is necessary to use consistent scan parameter for comparing or following up cases quantitatively.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10256637","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":"Novel T1 Analysis Method to Address Reduced Measurement Accuracy Due to Irregular Heart Rate Variability in Myocardial T1 Mapping Using Polarity-corrected Inversion Time Preparation.","authors":"Yuta Endo,&nbsp;Sanae Takahashi,&nbsp;Haruna Shibo,&nbsp;Makoto Amanuma,&nbsp;Kuninori Kobayashi,&nbsp;Shigehide Kuhara","doi":"10.2463/mrms.mp.2023-0029","DOIUrl":"https://doi.org/10.2463/mrms.mp.2023-0029","url":null,"abstract":"<p><strong>Purpose: </strong>Polarity-corrected inversion time preparation (PCTIP), a myocardial T1 mapping technique, is expected to reduce measurement underestimation in the modified Look-Locker inversion recover method. However, measurement precision is reduced, especially for heart rate variability. We devised an analysis using a recurrence formula to overcome this problem and showed that it improved the measurement accuracy, especially at high heart rates. Therefore, this study aimed to determine the effect of this analysis on the accuracy and precision of T1 measurements for irregular heart rate variability.</p><p><strong>Methods: </strong>A PCTIP scan using a 3T MRI scanner was performed in phantom experiment. We generated the simulated R-waves required for electrocardiogram (ECG)-gated acquisition using a signal generator set to 30 combinations. T1 map was generated using the signal train of the PCTIP images by nonlinear curve fitting using conventional and recurrence formulas. Accuracy against reference T1 and precision of heart rate variability were evaluated. To evaluate the fitting accuracy of both analyses, the relative fitting error was calculated.</p><p><strong>Results: </strong>For the longer T1, the fitting error was larger than the short T1, with the conventional analysis showing 10.1±2.0%. The recurrence formula analysis showed a small fitting error less than 1%, which was consistent for all heart rate variability patterns. In the conventional analysis, the accuracy, especially for longer T1, showed a large underestimation of the measurements and poor linearity. However, in the recurrence formula analysis, the accuracy improved at a long T1, and linearity also improved. The Bland-Altman plot showed that it varied greatly depending on the heart rate variability pattern for the longer T1 in the conventional analysis, whereas the recurrence formula analysis suppressed this variation.</p><p><strong>Conclusion: </strong>T1 analysis of PCTIP using the recurrence formula analysis achieved accurate and precise T1 measurements, even for irregular heart rate variability.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10500239","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":"Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.","authors":"Daisuke Takenaka,&nbsp;Yoshiyuki Ozawa,&nbsp;Kaori Yamamoto,&nbsp;Maiko Shinohara,&nbsp;Masato Ikedo,&nbsp;Masao Yui,&nbsp;Yuka Oshima,&nbsp;Nayu Hamabuchi,&nbsp;Hiroyuki Nagata,&nbsp;Takahiro Ueda,&nbsp;Hirotaka Ikeda,&nbsp;Akiyoshi Iwase,&nbsp;Takeshi Yoshikawa,&nbsp;Hiroshi Toyama,&nbsp;Yoshiharu Ohno","doi":"10.2463/mrms.mp.2023-0068","DOIUrl":"https://doi.org/10.2463/mrms.mp.2023-0068","url":null,"abstract":"<p><strong>Purpose: </strong>Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in different body fields. However, there have been no reports regarding the utility of DLR for image quality and T-factor assessment improvements on T2-weighted imaging (T2WI), short inversion time (TI) inversion recovery (STIR) imaging, and unenhanced- and contrast-enhanced (CE) 3D fast spoiled gradient echo (GRE) imaging with and without CS in comparison with thin-section multidetector-row CT (MDCT) for non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the utility of DLR for improving image quality and the appropriate sequence for T-category assessment for NSCLC patients.</p><p><strong>Methods: </strong>As subjects for this study, 213 pathologically diagnosed NSCLC patients who underwent thin-section MDCT and MR imaging as well as T-factor diagnosis were retrospectively enrolled. SNR of each tumor was calculated and compared by paired t-test for each sequence with and without DLR. T-factor for each patient was assessed with thin-section MDCT and all MR sequences, and the accuracy for T-factor diagnosis was compared among all sequences and thin-section CT by means of McNemar's test.</p><p><strong>Results: </strong>SNRs of T2WI, STIR imaging, unenhanced thin-section Quick 3D imaging, and CE-thin-section Quick 3D imaging with DLR were significantly higher than SNRs of those without DLR (P < 0.05). Diagnostic accuracy of STIR imaging and CE-thick- or thin-section Quick 3D imaging was significantly higher than that of thin-section CT, T2WI, and unenhanced thick- or thin-section Quick 3D imaging (P < 0.05).</p><p><strong>Conclusion: </strong>DLR is thus considered useful for image quality improvement on MR imaging. STIR imaging and CE-Quick 3D imaging with or without CS were validated as appropriate MR sequences for T-factor evaluation in NSCLC patients.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10202082","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":"Microstructural Abnormalities in the Contralateral Normal-appearing White Matter of Glioblastoma Patients Evaluated with Advanced Diffusion Imaging.","authors":"Barış Genç,&nbsp;Kerim Aslan,&nbsp;Ali Özçağlayan,&nbsp;Lütfi İncesu","doi":"10.2463/mrms.mp.2023-0054","DOIUrl":"https://doi.org/10.2463/mrms.mp.2023-0054","url":null,"abstract":"<p><strong>Purpose: </strong>Glioblastoma patients develop recurrence in the opposite hemisphere far from the primary tumor site even after complete resection. This is one of the main reasons for short disease survival. Our aim in this study is to detect microstructural changes in the contralateral hemisphere of glioblastoma patients using different diffusion models with the fully automated tract-based spatial statistics (TBSS) method.</p><p><strong>Methods: </strong>Fourteen right-sided and eleven left-sided glioblastoma patients without any treatment and eighteen age- and gender-matched controls were included in the study. Multi-shell diffusion weighted images were created with a 3T MRI device. After various preprocessing steps, images of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), axial kurtosis (AK), mean kurtosis (MK), radial kurtosis (RK), intracellular volume fraction (ICVF), orientation dispersion index (ODI), and isotropic water fraction (ISO) were obtained. TBSS was used to compare diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging parameters of right- and left-sided glioblastoma patients with the control group for the contralateral hemisphere.</p><p><strong>Results: </strong>Both right-sided and left-sided glioblastoma patients have shown an increase in MD and ODI in the contralateral hemisphere. While right-sided glioblastoma patients showed an increase in RD, AD, and ISO in a more limited area in the contralateral hemisphere, left-sided glioblastoma patients showed an increase in MK and AK. FA, ICVF, and RK did not show any difference in both groups.</p><p><strong>Conclusion: </strong>There are microstructural changes in the contralateral hemisphere in glioblastoma patients, and these changes differ between right-sided and left-sided glioblastoma patients.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9926992","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":"Numerical and Clinical Evaluation of the Robustness of Open-source Networks for Parallel MR Imaging Reconstruction.","authors":"Naoto Fujita,&nbsp;Suguru Yokosawa,&nbsp;Toru Shirai,&nbsp;Yasuhiko Terada","doi":"10.2463/mrms.mp.2023-0031","DOIUrl":"https://doi.org/10.2463/mrms.mp.2023-0031","url":null,"abstract":"<p><strong>Purpose: </strong>Deep neural networks (DNNs) for MRI reconstruction often require large datasets for training. Still, in clinical settings, the domains of datasets are diverse, and how robust DNNs are to domain differences between training and testing datasets has been an open question. Here, we numerically and clinically evaluate the generalization of the reconstruction networks across various domains under clinically practical conditions and provide practical guidance on what points to consider when selecting models for clinical application.</p><p><strong>Methods: </strong>We compare the reconstruction performance between four network models: U-Net, the deep cascade of convolutional neural networks (DC-CNNs), Hybrid Cascade, and variational network (VarNet). We used the public multicoil dataset fastMRI for training and testing and performed a single-domain test, where the domains of the dataset used for training and testing were the same, and cross-domain tests, where the source and target domains were different. We conducted a single-domain test (Experiment 1) and cross-domain tests (Experiments 2-4), focusing on six factors (the number of images, sampling pattern, acceleration factor, noise level, contrast, and anatomical structure) both numerically and clinically.</p><p><strong>Results: </strong>U-Net had lower performance than the three model-based networks and was less robust to domain shifts between training and testing datasets. VarNet had the highest performance and robustness among the three model-based networks, followed by Hybrid Cascade and DC-CNN. Especially, VarNet showed high performance even with a limited number of training images (200 images/10 cases). U-Net was more robust to domain shifts concerning noise level than the other model-based networks. Hybrid Cascade showed slightly better performance and robustness than DC-CNN, except for robustness to noise-level domain shifts. The results of the clinical evaluations generally agreed with the results of the quantitative metrics.</p><p><strong>Conclusion: </strong>In this study, we numerically and clinically evaluated the robustness of the publicly available networks using the multicoil data. Therefore, this study provided practical guidance for clinical applications.</p>","PeriodicalId":18119,"journal":{"name":"Magnetic Resonance in Medical Sciences","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9897830","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}