Magnetic Resonance Materials in Physics, Biology and Medicine最新文献_第9页

ESMRMB 2024 focus topic "MR Beyond Structures: The dynamic body at different scales". ESMRMB 2024 重点专题 "MR 超越结构：不同尺度的动态人体"。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-06-08 DOI: 10.1007/s10334-024-01175-6

Joana Pinto, Allison McGee, Hendrik Mattern, Karin Markenroth Bloch, Roy A M Haast, Thomas Küstner, S Johanna Vannesjo

引用次数: 0

MR beyond diagnostics at the ESMRMB annual meeting: MR theranostics and intervention. ESMRMB年会上的磁共振超越诊断：磁共振治疗和干预。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-06-12 DOI: 10.1007/s10334-024-01176-5

Milan Hájek, Ulrich Flögel, Adriana A S Tavares, Lucia Nichelli, Aneurin Kennerley, Thomas Kahn, Jurgen J Futterer, Aikaterini Firsiori, Holger Grüll, Nandita Saha, Felipe Couñago, Dogu Baran Aydogan, Maria Eugenia Caligiuri, Cornelius Faber, Laura C Bell, Patrícia Figueiredo, Joan C Vilanova, Francesco Santini, Ralf Mekle, Sonia Waiczies

引用次数: 0

Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck. 基于模型的深度学习重建头颈部评估弥散加权成像图像质量改进

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2023-11-21 DOI: 10.1007/s10334-023-01129-4

Noriyuki Fujima, Junichi Nakagawa, Hiroyuki Kameda, Yohei Ikebe, Taisuke Harada, Yukie Shimizu, Nayuta Tsushima, Satoshi Kano, Akihiro Homma, Jihun Kwon, Masami Yoneyama, Kohsuke Kudo

{"title":"Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck.","authors":"Noriyuki Fujima, Junichi Nakagawa, Hiroyuki Kameda, Yohei Ikebe, Taisuke Harada, Yukie Shimizu, Nayuta Tsushima, Satoshi Kano, Akihiro Homma, Jihun Kwon, Masami Yoneyama, Kohsuke Kudo","doi":"10.1007/s10334-023-01129-4","DOIUrl":"10.1007/s10334-023-01129-4","url":null,"abstract":"Objectives: To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).Materials and methods: We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.Results: Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001).Discussion: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"439-447"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138291348","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}

引用次数: 0

Artificial intelligence for neuro MRI acquisition: a review. 神经磁共振成像采集的人工智能：综述。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-06-26 DOI: 10.1007/s10334-024-01182-7

Hongjia Yang, Guanhua Wang, Ziyu Li, Haoxiang Li, Jialan Zheng, Yuxin Hu, Xiaozhi Cao, Congyu Liao, Huihui Ye, Qiyuan Tian

{"title":"Artificial intelligence for neuro MRI acquisition: a review.","authors":"Hongjia Yang, Guanhua Wang, Ziyu Li, Haoxiang Li, Jialan Zheng, Yuxin Hu, Xiaozhi Cao, Congyu Liao, Huihui Ye, Qiyuan Tian","doi":"10.1007/s10334-024-01182-7","DOIUrl":"10.1007/s10334-024-01182-7","url":null,"abstract":"Object: To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts.Materials and methods: A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods.Results: The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency.Discussion: The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"383-396"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141450833","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}

引用次数: 0

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy. 基于卷积神经网络的方法与 LCM 模型在活体磁共振光谱量化方面的比较。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2023-09-15 DOI: 10.1007/s10334-023-01120-z

Yu-Long Huang, Yi-Ru Lin, Shang-Yueh Tsai

{"title":"Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.","authors":"Yu-Long Huang, Yi-Ru Lin, Shang-Yueh Tsai","doi":"10.1007/s10334-023-01120-z","DOIUrl":"10.1007/s10334-023-01120-z","url":null,"abstract":"Background: Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful.Materials and methods: This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system.Results: The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content.Conclusion: In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"477-489"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10235915","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}

引用次数: 0

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs. 利用多对比度输入的三维卷积神经网络在纵向磁共振成像上自动进行腹部脂肪组织分割和体积量化。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-02-01 DOI: 10.1007/s10334-023-01146-3

Sevgi Gokce Kafali, Shu-Fu Shih, Xinzhou Li, Grace Hyun J Kim, Tristan Kelly, Shilpy Chowdhury, Spencer Loong, Jeremy Moretz, Samuel R Barnes, Zhaoping Li, Holden H Wu

{"title":"Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.","authors":"Sevgi Gokce Kafali, Shu-Fu Shih, Xinzhou Li, Grace Hyun J Kim, Tristan Kelly, Shilpy Chowdhury, Spencer Loong, Jeremy Moretz, Samuel R Barnes, Zhaoping Li, Holden H Wu","doi":"10.1007/s10334-023-01146-3","DOIUrl":"10.1007/s10334-023-01146-3","url":null,"abstract":"Objective: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs.Materials and methods: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC).Results: ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis.Discussion: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"491-506"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139651021","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}

引用次数: 0

Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data. 利用分布式高效记忆物理引导深度学习，在有限的训练数据下进行大规模三维非笛卡尔冠状动脉磁共振成像重建。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-05-14 DOI: 10.1007/s10334-024-01157-8

Chi Zhang, Davide Piccini, Omer Burak Demirel, Gabriele Bonanno, Christopher W Roy, Burhaneddin Yaman, Steen Moeller, Chetan Shenoy, Matthias Stuber, Mehmet Akçakaya

{"title":"Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.","authors":"Chi Zhang, Davide Piccini, Omer Burak Demirel, Gabriele Bonanno, Christopher W Roy, Burhaneddin Yaman, Steen Moeller, Chetan Shenoy, Matthias Stuber, Mehmet Akçakaya","doi":"10.1007/s10334-024-01157-8","DOIUrl":"10.1007/s10334-024-01157-8","url":null,"abstract":"Object: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability.Materials and methods: While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI.Results: Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality.Discussion: PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"429-438"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140922652","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}

引用次数: 0

Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI. 学习深度学习:选择UNet架构增强MRI的统计和范式测试。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2023-11-21 DOI: 10.1007/s10334-023-01127-6

Rishabh Sharma, Panagiotis Tsiamyrtzis, Andrew G Webb, Ernst L Leiss, Nikolaos V Tsekos

{"title":"Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.","authors":"Rishabh Sharma, Panagiotis Tsiamyrtzis, Andrew G Webb, Ernst L Leiss, Nikolaos V Tsekos","doi":"10.1007/s10334-023-01127-6","DOIUrl":"10.1007/s10334-023-01127-6","url":null,"abstract":"Objective: This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics.Materials and methods: To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI). We employed both Analysis of Variance (ANOVA) and Mixed Effects Model (MEM) to assess the statistical significance of the independent parameters, aiming to compare their efficacy in revealing differences and interactions among fixed parameters.Results: ANOVA analysis showed that, except for the acquisition protocol, fixed variables were statistically insignificant. In contrast, MEM analysis revealed that all fixed parameters and their interactions held statistical significance. This emphasizes the need for advanced statistical analysis in comparative studies, where MEM can uncover finer distinctions often overlooked by ANOVA.Discussion: These findings highlight the importance of utilizing appropriate statistical analysis when comparing different deep learning models. Additionally, the surprising effectiveness of the UNet architecture in enhancing various acquisition protocols underscores the potential for developing improved methods for characterizing and training deep learning models. This study serves as a stepping stone toward enhancing the transparency and comparability of deep learning techniques for medical imaging applications.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"507-528"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138291349","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}

引用次数: 0

ESMRMB 2024 focus topic: MR beyond trends-fact-checking MR. ESMRMB 2024 焦点话题：超越趋势的 MR--事实核查 MR。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-06-28 DOI: 10.1007/s10334-024-01177-4

Christian Langkammer, Lena Václavů, Thomas Kuestner, Melanie Bauer, Najat Salameh, Marco Palombo, Raquel Perez Lopez

引用次数: 0

Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement. 基于深度学习的 1.5 T 脑结构磁共振成像超分辨率：应用于定量体积测量。

IF 2 4区医学

Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-07-01 Epub Date: 2024-05-17 DOI: 10.1007/s10334-024-01165-8

Atita Suwannasak, Salita Angkurawaranon, Prapatsorn Sangpin, Itthi Chatnuntawech, Kittichai Wantanajittikul, Uten Yarach

{"title":"Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement.","authors":"Atita Suwannasak, Salita Angkurawaranon, Prapatsorn Sangpin, Itthi Chatnuntawech, Kittichai Wantanajittikul, Uten Yarach","doi":"10.1007/s10334-024-01165-8","DOIUrl":"10.1007/s10334-024-01165-8","url":null,"abstract":"Objective: This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM).Materials and methods: In vivo brain images acquired with 3D-T1W from various MRI scanners were utilized. For model training, LR images were generated by downsampling the original 1 mm-2 mm isotropic resolution images. Pairs of LR and HR images were used for training 3D residual dense net (RDN). For model testing, actual scanned 2 mm isotropic resolution 3D-T1W images with one-minute scan time were used. Normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used for model evaluation. The evaluation also included brain volume measurement, with assessments of subcortical brain regions.Results: The results showed that DL-SR model improved the quality of LR images compared with cubic interpolation, as indicated by NRMSE (24.22% vs 30.13%), PSNR (26.19 vs 24.65), and SSIM (0.96 vs 0.95). For volumetric assessments, there were no significant differences between DL-SR and actual HR images (p > 0.05, Pearson's correlation > 0.90) at seven subcortical regions.Discussion: The combination of LR MRI and DL-SR enables addressing prolonged scan time in 3D MRI scans while providing sufficient image quality without affecting brain volume measurement.","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"465-475"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958213","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}

引用次数: 0