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

{"title":"The beating heart: artificial intelligence for cardiovascular application in the clinic.","authors":"Manuel Villegas-Martinez, Victor de Villedon de Naide, Vivek Muthurangu, Aurélien Bustin","doi":"10.1007/s10334-024-01180-9","DOIUrl":"10.1007/s10334-024-01180-9","url":null,"abstract":"<p><p>Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"369-382"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141440585","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 for accelerated and robust MRI reconstruction.","authors":"Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman, Efrat Shimron","doi":"10.1007/s10334-024-01173-8","DOIUrl":"10.1007/s10334-024-01173-8","url":null,"abstract":"<p><p>Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"335-368"},"PeriodicalIF":2.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141748522","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":"Magnetic resonance metrics for identification of cuprizone-induced demyelination in the mouse model of neurodegeneration: a review","authors":"Emma Friesen, Kamya Hari, Maxina Sheft, Jonathan D. Thiessen, Melanie Martin","doi":"10.1007/s10334-024-01160-z","DOIUrl":"https://doi.org/10.1007/s10334-024-01160-z","url":null,"abstract":"<p>Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler’s Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T₁ Weighted (T₁W) and T₂ Weighted (T₂W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"468 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609711","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":"Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time","authors":"Rodrigo Pommot Berto, Hanna Bugler, Gabriel Dias, Mateus Oliveira, Lucas Ueda, Sergio Dertkigil, Paula D. P. Costa, Leticia Rittner, Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab, Gerhard S. Drenthen, Mitko Veta, Jacobus F. A. Jansen, Marcel Breeuwer, Ruud J. G. van Sloun, Abdul Qayyum, Cristobal Rodero, Steven Niederer, Roberto Souza, Ashley D. Harris","doi":"10.1007/s10334-024-01156-9","DOIUrl":"https://doi.org/10.1007/s10334-024-01156-9","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"41 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574574","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":"Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping","authors":"Vaddadi Venkatesh, Raji Susan Mathew, Phaneendra K. Yalavarthy","doi":"10.1007/s10334-024-01158-7","DOIUrl":"https://doi.org/10.1007/s10334-024-01158-7","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3><p>Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribution/local tissue field (effect) inherent in the MR phase images is estimated by numerically solving the inverse source-effect problem. This study aims to develop an effective model-based deep-learning framework to solve the inverse problem of QSM.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>This work proposes a Schatten <span>(textit{p})</span>-norm-driven model-based deep learning framework for QSM with a learnable norm parameter <span>(textit{p})</span> to adapt to the data. In contrast to other model-based architectures that enforce the <i>l</i><span>(_{text {2}})</span>-norm or <i>l</i><span>(_{text {1}})</span>-norm for the denoiser, the proposed approach can enforce any <span>(textit{p})</span>-norm (<span>(text {0}&lt;textit{p}le text {2})</span>) on a trainable regulariser.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The proposed method was compared with deep learning-based approaches, such as QSMnet, and model-based deep learning approaches, such as learned proximal convolutional neural network (LPCNN). Reconstructions performed using 77 imaging volumes with different acquisition protocols and clinical conditions, such as hemorrhage and multiple sclerosis, showed that the proposed approach outperformed existing state-of-the-art methods by a significant margin in terms of quantitative merits.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The proposed SpiNet-QSM showed a consistent improvement of at least 5% in terms of the high-frequency error norm (HFEN) and normalized root mean squared error (NRMSE) over other QSM reconstruction methods with limited training data.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"30 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574453","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":"Conversion map from quantitative parameter mapping to myelin water fraction: comparison with R1·R2* and myelin water fraction in white matter","authors":"Shun Kitano, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Hiroaki Hayashi, Yuki Matsumoto, Kosuke Ito, Yoshitaka Bito, Akihiro Haga","doi":"10.1007/s10334-024-01155-w","DOIUrl":"https://doi.org/10.1007/s10334-024-01155-w","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3><p>To clarify the relationship between myelin water fraction (MWF) and <i>R</i>₁⋅<i>R</i>₂^* and to develop a method to calculate MWF directly from parameters derived from QPM, i.e., MWF converted from QPM (MWF_QPM).</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>Subjects were 12 healthy volunteers. On a 3 T MR scanner, dataset was acquired using spoiled gradient-echo sequence for QPM. MWF and <i>R</i>₁⋅<i>R</i>₂^* maps were derived from the multi-gradient-echo (mGRE) dataset. Volume-of-interest (VOI) analysis using the JHU-white matter (WM) atlas was performed. All the data in the 48 WM regions measured VOI were plotted, and quadratic polynomial approximations of each region were derived from the relationship between <i>R</i>₁·<i>R</i>₂^* and the two-pool model-MWF. The <i>R</i>₁·<i>R</i>₂^* map was converted to MWF_QPM map. MWF atlas template was generated using converted to MWF from <i>R</i>₁·<i>R</i>₂^* per WM region.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The mean MWF and <i>R</i>₁·<i>R</i>₂^* values for the 48 WM regions were 11.96 ± 6.63%, and 19.94 ± 4.59 s⁻², respectively. A non-linear relationship in 48 regions of the WM between MWF and <i>R</i>₁·<i>R</i>₂^* values was observed by quadratic polynomial approximation (<i>R</i>² ≥ 0.963, <i>P</i> &lt; 0.0001).</p><h3 data-test=\"abstract-sub-heading\">Discussion</h3><p>MWF_QPM map improved image quality compared to the mGRE-MWF map. Myelin water atlas template derived from MWF_QPM may be generated with combined multiple WM regions.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"41 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574463","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":"Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI","authors":"Wenbo Sun, Dan Xu, Huan Li, Sirui Li, Qingjia Bao, Xiaopeng Song, Daniel Topgaard, Haibo Xu","doi":"10.1007/s10334-024-01154-x","DOIUrl":"https://doi.org/10.1007/s10334-024-01154-x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3><p>To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&amp;E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80–200 ms) with seven <i>b</i>-values (0–3000 s/mm²). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&amp;E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>HM-MRI metrics <i>F</i>_{<i>D</i>very-slow} and <i>F</i>_<i>D</i>slow demonstrated a significant correlation with the H&amp;E staining results (<i>p</i> &lt; .05). Besides, <i>F</i>_{<i>D</i>very-slow} showed the highest area under ROC curve (AUC = 0.854) for grading, while <i>D</i>slow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics <i>F</i>_{<i>D</i>very-slow} and T2_<i>D</i>slow improved the diagnostic performance for grading (AUC = 0.876).</p><h3 data-test=\"abstract-sub-heading\">Discussion</h3><p>HM-MRI can aid in non-invasive diagnosis of gliomas.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":"21 1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574550","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":"Cross²SynNet: cross-device-cross-modal synthesis of routine brain MRI sequences from CT with brain lesion.","authors":"Minbo Jiang, Shuai Wang, Zhiwei Song, Limei Song, Yi Wang, Chuanzhen Zhu, Qiang Zheng","doi":"10.1007/s10334-023-01145-4","DOIUrl":"10.1007/s10334-023-01145-4","url":null,"abstract":"<p><strong>Objectives: </strong>CT and MR are often needed to determine the location and extent of brain lesions collectively to improve diagnosis. However, patients with acute brain diseases cannot complete the MRI examination within a short time. The aim of the study is to devise a cross-device and cross-modal medical image synthesis (MIS) method Cross²SynNet for synthesizing routine brain MRI sequences of T1WI, T2WI, FLAIR, and DWI from CT with stroke and brain tumors.</p><p><strong>Materials and methods: </strong>For the retrospective study, the participants covered four different diseases of cerebral ischemic stroke (CIS-cohort), cerebral hemorrhage (CH-cohort), meningioma (M-cohort), glioma (G-cohort). The MIS model Cross²SynNet was established on the basic architecture of conditional generative adversarial network (CGAN), of which, the fully convolutional Transformer (FCT) module was adopted into generator to capture the short- and long-range dependencies between healthy and pathological tissues, and the edge loss function was to minimize the difference in gradient magnitude between synthetic image and ground truth. Three metrics of mean square error (MSE), peak signal-to-noise ratio (PSNR), and structure similarity index measure (SSIM) were used for evaluation.</p><p><strong>Results: </strong>A total of 230 participants (mean patient age, 59.77 years ± 13.63 [standard deviation]; 163 men [71%] and 67 women [29%]) were included, including CIS-cohort (95 participants between Dec 2019 and Feb 2022), CH-cohort (69 participants between Jan 2020 and Dec 2021), M-cohort (40 participants between Sep 2018 and Dec 2021), and G-cohort (26 participants between Sep 2019 and Dec 2021). The Cross²SynNet achieved averaged values of MSE = 0.008, PSNR = 21.728, and SSIM = 0.758 when synthesizing MRIs from CT, outperforming the CycleGAN, pix2pix, RegGAN, Pix2PixHD, and ResViT. The Cross²SynNet could synthesize the brain lesion on pseudo DWI even if the CT image did not exhibit clear signal in the acute ischemic stroke patients.</p><p><strong>Conclusions: </strong>Cross²SynNet could achieve routine brain MRI synthesis of T1WI, T2WI, FLAIR, and DWI from CT with promising performance given the brain lesion of stroke and brain tumor.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"241-256"},"PeriodicalIF":2.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139692224","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":"Rule-based deep learning method for prognosis of neonatal hypoxic-ischemic encephalopathy by using susceptibility weighted image analysis.","authors":"Zhen Tang, Sasan Mahmoodi, Di Meng, Angela Darekar, Brigitte Vollmer","doi":"10.1007/s10334-023-01139-2","DOIUrl":"10.1007/s10334-023-01139-2","url":null,"abstract":"<p><strong>Objective: </strong>Susceptibility weighted imaging (SWI) of neonatal hypoxic-ischemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE.</p><p><strong>Materials and methods: </strong>Due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. We randomly select 11 datasets from patients with normal neurology outcomes (n = 31) and patients with abnormal neurology outcomes (n = 11) at 24 months of age to avoid bias in classification due to any imbalance in the data.</p><p><strong>Results: </strong>We develop a rule-based system to improve the classification performance, with an accuracy of 0.93 ± 0.09. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification patients with abnormal neurology outcome.</p><p><strong>Conclusion: </strong>Such regions that are important in the classification accuracy can interpret the relationship between the brain regions affected by hypoxic-ischemic and neurodevelopmental outcomes of infants with HIE at the age of 2 years.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"227-239"},"PeriodicalIF":2.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139513241","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":"STEAM-SASHA: a novel approach for blood- and fat-suppressed native T1 measurement in the right ventricular myocardium.","authors":"Malte Roehl, Miriam Conway, Sarah Ghonim, Pedro F Ferreira, Sonia Nielles-Vallespin, Sonya V Babu-Narayan, Dudley J Pennell, Peter D Gatehouse, Andrew D Scott","doi":"10.1007/s10334-023-01141-8","DOIUrl":"10.1007/s10334-023-01141-8","url":null,"abstract":"<p><strong>Objective: </strong>The excellent blood and fat suppression of stimulated echo acquisition mode (STEAM) can be combined with saturation recovery single-shot acquisition (SASHA) in a novel STEAM-SASHA sequence for right ventricular (RV) native T1 mapping.</p><p><strong>Materials and methods: </strong>STEAM-SASHA splits magnetization preparation over two cardiac cycles, nulling blood signal and allowing fat signal to decay. Breath-hold T1 mapping was performed in a T1 phantom and twice in 10 volunteers using STEAM-SASHA and a modified Look-Locker sequence at peak systole at 3T. T1 was measured in 3 RV regions, the septum and left ventricle (LV).</p><p><strong>Results: </strong>In phantoms, MOLLI under-estimated while STEAM-SASHA over-estimated T1, on average by 3.0% and 7.0% respectively, although at typical 3T myocardial T1 (T1 > 1200 ms) STEAM-SASHA was more accurate. In volunteers, T1 was higher using STEAM-SASHA than MOLLI in the LV and septum (p = 0.03, p = 0.006, respectively), but lower in RV regions (p > 0.05). Inter-study, inter-observer and intra-observer coefficients of variation in all regions were < 15%. Blood suppression was excellent with STEAM-SASHA and noise floor effects were minimal.</p><p><strong>Discussion: </strong>STEAM-SASHA provides accurate and reproducible T1 in the RV with excellent blood and fat suppression. STEAM-SASHA has potential to provide new insights into pathological changes in the RV in future studies.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":" ","pages":"295-305"},"PeriodicalIF":2.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10995026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139432685","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}