Background: FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow, which are difficult to correct by analytical modeling. In this study, a deep learning (DL)-based synthetic FLAIR method was developed, which does not require analytical modeling of the signal.
Purpose: To correct artifacts in synthetic FLAIR using a DL method.
Study Type: Retrospective.
Subjects: A total of 80 subjects with clinical indications (60.6 ± 16.7 years, 38 males, 42 females) were divided into three groups: a training set (56 subjects, 62.1 ± 14.8 years, 25 males, 31 females), a validation set (1 subject, 62 years, male), and the testing set (23 subjects, 57.3 ± 20.4 years, 13 males, 10 females).
Field Strength/sequence: 3 T MRI using a multiple-dynamic multiple-echo acquisition (MDME) sequence for synthetic MRI and a conventional FLAIR sequence.
Assessment: Normalized root mean square (NRMSE) and structural similarity (SSIM) were computed for uncorrected synthetic FLAIR and DL-corrected FLAIR. In addition, three neuroradiologists scored the three FLAIR datasets blindly, evaluating image quality and artifacts for sulci/periventricular and intraventricular/cistern space regions.
Statistical Tests: Pairwise Student's t-tests and a Wilcoxon test were performed.
Results: For quantitative assessment, NRMSE improved from 4.2% to 2.9% (P < 0.0001) and SSIM improved from 0.85 to 0.93 (P < 0.0001). Additionally, NRMSE values significantly improved from 1.58% to 1.26% (P < 0.001), 3.1% to 1.5% (P < 0.0001), and 2.7% to 1.4% (P < 0.0001) in white matter, gray matter, and cerebral spinal fluid (CSF) regions, respectively, when using DL-corrected FLAIR. For qualitative assessment, DL correction achieved improved overall quality, fewer artifacts in sulci and periventricular regions, and in intraventricular and cistern space regions.
Data Conclusion: The DL approach provides a promising method to correct artifacts in synthetic FLAIR.
Level Of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1413-1423.
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http://dx.doi.org/10.1002/jmri.26712 | DOI Listing |
Front Parasitol
July 2024
Center for Global Health, Universidad Peruana Cayetano Heredia, Lima, Peru.
Neurocysticercosis (NCC) is caused by the invasion of larvae in the central nervous system (CNS) and stands as the predominant cause of epilepsy and other neurological disorders in many developing nations. NCC diagnosis is challenging because it relies on brain imaging exams (CT or MRI), which are poorly available in endemic rural or resource-limited areas. Moreover, some NCC cases cannot be easily detected by imaging, leading to inconclusive results.
View Article and Find Full Text PDFStroke
January 2025
Department of Clinical Neuroscience and Therapeutics, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan (M.T., T.N., S.A., H.M.).
Background: Synthetic magnetic resonance imaging (MRI) is an innovative MRI technology that enables the acquisition of multiple quantitative values, including T1 and T2 values, proton density, and myelin volume, in a single scan. Although the usefulness of myelin measurement with synthetic MRI has been reported for assessing several diseases, investigations in patients with stroke have not been reported. We aimed to explore the utility of myelin quantification using synthetic MRI in predicting outcomes in patients with acute ischemic stroke.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
Department of Radiology and Medical Imaging, King Saud University Medical City, King Saud University, Riyadh, KSA.
Background: Multiple sclerosis (MS) is one of the most common disabling central nervous system diseases affecting young adults. Magnetic resonance imaging (MRI) is an essential tool for diagnosing and following up multiple sclerosis. Over the years, many MRI techniques have been developed to improve the sensitivity of MS disease detection.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
January 2025
From the Orthopedic Data Innovation Lab (ODIL), Hospital for Special Surgery (A.M.L.S., M.A.F.), Department of Radiology and Imaging, Hospital for Special Surgery Centre (E.E.X, Z.I, E.T.T, D.B.S, J.L.C)and Department of Population Health Sciences, Weill Cornell Medicine (M.A.F), New York, New York, USA.
Background And Purpose: To train and evaluate an open-source generative adversarial networks (GANs) to create synthetic lumbar spine MRI STIR volumes from T1 and T2 sequences, providing a proof-of-concept that could allow for faster MRI examinations.
Materials And Methods: 1817 MRI examinations with sagittal T1, T2, and STIR sequences were accumulated and randomly divided into training, validation, and test sets. GANs were trained to create synthetic STIR volumes using the T1 and T2 volumes as inputs, optimized using the validation set, then applied to the test set.
Eur J Radiol
January 2025
Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province 214062, China. Electronic address:
Purpose: To construct a nomogram combining Kaiser score (KS), synthetic MRI (syMRI) parameters, apparent diffusion coefficient (ADC), and clinical features to distinguish benign and malignant breast lesions better.
Methods: From December 2022 to February 2024, a retrospective cohort of 168 patients with breast lesions diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 by ultrasound and/or mammography was included. The research population was divided into the training set (n = 117) and the validation set (n = 51) by random sampling with a ratio of 7:3.
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