Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Mais filtros

Base de dados
Intervalo de ano de publicação
Cerebrovasc Dis ; 50(3): 347-355, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33730735


INTRODUCTION: Sodium MRI (23Na MRI) derived biomarkers such as tissue sodium concentration (TSC) provide valuable information on cell function and brain tissue viability and has become a reliable tool for the assessment of brain tumors and ischemic stroke beyond pathoanatomical morphology. Patients with major stroke often suffer from different degrees of underlying white matter lesions (WMLs) attributed to chronic small vessel disease. This study aimed to evaluate the WM TSC in patients with an acute ischemic stroke and to correlate the TSC with the extent of small vessel disease. Furthermore, the reliability of relative TSC (rTSC) compared to absolute TSC in these patients was analyzed. METHODOLOGY: We prospectively examined 62 patients with acute ischemic stroke (73 ± 13 years) between November 2016 and August 2019 from which 18 patients were excluded and thus 44 patients were evaluated. A 3D 23Na MRI was acquired in addition to a T2-TIRM and a diffusion-weighted image. Coregistration and segmentation were performed with SPM 12 based on the T2-TIRM image. The extension of WM T2 hyperintense lesions in each patient was classified using the Fazekas scale of WMLs. The absolute TSC in the WM region was correlated to the Fazekas grades. The stroke region was manually segmented on the coregistered absolute diffusion coefficient image and absolute, and rTSC was calculated in the stroke region and compared to nonischemic WM region. Statistical significance was evaluated using the Student t-test. RESULTS: For patients with Fazekas grade I (n = 25, age: 68.5 ± 15.1 years), mean TSC in WM was 55.57 ± 7.43 mM, and it was not statistically significant different from patients with Fazekas grade II (n = 7, age: 77.9 ± 6.4 years) with a mean TSC in WM of 53.9 ± 6.4 mM, p = 0.58. For patients with Fazekas grade III (n = 9, age: 81.4 ± 7.9 years), mean TSC in WM was 68.7 ± 10.5 mM, which is statistically significantly higher than the TSC in patients with Fazekas grade I and II (p < 0.001 and p = 0.05, respectively). There was a positive correlation between the TSC in WM and the Fazekas grade with r = 0.48 p < 0.001. The rTSC in the stroke region was statistically significant difference between low (0 and I) and high (2 and 3) Fazekas grades (p = 0.0353) whereas there was no statistically significant difference in absolute TSC in the stroke region between low (0 and I) and high (2 and 3) Fazekas grades. CONCLUSION: The significant difference in absolute TSC in WM in patients with severe small vessel disease; Fazekas grade 3 can lead to inaccuracies using rTSC quantification for evaluation of acute ischemic stroke using 23 Na MRI. The study, therefore, emphasizes the importance of absolute tissue sodium quantification.

Doenças de Pequenos Vasos Cerebrais/diagnóstico por imagem , AVC Isquêmico/diagnóstico por imagem , Leucoencefalopatias/diagnóstico por imagem , Imageamento por Ressonância Magnética , Isótopos de Sódio/metabolismo , Substância Branca/diagnóstico por imagem , Idoso , Idoso de 80 Anos ou mais , Doenças de Pequenos Vasos Cerebrais/metabolismo , Imagem de Difusão por Ressonância Magnética , Feminino , Humanos , Interpretação de Imagem Assistida por Computador , AVC Isquêmico/metabolismo , Leucoencefalopatias/metabolismo , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudos Prospectivos , Reprodutibilidade dos Testes , Substância Branca/metabolismo
NMR Biomed ; 34(4): e4474, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33480128


Quantitative 23 Na magnetic resonance imaging (MRI) provides tissue sodium concentration (TSC), which is connected to cell viability and vitality. Long acquisition times are one of the most challenging aspects for its clinical establishment. K-space undersampling is an approach for acquisition time reduction, but generates noise and artifacts. The use of convolutional neural networks (CNNs) is increasing in medical imaging and they are a useful tool for MRI postprocessing. The aim of this study is 23 Na MRI acquisition time reduction by k-space undersampling. CNNs were applied to reduce the resulting noise and artifacts. A retrospective analysis from a prospective study was conducted including image datasets from 46 patients (aged 72 ± 13 years; 25 women, 21 men) with ischemic stroke; the 23 Na MRI acquisition time was 10 min. The reconstructions were performed with full dataset (FI) and with a simulated dataset an image that was acquired in 2.5 min (RI). Eight different CNNs with either U-Net-based or ResNet-based architectures were implemented with RI as input and FI as label, using batch normalization and the number of filters as varying parameters. Training was performed with 9500 samples and testing included 400 samples. CNN outputs were evaluated based on signal-to-noise ratio (SNR) and structural similarity (SSIM). After quantification, TSC error was calculated. The image quality was subjectively rated by three neuroradiologists. Statistical significance was evaluated by Student's t-test. The average SNR was 21.72 ± 2.75 (FI) and 10.16 ± 0.96 (RI). U-Nets increased the SNR of RI to 43.99 and therefore performed better than ResNet. SSIM of RI to FI was improved by three CNNs to 0.91 ± 0.03. CNNs reduced TSC error by up to 15%. The subjective rating of CNN-generated images showed significantly better results than the subjective image rating of RI. The acquisition time of 23 Na MRI can be reduced by 75% due to postprocessing with a CNN on highly undersampled data.