MRI Texture Analysis in Multiple Sclerosis: Toward a Clinical Analysis Protocol

Harrison, Lara; Raunio, Minna; Holli, K. K.; Luukkaala, Tiina; Savio, Sami; Elovaara, Irina; Soimakallio, Seppo; Eskola, Hannu; Dastidar, Prasun

doi:10.1016/j.acra.2010.01.005

Cited by 51 publications

(35 citation statements)

References 25 publications

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“…Co-occurrence matrix-based texture features describe changes in SI with increasing distance and reflect homogeneity/heterogeneity of SI in the ROI. The finding of high frequency of co-occurrence matrix-based features to be selected is consistent with other studies (19,30,31). We speculate that it may be caused by the relative uniform density of minimal fat AML and heterogenous attenuation of RCC on CT images, especially on the contrast-enhanced CT images (6,7,10,32).…”

Section: Discussionsupporting

confidence: 85%

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Angiomyolipoma with Minimal Fat

Lu¹,

Liu²,

Wang³

et al. 2015

Academic Radiology

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Section: Discussionsupporting

confidence: 85%

“…The causes are not clear. We speculate that it may be related with the imaging methods because most of the studies performed TA on MR images (19,30,31), whereas our study performed on CT images. Co-occurrence matrix-based texture features describe changes in SI with increasing distance and reflect homogeneity/heterogeneity of SI in the ROI.…”

Section: Discussionmentioning

confidence: 58%

Angiomyolipoma with Minimal Fat

Lu¹,

Liu²,

Wang³

et al. 2015

Academic Radiology

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“…31,32 White matter lesion can be present not only in stroke 33 but also in multiple sclerosis and pyridoxine deficiency. 34,35 Therefore, we consider SBI as the better marker for lacunar stroke in brain MRI images.…”

Section: Discussionmentioning

confidence: 99%

Adults With Late Stage 3 Chronic Kidney Disease Are at High Risk for Prevalent Silent Brain Infarction

Chou

Lien

Chen

et al. 2011

Stroke

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Background and Purpose-The close relationship between stroke and chronic kidney disease (CKD) has been well-documented. However, few studies have focused on silent brain infarction (SBI) in CKD. We investigated the prevalence of SBI in different stages of CKD. Methods-We included 1312 participants aged 30 to 93 years who came from either a random sample of residents or from a group of physically examined subjects in the same community. Basic information, clinical evaluations, laboratory tests, and MRI images were assessed. Subjects were divided into groups 1, 2, 3a, and 3b, corresponding to the estimated glomerular filtration rate (eGFR) levels of Ն90.0, 60.0 to 89.9, 45.0 to 59.9, and 30.0 to 44.9 mL/min/1.73 m 2 . Results-The crude prevalence was 4.7%: 2.6% (20 of 759 subjects) in group 1; 6.3% (32 of 506) in group 2; 12.9% (4 of 31) in group 3a; and 37.5% (6 of 16) in group 3b (PϽ0.001). Additionally, SBI also correlated with age, male sex, hypertension, diabetes, moderate carotid plaque, higher blood pressures, obesity, and levels of triglyceride, high-density lipoprotein cholesterol, high-sensitivity C-reactive protein, and uric acid (all PϽ0.05).

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“…Euclidean wavelet approaches have been used to classify structural brain data (Canales-Rodriguez et al, 2013;Lao et al, 2004) as a means to assess structural morphometric differences between different populations of subjects. They have also been used to discriminate between healthy and pathological tissue by characterizing subtle changes in brain structure in a variety of diseases such as Alzheimer's disease, mild cognitive impairment and multiple sclerosis (Hackmack et al, 2012;Harrison et al, 2010). Interestingly, the recent proposal in Kim et al (2014), also uses the SGWT to derive multi-scale shape descriptors that can be used to detect group-level effects.…”

Section: Extension To Structural Studiesmentioning

confidence: 99%

Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping

et al. 2015

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A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar gray matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity.

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MRI Texture Analysis in Multiple Sclerosis: Toward a Clinical Analysis Protocol

Cited by 51 publications

References 25 publications

Angiomyolipoma with Minimal Fat

Angiomyolipoma with Minimal Fat

Adults With Late Stage 3 Chronic Kidney Disease Are at High Risk for Prevalent Silent Brain Infarction

Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping

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