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The original version of this article, published on 04 June 2018, unfortunately contained a mistake.
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INTRODUCTION: Dermatomyositis (DM) is an idiopathic inflammatory myopathy involving severe debilitation in need of diagnostics. We evaluated the proximal lower extremity musculature with diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM) and dynamic DTI in DM patients and controls and compared with standard clinical workup. METHODS: In this IRB-approved, HIPAA-compliant study with written informed consent, anatomical, Dixon fat/water and diffusion imaging were collected in bilateral thigh MRI of 22 controls and 27 DM patients in a 3T scanner. Compartments were scored on T1/T2 scales. Single voxel dynamic DTI metrics in quadriceps before and after 3-min leg exercise were measured. Spearman rank correlation and mixed model analysis of variance/covariance (ANOVA/ANCOVA) were used to correlate with T1 and T2 scores and to compare patients with controls. RESULTS: DM patients showed significantly lower pseudo-diffusion and volume in quadriceps than controls. All subjects showed significant correlation between T1 score and signal-weighted fat fraction; tissue diffusion and pseudo-diffusion varied significantly with T1 and T2 score in patients. Radial and mean diffusion exercise response in patients was significantly higher than controls. CONCLUSION: Static and dynamic diffusion imaging metrics show correlation with conventional imaging scores, reveal spatial heterogeneity, and provide means to differentiate dermatomyositis patients from controls. KEY POINTS: ⢠Diffusion imaging shows regional differences between thigh muscles of dermatomyositis patients and controls. ⢠Signal-weighted fat fraction and diffusion metrics correlate with T1/T2 scores of disease severity. ⢠Dermatomyositis patients show significantly higher radial diffusion exercise response than controls.
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Dermatomiosite/diagnóstico , Imagem de Tensor de Difusão/métodos , Exercício Físico/fisiologia , Músculo Esquelético/diagnóstico por imagem , Adulto , Idoso , Dermatomiosite/fisiopatologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Músculo Esquelético/fisiopatologia , Reprodutibilidade dos Testes , Coxa da Perna , Adulto JovemRESUMO
AIM: To compare image quality and lesion detection in the liver using magnetic resonance imaging (MRI) at 3T between T2-weighted imaging using a standard rectilinear k-space trajectory (standard T2WI) and using the BLADE technique (BLADE-T2WI), a technique that employs periodically rotated overlapping parallel lines with enhanced reconstruction for motion correction. MATERIALS AND METHODS: Twenty-eight consecutive patients who underwent MRI examination of the liver at 3T including standard T2WI and BLADE-T2WI, both performed using multiple breath-holds, comprised the study cohort. Images were reviewed in consensus by two radiologists during separate sessions for a number of measures regarding artefacts and image quality. These two readers also assessed the two image sets for the presence of liver lesions and measured liver-to-lesion contrast. Binary logistic regression for correlated data was used to compare the sequences in terms of sensitivity and positive predictive value (PPV) for lesion detection. RESULTS: BLADE-T2WI received significantly higher scores than did standard T2WI for in-plane respiratory motion (p=0.0195), other ghosting artefacts (p<0.0001), sharpness of the liver edge (p=0.0004), sharpness of intra-hepatic vessels (p<0.0001), flow signal suppression (p<0.0001), and overall image quality (p<0.0001). There was a non-significant trend toward improved B(1)-inhomogeneity artefact with BLADE-T2WI (p=0.0571). There was no difference in through-plane respiratory motion (p=0.6836). BLADE-T2WI demonstrated a significant improvement in PPV for lesion detection (p=0.0129) as well as in liver-to-lesion contrast (p=0.0054). There was no difference regarding lesion sensitivity (p=1.0). CONCLUSIONS: Use of the BLADE technique for T2-weighted MRI of the liver at 3T may lead to a significant improvement in image artefacts and improved PPV for lesion detection.