RESUMO
Magnetic resonance elastography (MRE) is used to assess the stiffness of the liver to rule out cirrhosis or fibrosis. The image, nevertheless, is regarded as shear-wave imaging and does not depict any anatomical features. Multimodality medical image fusion (MMIF), such as the fusion of MRE with computed tomography (CT) scan or magnetic resonance imaging (MRI), can help doctors optimize the advantages of each imaging technique. As a result, perceptions serve as valid and valuable assessment criteria. The contrast sensitivity function (CSF), which describes the rates of visual contrast sensitivity through the changing of spatial frequencies, is used mathematically to characterize the human visual system (HVS). As a result, we suggest novel methods for fusing images that use discrete wavelets transform (DWT) based on HVS and CSF models. Images from MRI or CT scan were combined with MRE images, and the outcomes were assessed both subjectively and objectively. Visual inspection of merging images was done throughout the qualitative analysis. The CT-MRE fused images in all four datasets were shown to be superior at maintaining bones and spatial resolution, despite the MRI-MRE being better at exhibiting soft tissues and contrast resolution. It is clear from all four datasets that the liver soft tissue in MRI and CT images mixed successfully with the red-colored stiffness distribution seen in MRE images. The proposed approach outperformed DWT, which produced visual artifacts such as signal loss. Quantitative evaluation using mean, standard deviation, and entropy showed that the generated images from the proposed technique performed better than the source images and DWT. Additionally, peak signal-to-noise ratio, mean square error, correlation coefficient, and structural similarity index measure were employed to compare the two fusion approaches, namely, MRI-MRE and CT-MRE. The comparison did not show the superiority of one approach over the other. In conclusion, both subjective and objective evaluation approaches revealed that the combined images contained more information and characteristics. Hence, the proposed method might be a useful procedure to diagnose and localize the stiffness regions on the liver soft tissue by fusion of MRE with MRI or CT.
RESUMO
OBJECTIVE: This systematic review aims to synthesise and evaluate structural MRI (sMRI) and functional MRI (fMRI) studies in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). METHODS: We systematically searched Medline and Ovid and included articles from 1991 (date of Oxford diagnostic criteria for CFS/ME) to first April 2019. Studies were selected by predefined inclusion and exclusion criteria. Two reviewers independently reviewed the titles and abstracts to determine articles for inclusion, full text and quality assessment for risk of bias. RESULTS: sMRI studies report differences in CFS/ME brain anatomy in grey and white matter volume, ventricular enlargement and hyperintensities. Three studies report no neuroanatomical differences between CFS/ME and healthy controls. Task-based fMRI investigated working memory, attention, reward and motivation, sensory information processing and emotional conflict. The most consistent finding was CFS/ME exhibited increased activations and recruited additional brain regions. Tasks with increasing load or complexity produced decreased activation in task-specific brain regions. CONCLUSIONS: There were insufficient data to define a unique neural profile or biomarker of CFS/ME. This may be due to inconsistencies in finding neuroanatomical differences in CFS/ME and the variety of different tasks employed by fMRI studies. But there are also limitations with neuroimaging. All brain region specific volumetric differences in CFS/ME were derived from voxel-based statistics that are biased towards group differences that are highly localised in space. fMRI studies demonstrated both increases and decreases in activation patterns in CFS/ME, this may be related to task demand. However, fMRI signal cannot differentiate between neural excitation and inhibition or function-specific neural processing. Many studies have small sample sizes and did not control for the heterogeneity of this clinical population. We suggest that with robust study design, subgrouping and larger sample sizes, future neuroimaging studies could potentially lead to a breakthrough in our understanding of the disease.
