Quantitative MRI data in Multiple Sclerosis patients: a pattern recognition study

<div><p>Abstract Introduction Multiple Sclerosis (MS) is a neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Quantitative Magnetic Resonance Imaging (qMRI) enables a detailed characterization of brain tissue, but generates a large number of numerical results. In this study, we elucidated the main qMRI techniques and the brain regions that allow the identification of MS patients from neuroimaging data and pattern recognition techniques. Methods The data came from the combination of computational tools of image processing and neuroimaging acquired in a 3 Tesla scanner using different techniques: Diffusion, T2 Relaxometry, Magnetization Transfer Ratio (MTR) and Structural Morphometry. Data from 126 brain regions of 203 healthy individuals and 124 MS patients were separated into two groups and processed in a data-mining program using the k-nearest-neighbor (KNN) algorithm. Results The most relevant anatomical structures in the classification procedure were: corpus callosum, precuneus, left cerebellum and fusiform. Among the quantitative techniques the most relevant was the MTR, being indicated for longitudinal studies of this disease. KNN with 5 neighbors and pre-selected attributes had a better performance with an area under the ROC curve (97.3%) and accuracy (95.7%). A restricted classification considering only brain regions previously reported in the literature as affected by MS brought slightly lower scores, area: 97.1% and accuracy: 93.2%. Conclusion The use of standard recognition techniques from quantitative neuroimaging techniques has confirmed that the white matter of the brain is the most affected tissue by MS following a global pattern with greater involvement of the left hemisphere.</p></div

Pattern recognition of abscesses and brain tumors through MR spectroscopy: Comparison of experimental conditions and radiological findings

<div><p>Abstract Introduction The interpretation of brain tumors and abscesses MR spectra is complex and subjective. In clinical practice, different experimental conditions such as field strength or echo time (TE) reveal different metabolite information. Our study aims to show in which scenarios magnetic resonance spectroscopy can differentiate among brain tumors, normal tissue and abscesses using classification algorithms. Methods Pairwise classification between abscesses, brain tumor classes, and healthy subjects tissue spectra was performed, also the multiclass classification between meningiomas, grade I-II-III gliomas, and glioblastomas and metastases, in 1.5T short TE (n = 195), 1.5T long TE (n = 231) and 3.0T long TE (n = 59) point resolved spectroscopy setups, using LCModel metabolite concentration as input to classifiers. Results Areas under the curve of the Receiver Operating Characteristic above 0.9 were obtained for the classification between abscesses and all classes except glioblastomas, reaching 0.947 when classifying against metastases, grade I-II gliomas and glioblastomas (0.980), meningiomas and glioblastomas (0.956), grade I-II gliomas and metastases (0.989), meningiomas and metastases (0.990), and between healthy tissue and all other classes in both conditions except for anaplastic astrocytomas in short TE 1.5T setup. When the multiclass classification agrees with radiological diagnosis the accuracy reaches 96.8% for short TE and 98.9% for long TE. Conclusions The results in the three conditions were similar, highlighting comparable quality, robust quantification and good regularization and flexibility in either algorithm. Multiclass classification provides useful information to the radiologist. These findings show the potential of the development of decision support systems as well as tools for the accompaniment of treatments.</p></div

Quantitative MRI data in Multiple Sclerosis patients: a pattern recognition study

<div><p>Abstract Introduction Multiple Sclerosis (MS) is a neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Quantitative Magnetic Resonance Imaging (qMRI) enables a detailed characterization of brain tissue, but generates a large number of numerical results. In this study, we elucidated the main qMRI techniques and the brain regions that allow the identification of MS patients from neuroimaging data and pattern recognition techniques. Methods The data came from the combination of computational tools of image processing and neuroimaging acquired in a 3 Tesla scanner using different techniques: Diffusion, T2 Relaxometry, Magnetization Transfer Ratio (MTR) and Structural Morphometry. Data from 126 brain regions of 203 healthy individuals and 124 MS patients were separated into two groups and processed in a data-mining program using the k-nearest-neighbor (KNN) algorithm. Results The most relevant anatomical structures in the classification procedure were: corpus callosum, precuneus, left cerebellum and fusiform. Among the quantitative techniques the most relevant was the MTR, being indicated for longitudinal studies of this disease. KNN with 5 neighbors and pre-selected attributes had a better performance with an area under the ROC curve (97.3%) and accuracy (95.7%). A restricted classification considering only brain regions previously reported in the literature as affected by MS brought slightly lower scores, area: 97.1% and accuracy: 93.2%. Conclusion The use of standard recognition techniques from quantitative neuroimaging techniques has confirmed that the white matter of the brain is the most affected tissue by MS following a global pattern with greater involvement of the left hemisphere.</p></div

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

<div><p>Abstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.</p></div

Correlation of anthropometric characteristics with fat fraction and volume values on lumbar musculature.

<p>* Denotes a significant correlation (p <0.05).</p

Lumbar musculature morphometric measurements of cross sectional areas (mm²) and fat fraction (%) according to intervertebral level expressed as mean and standard deviation (SD).

<p>Lumbar musculature morphometric measurements of cross sectional areas (mm²) and fat fraction (%) according to intervertebral level expressed as mean and standard deviation (SD).</p

Spinopelvic parameters measured.

<p>Superior line: pelvic incidence—PI (<i>left</i>); pelvic tilt—PT (<i>center</i>); sacral slope—SS (<i>right</i>). Middle line: lumbar lordosis—LL (<i>left</i>); thoracic kyphosis—TK (<i>left-middle</i>); thoracolumbar alignment—TL (<i>middle-right</i>); C2 pelvic angle—CPA (<i>right</i>). Inferior line: spinosacral angle—SSA (<i>left</i>); sagittal vertical axis—SVA (<i>center</i>); L1S1 and T1S1 length (<i>right</i>).</p

Relationship of spinal alignment with muscular volume and fat infiltration of lumbar trunk muscles - Fig 2

<p>A) Lumbar lordosis angle measured by L3 inferior endplate to sacral endplate (L3-S1). B) Lumbar intervertebral angles: L3-L4, L4-L5, and L5-S1.</p

Spinopelvic parameters expressed as mean, standard deviation (SD) and range (minimum and maximum).

<p>Spinopelvic parameters expressed as mean, standard deviation (SD) and range (minimum and maximum).</p

Correlation (r) of the volume and fat fraction values on lumbar musculature with the spinopelvic parameters.

<p>* Denotes a significant correlation (p <0.05).</p