NMR in biomedicine
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Emphysema is a life-threatening pathology that causes irreversible destruction of alveolar walls. In vivo imaging techniques play a fundamental role in the early non-invasive pre-clinical and clinical detection and longitudinal follow-up of this pathology. In the present study, we aimed to evaluate the feasibility of using high resolution radial three-dimensional (3D) zero echo time (ZTE) and 3D ultra-short echo time (UTE) MRI to accurately detect lung pathomorphological changes in a rodent model of emphysema. ⋯ UTE yielded significantly higher SNR compared with ZTE (14% and 30% higher in PPE-treated and non-PPE-treated lungs, respectively). This study showed that optimized 3D radial UTE and ZTE MRI can provide lung images of excellent quality, with high isotropic spatial resolution (400 µm) and SNR in parenchymal tissue (>25) and negligible motion artifacts in freely breathing animals. These techniques were shown to be useful non-invasive instruments to accurately and reliably detect the pathomorphological alterations taking place in emphysematous lungs, without incurring the risks of cumulative radiation exposure typical of micro-computed tomography.
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This study used quantitative MRI to study normal appearing white matter (NAWM) in patients with clinically isolated syndromes suggestive of multiple sclerosis and relapsing-remitting multiple sclerosis (RRMS). This was done at ultrahigh field (7 T) for greater spatial resolution and sensitivity. 17 CIS patients, 11 RRMS patients, and 20 age-matched healthy controls were recruited. They were scanned using a 3D inversion recovery turbo field echo sequence to measure the longitudinal relaxation time (T1). ⋯ There was no significant correlation between the median of T1, MTR(-), or MTR(+) and the age of healthy controls. Furthermore, no significant correlation was observed between EDSS or disease duration and T1, MTR(-), or MTR(+) for either CIS or RRMS patients. In conclusion, MTR was found to be more sensitive to early changes in MS disease than T1.
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Comparative Study
Hemisphere, gender and age-related effects on iron deposition in deep gray matter revealed by quantitative susceptibility mapping.
The purpose of this work was to investigate the effects of hemispheric location, gender and age on susceptibility value, as well as the association between susceptibility value and diffusional metrics, in deep gray matter. Iron content was estimated in vivo using quantitative susceptibility mapping. Microstructure was probed using diffusional kurtosis imaging. ⋯ Notably, the putamen exhibited the highest rate of increase in susceptibility with aging. Correlations between susceptibility value and microstructural measurements were inconclusive. These findings could provide new clues for unveiling mechanisms underlying iron-related neurodegenerative diseases.
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Conventional MRI for brain tumor diagnosis employs T2 -weighted and contrast-enhanced T1 -weighted sequences. Non-enhanced T1 -weighted images provide improved anatomical details for precise tumor location, but reduced tumor-to-background contrast as elevated T1 and proton density (PD) values in tumor tissue affect the signal inversely. Radiofrequency (RF) coil inhomogeneities may further mask tumor and edema outlines. ⋯ Pure T1 weighting yielded lower SNR, but high CNR, because of increased optical contrasts. In patients with brain tumors, synthetic anatomies with pure T1 weighting yielded significant increases in optical contrast and improved visibility of tumor and edema in comparison with anatomies reflecting conventional T1 contrasts. In summary, the optimized purely T1 -weighted synthetic anatomy with an isotropic resolution of 1 mm, as proposed in this work, considerably enhances optical contrast and visibility of brain tumors and edema.
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Multiparametric medical imaging data can be large and are often complex. Machine learning algorithms can assist in image interpretation when reliable training data exist. In most cases, however, knowledge about ground truth (e.g. histology) and thus training data is limited, which makes application of machine learning algorithms difficult. ⋯ We observed an improvement in accuracy with increasing number of imaging parameters used for clustering and SVM training. In particular, including PET SUVs as an additional parameter markedly improved classification results. A variety of applications are conceivable, especially for imaging of tissues without easily available histopathological correlation.