Diffusion neuro-MRI has benefited significantly from sophisticated pre-processing procedures aimed at improving image quality and diagnostic. In this work, diffusion-weighted imaging (DWI) was used with artifact correction and the apparent diffusion coefficient (ADC) was quantified to investigate fetal brain development. The DWI protocol was designed in order to limit the acquisition time and to estimate ADC without perfusion bias. The ADC in normal fetal brains was compared to cases with isolated ventriculomegaly (VM), a common fetal disease whose DWI studies are still scarce. DWI was performed in 58 singleton fetuses (Gestational age (GA) range: 19-38w) at 1.5T. In 31 cases, VM was diagnosed on ultrasound. DW-Spin Echo EPI with b-values = 50, 200, 700 s/mm 2 along three orthogonal axes was used. All images were corrected for noise, Gibbs-ringing, and motion artifacts. The signal-to-noise ratio (SNR) was calculated and the ADC was measured with a linear least-squared algorithm. A multi-way ANOVA was used to evaluate differences in ADC between normal and VM cases and between second and third trimester in different brain regions. Correlation between ADC and GA was assessed with linear and quadratic regression analysis. Noise and artifact correction considerably increased SNR and the goodness-of-fit. ADC measurements were significantly different between second and third trimester in centrum semiovale, frontal white matter, thalamus, cerebellum and pons of both normal and VM brains (p ≤ 0.03). ADC values were significantly different between normal and VM in centrum semiovale and frontal white matter (p ≤ 0.02). ADC values in centrum semiovale, thalamus, cerebellum and pons linearly decreased with GA both in normal and VM brains, while a quadratic relation with GA was found in basal ganglia and occipital white matter of normal brains and in frontal white matter of VM (p ≤ 0.02). ADC values in all fetal brain regions were lower than those reported in literature where DWI with b = 0 was performed. Conversely, they were in agreement with the results of other authors who measured perfusion and diffusion contributions separately. By optimizing our DWI protocol we achieved an unbiased quantification of brain ADC in reasonable scan time. Our findings suggested that ADC can be a useful biomarker of brain abnormalities associated with VM.
In cultural heritage conservation science, moisture content (MC) is an essential factor to determine. At the same time, it is essential to choose non-destructive and non-invasive approaches for more sustainable investigations and make them safe for the environment and the sample. The question addressed in this work concerns the possibility and the opportunity to investigate waterlogged wood by using nuclear magnetic resonance imaging (MRI) clinical scanners to carry out non-destructive volumetric diagnostics. In this study, MRI, the most important non-invasive medical imaging technique for human tissue analysis, was applied to study archaeological waterlogged wood samples. This type of archaeological material has a very high moisture content (400%–800%), thus, it is an ideal investigative subject for MRI which detects water molecules inside matter. By following this methodology, it was possible to obtain information about water content and conservation status through a T1, T2, and T2* weighted image analysis, without any sampling or handling, and the samples were directly scanned in the water where they were stored. Furthermore, it permited processing 3D reconstruction, which could be an innovative tool for the digitalization of marine archaeological collections. In this work, 16 modern species of wood and a waterlogged archaeological wood sample were studied and investigated using a clinical NMR scanner operating at 3T. The results were compared with X-ray computed tomography (CT) images, as they had already been used for dendrochronology. The comparison highlights the similar, different, and complementary information about moisture content and conservation status in an all-in-one methodology obtainable from both MRI and CT techniques.
This study aimed to investigate the Diffusion-Tensor-Imaging (DTI) potential in the detection of microstructural changes in prostate cancer (PCa) in relation to the diffusion weight (b-value) and the associated diffusion length lD. Thirty-two patients (age range = 50–87 years) with biopsy-proven PCa underwent Diffusion-Weighted-Imaging (DWI) at 3T, using single non-zero b-value or groups of b-values up to b = 2500 s/mm2. The DTI maps (mean-diffusivity, MD; fractional-anisotropy, FA; axial and radial diffusivity, D// and D┴), visual quality, and the association between DTI-metrics and Gleason Score (GS) and DTI-metrics and age were discussed in relation to diffusion compartments probed by water molecules at different b-values. DTI-metrics differentiated benign from PCa tissue (p ≤ 0.0005), with the best discriminative power versus GS at b-values ≥ 1500 s/mm2, and for b-values range 0–2000 s/mm2, when the lD is comparable to the size of the epithelial compartment. The strongest linear correlations between MD, D//, D┴, and GS were found at b = 2000 s/mm2 and for the range 0–2000 s/mm2. A positive correlation between DTI parameters and age was found in benign tissue. In conclusion, the use of the b-value range 0–2000 s/mm2 and b-value = 2000 s/mm2 improves the contrast and discriminative power of DTI with respect to PCa. The sensitivity of DTI parameters to age-related microstructural changes is worth consideration.
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