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Quantitative susceptibility mapping (QSM) is used to quantify iron deposition in non-human primates in our study. Although QSM has many applications in detecting iron deposits in the human brain, including the distribution of iron deposits in specific brain regions, the change of iron deposition with aging, and the comparison of iron deposits between diseased groups and healthy controls, few studies have applied QSM to non-human primates, while most animal brain experiments focus on biochemical and anatomical results instead of non-invasive experiments. Additionally, brain imaging in children's research is difficult, but can be substituted using young rhesus monkeys, which are very similar to humans, as research animals. Therefore, understanding the relationship between iron deposition and age in rhesus macaques' brains can offer insights into both the developmental trajectory of magnetic susceptibility in the animal model and the correlated evidence in children's research. Twenty-three healthy rhesus macaque monkeys (23 ± 7.85 years, range 2-29 years) were included in this research. Seven regions of interest (ROIs-globus pallidus, substantia nigra, dentate nucleus, caudate nucleus, putamen, thalamus, red nucleus) have been analyzed in terms of QSM and R

Purpose To probe cerebral microstructural abnormalities and assess changes of neuronal density in Disrupted-in-Schizophrenia-1 (DISC1) mice using non-Gaussian diffusion and quantitative susceptibility mapping (QSM). Materials and methods Brain specimens of transgenic DISC1 mice (n = 8) and control mice (n = 7) were scanned. Metrics of neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging (DKI), as well as QSM, were acquired. Cell counting was performed on Nissl-stained sections. Group differences of imaging metrics and cell density were assessed. Pearson correlations between imaging metrics and cell densities were also examined. Results Significant increases of neuronal density were observed in the hippocampus of DISC1 mice. DKI metrics such as mean kurtosis exhibited significant group differences in the caudate putamen (P = 0.015), cerebral cortex (P = 0.021), and hippocampus (P = 0.011). However, DKI metrics did not correlate with cell density. In contrast, significant positive correlation between density of neurons and the neurite density index of NODDI in the hippocampus was observed (r = 0.783, P = 0.007). Significant correlation between density of neurons and susceptibility (r = 0.657, P = 0.039), as well as between density of neuroglia and susceptibility (r = 0.750, P = 0.013), was also observed in the hippocampus. Conclusion The imaging metrics derived from DKI were not sensitive specifically to cell density, while NODDI could provide diffusion metrics sensitive to density of neurons. The magnetic susceptibility values derived from the QSM method can serve as a sensitive biomarker for quantifying neuronal density.

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. Regional susceptibility and diffusional metrics were measured for the putamen, caudate nucleus, globus pallidus, thalamus, substantia nigra and red nucleus in 42 healthy adults (age range 25-78 years). Susceptibility value was significantly higher in the left than the right side of the caudate nucleus (P = 0.043) and substantia nigra (P < 0.001). Women exhibited lower susceptibility values than men in the thalamus (P < 0.001) and red nucleus (P = 0.032). Significant age-related increases of susceptibility were observed in the putamen (P < 0.001), red nucleus (P < 0.001), substantia nigra (P = 0.004), caudate nucleus (P < 0.001) and globus pallidus (P = 0.017). The putamen exhibited the highest rate of iron accumulation with aging (slope of linear regression = 0.73 × 10(-3) ppm/year), which was nearly twice those in substantia nigra (slope = 0.40 × 10(-3) ppm/year) and caudate nucleus (slope = 0.39 × 10(-3) ppm/year). Significant positive correlations between the susceptibility value and diffusion measurements were observed for fractional anisotropy (P = 0.045) and mean kurtosis (P = 0.048) in the putamen without controlling for age. Neither correlation was significant after controlling for age. Hemisphere, gender and age-related differences in iron measurements were observed in deep gray matter. 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.

We compared the parameters derived from diffusion-weighted imaging (DWI) and positron emission tomography/computed tomography (PET/CT) for treatment response evaluation and response prediction in patients with gastrointestinal stromal tumor (GIST). Seven patients with histologically proven metastatic disease were enrolled. DWI and PET/CT data were collected from all patients at diagnosis and from six at follow-up. All 37 lesions were identifiable in DWI with a sensitivity of 100%. To achieve higher accuracy, we used the apparent diffusion coefficient (ADC) of liver and background noise as thresholds for the measurement of the ADCs of lesions. Significant inverse correlations were found between ADCmean_thr (ADCmean with thresholds) and SUVmean (mean standardized uptake value) (R2 = 0.523, p

We compared the parameters derived from diffusion-weighted imaging (DWI) and positron emission tomography/computed tomography (PET/CT) for treatment response evaluation and response prediction in patients with gastrointestinal stromal tumor (GIST). Seven patients with histologically proven metastatic disease were enrolled. DWI and PET/CT data were collected from all patients at diagnosis and from six at follow-up. All 37 lesions were identifiable in DWI with a sensitivity of 100%. To achieve higher accuracy, we used the apparent diffusion coefficient (ADC) of liver and background noise as thresholds for the measurement of the ADCs of lesions. Significant inverse correlations were found between ADC(mean_thr) (ADC(mean) with thresholds) and SUV(mean) (mean standardized uptake value) (R(2)=0.523, p0.001 at diagnosis, and R(2)=0.916, p0.001 at follow-up), between ADC(mean_thr) and SUV(max) (maximum SUV) (R(2)=0.529, p0.001 at diagnosis, and R(2)=0.761, p0.001 at follow-up), between ΔADC(mean_thr) (percentage change in ADC(mean_thr) ) and ΔSUV(mean) (percentage change in SUV(mean) ) (R(2)=0.384, p0.001), and between ΔADC(mean_thr) and ΔSUV(max) (percentage change in SUV(max) ) (R(2)=0.500, p0.001). In lesion-based analysis, pre-treatment ADC(mean_thr) outperformed SUV(mean) and SUV(max) in treatment response prediction, with an area under the receiver operating characteristic curve of 0.706. These results show that DWI can provide a quantitative assessment comparable with PET/CT in GIST lesion characterization, treatment response evaluation and response prediction.