Animal models of Parkinson\'s disease are useful to evaluate new treatments and to elucidate the etiology of the disease. Hence, it is necessary to have methods that allow quantification of their effectiveness. [18 F]FDOPA-PET (FDOPA-PET) imaging is outstanding for this purpose because of its capacity to measure changes in the dopaminergic pathway noninvasively and in vivo. Nevertheless, PET acquisition and quantification is time-consuming making it necessary to find faster ways to quantify FDOPA-PET data. This study evaluated Male Wistar rats by FDOPA, before and after being partially injured with 6-OHDA unilaterally. MicroPET scans with a duration of 120 min were acquired and Patlak reference plots were created to estimate the influx constant Kc in the striatum using the full dynamic scan data. Additionally, simple striatal-to-cerebral ratios (SCR) of short static acquisitions were computed and compared with the Kc values. Good correlation (r > 0.70) was obtained between Kc and SCR, acquired between 80-120 min after FDOPA administration with frames of 10 or 20 min and both methods were able to separate the FDOPA-uptake of healthy controls from that of the PD model (SCR -28%, Kc -71%). The present study concludes that Kc and SCR can be trustfully used to discriminate partially lesioned rats from healthy controls.

OBJECTIVE: Glenoid bone integrity is crucial for shoulder stability. The purpose of this study was to investigate a non-invasive method for quantifying bone loss regarding reliability and accuracy to detect glenoid bone deficiency in standard two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) measurements at different time points. It was hypothesized that the diameter of the circle used would significantly differ between raters, rendering this method inaccurate and not allowing for an exact estimation of glenoid defect size.
METHODS: Fifty-two shoulder CTs from 26 patients (26 2D-CTs; 26 3D-CTs) with anterior glenoid bone defects were evaluated by 6 raters at time 0 (T0) and at least 3 weeks after (T1) to assess the glenoid bone defect using the ratio method (\"best fit circle\"). Inter- and intra-rater differences concerning circle dimensions (circle diameter), measured width of bone loss and calculated percentage of bone loss (length-width-ratio) were compared in 2D- versus 3D-CT scans. The intraclass coefficient (ICC) was used to determine the inter- and intra-rater agreement.
RESULTS: The mean circle diameter difference in 2D-CT was 2.0 ± 1.9 mm versus 1.8 ± 1.5 mm in 3D-CT, respectively (p < 0.01). Mean width of bone loss in 2D-CT was 1.9 ± 1.7 mm compared to 1.7 ± 1.5 mm in 3D-CT, respectively (p < 0.01). The mean difference of bone loss percentage was 5.1 ± 4.8% in 2D-CT and 4.8 ± 4.5% in 3D-CT (p < 0.01). No significant differences concerning circle diameter, bone loss width and bone loss percentage were detected comparing T0 and T1. Circle diameter, bone loss width and bone loss percentage measurements in 3D-CT were significantly smaller compared to 2D-CT at T0 and T1 (p < 0.01). Agreement (ICC) was fair to good for all indicators of circle diameter (range 0.76-0.83), bone loss width (range 0.76-0.86) and percentage of bone loss (range 0.85-0.91). Overall, 3D-CT showed superior agreement compared to 2D-CT.
CONCLUSIONS: The ratio method varies in all glenoid parameters and is not valid for consistently quantifying glenoid bone defects even in 3D computed tomography. This must be taken into consideration when determining proper surgical treatment. The degree of glenoid bone loss alone should not be used to decide for or against a bony procedure. Rather, it is more important to define a defect size as \"critical\" and to also take other patient-specific factors into consideration so that the best treatment option can be undertaken. Application of the \"best fitting circle\" is a source of error when using the ratio method; therefore, care should be taken when measuring the circle diameter.
METHODS: III.

All empirical water column correction methods have consistently been reported to require existing depth sounding data for the purpose of calibrating a simple depth retrieval model; they yield poor results over very bright or very dark bottoms. In contrast, we set out to (i) use only the relative radiance data in the image along with published data, and several new assumptions; (ii) in order to specify and operate the simplified radiative transfer equation (RTE); (iii) for the purpose of retrieving both the satellite derived bathymetry (SDB) and the water column corrected spectral reflectance over shallow seabeds. Sea truth regressions show that SDB depths retrieved by the method only need tide correction. Therefore it shall be demonstrated that, under such new assumptions, there is no need for (i) formal atmospheric correction; (ii) conversion of relative radiance into calibrated reflectance; or (iii) existing depth sounding data, to specify the simplified RTE and produce both SDB and spectral water column corrected radiance ready for bottom typing. Moreover, the use of the panchromatic band for that purpose is introduced. Altogether, we named this process the Self-Calibrated Supervised Spectral Shallow-sea Modeler (4SM). This approach requires a trained practitioner, though, to produce its results within hours of downloading the raw image. The ideal raw image should be a \"near-nadir\" view, exhibit homogeneous atmosphere and water column, include some coverage of optically deep waters and bare land, and lend itself to quality removal of haze, atmospheric adjacency effect, and sun/sky glint.

Two methods for scaling of multicrystal data collected in time-resolved photocrystallography experiments are discussed. The WLS method is based on a weighted least-squares refinement of laser-ON/laser-OFF intensity ratios. The other, previously applied, is based on the average absolute system response to light exposure. A more advanced application of these methods for scaling within a data set, necessary because of frequent anisotropy of light absorption in crystalline samples, is proposed. The methods are applied to recently collected synchrotron data on the tetra-nuclear compound Ag2Cu2L4 with L = 2-diphenylphosphino-3-methylindole. A statistical analysis of the weighted least-squares refinement residual terms is performed to test the importance of the scaling procedure.

Alzheimer disease (AD) is associated with an increase in the brain of the 18-kDa translocator protein (TSPO), which is overexpressed in activated microglia and reactive astrocytes. Measuring the density of TSPO with PET typically requires absolute quantitation with arterial blood sampling, because a reference region devoid of TSPO does not exist in the brain. We sought to determine whether a simple ratio method could substitute for absolute quantitation of binding with (11)C-PBR28, a second-generation radioligand for TSPO.
METHODS: (11)C-PBR28 PET imaging was performed in 21 healthy controls, 11 individuals with mild cognitive impairment, and 25 AD patients. Group differences in (11)C-PBR28 binding were compared using 2 methods. The first was the gold standard method of calculating total distribution volume (V(T)), using the 2-tissue-compartment model with the arterial input function, corrected for plasma-free fraction of radiotracer (f(P)). The second method used a ratio of brain uptake in target regions to that in cerebellum-that is, standardized uptake value ratio (SUVR).
RESULTS: Using absolute quantitation, we confirmed that TSPO binding (V(T)/f(P)) was greater in AD patients than in healthy controls in expected temporoparietal regions and was not significantly different among the 3 groups in the cerebellum. When the cerebellum was used as a pseudo-reference region, the SUVR method detected greater binding in AD patients than controls in the same regions as absolute quantification and in 1 additional region, suggesting SUVR may have greater sensitivity. Coefficients of variation of SUVR measurements were about two-thirds lower than those of absolute quantification, and the resulting statistical significance was much higher for SUVR when comparing AD and healthy controls (e.g., P < 0.0005 for SUVR vs. P = 0.023 for VT/fP in combined middle and inferior temporal cortex).
CONCLUSIONS: To measure TSPO density in AD patients and control subjects, a simple ratio method SUVR can substitute for, and may even be more sensitive than, absolute quantitation. The SUVR method is expected to improve subject tolerability by allowing shorter scanning time and not requiring arterial catheterization. In addition, this ratio method allows smaller sample sizes for comparable statistical significance because of the relatively low variability of the ratio values.