English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Traditional autoradiographic image analysis has been restricted to the two-dimensional assessment of local cerebral glucose utilization (LCMR glc ) or blood flow in individual brains. It is advantageous, however, to generate an entire three-dimensional (3D) data set and to develop the ability to map replicate images derived from multiple studies into the same 3D space, so as to generate average and standard deviation images for the entire series. We have developed a novel method, termed “disparity analysis,” for the alignment and mapping of autoradiographic images. We present the theory of this method, which is based upon a linear affine model, to analyze point-to-point disparities in two images. The method is a direct one that estimates scaling, translation, and rotation parameters simultaneously. Disparity analysis is general and flexible and deals well with damaged or asymmetric sections. We applied this method to study LCMR glc  in nine awake male Wistar rats by the [ 14 C]2-deoxyglucose method. Brains were physically aligned in the anteroposterior axis and were sectioned subserially at 100-μm intervals. For each brain, coronal sections were aligned by disparity analysis. The nine brains were then registered in the z-axis with respect to a common coronal reference level (bregma + 0.7 mm). Eight of the nine brains were mapped into the remaining brain, which was designated the “template,” and aggregate 3D data sets were generated of the mean and standard deviation for the entire series. The averaged images retained the major anatomic features apparent in individual brains but with some defocusing. Internal anatomic features of the averaged brain were smooth, continuous, and readily identifiable on sections through the 3D stack. The fidelity of the internal architecture of the averaged brain was compared with that of individual brains by analysis of line scans at four representative levels. Line scan comparisons between corresponding sections and their template showed a high degree of correlation, as did similar comparisons performed on entire sections. Fourier analysis of line scan data showed retention of low-frequency information with the expected attenuation of high-frequency components produced by averaging. Region-of-interest (ROI) analysis of the averaged brain yielded LCMR glc  values virtually identical to those derived from measurements and subsequent averaging of data from individual brains. In summary, 3D reconstruction of averaged autoradiographic image data by disparity analysis is a feasible approach, which vastly simplifies ROI analysis, facilitates the assessment of hemodynamic or metabolic patterns in three dimensions, permits the computation of threshold-defined volumes of interest on averaged 3D data sets, makes possible atlas-based ROI strategies, and importantly provides the capability of generating 3D image data sets derived from arithmetic manipulations on two or more primary data sets (e.g., percent difference or ratio images).

Three-Dimensional Quantitative Autoradiography by Disparity Analysis: Theory and Application to Image Averaging of Local Cerebral Glucose Utilization