Simon R. Cherry

A computer algorithm for the three-dimensional (3D) alignment of PET images is described. To align two images, the algorithm calculates the ratio of one image to the other on a voxel-by-voxel basis and then iteratively moves the images relative to one another to minimize the variance of this ratio across voxels. Since the method relies on anatomic information in the images rather than on external fiducial markers, it can be applied retrospectively. Validation studies using a 3D brain phantom show that the algorithm aligns images acquired at a wide variety of positions with maximum positional errors that are usually less than the width of a voxel (1.745 mm). Simulated cortical activation sites do not interfere with alignment. Global errors in quantitation from realignment are <2%. Regional errors due to partial volume effects are largest when the gantry is rotated by large angles or when the bed is translated axially by one-half the interplane distance. To minimize such partial volume effects, the algorithm can be used prospectively, during acquisition, to reposition the scanner gantry and bed to match an earlier study. Computation requires 3–6 min on a Sun SPARCstation 2.

PURPOSE: We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities. METHOD: Different cost functions, different minimization methods, and various sampling, smoothing, and editing strategies were compared. Internal consistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phantom for PET data. RESULTS: All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accuracy of registration of structural MRI images was in the 75 to 150 microns range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy. CONCLUSION: The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.

Our findings suggest that ulnar nerves are more commonly involved in men, with lower CMAP slower NCV values, and longer DL values.

PET is widely considered the most sensitive technique available for noninvasively studying physiology, metabolism, and molecular pathways in the living human being. However, the utility of PET, being a photon-deficient modality, remains constrained by factors including low signal-to-noise ratio, long imaging times, and concerns about radiation dose. Two developments offer the potential to dramatically increase the effective sensitivity of PET. First by increasing the geometric coverage to encompass the entire body, sensitivity can be increased by a factor of about 40 for total-body imaging or a factor of about 4-5 for imaging a single organ such as the brain or heart. The world's first total-body PET/CT scanner is currently under construction to demonstrate how this step change in sensitivity affects the way PET is used both in clinical research and in patient care. Second, there is the future prospect of significant improvements in timing resolution that could lead to further effective sensitivity gains. When combined with total-body PET, this could produce overall sensitivity gains of more than 2 orders of magnitude compared with existing state-of-the-art systems. In this article, we discuss the benefits of increasing body coverage, describe our efforts to develop a first-generation total-body PET/CT scanner, discuss selected application areas for total-body PET, and project the impact of further improvements in time-of-flight PET.