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Biomedical subjects

S R Cherry

Publications and source records attributed to S R Cherry.

62 records · Page 4Linked to original sources
PubMed1989

Image quantification with a large area multiwire proportional chamber positron camera (MUP-PET).

A large area multi wire proportional chamber positron camera system is under evaluation for clinical Nuclear Medicine investigation using isotopes both from the house generators (68Ga) and from remote cyclotrons (18F, 124I, 64Cu and 81Rb). Images are reconstructed using a fully three dimensional (3D) algorithm and exhibit equal resolution in all three orthogonal directions. The axial field of view of 15 cm is large enough to allow coverage of the whole brain. This paper discusses the performance of the MUP-PET system with particular emphasis on quantification and the development of an attenuation correction scheme for a large area detector system. The distribution of scattered and accidental events is investigated and observed to contribute a fairly uniform background to the image. Dead time correction factors are calculated from an analytical expression obtained by considering the various sources of dead time present in the system. Following correction for attenuation, accidentals, scattered events and dead time, reconstructed phantom images show a strong linear correlation (r = 0.998) between count density and regional isotope concentration. The extension of the methods to clinical studies is discussed.

Image Processing, Computer-Assisted↗
PubMed1989

The performance of a multiwire proportional chamber positron camera for clinical use.

The Rutherford Appleton Laboratory clinical positron camera consists of two opposed multiwire proportional chambers (MWPCS) mounted on a rotating gantry capable of performing routine nuclear medicine studies. The system has operated since the end of 1986 with complete reliability. It has a sensitivity of 37 kcps MBq-1 cm3 per axial cm for a 20 cm diameter uniformly filled cylinder of activity. The best spatial resolution obtainable is about 6 mm, although in practice images are smoothed in order to reduce statistical noise with a corresponding decrease in resolution. Cross-plane rays are utilised during image reconstruction and the resulting three-dimensional images exhibit the same spatial resolution in three orthogonal directions over a large cylindrical field of view 15 cm high by 30 cm in diameter. The maximum data-taking rate is limited to 1.5 to 2 kcps at present due to deadtime in the read-out system. The performance of the system is described with particular reference to the problems of imaging with the large fractions of random and scattered events which are a consequence of using large-area detectors. Images of phantoms and patients are presented and proposed modifications to the camera are described.

Algorithms↗
PubMedDate not supplied

Imaging transgene expression with radionuclide imaging technologies.

A variety of imaging technologies are being investigated as tools for studying gene expression in living subjects. Noninvasive, repetitive and quantitative imaging of gene expression will help both to facilitate human gene therapy trials and to allow for the study of animal models of molecular and cellular therapy. Radionuclide approaches using single photon emission computed tomography (SPECT) and positron emission tomography (PET) are the most mature of the current imaging technologies and offer many advantages for imaging gene expression compared to optical and magnetic resonance imaging (MRI)-based approaches. These advantages include relatively high sensitivity, full quantitative capability (for PET), and the ability to extend small animal assays directly into clinical human applications. We describe a PET scanner (microPET) designed specifically for studies of small animals. We review "marker/reporter gene" imaging approaches using the herpes simplex type 1 virus thymidine kinase (HSV1-tk) and the dopamine type 2 receptor (D2R) genes. We describe and contrast several radiolabeled probes that can be used with the HSV1-tk reporter gene both for SPECT and for PET imaging. We also describe the advantages/disadvantages of each of the assays developed and discuss future animal and human applications.

Animals↗
PubMedDate not supplied

3D PET using a conventional multislice tomograph without septa.

A conventional multislice positron emission tomography scanner was modified to operate without interplane septa to evaluate its performance in collecting and reconstructing data in a three-dimensional (3D) format, thereby significantly increasing system sensitivity. A 3D filtered backprojection algorithm was implemented and tested, using both computer simulations and phantom measurements. No artifacts were apparent in the test images, although the algorithm was shown to lead to a 11% degradation in transaxial resolution in the outer planes. Following septa removal, sensitivity was found to increase by a factor of 7 with an increase in scatter fraction from 16 to 41%. Axial resolution degraded from 6.9 to 7.7 mm full width at half maximum at the center of the field of view. The maximum count rate without septa was 2.4 x 10(5) cps, at a concentration of 0.4 microCi/ml, compared with 1.3 x 10(5) cps at 1.5 microCi/ml with septa. Brain studies were performed with volunteers using 18F-fluorodeoxyglucose, 18F-fluorodopa, and H2 15O to compare noise-equivalent count rates and qualitatively assess image quality over a wide range of imaging conditions.

Algorithms↗
PubMedDate not supplied

Rapid automated algorithm for aligning and reslicing PET images.

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 less than 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.

Algorithms↗
PubMedDate not supplied

MRI-PET registration with automated algorithm.

OBJECTIVE: We have previously reported an automated method for within-modality (e.g., PET-to-PET) image alignment. We now describe modifications to this method that allow for cross-modality registration of MRI and PET brain images obtained from a single subject. METHODS: This method does not require fiducial markers and the user is not required to identify common structures on the two image sets. To align the images, the algorithm seeks to minimize the standard deviation of the PET pixel values that correspond to each MRI pixel value. The MR images must be edited to exclude nonbrain regions prior to using the algorithm. RESULTS AND CONCLUSION: The method has been validated quantitatively using data from patients with stereotaxic fiducial markers rigidly fixed in the skull. Maximal three-dimensional errors of < 3 mm and mean three-dimensional errors of < 2 mm were measured. Computation time on a SPARCstation IPX varies from 3 to 9 min to align MR image sets with [18F]fluorodeoxyglucose PET images. The MR alignment with noisy H2(15)O PET images typically requires 20-30 min.

Adult↗
PubMedDate not supplied

Automated image registration: I. General methods and intrasubject, intramodality validation.

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.

Algorithms↗