Radiant energy – Invisible radiant energy responsive electric signalling – With or including a luminophor
Reexamination Certificate
2001-09-07
2003-07-08
Hannaher, Constantine (Department: 2878)
Radiant energy
Invisible radiant energy responsive electric signalling
With or including a luminophor
C250S363020
Reexamination Certificate
active
06590213
ABSTRACT:
BACKGROUND OF THE INVENTION
The field of the invention is positron emission tomography (PET) scanners, and particularly PET scanners that can acquire data in a three-dimensional (3D) mode.
Positrons are positively charged electrons which are emitted by radionuclides that have been prepared using a cyclotron or other device. These are employed as radioactive tracers called “radiopharmaceuticals” by incorporating them into substances, such as glucose or carbon dioxide. The radiopharmaceuticals are injected in the patient and become involved in such processes as blood flow, fatty acid, glucose metabolism, and protein synthesis. As the radionuclides decay, they emit positrons. The positrons travel a very short distance before they encounter an electron, and when this occurs, they are annihilated and converted into two photons, or gamma rays. This annihilation is characterized by two features which are pertinent to PET scanners—each gamma ray has an energy of 511 keV and the two gamma rays are directed in nearly opposite directions. An image is created by determining the number of such annihilations at each location within the field of view.
A typical PET scanner is cylindrical and includes a detector ring assembly composed of rings of detectors which encircle the patient and which convert the energy of each 511 keV photon into a flash of light that is sensed by a photomultiplier tube (PMT). Coincidence detection circuits connect to the detectors and record only those photons which are detected simultaneously by detectors located on opposite sides of the patient. The number of such simultaneous events (coincidence events) indicates the number of positron annihilations that occurred along a line joining the two opposing detectors. During an acquisition, coincidence events are recorded to indicate the number of annihilations along lines joining pairs of detectors in the detector ring. These numbers are employed to reconstruct an image using well-known computed tomography techniques.
When originally developed, PET scanners were strictly multiplanar scanners. In such PET scanners, each detector ring is configured to detect annihilations occurring only within the plane of that respective ring alone, or at most within planes defined by detectors on adjacent rings, and not annihilations occurring at other positions within the PET scanner. Because each detector within each detector ring is capable of receiving photons coming in toward the detector from a variety of angles (rather than merely coming in toward the detector from the center of the ring of which the detector is a part), fixed slice septa are positioned in between each of the detector rings of the PET scanners for imaging in what is known as “2D mode”. The septa, which are commonly composed of lead or tungsten alloy, shield the detectors of each individual detector ring from photons that have not originated from annihilations within the plane defined by the detector ring. The septa further have the function of shielding the detectors of the detector rings from out-of-plane scattered photons or other photons that are not resulting from annihilations (i.e., photons entering at either end of the cylindrical PET scanner).
A major innovation in PET scanners that occurred in the late 1980s and early 1990s has been the development of 3D PET scanners, which include true-3D (or “volumetric”) PET scanners and pseudo-3D PET scanners. In contrast to multiplanar scanners, true-3D PET scanners have no septa and consequently the detectors of each detector ring of the scanners can receive photons from a wider range of angles with respect to the plane of the respective ring than in multiplanar PET scanners. Although pseudo-3D PET scanners do employ septa, the septa are short so as to primarily reduce out of field-of-view (FOV) scatter. 3D PET scanners became feasible partly as a result of the increased speed of computers, since PET imaging in such scanners requires determining the existence of, and processing information related to, coincidence events that occur not merely between pairs of detectors positioned on individual (or adjacent) detector rings, but also between pairs of detectors positioned on different detector rings (or different detector rings that are spaced more than one ring apart from one another). 3D PET scanners allow for increased sensitivity relative to multiplanar scanners, since more true coincidence events can be recorded. However, 3D PET scanners also admit more scattered and random coincidence events to the data set from which the image is reconstructed than multiplanar PET scanners. In particular, scattered coincidence events can account for more than 50% of recorded coincidence events in the case of procedures such as torso imaging.
To address the problem of correcting for scatter in 3D PET scanners, model-based scatter correction methods have been proposed. Model-based scatter correction methods generally involve algorithms that use the acquired PET emission and transmission data to form a set of images, downsample the data to reduce the number of pixels, determine contributed-to detector pairs and calculate the expected flux of single-scatter radiation that is detected in different lines-of-response between different detectors. One such model-based scatter correction method was set forth in an article by John M. Ollinger entitled “Model-Based Scatter Correction for Fully 3D-PET” (Phys. Med. Biol. 41, pages 153-176, 1996), which is hereby incorporated by reference herein. Another model-based scatter correction method was set forth in an article by C. C. Watson entitled “New, Faster, Image-Based Scatter Correction for 3D-PET” (IEEE Trans. Nucl. Sci., 44, 90-97, 1997), which also is hereby incorporated by reference herein.
The aforementioned model-based scatter correction methods are pixel (or voxel) driven routines and that use measured data as the inputs. To obtain output results that are based mainly upon true coincidence event data and not scattered coincidence events, the model-based scatter correction methods involve performing multiple iterations of the scatter estimation, where each iteration involves nested looping through several dimensions. For example, the model-based scatter correction method set forth by Ollinger involves processing the data set obtained by the PET scanner by looping through such dimensions as the transaxial distance, the theta angle (angular orientation within a particular detector ring with respect to horizontal) and the azimuthal angle (angular orientation between different detector rings). The model-based scatter correction method set forth by Watson proceeds in a similar manner.
Because of the iterative nature of model -based scatter correction methods, and particularly the nested looping through multiple parameters that is performed according to those methods, the model-based scatter correction methods are limited in their usefulness in conventional 3D PET scanners insofar as the methods employ intensive processing. For example, to perform the first of the above-identified model-based scatter correction methods, nested looping is performed over several parameters, including a first parameter concerning the detector hit by an unscattered photon (d
1
), a second parameter concerning azimuthal angle, a third parameter concerning in-transverse-plane angle, a fourth parameter concerning the transverse distance across the image pixels, a fifth parameter concerning the transverse distance down the image pixels away from the detector d
1
(defined scatter voxel S), and a sixth parameter concerning detectors contributed to for scatter voxel S. Typically, in order to perform three iterations of these nested loops for this model-based scatter correction method using a conventional Sun Ultra-Sparc 360 MHz processor, up to 8-12 minutes of processing time is required.
Because of the continuing need for improvements in the speed and accuracy with which 3D PET images can be produced, it would therefore be advantageous if a method and system was developed in PET scanners that allowed scatter to be c
Gabor Otilia
GE Medical Systems Global Technology Company LLC
Hannaher Constantine
Horton Carl
Quarles & Brady LLP
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