Image reconstruction using data sorting

Abstract

Methods, systems and apparatus for processing data associated with nuclear medicine imaging techniques are provided. Sort data in LUTs and memory structures. For a computer implementing aspects of the invention, an article of manufacture is provided. The data elements are sorted into a plurality of ordered data groups according to a spatial index order, and the data elements are retrieved and processed in the spatial index order. The data elements include sensitivity matrix elements, PET annihilation event data, and system and image matrix elements, grouping the data in an order corresponding to processing of the data. On the one hand, the geometric symmetry of the PET scanner FOV is used in sorting and processing the data. In one aspect, the system matrix LUT contains a total number of system matrix elements equal to the total number of image matrix elements divided by the total number of possible third index values.

Description

technical field [0001] The present invention relates to the field of positron emission tomography and, more particularly, to the use of data ranking in the reconstruction of annihilation event data obtained by positron emission tomography (PET). Data sorting applications include design and implementation of look-up tables (LUTs), dynamic reconstruction calculations in non-LUT reconstruction methods, tabular data sorting, and sensitivity matrix calculations. Background technique [0002] Positron emission tomography (PET) is a branch of nuclear medicine in which positron-emitting radiopharmaceuticals such as 18-fluorodeoxyglucose (FDG) are introduced into a patient. As the radiopharmaceutical decays, positrons are generated. More specifically, each of multiple positrons can react with an electron in what is known as a positron annihilation event, generating a coincident pair of 511 KeV gamma rays along the line of response (LOR) propagate in the opposite direction. [0003...

AI Technical Summary

Problems solved by technology

[0007] Although precomputation of system matrices can provide time efficiency for dynamic system matrix determination, memory size and memory access bandwidth issues limit the application of LUTs in iterative reconstruction

System matrices are usually large and require a considerable amount of memory resources: therefore, the LUT size is limited by the available physical memory size

Furthermore, even though larger LUTs can be accommodated in system memory resources, accessing a LUT from memory can take considerable time: in one example, retrieving a system matrix element from a LUT in memory for an operation may require 100 clock cycles

Therefore, accessing larger LUTs and / or larger system matrix elements from memory requires correspondingly larger memory access times

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