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A MODEL BASED SCATTER CORRECTION METHOD FOR POSITRON EMISSION TOMOGRAPHY

IP.com Disclosure Number: IPCOM000117356D
Publication Date: 2005-Mar-31
Document File: 11 page(s) / 75K

Publishing Venue

The IP.com Prior Art Database

Abstract

In general, a model-based scatter algorithm according to this invention includes using pre-acquired attenuation measurement data (from CTAC or rod-source) to add on to the end super-slices of the data to include attenuation data from outside the AFOV. To extend the attenuation data, axial slices are added into the averaging for the two distal super-slices.

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A MODEL BASED SCATTER CORRECTION METHOD FOR POSITRON EMISSION TOMOGRAPHY

FIELD OF THE INVENTION

[0001]   This invention relates to a model-based scatter correction method for positron emission tomography (PET), especially in medical imaging.

BACKGROUND OF THE INVENTION

 

[0002]   Model-based scatter estimation in 3D has been adopted in the field of PET due to its superior correction accuracy as compared to other scatter correction methods.  However, as with many complex algorithmic methods, it is only an estimation and does not always reflect the true scatter distribution with a robust high degree of accuracy.  The currently implemented algorithm calculates from the measured PET emission data the amount of single-scatter, and then using a convolution method estimates the multiple scatter.  This total scatter estimate is then scaled to each plane of the measured (True+SingleScatter+MultipleScatter) data by finding the boundaries of the object in each plane. The ratio of the integral outside the object in the measured data and the total scatter estimate is used to scale the scatter estimate.

[0003]   Various approximations are made in the model-based scatter correction that reduce the accuracy of the scatter.  Most of the approximations were made to speed the calculation of the single-scatter estimate.  Further, current 3D reconstruction code on all PET platforms use frame-based reconstruction (single axial FOV, or AFOV), when in fact the emission data acquired inside a PET frame are affected by the source and attenuator distribution outside the AFOV.  The largest errors in scatter correction are found when either there is a high activity concentration source just outside the axial frame or when the attenuator just outside the frame changes shape rapidly (in the transaxial dimension) as compared to the frame undergoing data collection.  In the latter case, the problem is exacerbated when the emitter within the acquisition AFOV has a significantly higher activity concentration than the adjacent FOV with a large attenuator.  A good example of where this occurs in a clinical setting is the transition from imaging the brain to imaging the shoulders.  It is the intention of the disclosed invention to address the second problem described above.

[0004]   The current MBSC algorithm uses the concept of “super-slices”.  Data within these 4 slices (current implementation), for both attenuation and emission, are averaged together to allow scatter estimation from only a subset of the total data.  Current super-slice thickness (W) is 10cm.  The main reason for this is to reduce the computational time required while maintaining accuracy.  This can be done, in general, since scatter is a slowly-varying function in most cases.  However, the 2 super-slices at the edge of the AFOV do not currently contain as much axial data as the interior two super-slices, and would benefit from both the emission and attenuation data from outside the AFOV.  To perfor...