Completion of a truncated attenuation image from the attenuated PET emission data

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Image Reconstruction

Background:

• Hybrid positron emission tomographs (PETs), primarily PET/CT and emerging PET/MRI systems, are standard in medical diagnostics.
• A key challenge is the truncated transaxial field-of-view (FOV) of the secondary imaging modality (CT or MRI) compared to the PET scanner.
• This FOV mismatch leads to incomplete attenuation data, causing significant bias in the corrected PET activity images.

Purpose of the Study:

• To develop and evaluate a novel algorithm for estimating missing attenuation map data in hybrid PET systems.
• To address the image artifacts caused by truncated attenuation data in PET/CT and PET/MRI.
• To improve the quantitative accuracy of PET emission data reconstruction.

Main Methods:

• Proposed a maximum-a-posteriori (MAP) algorithm to estimate the missing portions of the attenuation map.
• Utilized PET emission data as the primary input for the estimation process.
• Evaluated the algorithm on artificially truncated 18F-FDG PET/CT studies and a clinical PET/MRI patient study.

Main Results:

• The proposed MAP algorithm significantly reduced reconstruction errors in artificially truncated 18F-FDG PET/CT studies.
• Reconstruction errors decreased from an average of 20% to below 7% after applying the algorithm.
• Successful application demonstrated on a clinical 18F-FDG PET/MRI patient dataset.

Conclusions:

• The developed MAP algorithm effectively reconstructs missing attenuation data in hybrid PET systems.
• This method offers a viable solution to mitigate artifacts and improve quantitative accuracy in PET imaging.
• The findings have implications for enhancing the diagnostic utility of both PET/CT and PET/MRI.