Image improvement method for positron emission mammography

Abstract

Purpose: To evaluate in clinical use a rapidly converging, efficient iterative deconvolution algorithm (RSEMD) for improving the quantitative accuracy of previously reconstructed breast images by a commercial positron emission mammography (PEM) scanner.

Materials and methods: The RSEMD method was tested on imaging data from clinical Naviscan Flex Solo II PEM scanner. This method was applied to anthropomorphic like breast phantom data and patient breast images previously reconstructed with Naviscan software to determine improvements in image resolution, signal to noise ratio (SNR) and contrast to noise ratio (CNR).

Results: In all of the patients' breast studies the improved images proved to have higher resolution, contrast and lower noise as compared with images reconstructed by conventional methods. In general, the values of CNR reached a plateau at an average of 6 iterations with an average improvement factor of about 2 for post-reconstructed Flex Solo II PEM images. Improvements in image resolution after the application of RSEMD have also been demonstrated.

Conclusions: A rapidly converging, iterative deconvolution algorithm with a resolution subsets-based approach (RSEMD) that operates on patient DICOM images has been used for quantitative improvement in breast imaging. The RSEMD method can be applied to PEM images to enhance the resolution and contrast in cancer diagnosis to monitor the tumor progression at the earliest stages.

Keywords: Image resolution improvement; Positron emission mammography (PEM); Resolution subsets-based iterative method; SNR and CNR improvement.