Image improvement method for positron emission mammography

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)164-173
Number of pages10
JournalPhysica Medica
Volume39
DOIs
StatePublished - Jul 1 2017

Fingerprint

Mammography
positrons
Breast
Electrons
breast
Noise
image resolution
scanners
Signal-To-Noise Ratio
Neoplasms
Software
progressions
low noise
set theory
iteration
plateaus
signal to noise ratios
tumors
cancer
computer programs

Keywords

  • Image resolution improvement
  • Positron emission mammography (PEM)
  • Resolution subsets-based iterative method
  • SNR and CNR improvement

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging
  • Physics and Astronomy(all)

Cite this

Image improvement method for positron emission mammography. / Slavine, Nikolai V.; Seiler, Stephen J.; McColl, Roderick W.; Lenkinski, Robert E.

In: Physica Medica, Vol. 39, 01.07.2017, p. 164-173.

Research output: Contribution to journalArticle

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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.",
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