Prof. Stephen Seiler Profile

Prof. Stephen Seiler

at UT Southwestern Medical Ctr

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Publications (2)

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Proceedings Article | 2 March 2018 Presentation + Paper

Image enhancement method for digital mammography

Nikolai Slavine, Stephen Seiler, Timothy Blackburn, Robert Lenkinski

Proceedings Volume 10574, 105740G (2018) https://doi.org/10.1117/12.2293604

KEYWORDS: Image enhancement, Image resolution, Image quality, Breast, Reconstruction algorithms, Digital imaging, Digital breast tomosynthesis, Image processing, Digital mammography

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Purpose: To evaluate in clinical use a practical iterative deconvolution method to enhance contrast and image resolution in digital breast tomosynthesis. A novel rapidly converging, iterative deconvolution algorithm for improving the quantitative accuracy of previously reconstructed breast images by commercial breast tomosynthesis system is demonstrated. Materials and Methods: The method was tested on phantoms and clinical breast imaging data. Data acquisition was performed on a commercial Hologic Selenia Dimensions digital breast tomosynthesis system. The method was applied to patient breast images previously processed with Hologic Selenia conventional and C-View software to determine improvements in resolution and contrast to noise ratio. Results: In all of the phantom and patients’ breast studies the post-processed images proved to have higher resolution and contrast as compared with images reconstructed by Hologic methods. In general, the values of CNR reached a plateau at around 8 iterations with an average improvement factor of about 1.8 for processed Hologic Selenia images. Improvements in image resolution after the application of the method are also demonstrated. Conclusions: A rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on patient DICOM images has been used for quantitative improvement in digital breast tomosynthesis. The method can be applied to clinical breast images to improve image quality to diagnostically acceptable levels and will be crucial in order to facilitate diagnosis of tumor progression at the earliest stages. The method can be considered as an extended blind deblurring (or Richardson-Lucy like) algorithm with multiple resolution levels

Proceedings Article | 24 February 2017 Paper

Image enhancement in positron emission mammography

Nikolai Slavine, Stephen Seiler, Roderick McColl, Robert Lenkinski

Proceedings Volume 10133, 101331J (2017) https://doi.org/10.1117/12.2248758

KEYWORDS: Image resolution, Signal to noise ratio, Image enhancement, Mammography, Breast, Iterative methods, Cameras, Deconvolution, Image quality, Breast cancer, Reconstruction algorithms, Image restoration, Positron emission tomography

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Purpose: To evaluate an efficient iterative deconvolution method (RSEMD) for improving the quantitative accuracy of previously reconstructed breast images by commercial positron emission mammography (PEM) scanner. Materials and Methods: The RSEMD method was tested on breast phantom data and clinical PEM imaging data. Data acquisition was performed on a commercial Naviscan Flex Solo II PEM camera. This method was applied to patient breast images previously reconstructed with Naviscan software (MLEM) to determine improvements in resolution, signal to noise ratio (SNR) and contrast to noise ratio (CNR.) Results: In all of the patients’ breast studies the post-processed images proved to have higher resolution and lower noise as compared with images reconstructed by conventional methods. In general, the values of SNR reached a plateau at around 6 iterations with an improvement factor of about 2 for post-processed 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 novel 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 clinical PEM images to improve image quality to diagnostically acceptable levels and will be crucial in order to facilitate diagnosis of tumor progression at the earliest stages. The RSEMD method can be considered as an extended Richardson-Lucy algorithm with multiple resolution levels (resolution subsets).

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