Image improvement method for positron emission mammography
| Autor: | Stephen J. Seiler, Roderick W McColl, Robert E. Lenkinski, Nikolai V Slavine |
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| Rok vydání: | 2016 |
| Předmět: |
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Computer science Breast imaging Biophysics General Physics and Astronomy Breast Neoplasms Electrons Signal-To-Noise Ratio 030218 nuclear medicine & medical imaging 03 medical and health sciences DICOM 0302 clinical medicine Signal-to-noise ratio Contrast-to-noise ratio Image Processing Computer-Assisted Positron emission mammography Humans Radiology Nuclear Medicine and imaging Computer vision Breast Image resolution business.industry Noise (signal processing) Phantoms Imaging General Medicine 030220 oncology & carcinogenesis Positron-Emission Tomography Female Artificial intelligence Nuclear medicine business Algorithms Mammography |
| Zdroj: | Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB). 39 |
| ISSN: | 1724-191X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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