Deconvolution of x-ray phase contrast images as a way to retrieve phase information lost due to insufficient resolution
Creators
- 1. Department of Medical Physics and Bioengineering, UCL, London WC1E 6BT (United Kingdom)
Description
When free-space propagation x-ray phase contrast imaging is implemented outside synchrotron radiation facilities, the combined effect of detector resolution and source size swamps the fine phase contrast fringes, often making them almost undetectable. In an attempt to mitigate this effect, a simple deconvolution procedure based on division in the Fourier space plus multiplication by an appropriate filter was applied to experimental x-ray phase contrast images of a simple geometric phantom. The filter parameter was varied in order to assess its impact on the level of retrieved phase signal. The deconvolved images were compared to simulated ones obtained under different resolution conditions, showing that this simple procedure provided signals equivalent to those that would be obtained with a detector with three times better resolution. By accepting an increase in the overall image noise, the method also appears to bring up secondary phase contrast fringes, which are not visible in the unprocessed signal. (note)
Availability note (English)
Available from http://dx.doi.org/10.1088/0031-9155/54/15/N02Additional details
Identifiers
- DOI
- 10.1088/0031-9155/54/15/N02;
- PII
- S0031-9155(09)16254-1;
Publishing Information
- Journal Title
- Physics in Medicine and Biology
- Journal Volume
- 54
- Journal Issue
- 15
- Journal Page Range
- p. N347-N354
- ISSN
- 0031-9155
- CODEN
- PHMBA7
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 41050855
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- IMAGES; NOISE; PHANTOMS; RADIATION DETECTORS; RESOLUTION; SIMULATION; X RADIATION
- Descriptors DEC
- ELECTROMAGNETIC RADIATION; IONIZING RADIATIONS; MEASURING INSTRUMENTS; MOCKUP; RADIATIONS; STRUCTURAL MODELS