1. Blind Image Restoration Enhances Digital Autoradiographic Imaging of Radiopharmaceutical Tissue Distribution
- Author
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Russell K. Pachynski, Brian W. Simons, Wen Jiang, Nadia Benabdallah, Daniel L.J. Thorek, Peng Lu, Abhinav K. Jha, Hanwen Zhang, Robert F. Hobbs, Brian C. Baumann, and David Ulmert
- Subjects
Diagnostic Imaging ,Male ,Point spread function ,Computer science ,Noise reduction ,Image processing ,Background noise ,Mice ,Contrast-to-noise ratio ,Fluorodeoxyglucose F18 ,Image Processing, Computer-Assisted ,Animals ,Humans ,Tissue Distribution ,Radiology, Nuclear Medicine and imaging ,Image resolution ,Image restoration ,Phantoms, Imaging ,business.industry ,Pattern recognition ,Radionuclide therapy ,Autoradiography ,Artificial intelligence ,Radiopharmaceuticals ,business ,Algorithms - Abstract
Digital autoradiography (DAR) is a powerful tool to quantitatively determine the distribution of a radiopharmaceutical within a tissue section and is widely used in drug discovery and development. However, the low image resolution and significant background noise can result in poor correlation, even errors, between radiotracer distribution, anatomical structure, and molecular expression profiles. Differing from conventional optical systems, the point spread function (PSF) in DAR is determined by properties of radioisotope decay, phosphor and digitizer. Calibration of an experimental PSF a priori is difficult, prone to error, and impractical. We have developed a content-adaptive restoration algorithm to address these problems. Methods: We model the DAR imaging process using a mixed Poisson-Gaussian model, and blindly restore the image by a Penalized Maximum-Likelihood Expectation-Maximization algorithm (PG- PEM). PG-PEM implements a patch-based estimation algorithm with "Density-Based Spatial Clus- tering of Applications with Noise" to estimate noise parameters, and utilizes L2 and Hessian Frobenius (HF) norms as regularization functions to improve performance. Results: First, PG-PEM outperformed other restoration algorithms at the denoising task (p
- Published
- 2021
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