TY - JOUR
T1 - Blind Image Restoration Enhances Digital Autoradiographic Imaging of Radiopharmaceutical Tissue Distribution
AU - Lu, Peng
AU - Benabdallah, Nadia
AU - Jiang, Wen
AU - Simons, Brian W.
AU - Zhang, Hanwen
AU - Hobbs, Robert F.
AU - Ulmert, David
AU - Baumann, Brian C.
AU - Pachynski, Russell K.
AU - Jha, Abhinav K.
AU - Thorek, Daniel L.J.
N1 - Funding Information:
This work was funded in part by the National Cancer Institute of the National Institutes of Health (R01CA229893, R01CA20 1035, and R01CA240711 [all to Daniel Thorek]) and by the Society of Nuclear Medicine and Molecular Imaging Student Research Award (Peng Lu). No other potential conflict of interest relevant to this article was reported.
Publisher Copyright:
Copyright © 2022 by the Society of Nuclear Medicine and Molecular Imaging.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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, anatomic structure, and molecular expression profiles. Differing from conventional optical systems, the point-spread function in DAR is determined by properties of radioisotope decay, phosphor, and digitizer. Calibration of an experimental point-spread function 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 clustering of applications with noise to estimate noise parameters and uses L2 and Hessian Frebonius norms as regularization functions to improve performance. Results: First, PG-PEM outperformed other restoration algorithms at the denoising task (P, 0.01). Next, we implemented PG-PEM on preclinical DAR images (18F-FDG, treated mouse tumor and heart; 18F-NaF, treated mouse femur) and clinical DAR images (bone biopsy sections from 223RaCl2-treated castration-resistant prostate cancer patients). DAR images restored by PG-PEM of all samples achieved a significantly higher effective resolution and contrast-to-noise ratio and a lower SD of background (P, 0.0001). Additionally, by comparing the registration results between the clinical DAR images and the segmented bone masks from the corresponding histologic images, we found that the radiopharmaceutical distribution was significantly improved (P, 0.0001). Conclusion: PG-PEM is able to increase resolution and contrast while robustly accounting for DAR noise and demonstrates the capacity to be widely implemented to improve preclinical and clinical DAR imaging of radiopharmaceutical distribution.
AB - 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, anatomic structure, and molecular expression profiles. Differing from conventional optical systems, the point-spread function in DAR is determined by properties of radioisotope decay, phosphor, and digitizer. Calibration of an experimental point-spread function 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 clustering of applications with noise to estimate noise parameters and uses L2 and Hessian Frebonius norms as regularization functions to improve performance. Results: First, PG-PEM outperformed other restoration algorithms at the denoising task (P, 0.01). Next, we implemented PG-PEM on preclinical DAR images (18F-FDG, treated mouse tumor and heart; 18F-NaF, treated mouse femur) and clinical DAR images (bone biopsy sections from 223RaCl2-treated castration-resistant prostate cancer patients). DAR images restored by PG-PEM of all samples achieved a significantly higher effective resolution and contrast-to-noise ratio and a lower SD of background (P, 0.0001). Additionally, by comparing the registration results between the clinical DAR images and the segmented bone masks from the corresponding histologic images, we found that the radiopharmaceutical distribution was significantly improved (P, 0.0001). Conclusion: PG-PEM is able to increase resolution and contrast while robustly accounting for DAR noise and demonstrates the capacity to be widely implemented to improve preclinical and clinical DAR imaging of radiopharmaceutical distribution.
KW - A-particle emission
KW - Blind image restoration
KW - Digital autoradiography
KW - Poisson–gaussian noise model
KW - Positron
UR - http://www.scopus.com/inward/record.url?scp=85128122679&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128122679&partnerID=8YFLogxK
U2 - 10.2967/jnumed.121.262270
DO - 10.2967/jnumed.121.262270
M3 - Article
C2 - 34385337
AN - SCOPUS:85128122679
SN - 0161-5505
VL - 63
SP - 591
EP - 597
JO - Journal of Nuclear Medicine
JF - Journal of Nuclear Medicine
IS - 4
ER -