Image deconvolution in digital autoradiography: a preliminary study.
Digital autoradiography (DAR) is a powerful method to determine quantitatively the "small-scale" (i.e., submillimeter) distribution of a radiotracer within a tissue section. However, the limited spatial resolution of the DAR image, due to blurring by the point spread function (PSF), can result in a poor correlation with tissue histology and immunohistochemistry. The authors attempt to overcome this limitation by recovering the radiotracer distribution by image deconvolution using the Richardson-Lucy algorithm and a measured PSF obtained from a small radioactive source on hydrophobic microscope slide. Simulation studies have shown that the deconvolution algorithm reliably recovers the pixel values corresponding to the radioactivity distributions. As an example, the proposed image restoration approach has been tested with DAR images of different radiolabeled markers on tumor sections obtained from clinical and preclinical animal model studies. Digital autoradiograms following deconvolution show improved sharpness and contrast relative to the unprocessed autoradiograms.
Zhang
Mutian
M
Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.
Chen
Qing
Q
Li
Xiao-Feng
XF
O'Donoghue
Joseph
J
Ruan
Shutian
S
Zanzonico
Pat
P
Ling
C Clifton
CC
Humm
John L
JL
D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001345:Autoradiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009369:Neoplasms / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D010865:Pilot Projects; D011856:Radiographic Image Enhancement / Q000379:methods*; D011877:Radionuclide Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted