Method for simulating dose reduction in digital mammography using the Anscombe transformation.
OBJECTIVE
This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose.


METHODS
The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose.


RESULTS
The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%.


CONCLUSIONS
A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.
Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, 400 Trabalhador São-Carlense Avenue, São Carlos 13566-590, Brazil.
Lucas R
LR
Borges
Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, 400 Trabalhador São-Carlense Avenue, São Carlos 13566-590, Brazil.
Helder C R de
HCR
Oliveira
Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, 400 Trabalhador São-Carlense Avenue, São Carlos 13566-590, Brazil.
Polyana F
PF
Nunes
Department of Radiology, Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, Pennsylvania 19104.
Predrag R
PR
Bakic
Department of Radiology, Hospital of the University of Pennsylvania, University of Pennsylvania, 3400 Spruce Street, Philadelphia, Pennsylvania 19104.
Andrew D A
ADA
Maidment
Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, 400 Trabalhador São-Carlense Avenue, São Carlos 13566-590, Brazil.
Marcelo A C
MAC
Vieira
D000465:Algorithms; D016477:Artifacts; D001940:Breast / Q000528:radiation effects; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008327:Mammography / Q000295:instrumentation / Q000379:methods*; D008953:Models, Anatomic; D019047:Phantoms, Imaging; D011829:Radiation Dosage