Title :
Objective Assessment of Sonographic: Quality II Acquisition Information Spectrum
Author :
Nguyen, Nghia Q. ; Abbey, Craig K. ; Insana, Michael F.
Author_Institution :
Earth & Environ. Sci. Div., Los Alamos Nat. Lab., Los Alamos, NM, USA
Abstract :
This paper describes a task-based, information-theoretic approach to the assessment of image quality in diagnostic sonography. We expand the Kullback-Leibler divergence metric J, which quantifies the diagnostic information contained within recorded radio-frequency echo signals, into a spatial-frequency integral comprised of two spectral components: one describes patient features for low-contrast diagnostic tasks and the other describes instrumentation properties. The latter quantity is the acquisition information spectrum (AIS), which measures the density of object information that an imaging system is able to transfer to the echo data at each spatial frequency. AIS is derived based on unique properties of acoustic scattering in tissues that generate object contrast. Predictions made by the J integral expression were validated through Monte Carlo studies using echo-signal data from simulated lesions. Our analysis predicts the diagnostic performance of any sonographic system at specific diagnostic tasks based on engineering properties of the instrument that constitute image quality.
Keywords :
Monte Carlo methods; biomedical ultrasonics; echo; medical image processing; patient diagnosis; AIS; J integral expression; Kullback-Leibler divergence metric; Monte Carlo studies; Quality II acquisition information spectrum; acoustic scattering; diagnostic information; diagnostic sonography; echo-signal data; image quality; imaging system; information-theoretic approach; low-contrast diagnostic tasks; object information; objective assessment; radio-frequency echo signals; sonographic system; spatial frequency; spatial-frequency integral; spectral components; task-based approach; Covariance matrix; Imaging; Instruments; Lesions; Noise; Observers; Vectors; Breast lesions; Kullback–Leibler divergence; ideal observer; image quality; Breast Neoplasms; Computer Simulation; Female; Humans; Image Processing, Computer-Assisted; Models, Biological; Monte Carlo Method; Ultrasonography;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2012.2231963