Medical image restoration of dynamic lungs using optical transfer function of lung motion

Abstract

When carrying out medical imaging based on detection of isotopic radiation levels of internal organs such a lungs or heart, distortions, and blur arise as a result of the organ motion during breathing and blood supply. Consequently, image quality declines, despite the use of expensive high resolution devices and, such devices are not exploited fully. A method with which to overcome the problem is image restoration. Previously, we suggested and developed a method for calculating numerically the optical transfer function (OTF) for any type of image motion. The purpose of this research is restoration of original isotope images (of the lungs) by restoration methods that depend on the OTF of the real time relative motion between the object and the imaging system. This research uses different algorithms for the restoration of an image, according to the OTF of the lung motion, which is in several directions simultaneously. One way of handling the three-dimensional movement is to decompose the image into several portions, to restore each portion according to its motion characteristics, and then to combine all the image portions back into a single image. An additional complication is that the image was recorded at different angles. The application of this research is in medical systems requiring high resolution imaging. The main advantage of this approach is its low cost versus conventional approaches.

Including the effect of motion artifacts in noise and performance analysis of dual-energy contrast-enhanced mammography

“…linear or sinusoidal/vibrational, that occurs within the exposure time (i.e. intra-image motion) is of interest (Hadar et al 1994, Arbel et al 2001, Xu et al 2011. In the current study we are interested in the motion that occurs between exposures (i.e.…”

Respiratory motion blur identification and reduction in ungated thoracic PET imaging

“…A minimum is expected in the auto-correlation function (ACF) of the image derivative; the blur extent is the distance between the zero-shift center of the average auto-correlation and its global minimum. Implementation of a pseudo-whitening filter will form patterns similar to the high-pass-filtered PSF, surrounded by extremely suppressed de-correlated regions (Arbel et al 2001, Yitzhaky and Kopeika 1997, Yitzhaky et al 1999. Detailed steps are given as follows.…”

“…Respiratory motion is close to back-and-forth uniform linear motion (abbreviated as 'uniform motion') due to its low velocity and short displacement; it can also be roughly regarded as highfrequency sinusoidal vibration (abbreviated as 'sinusoidal vibration') since the displacement of markers attached on human pectoral or abdomen changes sinusoidally during respiration and the 'exposure time' of the PET or SPECT system is much longer than breathing cycle (Arbel et al 2001). Therefore, after both the blur direction and blur extent are identified, the initial estimate of the respiratory PSF can be derived according to the uniform motion or sinusoidal vibration model.…”

“…Therefore, after both the blur direction and blur extent are identified, the initial estimate of the respiratory PSF can be derived according to the uniform motion or sinusoidal vibration model. Equation (11) gives the 1D PSF of uniform motion and sinusoidal vibration, where L is the blur extent (peak-to-peak displacement) and D is the amplitude of the vibration (d = 2D) (Arbel et al 2001, Yitzhaky et al 2000.…”