KEYWORDS: Signal to noise ratio, Reconstruction algorithms, Computed tomography, Target detection, Medical imaging, Sensors, Imaging systems, Optical spheres, Detection and tracking algorithms, 3D acquisition
In medical imaging systems, several factors (e.g., reconstruction algorithm, noise structures, target size, contrast, etc) affect the detection performance and need to be considered for object detection. In a cone beam CT system, FDK reconstruction produces different noise structures in axial and coronal slices, and thus we analyzed directional dependent detectability of objects using detection SNR of Channelized Hotelling observer. To calculate the detection SNR, difference-of-Gaussian channel model with 10 channels was implemented, and 20 sphere objects with different radius (i.e., 0.25 (mm) to 5 (mm) equally spaced by 0.25 (mm)), reconstructed by FDK algorithm, were used as object templates. Covariance matrix in axial and coronal direction was estimated from 3000 reconstructed noise volumes, and then the SNR ratio between axial and coronal direction was calculated. Corresponding 2D noise power spectrum was also calculated. The results show that as the object size increases, the SNR ratio decreases, especially lower than 1 when the object size is larger than 2.5 mm radius. The reason is because the axial (coronal) noise power is higher in high (low) frequency band, and therefore the detectability of a small (large) object is higher in coronal (axial) images. Our results indicate that it is more beneficial to use coronal slices in order to improve the detectability of a small object in a cone beam CT system.
To measure a spatial resolution of CT scanner, several methods have been developed using bar pattern, wires and thin
plates. While these approaches are effective to measure two dimensional MTF, it is not easy to measure directional MTF
using those phantoms. To overcome these limitations, Thornton et al. proposed a method to measure directional MTF
using sphere phantoms, which is effective only when the cone angle is small. Recently, Baek et al. developed a method
to estimate the directional MTF even with a larger cone angle, but the proposed method was analyzed using a noiseless
data set. In this work, we present Wiener and Richardson-Lucy deconvolution techniques to estimate the directional MTF,
and compare the estimation performance with that of the previous methods (i.e., Thornton’s and Baek’s methods). To
estimate directional MTF, we reconstructed a sphere object centered at (0.01 cm, 0.01 cm, 10.01 cm) using FDK
algorithm, and then calculated plane integrals of the reconstructed sphere object and the ideal sphere object. The plane
integrals of sphere objects were used to estimate the directional MTF using Wiener and Richardson-Lucy deconvolution
techniques. The estimated directional MTF was compared with the ideal MTF calculated from a point object, and
showed an excellent agreement.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.