Photograph of the experimental DE imaging bench, showing (1) the x-ray tube, (2) an anthropomorphic chest phantom, and (3) the flat-panel detector.
Example DE images of an anthropomorphic chest phantom produced by various noise reduction algorithms. Each image is zoomed-in about a region of the right lung to illustrate subtle differences in spatial resolution and noise, (a,b) Low- and high-energy projections. (c,e,g) Soft-tissue images decomposed using SLS, SSH, and ACNR algorithms, respectively. (d,f,h) Bone-only images decomposed using SLS, SSH, and ACNR algorithms, respectively. DE image decomposition parameters associated with each case are summarized in Table II.
Measured (data points) and theoretical (straight lines) NNPS for (a) “soft-tissue” and (b) “bone-only” flat-field images formed using the three decomposition algorithms. SSH and ACNR are seen to reduce noise significantly compared to SLS. Theoretical calculations using cascaded systems analysis show excellent agreement with measurements.
Flood-field images depicting quantum mottle under different DE image decomposition algorithms corresponding to the NPS plots shown in Fig. 3. The algorithms are seen to dramatically affect the magnitude and spatial-frequency content of the noise.
Measured and theoretical MTF associated with DE images formed using various noise reduction algorithms. The dashed line is the MTF of the detector as determined from ESF measurements using an angled Pb edge. For the SSH and ACNR algorithms, the MTF depends on the signal differences presenting in low- and high-energy images. (a,b) The MTF for SSH in soft-tissue and bone-only images. (c,d) The MTF for ACNR in soft-tissue and bone-only images. The data points with error bars are measurements, and the solid lines are theoretical calculations.
Calculations of the DE MTF for various decomposition algorithms. (a) MTF for the soft-tissue image, for which the structure of interest is HDPE . (b) MTF for the bone-only image, for which the structure of interest is a Teflon sphere .
NEQ calculated from the NNPS and MTF of Figs. 3 and 6, respectively, (a) NEQ of the soft-tissue image, taking HDPE (similar to a solid pulmonary nodule) as the material of interest in the decomposition, (b) NEQ of the bone-only image, taking Teflon (similar to cortical bone) as the material of interest in the decomposition.
Detectability index computed as a function of dose allocation for the detection of: (a) a HDPE sphere in a soft-tissue image and (b) a Teflon sphere in a bone-only image.
Soft-tissue DE images of a HDPE sphere at various dose allocation and decomposition algorithms corresponding to the calculations of Fig. 8(a). The detectability index (soft-tissue sphere detection task) computed using Eq. (18) is superimposed. Qualitative agreement is observed between the trends in and conspicuity of the sphere.
Bone-only DE images of a Teflon sphere at various dose allocation and decomposition algorithms corresponding to the calculations of Fig. 8(b). The detectability index (bony sphere detection task) computed using Eq. (18) is superimposed. Qualitative agreement is observed between the trends in and conspicuity of the sphere.
DE images decomposed using GLNR decomposition for soft-tissue and bone-only image compared to optimal SLS, SSH, and ACNR images. Top row: soft-tissue images of a polyethylene sphere. Bottom row: bone-only images of a Teflon sphere.
Summary of low- and high-energy convolution filters for four DE decomposition algorithms (SLS, SSH, ACNR, and GLNR), shown to be special cases of the general form in Eq. (1).
Summary of DE image decomposition parameters.
Article metrics loading...
Full text loading...