Rebuilding IVUS images from raw data of the RF signal exported by IVUS equipment - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 77

Figure 5(e) and (h) shows a bifurcation region, with calcification. A stent is visible in Figure
5(d) and (i) , and it is possible to identify the malposition of the stent in Figure 5(i) .
Figure 5(f) shows the shadow of the pericardium and Figure 5(g) the acoustic shadow of a
big calcification and the lumen and media-adventitia borders.
Figure 6 shows both the rebuild image and the DICOM images, Figure 6(a)-(d) as possible
to see DICOM images and in Figures 6(e)-(h) the images rebuilding by the method propose in
this article.
FIGURE 6 (a)-(d) DICOM images and (e)-(h) rebuilt images.
As can be seen, the rebuilt images show the same structures as DICOM images, and in all
cases the contrast of the rebuild images is beter than the DICOM images. What is perhaps
most noticeable is the difference in visibility in the outer region of the lumen. The reconstruc-
ted image shows fine detail where the DICOM shows only a black region.
4 Discussion, conclusion, and future work
IVUS is an examination that can provide a good quality image of the cross-section of blood
vessel allowing the assessment of inner structures.
In an IVUS medical examination, sets of hundreds or even thousands of images are acquired
and used as the basis for a medical diagnosis.
These images are subject to a variability of interpretation inter and intra operator because
a set of parameter are adjusted to improve the visualization of an ROI. Once the images are
acquired, these parameters cannot be changed, restricting the comparison between diferent
examinations or patients.
To avoid this limitation, this work describes a methodology for reconstructing IVUS images
from RF raw data, which are independent of the parameters adjusted by the physician during
the exam and which can be processed to improve the CNR of the image.
The RF signal is processed according to the theoretic model proposed in Section 2 and il-
il-lustrated in Figure 2 . The parameters used in the model were adjusted to maximize CNR en-
abling identification of the main structures of the vessel.
The results of the proposed model were presented in Figures 5 and 6 and compared with
the DICOM images generated by the equipment. The proposed model produces images with
superior CNR which can be used for clinical purposes.
In the figures, it is possible to see the main structures of the vessel and this result can be used
to perform segmentation to help the physician in diagnosis process. Beyond this, it is possible
to identify bifurcations and calciications regions to be submited a percutaneous coronary in-
tervention.
Considering the data used in this work, the propose method was proved to be robust with
regard to fidelity in the reconstruction of structures in comparison with DICOM image and, in
all cases the CNR in reconstructed images was higher than DICOM images ( Figure 6 ) .
The study of RF signal plays a fundamental role in the rebuilding process and can be used
to development of automatic segmentation algorithm of the structures of the vessel and to de-
velopment of automatic classifiers by tissue characterization.