Learning objectives
To understand the technical background and mathematical models of CT perfusion.
To understand the value of different perfusion parameters.
To identify the various clinical situations that may cause perfusion changes.
To understand the potential and limitations of artificial intelligence applications used with perfusion imaging.
Background
CT perfusion (CTP) adds valuable information to the standard anatomical imaging techniques,
consequently assisting diagnosis and guiding therapy in several neurological conditions.
Understanding the postprocessing techniques,
the value of each parameter and the perfusion changes seen in different conditions is essential for correct interpretation.
This poster will provide an overview of the techniques used in CTP,
the value and interpretation of perfusion parameter,
the different clinical conditions that may cause perfusion changes and,
finally,
the potential and limitations of artificial intelligence.
Findings and procedure details
1. Advantages and disadvantages of CTP:
Fig. 1Fig. 2
2.Acquisition:
Fig. 3Fig. 4Fig. 5Fig. 6Fig. 7
3.Theoretical background[1]
a.Nondeconvolution:
Fig. 8
b.Deconvolution:
Fig. 9Fig. 10
Another important mathematical theory is delay and dispersion
Fig. 11Fig. 12[2]
These different theories lead to substantial differences between the commercially available software that may drastically affect the results of processing[3].
Radiologists should be familiar with the type of postprocessing used,
along with the limitations to avoid misinterpretation.
4.Postprocessing:
Fig. 13
5.Interpretation:
a.Tissue attenuation curve (TAC):
Fig. 14
b.Parameters:
Fig....
Conclusion
CTP is a functional technique that may indicate hemodynamic changes in a wide variety of diseases.
Several factors can affect the final results; therefore,
they should be known by radiologists.
Advances in artificial intelligence have a high impact on perfusion processing and interpretations.
Understanding the potentials as well as the limitations of this technique is important to reach sufficient results to improve patient care.
References
1.Konstas A et al (2009) Theoretic basis and technical implementations of CT perfusion in acute ischemic stroke,
part1: Theoretic basis.
AJNR Am J Neuroradiol 30:662–8.
https://doi.org/10.3174/ajnr.A1487
2.Konstas A et al (2009) Theoretic basis and technical implementations of CT perfusion in acute ischemic stroke,
part2: technical implementations.
AJNR Am J Neuroradiol 30:885–92.
https://doi.org/10.3174/ajnr.A1492
3.Zussman BM et al (2011) The Relative Effect of Vendor Variability in CT Perfusion Results: A Method Comparison Study.
Am J Roentgenol 197:468–473.
https://doi.org/10.2214/AJR.10.6058
4.Calamante F et al (2010) The Physiological Significance of...