Clinical Significance of CT and MR Perfusion Imaging in Cancer Diagnosis: Techniques, Parameters, and Diagnostic Implications
Perfusion imaging is an advanced technique for evaluating the blood flow of the microvasculature of any tissue or pathology. It refers to the passing of blood from the arterial system to the vascular system through the microvasculature of tissues. It may be affected in abnormal tissues; thus, perfusion imaging techniques prove helpful in disease characterization. Many authors have mentioned these techniques and their significance in different disease diagnoses. However, there is a lack of information about the fundamentals of perfusion and its parameters, particularly the relationship between its parameters when diagnosing diseases, such as cancer. This review fills this gap and will provide information about fundamentals, different parameters, and their clinical relationships. It focuses on techniques of CT and MR perfusion imaging and is helpful in the diagnosis of altered blood flow in different tissues. Cancer is one of the leading causes of death across the world. It increases the vascular permeability, which alters the blood flow in different tissue. So, perfusion imaging proves to be very helpful in tumor identification, differentiation, and grading. The results of this study are based on perfusion maps. These maps include blood flow (CBF), blood volume (CBV), time to peak (TTP), and mean transit time (MTP). Different maps provide specific information about the tissue or pathology. CT imaging and MRI are commonly used modalities for perfusion imaging. MR perfusion has an ASL technique as a special advanced technique for perfusion study.
Authors
- Mohd Faraz (ORCID: https://orcid.org/0000-0003-0095-8166)
- Md Rashid Anwer
- Aliyeva Nigar
- Noor Khan (ORCID: https://orcid.org/0000-0002-2146-8097)
- Arafat Ali Farooqui (ORCID: https://orcid.org/0000-0001-7391-8419)
- Zeeshan Akram
- Rezina Parveen
- Saqib Zameer
Publication Details
- Journal
- Journal of Cancer and Tumor International
- Published
- 2025-06-28
- DOI
- https://doi.org/10.9734/jcti/2025/v15i3301
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36