In modern radiology, computed tomography (CT) imaging is vital in diagnosing liver diseases, particularly malignancies. The use of intravenous contrast agents is essential for enhancing liver visibility in CT scans. However, the one-size-fits-all approach in contrast administration can lead to suboptimal results. A recently published review in European Radiology Experimental explores the development and validation of a personalised contrast injection algorithm designed to achieve consistent liver enhancement. By considering patient-specific characteristics, such as body composition, the algorithm aims to optimise liver CT imaging outcomes.
Personalised Contrast Protocols in Liver CT
Contrast agents are pivotal in improving the visualisation of liver lesions, which are often crucial for cancer detection and follow-up. Traditionally, the amount of contrast administered is based on general factors like total body weight or fixed injection rates. However, these methods may not consistently achieve the desired image quality for every patient. Personalised contrast protocols aim to address this challenge by tailoring the dose and injection parameters to each patient's specific physiological characteristics.
This study introduces a novel algorithm that personalises the contrast dose based on the patient's fat-free mass (FFM), which has been shown to better predict contrast enhancement than total body weight. The algorithm ensures optimal contrast enhancement across different patient demographics by integrating various clinical parameters—scanner type, imaging phase, and contrast concentration.
Methodology: Validating the Algorithm
The study collected CT data from 384 patients who underwent liver CT for cancer staging or follow-up between 2020 and 2022. Patients with abnormal liver conditions, such as cirrhosis or fatty liver, were excluded to maintain consistent comparison metrics. The algorithm calculated the appropriate contrast dose by incorporating data from each patient's FFM and the imaging centre’s radiology information system. The goal was to achieve consistent liver enhancement centred at 50 Hounsfield Units (HU), which was considered optimal for accurate lesion detection.
The patients underwent CT scans at varying tube potentials (kVp), contrast concentrations, and injection rates. Image quality was assessed based on the contrast-enhancement index of normal parenchyma, which was in the desirable range of 40–60 HU.
Results: High-Quality Liver Imaging
The study demonstrated that 92.7% of the CT scans achieved liver enhancement within the acceptable range (30–70 HU), while 72.1% fell within the more desirable 40–60 HU range. Notably, the algorithm performed well across different patient body types, contrast concentrations, and imaging parameters, providing consistent results. A notable finding was that the amount of administered iodine was higher in males than females, reflecting the difference in body composition (males generally have higher FFM). However, the algorithm’s adjustments ensured that both male and female patients received appropriate contrast doses, resulting in comparable liver enhancement.
Additionally, the algorithm’s flexibility allowed it to adapt to different scanner models and kVp settings, highlighting its potential for broad application in clinical settings. By considering FFM instead of total body weight, the algorithm optimised contrast administration, improving visibility and diagnostic accuracy for liver lesions.
The validation of this multi-parameter algorithm represents a significant advancement in personalised medicine within radiology. By tailoring contrast injection protocols to individual patient characteristics, the algorithm improves the reproducibility of liver CT images and enhances diagnostic accuracy. This approach ensures patient safety and minimises the risk of contrast overuse, reducing costs and potential side effects. The algorithm’s success across various clinical scenarios underscores its potential for widespread adoption in radiology departments, paving the way for more precise and effective imaging practices.
Source: European Radiology Experimental
Image Credit: iStock
