Development and Testing of a Clinical Tool to Predict and Optimize Liver Contrast-Enhanced CT Imaging

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2023-07-23

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Abstract

Achieving consistent and sufficient hepatic parenchyma contrast enhancement (HPCE) level can improve diagnostic performance and reduce enhancement variability; this raises the baseline image quality and optimize injection practices, both carries economic and safety implications. Patient factors, Iodine injection and scanning parameters (e.g. tube potential, scanning delay) affect HPCE in CT imaging. In this study, we developed and prospectively tested a clinical graphical user interface (GUI) tool which predicts enhancement level and suggests alternative injection/scanning parameters based on patient attributes (height, weight, sex, age). Methods: This work was based on our retrospectively-validated neural-network prediction model. We built a GUI to combine our model with an optimization algorithm, which suggests alternative injection/scanning parameters for patients with predicted-insufficient enhancement. The system was clinically-deployed and prospectively-tested in 24 patients considering a 110HU+/-10HU target portal-venous HPCE. For each patient, HPCE was calculated as the average HU-value of three ROIs and compared against the target value. Additionally, we compared the outcome with the patient’s previous similarly-protocoled scan to assess improvement and consistency. Results: The system suggested adjustment for 15 patients with median 8.8% and 9.1% reductions to volume and injection rate, respectively. All scan delays were reduced by an average of 42.6%. Comparison with previous scans shows increased consistency (CV=0.21 v. 0.11,p=0.012) while median enhancement remained relatively unchanged (111.3HU v. 108.7HU). The number of under-enhanced patients was halved, and all previously over-enhanced patients received enhancement reductions. Conclusion: We developed and tested a patient-informed clinical framework which predicts optimal patient’s HPCE; and suggests empiric injection/scanning parameters when predicted enhancement is deemed insufficient. The system improved HPCE consistency and decreased the number of under-enhanced patients as compared to their previous scans. This study demonstrated that the patient-informed clinical framework can predict an optimal patient's HPCE and suggest empiric injection/scanning parameters to achieve consistent and sufficient HPCE levels.

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10.1002/mp.16525

Scholars@Duke

Abadi

Ehsan Abadi

Assistant Professor in Radiology

Ehsan Abadi, PhD is an imaging scientist at Duke University. He serves as an Assistant Professor in the departments of Radiology and Electrical & Computer Engineering, a faculty member in the Medical Physics Graduate Program and Carl E. Ravin Advanced Imaging Laboratories, and a co-Lead in the Center for Virtual Imaging Trials. Ehsan’s research focuses on quantitative imaging and optimization, CT imaging, lung diseases, computational human modeling, and medical imaging simulation. He is actively involved in developing computational anthropomorphic models with various diseases such as COPD, and scanner-specific simulation platforms (e.g., DukeSim) for imaging systems. Currently, his work is centered on identifying and optimizing imaging systems to ensure accurate and precise quantifications of lung diseases.

Ria

Francesco Ria

Assistant Professor of Radiology

Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.

Dr. Ria is a member of the American Association of Physicists in Medicine task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the American Association of Physicists in Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.


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