Liver cancer presents an increasing burden on public health. Currently, a 3D multi-phase contrast-enhanced MR exam is used to separate the arterial from the venous blood supply to any lesion to determine whether it is malignant or not. It relies heavily on the ability of the patient to sustain a breath-hold, but in a considerable subset of patients, this ability is compromised, leading to a sharp decrease in image quality. It also relies on accurate bolus timing by the operator, such that the first so-called hepatic arterial phase is acquired at the right peak arterial enhancement. Bolus timing errors lead to a suboptimal view of the enhancement pattern, negatively affecting the ability to accurately diagnose liver disease. The long-term goal of this research is to improve the robustness and accuracy of MR imaging of the liver for the detection of hepatocellular carcinoma and other malignancies. The objective of this research, as part of that goal, is to develop a robust image acquisition and visualization technology. The hypothesis in this research is that this technology eliminates these requirements and optimally presents all available dynamic information to the reader. The rationale for the proposed research is that a robust and accurate dynamic imaging method enables the fast extraction of all necessary contrast uptake and washout information for the accurate diagnosis of liver lesions. To test the hypothesis, the following two specific aims are pursued: 1) develop a three-dimensional time-resolved contrast-enhanced free-breathing imaging sequence with high temporal and spatial resolution together with an interactive four-dimensional visualization and post-processing engine, and 2) evaluate the diagnostic performance of the developed free-breathing liver MRI sequence in a patient study. The expected outcome of this research is an interactive dynamic MR imaging method of the liver that will significantly improve the way liver lesions are detected and characterized. Such a technique is likely to have a positive impact, since it expected to increase the sensitivity and specificity of liver MRI and to reduce the need for liver biopsy and the reliance on patient cooperation and operator skill. .

Public Health Relevance

The proposed research is relevant to public health because, if completed successfully, it is expected to increase the sensitivity and specificity of liver MRI. It will reduce the need for liver biopsy and the reliance on patient cooperation and operator skill. It is likely to lead to significant improvements in the staging of hepatocellular carcinomas and in the assignment of ranking scores for liver transplantation.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1-SBIB-J (80))
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Zhang, Huiming
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Weill Medical College of Cornell University
Schools of Medicine
New York
United States
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Cooper, Mitchell A; Nguyen, Thanh D; Xu, Bo et al. (2014) Patch based reconstruction of undersampled data (PROUD) for high signal-to-noise ratio and high frame rate contrast enhanced liver imaging. Magn Reson Med :
Zhou, Dong; Liu, Tian; Spincemaille, Pascal et al. (2014) Background field removal by solving the Laplacian boundary value problem. NMR Biomed 27:312-9