Tuesday, January 25, 2022
1/25/2022
Abstract Liver is commonly involved in metastatic disease in colorectal cancer (CRC) and knowledge about the presence and location of these tumors affects treatment decisions. In patients with CRC, surgical or ablative treatment of liver metastases improves overall survival. Early diagnosis of colorectal metastases (i.e. while lesions are small) is expected to improve treatment outcomes by increasing the number of subjects that can undergo surgical resection or by identifying subjects early on, when non-surgical options are an alternative treatment. Magnetic Resonance Imaging (MRI) is regarded as the most effective imaging modality for the detection and characterization of liver neoplasms; T2-weighted (T2w) and T1-weighted (T1w) images - combined with administration of a gadolinium chelate agent and multi-phase dynamic contrast enhancement (DCE) - are the foundational acquisitions used for the detection and characterization of liver tumors. However, challenges remain for the detection and characterization of small lesions due to factors including inadequate spatial resolution, partial volume effects, physiological motion, and variations in timing of contrast arrival in DCE imaging. In this academic-industrial partnership the scientific and engineering teams at the University of Arizona and Siemens Medical Solutions are coming together to develop robust radial MRI techniques for T2w/T2 mapping and DCE imaging of the liver to improve detection and characterization of small tumors with the goal of bringing these techniques to routine clinical practice. The proposed work is based on a radial turbo spin- echo technique pioneered by the team at the University of Arizona for abdominal imaging and a radial stack-of-stars technique with continuous acquisition for DCE imaging. The specific aims of the partnership are: Aim 1: To develop radial T2w acquisition and reconstruction techniques with efficient full coverage of the liver for small tumor detection and accurate T2 quantification for tumor characterization. Aim 2: To implement a self-navigated 3D radial stack-of-stars technique for continuous acquisition of DCE data and retrospective reconstruction of the dynamic phases. Aim 3: To conduct a clinical evaluation of the techniques from Aims 1 and 2 against conventional T2w and DCE techniques. Aim 4: To streamline translation of the new radial methods to the clinic by developing a computationally efficient reconstruction pipeline. The endpoints of our study include technical advances in MRI acquisitions that markedly overcome limitations of current liver MRI for the diagnosis of early metastases. We expect our proposal to yield technology improvements that will increase precision of care and outcomes in patients with metastatic malignancies, in particular those with colorectal cancer.
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