The incidence of renal diseases is growing as the population ages. Diagnostic imaging serves a key role in detecting and monitoring renal diseases including renal cell carcinoma (RCC). Yet the risk of intravenous contrast agents used in imaging procedures rises for individuals with renal disease and aged 60 years or older. The objectives of the proposed research are to develop contrast free magnetic resonance imaging (MRI) techniques for application in RCC. The MRI procedures will include perfusion quantification to detect renal tumors. Respiratory motion compensation is a major challenge in MRI of the abdomen. Functional procedures like perfusion MRI require accurate motion compensation since alternatives like breathholds or respiratory gating increase the length of the exam and the strain on the patient. The objectives of the proposed research are to enhance ASL MRI for application in RCC and abdominal perfusion MRI.
The specific aims of this proposal are to: 1) identify and evaluate candidate respiratory motion prediction (RMP) algorithms using computer simulations and in vivo respiratory motion data;2) integrate the best RMP algorithms with 2D echo planar imaging (EPI) and 3D gradient and spin echo (GRASE) MRI to allow fast free-breathing MRI acquisitions of the abdomen, optimize the techniques using a respiratory motion phantom, and then compare their performances in human volunteers;3) integrate the best 2D EPI and 3D GRASE RMP techniques with pulsed continuous ASL (pCASL) MRI sequences, and evaluate and optimize its performance in human volunteers;4) apply the best RMP perfusion (2D vs. 3D pCASL) technique in human subjects with RCC to assess tumor perfusion and compare the results to the gold standard (CT with contrast). The hypotheses that will be tested in this study include: 1) kidney motion can be predicted 1 s into the future with an error of <2 mm;2) 3D GRASE with RMP using retrospective image post-processing will provide superior motion correction than 2D EPI with prospective image slice translation;3) 3D GRASE pCASL with RMP will provide superior perfusion signal-to-noise ratio (SNR) and less motion artifact than using 2D EPI pCASL with RMP;4) pCASL with RMP can detect RCC tumor boundaries with >90% power. The RMP method will permit free-breathing and real-time tracking of the organ of interest while minimizing discarded image data. Motion sensitive functional MRI sequences like arterial spin labeling, diffusion weighted imaging (DWI), and blood oxygenation level dependent (BOLD) that have utility in assessing organ health and tumor activity will also benefit. The technique may be further developed for other anatomical regions of interest or applications where physiological motion impedes accurate diagnosis or therapy (e.g., radiotherapy).
The proposed research seeks to integrate bioengineering technologies using imaging physics, mathematical modeling of respiratory motion, and quantification of tissue blood flow to develop a non-invasive method of detecting and characterizing renal tumors with magnetic resonance imaging. The technique will predict, and compensate for, the motion of organs during imaging without the need for repeated breathholds. The method will avoid the use of intravenous contrast agents since they pose a risk to individuals with renal issues.
|Liu, Wenyang; Ruan, Dan (2015) A unified variational segmentation framework with a level-set based sparse composite shape prior. Phys Med Biol 60:1865-77|