Due to recent breakthroughs in fast imaging techniques and hardware MRI now has the potential to surpass CT for diagnosing abdominal tumors. MRI has two fundamental advantages: it lacks ionizing radiation, and it shows superior soft tissue contrast. The disadvantages of conventional MRI are sensitivity to motion and inferior resolution. The work proposed here directly addresses these challenges. During the first phase of this grant we have developed several new rapid imaging techniques that clearly have the potential to make tumor imaging in the abdomen a viable clinical tool. These include high-speed interleaved spiral sequences that provide the necessary sensitivity, immunity to motion, and contrast for clinical imaging of tumors in the abdomen. This potential has been vividly confirmed after imaging over 50 patients and many more normal volunteers. These techniques include a novel RARE-spiral sequence that promises improved SNR and resolution. In addition we have studied several volumetric sequences in which 3D kappa- space is scanned efficiently and with insensitivity to motion. We have developed several fluoroscopic sequences to enable critical dynamic studies. For uncooperative patients who cannot hold their breath, we have developed a non-breath-held sequence using navigator-based motion detection system that in real time reacquires all data corrupted by excessive motion. In this renewal we propose to continue developing several important abdominal imaging protocols and also several core techniques critical to high speed imaging with time-varying gradients. Ultimately, this body of work will produce a suite of optimized clinical protocols including breath-held T/1 and T/2 weighted sequences, non-breath-held, motion- compensated T/1 and T/2 weighted sequences, and a fluoroscopic sequence. Although these are designed for current hardware, each can be adapted to exploit the capabilities of the next generation of gradient and RF hardware currently being released. The new protocols and core techniques will be optimized using anecdotal clinical scans.
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