MRSI is a noninvasive tool for measuring the spatial distribution of important metabolites in human tissue. MRSI signals are quite weak, so a lengthy data gathering period is required to obtain adequate signal levels from each measurement point. To reduce the total duration of the MRSI session, clinicians usually limit the number of MRSI measurement points. When these limited data are processed, the resulting image lacks contrast and exhibits poor spatial resolution. This poor image quality is believed to be caused by the processing algorithms employed in existing equipment. This effort will develop a different MRSI processing algorithm to improve image quality. Specifically, MRSI sampled data currently are processed using the FFT, which is a computationally efficient method of producing an image. However, the underlying assumptions required by the FFT are not satisfied in MRSI. In particular, MRSI data form frequency-limited records of a spatially finite tissue. For this situation, a different discrete transform based on Prolate Spheroidal Wave Functions (PSWF) is required. PSWF requires more computation than the FFT, but its superior accuracy permits greatly improved resolution from a limited number of measurements. PSWF image transforms for MRSI will lead to robust, automated image processing.Awardee's statement of the potential commercial applications of the research: The PSWF method would replace the FFT image processing algorithm now used in MRSI and MRI. The mature PSWF method should produce briefer measurement sessions and permit routine MRSI processing to be performed by instrument technicians. The small capital cost of this new software will be recovered quickly from the cost savings m instrument measurement and senior staff processing time at user hospitals.National Center for Research Resources (NCRR)
