Angel Ramon Pineda1, Atousa Sarcon1, Nasser Abbasi1, Doug Stang1, Siavash Jalal1, Kacie Jacklin1, Reed F. Busse2, Jean H. Brittain2
1Mathematics Department, California State University, Fullerton, CA, USA; 2Applied Science Laboratory, GE Healthcare, Madison, WI, USA
HYPR is a promising new method for time-resolved imaging that uses a time averaged image (composite) to improve images at individual time frames. We show that HYPR and a subsequently described modification by Huang and Wright (HW-HYPR) can be understood mathematically as the first step of iterative methods used in other imaging modalities: HYPR as an approximation to maximum likelihood expectation maximization (MLEM) and HW-HYPR as the multiplicative arithmetic reconstruction technique (MART) used to solve the normal equations, where the composite image serves as an initial estimate prior to iteration.