Dynamic Tomographic Reconstruction of Hepatobiliary scintigraphy from dynamic planar imaging followed by SPECT

Hepatobiliary scintigraphy allows the estimation of liver function but only for the whole liver and not for individual regions. A workaround for this consists in rotating the camera to the angle where maximum separation between the lobes is achieved, however, this does not fully provide the activity distribution. In this work, we introduce an algorithm to reconstruct a full 4D tomographic image by using the same conventional planar dynamic imaging sequence, followed by a tomographic acquisition. The algorithm is based on a dictionary learning approach, where multiple L1 and L2 regularization terms are applied both on the bases and on the coefficients. The novelty of this approach is in that we fully model the Poisson statistics of the problem and of photon attenuation, which improves image quality compared to previous approaches but makes convergence extremely slow. This was solved using an optimal diagonal preconditioner and Nesterov acceleration. In this way, some depth information can also be achieved by the projection consistency when attenuation correction is used. We report the results obtained on a patient case. The image quality improves better the closer a frame is to the tomographic acquisition. VOI analysis show activity trends similar to that of planar acquisitions. Visually the images produced seem to allow a much better discrimination of the different regions of the liver. The problem of validation remains open.