Advanced reconstruction of attenuation maps using SPECT emission data only

Abstract

Today, attenuation corrected SPECT, typically performed using CT or Gadolinium line source based transmission scans, is more and more becoming standard in many medical applications. Moreover, the information about the material density distribution provided by these scans is key for other artifact compensation approaches in advanced SPECT reconstruction. Major drawbacks of these approaches are the additional patient radiation and hardware/maintenance costs as well as the additional workflow effort, e.g. if the CT scans are not performed on a hybrid scanner. It has been investigated in the past, whether it is possible to recover this structural information solely from the SPECT scan data. However, the investigated methods often result in noticeable image artifacts due to cross-dependences between attenuation and activity distribution estimation. With the simultaneous reconstruction method presented in this paper, we aim to effectively prevent these typical cross-talk artifacts using a-priori known atlas information of a human body.At first, an initial 3D shape model is coarsely registered to the SPECT data using anatomical landmarks and each organ structure within the model is identified with its typical attenuation coefficient. During the iterative reconstruction based on a modified ML-EM scheme, the algorithm simultaneously adapts both, the local activity estimation and the 3D shape model in order to improve the overall consistency between measured and estimated sinogram data. By explicitly avoiding topology modifications resulting in a non-anatomical state, we ensure that the estimated attenuation map remains realistic.Several tests with simulated as well as real patient SPECT data were performed to test the proposed algorithm, which demonstrated reliable convergence behaviour in both cases. Comparing the achieved results with available reference data, an overall good agreement for both cold as well as hot activity regions could be observed (mean deviation: -5.98%).