Adaptive Autoregressive Model for Reduction of Noise in SPECT

This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-AR method). The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method). A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR%) and the full width at half maximum (FWHM) of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects.

Glucose Prediction and Hypoglycemia Alarms Based on Adaptive Model

The paper proposes a glucose prediction model and hypoglycemia alarms technology based on CGMS. Method: By using kalman filter to smooth the glucose data from the CGMS, reducing noise interference; Then according to the non-stationary characteristics of glucose concentration signal ,Using adaptive autoregressive model (AR) glucose prediction model is established; Finally, the prediction model is applied to hypoglycemia alarms. Results: The prediction model can dynamically capture the changes of the glucose and predict glucose of 30 min ahead, RMSE、SSGPE were 5.069,5.276; And hypoglycemia can be timely detected.