A noise robust decoder for multiplexing readout channels on an imaging sensor array

Stanford researchers have invented a decoder for multiplexed readouts of imaging arrays that optimizes the signal-to-noise ratio (SNR) of the decoded detector pixel signals. It uses maximum likelihood estimation, which is referred to as “maximum likelihood CS” (ML-CS) decoding. For noisy imaging applications, it can improve the signal-to-noise ratio (SNR) performance of multiplexed readouts for imaging arrays with a practical, affordable implementation. This invention can reduce the cost of an imaging sensor by reducing the number of readout channels. It is applicable to a wide range of imaging applications, ranging from medical imaging to digital cameras.

Stage of Research:
- For positron emission tomography (PET), simulations showed that the invention can improve the SNR of the decoded signal by 3-4 times over compressed sensing techniques on compressed sensing multiplexing topologies.
- The decoder can also be applied to conventional multiplexing topologies to provide a 50% decoded SNR improvement over conventional multiplexing decoders.