Koichi Ogawa

Multi-pinhole SPECT gains sensitivity but stacks overlapping projections with attenuation, scatter, quantum noise and aperture effects, and attenuation correction usually needs an attenuation coefficient map from X-ray CT. A U-Net++ convolutional network takes Monte Carlo-simulated overlapping projections as input and ray-traced ideal projections as target, separating overlaps and correcting the degradations together before maximum-likelihood expectation-maximization reconstruction. On 60 public brain cases under quasi-stationary acquisition, it reached NMSE 0.18±0.02, PSNR 18.42±0.69 dB and SSIM 0.81±0.04, better than 11-pinhole reconstruction and fan-beam reconstruction with 20-fold longer acquisition.

Multi-pinhole SPECT gains sensitivity but stacks overlapping projections with attenuation, scatter, quantum noise and aperture effects, and attenuation correction usually needs an attenuation coefficient map from X-ray CT. A U-Net++ convolutional network takes Monte Carlo-simulated overlapping projections as input and ray-traced ideal projections as target, separating overlaps and correcting the degradations together before maximum-likelihood expectation-maximization reconstruction. On 60 public brain cases under quasi-stationary acquisition, it reached NMSE 0.18±0.02, PSNR 18.42±0.69 dB and SSIM 0.81±0.04, better than 11-pinhole reconstruction and fan-beam reconstruction with 20-fold longer acquisition.