Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

EJNMMI Research

Table 3 Quantitative results of the animal experiment

Metric	Tracer	Method	Frame 4	Frame 8	Frame 12	Frame 16
MSE	\(^{18}\)F-FDG	FBP-CNN	0.0004	0.0003	0.0004	0.0004
		Proposed	0.0003	0.0003	0.0003	0.0004
	\(^{18}\)F-FET	FBP-CNN	0.0014	0.0013	0.0011	0.0010
		Proposed	0.0008	0.0007	0.0006	0.0006
MS-SSIM	\(^{18}\)F-FDG	FBP-CNN	0.8193	0.8822	0.9080	0.9115
		Proposed	0.8719	0.8993	0.9113	0.9055
	\(^{18}\)F-FET	FBP-CNN	0.7549	0.7532	0.7755	0.7863
		Proposed	0.8884	0.8787	0.8765	0.8727
PSNR	\(^{18}\)F-FDG	FBP-CNN	22.84	25.67	27.76	28.56
		Proposed	24.66	26.53	28.17	28.59
	\(^{18}\)F-FET	FBP-CNN	24.44	23.68	23.91	23.67
		Proposed	27.49	26.90	26.51	26.00

The lower MSE, higher MS-SSIM and higher PSNR are noted in bold font in the comparison of the proposed method and FBP-CNN. When calculating MSE, activity unit was converted to MBq/mL