TABLE I.

A Summary of The Data Sets

Purpose	Name	Phantom type	Recon algorithm	No. of patch pairs	Comments
Training	24mAs/target	Digital	SART	199,850	target = 72mAs, 120mAs, 360mAs, noiseless. For investigating the effect of the dose level of the HD training target.
	MC fine-tuning set	Digital	SART	3,048	Patches centered at individual MCs generated at known locations. For investigating the feasibility of a second fine-tuning stage.
	LD/HD-k	Physical	SART	k × 400,000	k = 20%, 35%, 50%, 65%, 80%, 100%. For investigating the effect of the training sample size.
	LD/HD-Pristina	Physical	Pristina	400,000	For training a matched denoiser when evaluating the generalizability of DNGAN in terms of the reconstruction algorithms.
Validation	24mAs as input, higher dose levels as reference truth	Digital	SART	/	Has ground truth scans simulated at multiple dose levels. Used for NPS comparison.
	LD as input, HD as reference for performance comparison	Physical	SART	/	Has individually marked MCs of three nominal diameters. Used for CNR, FWHM, fit success rate, d’, and visual comparisons.
			Pristina	/	For evaluating the generalizability of DNGAN in terms of the reconstruction algorithms.
Test	Human subject DBTs	/	SART	/	An independent test set. For demonstrating the robustness and the feasibility of applying a denoiser trained with phantom data to human DBTs.