A medical imaging system configured to generate a patient image normal local collective for use in evaluating patient images through voxel-wise statistical testing, comprising: a data store configured to store the normal local collective; and a quality assurance engine configured to iteratively define the normal local collective comprising one or more processors programmed to: a) receive a plurality of candidate images comprising locally generated patient images and associated patient data for inclusion in the normal local collective; b) apply at least one inclusion/exclusion rule to the plurality of candidate images and the associated data which results in a subset of candidate images; c) test the candidate images based on at least one quality measure to test compliance with the at least one quality measure, wherein the quality measure compares the voxel-wise distribution of candidate images with a maximum range to ensure sensitivity to a variation from normal; d) review the at least one quality measure and the candidate images; e) refine the at least one inclusion/exclusion rule by at least one of: adding a rule, modifying a rule, deleting a rule, removing a candidate image, and adding a candidate image; repeat b)-e) until an optimized normal local collective of images is generated based on a target size of the normal local collective and the reviewed at least one quality measure; and output at least one generated normal local collective to the data store. a data store configured to store the normal local collective; and a quality assurance engine configured to iteratively define the normal local collective comprising one or more processors programmed to: a) receive a plurality of candidate images comprising locally generated patient images and associated patient data for inclusion in the normal local collective; b) apply at least one inclusion/exclusion rule to the plurality of candidate images and the associated data which results in a subset of candidate images; c) test the candidate images based on at least one quality measure to test compliance with the at least one quality measure, wherein the quality measure compares the voxel-wise distribution of candidate images with a maximum range to ensure sensitivity to a variation from normal; d) review the at least one quality measure and the candidate images; e) refine the at least one inclusion/exclusion rule by at least one of: adding a rule, modifying a rule, deleting a rule, removing a candidate image, and adding a candidate image; a) receive a plurality of candidate images comprising locally generated patient images and associated patient data for inclusion in the normal local collective; b) apply at least one inclusion/exclusion rule to the plurality of candidate images and the associated data which results in a subset of candidate images; c) test the candidate images based on at least one quality measure to test compliance with the at least one quality measure, wherein the quality measure compares the voxel-wise distribution of candidate images with a maximum range to ensure sensitivity to a variation from normal; d) review the at least one quality measure and the candidate images; e) refine the at least one inclusion/exclusion rule by at least one of: adding a rule, modifying a rule, deleting a rule, removing a candidate image, and adding a candidate image; repeat b)-e) until an optimized normal local collective of images is generated based on a target size of the normal local collective and the reviewed at least one quality measure; and output at least one generated normal local collective to the data store.