Machine learning techniques for mri processing using regional scoring of non-parametric voxel integrity rankings

An apparatus for generating one or more predictive recommendations based at least in part on a magnetic resonance imaging (MRI) set characterized by one or more MRI images that are associated with a common target object having a common target object type, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least:

• generate, using an image preprocessing model for the common target object type, and based at least in part on the MRI set, a standardized MRI set;
• generate, using an image segmentation model, and based at least in part on the standardized MRI set:
• (i) a plurality of disjoint image regions of the standardized MRI set, and (ii) for each disjoint image region, a subset of a group of image voxels of the standardized MRI set;
• for each image voxel:
• generate, using a voxel integrity score generation machine learning model, and based at least in part on a voxel input representation for the image voxel, a voxel integrity score for the image voxel, and
• generate, using an integrity score normalization model characterized by a normalization space that is defined by one or more normalization variables associated with the common target object, and based at least in part on the voxel integrity score for the image voxel, a non-parametric integrity ranking for the image voxel;
• generate a group of region scores based at least in part on each non-parametric integrity ranking, wherein:
• each region score is associated with a respective disjoint image region and is generated based at least in part on each non-parametric integrity ranking for those image voxels that are in the respective disjoint image region, and
• the group of region scores comprise, for each disjoint image region:
• (i) a normality-distribution region score that is generated based at least in part on an average measure of each non-parametric integrity ranking for those image voxels that are in the respective disjoint image region, and (ii) a normality-ratio region score that is generated based at least in part on a ratio of those image voxels that are in the disjoint image region and that are associated with non-threshold-satisfying non-parametric integrity rankings and those image voxels that are in the disjoint image region and that are associated with threshold-satisfying non-parametric integrity rankings;
• generate, using a predictive recommendation model and based at least in part on the group of region scores, the one or more predictive recommendations, wherein:
• the predictive recommendation model is configured to map the group of region scores to a selected subset of a plurality of candidate predictive recommendations,
• the predictive recommendation model is characterized by a plurality of predictive recommendation mapping rules comprising one or more normality-distribution predictive recommendation mapping rules and one or more normality-ratio predictive recommendation mapping rules,
• each normality-distribution predictive recommendation mapping rule is characterized by a normality-distribution region score threshold, and
• each normality-ratio predictive recommendation mapping rule is characterized by a normality-ratio region score threshold, and
• perform one or more prediction-based actions based at least in part on the selected subset.

A computer program product for generating one or more predictive recommendations based at least in part on a magnetic resonance imaging (MM) acquisition protocol characterized by one or more MRI images that are associated with a common target object having a common target object type, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:

• generate, using an image preprocessing model for the common target object type, and based at least in part on the MRI set, a standardized MM set;
• generate, using an image segmentation model, and based at least in part on the standardized MRI set:
• (i) a plurality of disjoint image regions of the standardized MRI set, and (ii) for each disjoint image region, a subset of a group of image voxels of the standardized MRI set;
• for each image voxel:
• generate, using a voxel integrity score generation machine learning model, and based at least in part on a voxel input representation for the image voxel, a voxel integrity score for the image voxel, and
• generate, using an integrity score normalization model characterized by a normalization space that is defined by one or more normalization variables associated with the common target object, and based at least in part on the voxel integrity score for the image voxel, a non-parametric integrity ranking for the image voxel;
• generate a group of region scores based at least in part on each non-parametric integrity ranking, wherein:
• each region score is associated with a respective disjoint image region and is generated based at least in part on each non-parametric integrity ranking for those image voxels that are in the respective disjoint image region, and
• the group of region scores comprise, for each disjoint image region:
• (i) a normality-distribution region score that is generated based at least in part on an average measure of each non-parametric integrity ranking for those image voxels that are in the respective disjoint image region, and (ii) a normality-ratio region score that is generated based at least in part on a ratio of those image voxels that are in the disjoint image region and that are associated with non-threshold-satisfying non-parametric integrity rankings and those image voxels that are in the disjoint image region and that are associated with threshold-satisfying non-parametric integrity rankings;
• generate, using a predictive recommendation model and based at least in part on the group of region scores, the one or more predictive recommendations, wherein:
• the predictive recommendation model is configured to map the group of region scores to a selected subset of a plurality of candidate predictive recommendations,
• the predictive recommendation model is characterized by a plurality of predictive recommendation mapping rules comprising one or more normality-distribution predictive recommendation mapping rules and one or more normality-ratio predictive recommendation mapping rules,
• each normality-distribution predictive recommendation mapping rule is characterized by a normality-distribution region score threshold, and
• each normality-ratio predictive recommendation mapping rule is characterized by a normality-ratio region score threshold;
• and
• perform one or more prediction-based actions based at least in part on the selected subset.