Processing multidimensional signal

A system for processing multidimensional signals, comprising:

• an array representing a plurality of elements stored in a memory, comprising multidimensional signals representing a plurality of separated nonlinear manifolds captured by at least one detector from the object;
• at least one automated processor configured to construct a modified array of elements representing origin-centered manifolds in both space and time having unit covariance;
• and
• an output port presenting a reconstructed representation of the object, based on the modified array of elements, using the at least one automated processor.

A method of generating a modified representation of medical imaging signals representing volumetric elements modelled as black bodies in a physical space, comprising:

• receiving an array of medical imaging signals as an input dataset;
• determining distances between volumetric elements within the array;
• determining spatial neighbors of the volumetric elements in the array based on the determined distances;
• linearizing spatial indices of the array to produce a linearized array constrained to maintain a spatial neighbor relationship with the determined spatial neighbors of the volumetric elements, defining a Lebesgue space;
• generating reconstruction weights for reconstructing the linearized array, using locally linear embedding, at a minimized cost of reconstruction from the spatial neighbors;
• constructing a modified array of volumetric elements using a constrained global least squares optimization based on the reconstruction weights, wherein the volumetric elements of the modified array have unit covariance;
• and
• presenting a medical image, based on the modified array of volumetric elements.