A method of examining a sample using a charged-particle microscope, comprising the following steps: - Mounting the sample on a sample holder; - Using a particle-optical column to direct at least one beam of particulate radiation onto a surface S of the sample, thereby producing an interaction that causes emitted radiation to emerge from the sample surface S; - Using a detector arrangement to detect electrons in said emitted radiation; - Recording an output O n of said detector arrangement as a function of kinetic energy E n of said electrons, thus compiling a measurement set M = {(On, E n)} for a plurality of values of E n, the method further comprising using computer processing apparatus to automatically deconvolve the measurement set M and spatially resolve it into a result set R = {(V k, L k)}, in which a spatial variable V demonstrates a value V k at an associated discrete depth level L k referenced to the surface S, in a manner that comprises the following steps: - Defining a Point Spread Function that, for each value of n, has a kernel value K n representing a behavior of said beam of particulate radiation in a bulk of the sample as perceived by the detector arrangement for electron energy value E n; - Defining an imaging quantity that, for each value of n, has a value Q n that is a convolution of K n and V, such that Q n = K n * V; - For each value of n, determining a minimum divergence min D O n ‖ K n * V between O n and Q n, subject to a noise model, wherein it is solved for V while applying constraints on the values K n, whereby n and k are members of an integer sequence, and spatial variable V represents a physical property of the sample as a function of position in its bulk; characterised in that said convolution of Kn and V is a mutli-dimensional convolution and in that said determining a minimum divergence is computationally determining a minimum divergence. - Mounting the sample on a sample holder; - Using a particle-optical column to direct at least one beam of particulate radiation onto a surface S of the sample, thereby producing an interaction that causes emitted radiation to emerge from the sample surface S; - Using a detector arrangement to detect electrons in said emitted radiation; - Recording an output O n of said detector arrangement as a function of kinetic energy E n of said electrons, thus compiling a measurement set M = {(On, E n)} for a plurality of values of E n, the method further comprising using computer processing apparatus to automatically deconvolve the measurement set M and spatially resolve it into a result set R = {(V k, L k)}, in which a spatial variable V demonstrates a value V k at an associated discrete depth level L k referenced to the surface S, in a manner that comprises the following steps: - Defining a Point Spread Function that, for each value of n, has a kernel value K n representing a behavior of said beam of particulate radiation in a bulk of the sample as perceived by the detector arrangement for electron energy value E n; - Defining an imaging quantity that, for each value of n, has a value Q n that is a convolution of K n and V, such that Q n = K n * V; - For each value of n, determining a minimum divergence min D O n ‖ K n * V between O n and Q n, subject to a noise model, wherein it is solved for V while applying constraints on the values K n, whereby n and k are members of an integer sequence, and spatial variable V represents a physical property of the sample as a function of position in its bulk; characterised in that said convolution of Kn and V is a mutli-dimensional convolution and in that said determining a minimum divergence is computationally determining a minimum divergence. - Defining a Point Spread Function that, for each value of n, has a kernel value K n representing a behavior of said beam of particulate radiation in a bulk of the sample as perceived by the detector arrangement for electron energy value E n; - Defining an imaging quantity that, for each value of n, has a value Q n that is a convolution of K n and V, such that Q n = K n * V; - For each value of n, determining a minimum divergence min D O n ‖ K n * V between O n and Q n, subject to a noise model, wherein it is solved for V while applying constraints on the values K n, whereby n and k are members of an integer sequence, and spatial variable V represents a physical property of the sample as a function of position in its bulk; characterised in that said convolution of Kn and V is a mutli-dimensional convolution and in that said determining a minimum divergence is computationally determining a minimum divergence. between O n and Q n, subject to a noise model, wherein it is solved for V while applying constraints on the values K n, whereby n and k are members of an integer sequence, and spatial variable V represents a physical property of the sample as a function of position in its bulk; characterised in that said convolution of Kn and V is a mutli-dimensional convolution and in that said determining a minimum divergence is computationally determining a minimum divergence.