A system for determining a reconstruction of a particle distribution in an object based on electron paramagnetic resonance measurement data of the object comprising the distribution of particles, the system comprising - a data obtaining means configured to obtain electron paramagnetic resonance measurement data of the object under study, - a processor configured to process the obtained electron paramagnetic resonance measurement data, the processor being configured for: - applying a numerical inverse model for solving a numerical inverse problem of deriving, from the electron paramagnetic resonance measurement data (Smeas), a reconstruction of the particle distribution (C k ∗), and - generating estimated electron paramagnetic resonance data (S i) by applying a numerical forward model to the particle distribution (C k), wherein the processor comprises a quality determining means configured to determine a measure of quality as a difference between the estimated electron paramagnetic resonance data (S i) and the electron paramagnetic resonance measurement data (S meas), or between the particle distribution in a certain iteration of an iterative derivation of the particle distribution and the particle distribution in the previous iteration, - a controlling means configured to control the processing of the data to refine the derived reconstruction of the particle distribution based on the determined measure of quality by adjusting the numerical inverse model as function of the determined measure of the quality, - an output means for outputting data based on the derived reconstruction of the particle distribution, wherein the forward model is a linear model, a system matrix of the linear model relates a particle distribution to estimated electron paramagnetic resonance data, and the controlling means is configured to adjust the numerical inverse model by selecting a set of singular values used in a singular value decomposition of the system matrix, said set resulting from an optimization loop that numerically determines the singular values that optimize said measure of quality or, when the system matrix is a Leadfield matrix, by extending the system matrix with additional measurements in order to increase a size of singular values normalized with respect to the highest singular value in a singular value decomposition of the extended system matrix. - a data obtaining means configured to obtain electron paramagnetic resonance measurement data of the object under study, - a processor configured to process the obtained electron paramagnetic resonance measurement data, the processor being configured for: - applying a numerical inverse model for solving a numerical inverse problem of deriving, from the electron paramagnetic resonance measurement data (Smeas), a reconstruction of the particle distribution (C k ∗), and - generating estimated electron paramagnetic resonance data (S i) by applying a numerical forward model to the particle distribution (C k), wherein the processor comprises a quality determining means configured to determine a measure of quality as a difference between the estimated electron paramagnetic resonance data (S i) and the electron paramagnetic resonance measurement data (S meas), or between the particle distribution in a certain iteration of an iterative derivation of the particle distribution and the particle distribution in the previous iteration, - applying a numerical inverse model for solving a numerical inverse problem of deriving, from the electron paramagnetic resonance measurement data (Smeas), a reconstruction of the particle distribution (C k ∗), and - generating estimated electron paramagnetic resonance data (S i) by applying a numerical forward model to the particle distribution (C k), wherein the processor comprises a quality determining means configured to determine a measure of quality as a difference between the estimated electron paramagnetic resonance data (S i) and the electron paramagnetic resonance measurement data (S meas), or between the particle distribution in a certain iteration of an iterative derivation of the particle distribution and the particle distribution in the previous iteration, - a controlling means configured to control the processing of the data to refine the derived reconstruction of the particle distribution based on the determined measure of quality by adjusting the numerical inverse model as function of the determined measure of the quality, - an output means for outputting data based on the derived reconstruction of the particle distribution,