N-Peaks: MRI intensity normalization based on normal tissue histogram peak intensities

<i>Objective.</i>We propose a method called N-Peaks that normalizes intensity units in structural magnetic resonance (MR) images by harmonizing the intensities of certain reference tissues.<i>Approach.</i>The N-Peaks normalization requires an image and a number of normal tissue reference contours as input. A map of local intensity change is calculated, which is used to isolate homogeneous regions in each contour. An intensity histogram of the homogeneous voxels is calculated and the peak intensity on that histogram is identified to serve as a landmark. The image intensities are then transformed in a piecewise linear fashion to map the landmark intensities of the normal tissue to predefined target values. The approach was tested for a data set of 194 abdomen images acquired on a 0.35 T MR-Linac. The images were normalized with N-Peaks using the background, manually contoured liver and semi-automatically contoured fat as reference tissues. For comparison, a Nyul normalization and<i>Z</i>-Score normalizations based on the body and liver were applied. The heterogeneity of intensity histograms after each normalization was quantified using the Jensen-Shannon distance (JSD) of individual intensity histograms to the average intensity histogram for the body, liver and fat contours.<i>Main result.</i>The N-Peaks normalization resulted in consistently low JSD in all three tissues. In the body contour, Nyul normalization achieved the lowest JSD, but it resulted in a distorted histogram shape for fat.<i>Z</i>-Score based on the body showed high JSD in fat and liver, and<i>Z</i>-Score based on the liver resulted in the lowest JSD in the liver but high JSD in body and fat.<i>Significance.</i>We demonstrate that N-Peaks successfully normalizes MR image intensities of selected normal tissues, improving the consistency of the intensities regarding tissue types. An implementation of the method is provided, offering potential applications for different quantitative MR image analyses.