Pulmonary embolism is a common condition with high short–term morbidity. Pulmonary embolism can be treated successfully but diagnosis remains diﬃcult due to the large variability of symptoms, which are often non–speciﬁc including breath shortness, chest pain and cough. Dual energy CT produces 4–dimensional data by acquiring variation of attenuation with respect to spatial coordinates and also with respect to the energy level. This additional information opens the possibility of discriminating tissue with speciﬁc material content, such as bone and adjacent contrast. Despite having already been available for clinical use for a while, there are few applications where Dual energy CT is currently showing a clear clinical advantage. In this article we propose to use the additional energy–level data in a 4D dataset to quantify texture changes in lung parenchyma as a way of ﬁnding parenchyma perfusion deﬁcits characteristic of pulmonary embolism.