Analysis of contrast‐enhanced dynamic MR images of the lung

Recent studies have demonstrated the potential of dynamic contrast‐enhanced magnetic resonance imaging (MRI) describing pulmonary perfusion. However, breathing motion, susceptibility artifacts, and a low signal‐to‐noise ratio (SNR) make automatic pixel‐by‐pixel analysis difficult. In the present work, we propose a novel method to compensate for breathing motion. In order to test the feasibility of this method, we enrolled 53 patients with pulmonary embolism (N = 24), chronic obstructive pulmonary disease (COPD) (N = 14), and acute pneumonia (N = 15). A crucial part of the method, an automatic diaphragm detection algorithm, was evaluated in all 53 patients by two independent observers. The accuracy of the method to detect the diaphragm showed a success rate of 92%. Furthermore, a Bayesian noise reduction technique was implemented and tested. This technique significantly reduced the noise level without removing important clinical information. In conclusion, the combination of a motion correction method and a Bayesian noise reduction method offered a rapid, semiautomatic pixel‐by‐pixel analysis of the lungs with great potential for research and clinical use. J. Magn. Reson. Imaging 2001;13:577–587. © 2001 Wiley‐Liss, Inc.