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 wor k, we propose a novel method to compensate for breathing motion. In order t o test the feasibility of this method, we enrolled 53 patients with pulmona ry 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 Inde pendent observers. The accuracy of the method to detect the diaphragm showe d 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 c ombination of a motion correction method and a Bayesian noise reduction met hod offered a rapid, semiautomatic pixel-by-pixel analysis of the lungs wit h great potential for research and clinical use. (C) 2001 Wiley-Liss, Inc.