RATIONALE AND OBJECTIVES:
The authors present their initial experience using a 3-T whole-body scanner equipped with a 128-channel coil applied to lung motion assessment. Recent improvements in fast magnetic resonance imaging (MRI) technology have enabled several trials of free-breathing three-dimensional (3D) imaging of the lung. A large number of image frames necessarily increases the difficulty of image analysis and therefore warrants automatic image processing. However, the intensity homogeneities of images of prior dynamic 3D lung MRI studies have been insufficient to use such methods. In this study, initial data were obtained at 3 T with a 128-channel coil that demonstrate the feasibility of acquiring multiple sets of 3D pulmonary scans during free breathing and that have sufficient quality to be amenable to automatic segmentation.
MATERIALS AND METHODS:
Dynamic 3D images of the lungs of two volunteers were acquired with acquisition times of 0.62 to 0.76 frames/s and an image matrix of 128 x 128, with 24 to 30 slice encodings. The volunteers were instructed to take shallow and deep breaths during the scans. The variation of lung volume was measured from the segmented images.
Dynamic 3D images were successfully acquired for both respiratory conditions for each subject. The images showed whole-lung motion, including lifting of the chest wall and the displacement of the diaphragm, with sufficient contrast to distinguish these structures from adjacent tissues. The average time to complete segmentation for one 3D image was 4.8 seconds. The tidal volume measured was consistent with known tidal volumes for healthy subjects performing deep-breathing maneuvers. The temporal resolution was insufficient to measure tidal volumes for shallow breathing.
This initial experience with a 3-T whole-body scanner and a 128-channel coil showed that the scanner and imaging protocol provided dynamic 3D images with spatial and temporal resolution sufficient to delineate the diaphragmatic domes and chest wall during active breathing. In addition, the intensity homogeneities and signal-to-noise ratio were adequate to perform automatic segmentation.