Diaphragmatic Surface Reconstruction from MR Temporal Sequences of Images

The diaphragm is a flat muscle sheet and it is the biggest respiratory muscle. It is alone responsible for most of the volume changes during quiet, and to a somewhat lesser degree, during forced respiration. The quantification of true diaphragmatic motion is challenging, but at the same time a promising field of investigation in MRI of the lung. Compared to CT, MR imaging involves longer acquisition times and it is preferable because it does not involve radiation. On the other hand, MRI is hampered by several challenges: the low amount of tissue relates to a small number of protons leading to low signal, countless air-tissue interfaces cause substantial susceptibility artifacts, and respiratory and cardiac motion cause blurred imaging. MR signal is generated from protons within water molecules and organic material. The lung parenchyma contains only about 800 g of tissue and blood, which is distributed over a volume of 4 to 6 liters. Proton density and signal intensity are therefore extremely low compared to other parts of the body. Several groups used static MRI at different respiratory volumes to gain further insight into the function of the diaphragm. Images were acquired during a breath hold with a relaxed diaphragm in sagittal and coronal orientation. Most of the time during the respiratory cycle, the diaphragm is in motion, either actively contracting during inspiration or passively expanding during expiration. To truly understand its mode of function in contributing to lung volume changes, static analysis as described in the previous section is not ideal. This work proposes a method to reconstruct the 3D diaphragmatic movement from coronal and sagittal temporal sequences of MRI. It is known that the lung movement is not periodic and it is susceptible to variations in the degree of respiration. As coronal and sagittal sequences of images are orthogonal to each other, their intersection corresponds to a segment in the three dimensional space. A time sequence of this intersecting segment can be stacked, defining a two dimension spatio temporal image. The proposed method searches for breathing patterns present in spatio temporal images extracted from the intersection line between a coronal and sagittal sequences of temporal MRI. The breathing patterns are determined by assuming that the diaphragmatic movement is the principal movement and all the lungs structures do move almost synchronously. The synchronization was realized through a pattern named respiratory function. A Hough transform algorithm, using the respiratory function as input, searches for synchronized movements with the respiratory function. The temporal registration determines the instants with the same diaphragmatic level at the intersecting line. The registration is done without the use of any triggering information and any special gas to enhance the contrast. The temporal sequences of images are acquired in free breathing. Several results and conclusions are shown.