James A Scott1. 1. Division of Nuclear Medicine, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114, USA.
Abstract
PURPOSE: To use artificial intelligence methods to determine whether quantitative parameters describing the perfusion image can be synthesized to make a reasonable estimate of the pulmonary arterial (PA) pressure measured at angiography. MATERIALS AND METHODS: Radionuclide perfusion images were obtained in 120 patients with normal chest radiographs who also underwent angiographic PA pressure measurement within 3 days of the radionuclide study. An artificial neural network (ANN) was constructed from several image parameters describing statistical and boundary characteristics of the perfusion images. With use of a leave-one-out cross-validation technique, this method was used to predict the PA systolic pressure in cases on which the ANN had not been trained. A Pearson correlation coefficient was determined between the predicted and measured PA systolic pressures. RESULTS: ANN predictions correlated with measured pulmonary systolic pressures (r = 0.846, P <.001). The accuracy of the predictions was not influenced by the presence of pulmonary embolism. None of the 51 patients with predicted PA pressures of less than 29 mm Hg had pulmonary hypertension at angiography. All 13 patients with predicted PA pressures greater than 48 mm Hg had pulmonary hypertension at angiography. CONCLUSION: Meaningful information regarding PA pressure can be derived from noninvasive radionuclide perfusion scanning. The use of image analysis in concert with artificial intelligence methods helps to reveal physiologic information not readily apparent at visual image inspection. Copyright RSNA, 2002
PURPOSE: To use artificial intelligence methods to determine whether quantitative parameters describing the perfusion image can be synthesized to make a reasonable estimate of the pulmonary arterial (PA) pressure measured at angiography. MATERIALS AND METHODS: Radionuclide perfusion images were obtained in 120 patients with normal chest radiographs who also underwent angiographic PA pressure measurement within 3 days of the radionuclide study. An artificial neural network (ANN) was constructed from several image parameters describing statistical and boundary characteristics of the perfusion images. With use of a leave-one-out cross-validation technique, this method was used to predict the PA systolic pressure in cases on which the ANN had not been trained. A Pearson correlation coefficient was determined between the predicted and measured PA systolic pressures. RESULTS: ANN predictions correlated with measured pulmonary systolic pressures (r = 0.846, P <.001). The accuracy of the predictions was not influenced by the presence of pulmonary embolism. None of the 51 patients with predicted PA pressures of less than 29 mm Hg had pulmonary hypertension at angiography. All 13 patients with predicted PA pressures greater than 48 mm Hg had pulmonary hypertension at angiography. CONCLUSION: Meaningful information regarding PA pressure can be derived from noninvasive radionuclide perfusion scanning. The use of image analysis in concert with artificial intelligence methods helps to reveal physiologic information not readily apparent at visual image inspection. Copyright RSNA, 2002