Development of an automated region-of-interest-setting program based on a deep neural network for brain perfusion single photon emission computed tomography quantification methods

Abstract

A simple noninvasive microsphere (SIMS) method using 123I-IMP and an improved brain uptake ratio (IBUR) method using 99mTc-ECD for the quantitative measurement of regional cerebral blood flow have been recently reported. The input functions of these methods were determined using the administered dose, which was obtained by analyzing the time activity curve of the pulmonary artery (PA) for SIMS and the ascending aorta (AAo) for the IBUR methods for dynamic chest images. If the PA and AAo regions of interest (ROIs) can be determined using deep convolutional neural networks (DCNN) for segmentation, the accuracy of these ROI-setting methods can be improved through simple analytical operations to ensure repeatability and reproducibility. The purpose of this study was to develop new PA and AAo-ROI setting methods using a DCNN. A U-Net architecture based on convolutional neural networks was used to determine the PA and AAo candidate regions. Images of 290 patients who underwent 123I-IMP RI angiography and 108 patients who underwent 99mTc-ECD RI angiography were used. The PA and AAo results for the automated method were compared to those obtained using manual methods. The coincidence ratio for the locations of the PA and AAo-ROI obtained using the automated program and that for the manual methods was 100%. Strong correlations were observed between the DCNN and manual methods. New ROI-setting programs were developed using a DCNN for the SIMS and IBUR methods. The accuracy of these methods is comparable to that of the manual method.