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

Tomimatsu, Taeko; Yamshita, Kousuke; Sakata, Takumi; Kamezaki, Ryosuke; Ikeda, Ryuji; Shiraishi, Shinya; Uchiyama, Yoshikazu; Ito, Shigeki

doi:10.22038/aojnmb.2024.75375.1528

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

Document Type : Original Article

Authors

¹ Graduate School of Health Sciences, Kumamoto University, Kumamoto, Japan

² Department of Central Radiology Kumamoto University Hospital, Kumamoto, Japan

³ Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan

⁴ Department of Information and Communication Technology, Faculty of Engineering, University of Miyazaki

⁵ Department of Medical Imaging, Faculty of Life Sciences, Kumamoto, Kumamoto , Japan

10.22038/aojnmb.2024.75375.1528

Abstract

Objectives: 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 (DCNN ROI method).
Methods: 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 ROI results for the DCNN ROI method were compared to those obtained using manual methods. The counts for the input function on the PA and AAo ROI were determined by integrating the area under the curve (AUC) counts of the time-activity curve of PA and AAo ROI, respectively. The effectiveness of the DCNN ROI method was elucidated through a comparison with the integrated AUC counts of the DCNN ROI and the manual ROI.
Results: The coincidence ratio for the locations of the PA and AAo-ROI obtained using the DCNN method and that for the manual method was 100%. Strong correlations were observed between the AUC counts using the DCNN and manual methods.
Conclusion: New ROI setting programs were developed using a deep convolution neural network DCNN to determine the input functions for the SIMS and IBUR methods. The accuracy of these methods is comparable to that of the manual method.

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