A novel cardiac SPECT system and imaging method

ABSTRACT

A novel SPECT model with an elliptical orbit and multiple detectors is proposed for detecting the metabolism of the human heart. Under the structure of the proposed model, the distance between the detector and the patient becomes smaller, which can effectively improve the SNR. The slit-slat collimator is used to collimate the emitted photons. Modified iterative reconstruction algorithm is proposed to reconstruct a small centre field with severe truncation data under the constraints of body contour and total variation (TV). Simulations for the proposed SPECT are implemented to access the image reconstruction quality. The simulation results show that proposed imaging system with appropriate reconstruction methods can provide significant improvements in the spatial resolution and the image quality. The geometric efficiency of the proposed SPECT is about twice that of the conventional SPECT system, but the scanning time is only about one tenth.

KEYWORDS:

• Attenuation correction
• body contour
• component
• elliptical orbit
• OSEM
• slit-slat collimator
• SPECT
• total variation minimization

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61871168 and Zhejiang Provincial Natural Science Foundation under grant Y17E070024.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributors

Jinhua Sheng received his Ph. D degree in nuclear electronics from University of Science and Technology of China in 1997. From April, 2005 to December, 2008, he was a post–doctoral research fellow at University of Wisconsin, USA. Currently, he is a Distinguished Professor in Hangzhou Dianzi University and Director of Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, China. His research interests include image processing, medical imaging science, neuroscience, bioinformatics, genomic signal processing.

Yangjie Ma received his Bachelor's degree from Zhejiang University of Technology, China, in 2016 and the master's degree from Hangzhou Dianzi University, China in 2020. His research interests include low–dose CT imaging, biosignal processing, Single Photon Imaging Technology.

Rougang Zhou received his Ph. D degree from Huazhong University of science and technology, China in 2015. Currently, he is a Distinguished Professor in Hangzhou Dianzi University. His research interests include intelligent manufacturing, machine vision.

Xun Li received his Ph. D degree from Zhejiang University, China, in 2009. Currently, he is a lecturer in Hangzhou Dianzi University. His research interests imaging processing, machine learning.

Luyun Wang received her Bachelor's and Master's degree from Hangzhou Dianzi University, China, in 2015 and 2018, respectively. She is currently pursuing her Ph. D. degree in computer science with Hangzhou Dianzi University, China. Her research interests include biomedical signal processing, neuroscience, machine learning. Yuchen Shi received his Bachelor's degree from Hangzhou Dianzi University, China, in 2019. He is currently pursuing his master's degree in computer science with Hangzhou Dianzi University, China. His research interests include medical image processing, parallel MRI.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 61871168] and Zhejiang Provincial Natural Science Foundation [grant number Y17E070024].