Technical note: Rapid and high‐resolution deep learning–based radiopharmaceutical imaging with 3D‐CZT Compton camera and sparse projection data

Abstract

Background The Compton camera (CC) has great potential in nuclear medicine imaging due to the high detection efficiency and the ability to simultaneously detect multi‐energy radioactive sources. However, the finite resolution of the detectors will degrade the images that the real‐world CC can obtain. Besides, the CC sometimes can be limited by the detection efficiency, leading to difficulty in using sparse projection data to realize high‐resolution reconstruction with short‐time measurement, which limits its clinical application for real‐time or rapid radiopharmaceutical imaging. Purpose To overcome the difficulty and promote the usage of the CC in radiopharmaceutical imaging, we present a deep learning (DL)–based CC reconstruction method to realize rapid and high‐resolution imaging with short‐time measurement. Methods We developed a DL–based algorithm MCBP‐CCnet via Monte Carlo sampling–based back projection and a dedicated convolutional neural network, called CC‐Net, to realize the rapid and high‐resolution reconstruction with sparse projection data. A CC prototype based on a single three‐dimensional position‐sensitive CdZnTe (3D‐CZT) detector was used to demonstrate the feasibility of our proposed method. The simulations and experiments of radiopharmaceutical imaging used the 3D‐CZT CC and [18F]NaF. A 3D‐printing mouse phantom was also further used to evaluate the performance of the proposed method in animal molecular imaging. Results The simulation and experimental results showed that the proposed method could realize the images reconstruction within 5 s for list‐mode projection data and realized a rapid reconstruction within 35 s for experimental radiopharmaceutical imaging based on the 3D‐printing mouse phantom, as well as realized the high‐resolution imaging with an accuracy of within 0.78 mm in terms of the sparse projection data that only contained hundreds of events. Besides, the deviations between the reconstructed radiative activities and the exact values were less than 1.51%. Conclusion The results demonstrated that the proposed method could realize the rapid and high‐resolution CC reconstruction with sparse projection data obtained by the 3D‐CZT CC and realize the high‐resolution radiopharmaceutical imaging. The study in this paper also demonstrated the potential and feasibility of future applications of a 3D‐CZT CC for real‐time high‐resolution radiopharmaceutical imaging with short‐time measurement.

Hybrid PET/Compton-camera imaging: an imager for the next generation

“…The recent rise of deep learning (DL) has also found resonance in the field of image reconstruction for Compton cameras. Promising results have been obtained for point sources [123] and small hot lesions in a cold background [122], see Fig. 7.…”