Technological Advances in SPECT and SPECT/CT Imaging

Objective. Deep learning (DL) is becoming increasingly important in generating attenuation maps for accurate attenuation correction in cardiac perfusion SPECT imaging. Typically, DL models take inputs from initial reconstructed SPECT images, which are performed on the photopeak window and often also on scatter windows. While prior studies have demonstrated improvements in DL performance when scatter window images are incorporated into the DL input, the comprehensive analysis of the impact of employing different scatter windows remains unassessed. Additionally, existing research mainly focuses on applying DL to SPECT scans obtained at clinical standard count levels. This study aimed to assess utilities of DL from two aspects: 1) investigating the impact when different scatter windows were used as input to DL, and 2) evaluating the performance of DL when applied on SPECT scans acquired at a reduced count level. Approach. We utilized 1517 subjects, with 386 subjects for testing and the remaining 1131 for training and validation. Main results. The results showed that as scatter window width increased from 4% to 30%, a slight improvement was observed in DL estimated attenuation maps. The application of DL models to quarter-count (¼-count) SPECT scans, compared to full-count scans, showed a slight reduction in performance. Nonetheless, discrepancies across different scatter window configurations and between count levels were minimal, with all normalized mean square error (NMSE) values remaining within 2.1% when comparing the different DL attenuation maps to the reference CT maps. For attenuation corrected SPECT slices using DL estimated maps, NMSE values were within 0.5% when compared to CT correction. Significance. This study, leveraging an extensive clinical dataset, showed that the performance of DL seemed to be consistent across the use of varied scatter window settings. Moreover, our investigation into reduced count studies indicated that DL could provide accurate attenuation correction even at a ¼-count level.

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SPECT with a Compton camera for thyroid cancer imaging

Liang,

Li,

Han

et al. 2024

Developments in X-Ray Tomography XV

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Single photon emission computed tomography (SPECT) is commonly used with radioiodine scintigraphy to evaluate patients with multiple diseases such as thyroid cancer. The clinical gamma camera for SPECT contains a mechanical collimator that greatly compromises dose efficiency and limits diagnostic performance. The Compton camera is emerging as a promising alternative for mapping the distribution of radio-pharmaceuticals in the thyroid, since the Compton camera does not require mechanical collimation and in principle does not reject gamma ray photons. In this study, a high-efficiency tomographic imaging system is designed with a Compton camera for thyroid cancer imaging. A Timepix3-based Compton camera is selected for collecting gamma photons emitted from an I-131 phantom, and the backprojection filtration algorithm is applied for image reconstruction. The results demonstrate the feasibility of the Compton camera for high efficiency SPECT imaging and also the limitations that need further efforts to address.

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Technological Advances in SPECT and SPECT/CT Imaging

Cited by 5 publications

References 87 publications

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Targeting anticancer immunity in melanoma tumour microenvironment: unleashing the potential of adjuvants, drugs, and phytochemicals

Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging

SPECT with a Compton camera for thyroid cancer imaging

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