Single-photon emission computed tomography (SPECT) is a widely used clinical imaging modality with multiple applications such as diagnosis of coronary artery disease, management of Parkinson’s disease, and SPECT-based dosimetry. However, SPECT systems have low resolution and have low photon-sensitivity. Thus, there is an important need for computational methods to extract information from measurements measured by SPECT systems. We are making multiple advances in this area.

Conventional imaging systems typically bin the data into images and thus suffer from binning-related losses. However, recent advances in technology are enabling a new class of imaging systems that using optimal statistical techniques, compute and store the measured attributes of each detected photon, such as position of interaction of the photon with the detector and the energy of the detected photon. These systems can extract the maximum possible information from image data, and could potentially redefine the fundamental limits on task performance.

In a project supported by the NIBIB Trailblazer award, we investigated whether the energy attribute of scattered photons acquired by SPECT systems can be used to perform transmission-less attenuation compensation in myocardial perfusion SPECT. Conventional methods for attenuation compensation require an independent attenuation-map measurement using a transmission scan, e.g. CT scan. This leads to several issues such as increased scanning costs, radiation dose, hardware complexity, and potentially inaccurate diagnosis due to misalignment between SPECT and CT. Thus, a transmission-less attenuation compensation method would be highly significant. Our studies demonstrate that scattered photons acquired in list-mode format contain information to estimate the estimate the attenuation distribution. We then developed  a physics and deep-learning-based transmission-less attenuation compensation method that only uses the SPECT emission data. Further, we developed methods that use the energy attribute of scattered photons to estimate the activity map.  This provides an opportunity to improve the effective sensitivity of SPECT systems.

In another project in collaboration with the Center for Gamma Ray Imaging at the University of Arizona, we demonstrated using singular value decomposition (SVD) analysis that list-mode systems can potentially measure more features, such as sharp edges, in comparison to the photon-counting systems. Further, we also developed methods to reconstruct the radiotracer distribution from data acquired via these systems.

Fig. 1: The measurement and null components obtained with the photon-processing (PP) and photon-counting  (PC) systems for a digital phantom. Note that the PP system can capture many more features, such as edges, in comparison to a PC system.

We have also demonstrated that these systems provide improved quantification performance compared to conventional systems.


  • Z. Yu, M. A. Rahman, R. Laforest, T. H. Schindler, A.  K. Jha. “A physics and learning-based transmission-less attenuation compensation method for SPECT” (link to proceedings and presentation)
  • M. A. Rahman, A. K. Jha, “Task-based assessment of binned and list-mode SPECT systems” (link to proceedings and presentation)
  • M. A. Rahman, Y. Zhu, E. Clarkson, M. Kupinski, E. Frey, A. K. Jha, “Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution”, Inverse Problems 2020 (special issue on Modern Challenges in Imaging) (link)
  • M. A. Rahman, R. Laforest, A. K. Jha, “A list-mode OSEM-based attenuation and scatter compensation method for SPECT”, IEEE Symposium on Biomedical Imaging 2020 (link)
  • Z. Yu, M. A. Rahman, A. K. Jha, “A Transmission-less Attenuation Compensation Method for Brain SPECT Imaging”, IEEE International Symposium on Biomedical Imaging 2020
  • A. K. Jha, H. H. Barrett, E. Frey, E. Clarkson, L. Caucci, M. A. Kupinski, “Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions”, Phys. Med. Biol., 60, 7359-7385, 2015 (link)
  • A. K. Jha, E. Frey, “Estimating ROI activity concentration with photon-processing and photon-counting SPECT systems”, SPIE Med. Imag. Conf. 2015 (link)
  • A. K. Jha, E. Clarkson, M. A. Kupinski, H. H. Barrett. “Joint reconstruction of activity and attenuation map using LM SPECT emission data”, Proc. SPIE 8668 Med. Imag., 86681W, 2013 (link)
  • V. Bora, H. H. Barrett, A. K. Jha, E. Clarkson, “Impact of Fano factor on position and energy estimation in scintillation detectors”, IEEE Trans. Nucl. Sci., 99, pp. 11-15, 2015 link)