  Purpose: This study addresses the challenge of extended SPECT imaging
duration under low-count conditions, as encountered in Lu-177 SPECT imaging, by
developing a self-supervised learning approach to synthesize skipped SPECT
projection views, thus shortening scan times in clinical settings. Methods: We
employed a self-supervised coordinate-based learning technique, adapting the
neural radiance field (NeRF) concept in computer vision to synthesize
under-sampled SPECT projection views. For each single scan, we used
self-supervised coordinate learning to estimate skipped SPECT projection views.
The method was tested with various down-sampling factors (DFs=2, 4, 8) on both
Lu-177 phantom SPECT/CT measurements and clinical SPECT/CT datasets, from 11
patients undergoing Lu-177 DOTATATE and 6 patients undergoing Lu-177 PSMA-617
radiopharmaceutical therapy. Results: For SPECT reconstructions, our method
outperformed the use of linearly interpolated projections and partial
projection views in relative contrast-to-noise-ratios (RCNR) averaged across
different downsampling factors: 1) DOTATATE: 83% vs. 65% vs. 67% for lesions
and 86% vs. 70% vs. 67% for kidney, 2) PSMA: 76% vs. 69% vs. 68% for lesions
and 75% vs. 55% vs. 66% for organs, including kidneys, lacrimal glands, parotid
glands, and submandibular glands. Conclusion: The proposed method enables
reduction in acquisition time (by factors of 2, 4, or 8) while maintaining
quantitative accuracy in clinical SPECT protocols by allowing for the
collection of fewer projections. Importantly, the self-supervised nature of
this NeRF-based approach eliminates the need for extensive training data,
instead learning from each patient's projection data alone. The reduction in
acquisition time is particularly relevant for imaging under low-count
conditions and for protocols that require multiple-bed positions such as
whole-body imaging.
