A deep-learning noise reduction algorithm outperforms the spatial filters previously required for bone SPECT on a high-speed whole-body 360° CZT-camera.

backgroundSpatial filters are required to suppress the statistical noise of SPECT images but with an unavoidable smoothing effect that further decreases the SUV and contrast. This study assesses a deep-learning noise reduction (DLNR) algorithm, previously developed to further reduce bone SPECT recording time on a high-speed whole-body 360° CZT-camera, when used instead of, rather than in addition to, the conventional spatial filters (CSF) recommended for this camera.

resultsThe SUVmax of bone lesions (114 definite arthritis or metastasis lesions) and the resolution recovery coefficients of small and medium phantom spheres, were higher for DLNR than CSF or the combination of CSF plus DLNR (CSF-DLNR) (all p < 0.001), whereas the relative noises were lower for DLNR or CSF-DLNR, as compared with CSF (p < 0.001). Consequently, contrast-to-noise ratio (CNR) was dramatically higher for DLNR, as compared with CSF, and also CSF-DLNR, especially for small- and medium-sized structures. Compared with CSF, DLNR provided an almost two-fold CNR increase for the sphere and lesions in the range of one cm

conclusionWhen used alone, this DLNR algorithm enhances the contrast-to-noise ratio and quantification of bone lesions, especially those of small or medium sizes. It outperforms conventional spatial filters and provides remarkable image quality for routine analysis of bone SPECT from the high-speed whole-body 360° CZT camera. However, further research and validation studies are still necessary before a widespread adoption in clinical practice. KEY POINTS: Question: How does a deep-learning noise reduction algorithm, previously developed to further reduce bone SPECT recording times on a high-speed whole-body 360° CZT-camera, work when used instead of, rather than in addition to, the conventional spatial filters recommended for this camera. Pertinent findings: When used alone, this deep-learning noise reduction algorithm provides a high level of image denoising and better preserves the activities of small- to medium-sized bone structures and lesions than conventional spatial filters do, leading to a dramatic increase in the corresponding contrast-to-noise ratios. IMPLICATIONS FOR PATIENT CARE: Such a deep-learning noise reduction algorithm could be used not only to reduce SPECT recording time when added to conventional spatial filters, but also to improve image quality and resolution when used alone.

trial registrationclinicaltrials.gov, NCT06782438, Registered 27 February 2025, https://clinicaltrials.gov/search?id=NCT06782438 .