An observational study in Cardiac Disease, sponsored by Central Hospital, Nancy, France. Not yet recruiting at 1 site in France. Open to participants aged 18 Years to 90 Years. Per ClinicalTrials.gov, last updated 2026-10-22.
Sponsored by Central Hospital, Nancy, France · Observational
Spatial filtering is routinely required to reduce the statistical noise of gated SPECT myocardial perfusion imaging, but at the expense of image blurring. This study will evaluate a deep-learning-based noise reduction (DLNR) approach, developed to shorten acquisition times, when used as a replacement for conventional spatial filters (CSF).
SPECT image noise can also be reduced using deep-learning algorithms, which were initially developed to enable shorter acquisition times or lower injected activities while maintaining diagnostic image quality. More recently, when used alone rather than in combination with spatial filters, such an algorithm was shown to outperform the spatial filters previously required for bone SPECT, providing greater noise reduction with substantially less smoothing. This raises the question of whether the same approach could be applied to gated-SPECT images acquired with low-dose protocols.
3,674 studies on the registry are indexed under Heart Diseases; 458 are open to participants now.
This study's planned enrollment of 49 is below the median of 295 across 1,250 observational studies indexed under Heart Diseases.
Browse Heart Diseases studies →Central Hospital, Nancy, France is the lead sponsor of 796 studies on the registry; 187 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Patients referred for myocardial scintigraphy due to known or suspected coronary artery disease, who underwent the same one-day "low-dose" protocol.
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- Patients who underwent myocardial scintigraphy at the Nancy University Hospital (CHRU) under the conditions applicable to the groups described above.
Exclusion Criteria:
Compare deep learning-based noise reduction and conventional spatial filters for myocardial scintigraphy image reconstruction.
Experienced physicians will perform a visual analysis of the images reconstructed with and without deep learning and will complete a questionnaire that includes an image quality and interpretation score.
Time frame: Day 1
Compare left ventricle end-diastolic and end-systolic volumes and Left Ventricle Ejection Fraction between deep learning and conventional filters
Image quality: visual scale of cine loops for the two observers, categorized as good-to-excellent (low background noise, good edge definition), intermediate, or poor (very high background noise, edge definition making analysis difficult or impossible).
Time frame: Day 1
Start is estimated for Nov 2026; primary completion for Jan 2027, 1 month later.
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From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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