Cardiac DiseaseDEEPLAIMNCT07888218
Replacing Spatial Filtering With Deep-learning Noise Reduction
An observational study of Cardiac Disease, run by Central Hospital, Nancy, France at 1 site in France.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Cardiac Disease
- How many people
- 49 participants
- Where
- 1 siteFrance
- Study dates
- 25 Nov 2026to 3 Mar 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients referred for myocardial scintigraphy due to known or suspected coronary artery disease, who underwent the same one-day "low-dose" protocol.
Who may be able to join
- Patients who underwent myocardial scintigraphy at the Nancy University Hospital (CHRU) under the conditions applicable to the groups described above.
Who cannot join
- Significant arrhythmia, technical reconstruction issues, poor image quality (motion artifacts during acquisition, insufficient count statistics).
- Patients who objected to the use of their data for research purposes.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Replacing Spatial Filtering With Deep-learning Noise Reduction Improves Low-dose Gated SPECT Myocardial Perfusion Imaging
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).
Read the detailed description
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.
- Conditions
- Cardiac Disease
- Medical subject headings
- Heart Diseases
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Day 1
Secondary outcomes (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).
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Oct 2026
- Study start25 Nov 2026 (estimated)
- Primary completion23 Jan 2027 (estimated)
- Study completion3 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiac DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 49 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Central Hospital, Nancy, France
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Pierre Yves MARIE, Central Hospital, Nancy, France
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07888218
- Sponsor's protocol ID
- 2026PI207
- Acronym
- DEEPLAIM
- Status last verified
- Oct 2026
- First submitted
- 14 Oct 2026
- Last updated
- 22 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Replacing Spatial Filtering With Deep-learning Noise Reduction Improves Low-dose Gated SPECT Myocardial Perfusion Imaging
- ClinicalTrials.gov ID
- NCT07888218
- Acronym / sponsor ID
- DEEPLAIM / 2026PI207
- Conditions
- Cardiac Disease
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 49 participants (estimated)
- Lead sponsor
- Central Hospital, Nancy, FranceUniversity, hospital or other
- Study start
- 25 Nov 2026 (estimated)
- Primary completion
- 23 Jan 2027 (estimated)
- Study completion
- 3 Mar 2027 (estimated)
- First posted
- 22 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Detailed description
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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients referred for myocardial scintigraphy due to known or suspected coronary artery disease, who underwent the same one-day "low-dose" protocol.
Key inclusion criteria (1 listed)
- Patients who underwent myocardial scintigraphy at the Nancy University Hospital (CHRU) under the conditions applicable to the groups described above.
Key exclusion criteria (2 listed)
- Significant arrhythmia, technical reconstruction issues, poor image quality (motion artifacts during acquisition, insufficient count statistics).
- Patients who objected to the use of their data for research purposes.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Compare deep learning-based noise reduction and conventional spatial filters for myocardial scintigraphy image reconstruction.
Time frame: Day 1
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.
1 secondary outcome
Compare left ventricle end-diastolic and end-systolic volumes and Left Ventricle Ejection Fraction between deep learning and conventional filters
Time frame: Day 1
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Oct 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiac Disease, run by Central Hospital, Nancy, France at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Pierre-Yves Marie, MD,PhD (0383153909 ext. +33, py.marie@chru-nancy.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Compare deep learning-based noise reduction and conventional spatial filters for myocardial scintigraphy image reconstruction. (time frame: Day 1). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Oct 2026.