PET Images and Deep Neural Networks AlgorithmsNCT04140565
Positron Emission Tomography (PET) Images Using Deep Neural Networks
An observational study of PET Images and Deep Neural Networks Algorithms, run by Sheba Medical Center at 1 site in Israel.
Trial summary
PET images are based on detecting two annihilation 511 KeV photons that are produced by positron emitting isotopes. The longer the acquisition time, the more photons are detected and processed, resulting in better image quality. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- PET Images and Deep Neural Networks Algorithms
- How many people
- 200 participants
- Where
- 1 siteIsrael
- Study dates
- 1 Nov 2019to 1 Nov 2021
- How long will I take part?
- 2 Years
- Phase
- Observational
- Are there results?
- None posted in the registry
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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients who perform FDG PET/CT from vertex to mid thighs.
Who may be able to join
- Exclusion Criteria:1. Under 18 years old. 2. PET/CT performed with a radioisotope other then FDG.
- -
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country.
Israel1 site
Sheba Medical Center Hospital- Tel Hashomer
Ramat Gan, 52621
Liran Domachevsky, MD, 052-53-3387635, Liran.Domachevsky@sheba.health.gov.il
About this study
Official title: Extraction of Diagnostic Positron Emission Tomography (PET) Images From 10 Seconds Bed-position Acquisition, Using Deep Neural Networks
PET images are based on detecting two annihilation 511 KeV photons that are produced by positron emitting isotopes. The longer the acquisition time, the more photons are detected and processed, resulting in better image quality. However, long scan times (typically 20-40 minutes per scan) are less convenient to patients, and may result in patient motion and misalignment.
several studies have used machine learning to produce diagnostic images from low quality images.The goal of our study is to produce diagnostic PET images with 10 seconds acquisition time per bed position using DNN algorithms
Read the detailed description
Positron emission tomography (PET)/ computerized tomography (CT), with the use of several tracers, among which fluoro deoxyglucose (FDG) is the most prevalent, has become a principal imaging modality in oncology. The PET and CT components reflect metabolic and anatomic information, respectively. PET images are based on detecting two annihilation 511 KeV photons that are produced by positron emitting isotopes. The longer the acquisition time, the more photons are detected and processed, resulting in better image quality. However, long scan times (typically 20-40 minutes per scan) are less convenient to patients, and may result in patient motion and misalignment. Over the years, several methods, such as 3D and time of flight acquisitions, have been developed to compensate for the degradation in image quality as a result of shortening of the scanning time. Recently, several studies have used machine learning to produce diagnostic images from low quality images. Xiang et al compared PET images of the brain that were acquired in 3 minutes (i.e., low-quality PET (LPET)) with standard PET images (i.e., SPET) that were acquired in 12 minutes. They have combined LPET and T1 weighted images using deep neural networks (DNN) to produce diagnostic PET images equivalent to SPET images.
The goal of our study is to produce diagnostic PET images with 10 seconds acquisition time per bed position using DNN algorithms developed at the CILAB laboratory in the imaging department of Sheba.
The algorithms were previously successfully validated for the denoising of ultra-low dose chest CT scans, making them suitable for lung cancer screening. The algorithms are based on the locally-consistent non-local means (LC-NLM) algorithm.
The LC-NLM algorithm uses fast approximate nearest neighbors (ANN) to find the most similar high-SNR patch, in a purposely built database, for each noisy patch in the input image (Green et al.) ] We propose to use the recently introduced non-local neural networks (Wang et al.) in order to stack the LC-NLM into a fully trainable, locally-consistent nonlocal block (LC-NLB). The original non-local networks combines the ideas of the classical non-local means (NLM) algorithm (Buades et al.) into a neural network block, which computes the output at a specific position as a weighted sum of the features at all positions.
- Keywords
- Machine learning, deep neural networks, PET/CT
What is being measured
The primary outcome is the main question the study is designed to answer.
Production of diagnostic PET images using deep neural networks algorithms
To produce PET images form very short bed positions equivalent in quality to the standard PET images
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Oct 2019
- Study start1 Nov 2019 (estimated)
- Primary completion1 Nov 2021 (estimated)
- Study completion1 Nov 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PET Images and Deep Neural Networks AlgorithmsStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
- Target duration
- 2 Years
Sponsor and investigators
- Lead sponsor
- Sheba Medical Center
- Sponsor type
- Other government
- Collaborators
- Computational Imaging Lab , Dr. Arnaldo Mayer
- Responsible party
- Principal investigator, Dr. Liran Domachevsky, Sheba Medical Center
- Investigators
- Liran Domachevsky, MD, Principal investigator, Sheba Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04140565
- Sponsor's protocol ID
- SHEBA-19-6267-LD-CTIL
- Status last verified
- Oct 2019
- First submitted
- 24 Oct 2019
- Last updated
- 30 Oct 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: PET image quality of patients scored on a visual basis and with objective parameters.
Study at a glance
From the registry record, last updated by the sponsor on 30 Oct 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Extraction of Diagnostic Positron Emission Tomography (PET) Images From 10 Seconds Bed-position Acquisition, Using Deep Neural Networks
- ClinicalTrials.gov ID
- NCT04140565
- Acronym / sponsor ID
- SHEBA-19-6267-LD-CTIL
- Keywords
- Machine learning, deep neural networks, PET/CT
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Sheba Medical CenterOther government
- Collaborators
- Computational Imaging Lab , Dr. Arnaldo Mayer
- Study start
- 1 Nov 2019 (estimated)
- Primary completion
- 1 Nov 2021 (estimated)
- Study completion
- 1 Nov 2021 (estimated)
- First posted
- 28 Oct 2019 (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.
PET images are based on detecting two annihilation 511 KeV photons that are produced by positron emitting isotopes. The longer the acquisition time, the more photons are detected and processed, resulting in better image quality. However, long scan times (typically 20-40 minutes per scan) are less convenient to patients, and may result in patient motion and misalignment.
several studies have used machine learning to produce diagnostic images from low quality images.The goal of our study is to produce diagnostic PET images with 10 seconds acquisition time per bed position using DNN algorithms
Detailed description
Positron emission tomography (PET)/ computerized tomography (CT), with the use of several tracers, among which fluoro deoxyglucose (FDG) is the most prevalent, has become a principal imaging modality in oncology. The PET and CT components reflect metabolic and anatomic information, respectively. PET images are based on detecting two annihilation 511 KeV photons that are produced by positron emitting isotopes. The longer the acquisition time, the more photons are detected and processed, resulting in better image quality. However, long scan times (typically 20-40 minutes per scan) are less convenient to patients, and may result in patient motion and misalignment. Over the years, several methods, such as 3D and time of flight acquisitions, have been developed to compensate for the degradation in image quality as a result of shortening of the scanning time. Recently, several studies have used machine learning to produce diagnostic images from low quality images. Xiang et al compared PET images of the brain that were acquired in 3 minutes (i.e., low-quality PET (LPET)) with standard PET images (i.e., SPET) that were acquired in 12 minutes. They have combined LPET and T1 weighted images using deep neural networks (DNN) to produce diagnostic PET images equivalent to SPET images.
The goal of our study is to produce diagnostic PET images with 10 seconds acquisition time per bed position using DNN algorithms developed at the CILAB laboratory in the imaging department of Sheba.
The algorithms were previously successfully validated for the denoising of ultra-low dose chest CT scans, making them suitable for lung cancer screening. The algorithms are based on the locally-consistent non-local means (LC-NLM) algorithm.
The LC-NLM algorithm uses fast approximate nearest neighbors (ANN) to find the most similar high-SNR patch, in a purposely built database, for each noisy patch in the input image (Green et al.) ] We propose to use the recently introduced non-local neural networks (Wang et al.) in order to stack the LC-NLM into a fully trainable, locally-consistent nonlocal block (LC-NLB). The original non-local networks combines the ideas of the classical non-local means (NLM) algorithm (Buades et al.) into a neural network block, which computes the output at a specific position as a weighted sum of the features at all positions.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 2 Years
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who perform FDG PET/CT from vertex to mid thighs.
Key inclusion criteria (2 listed)
- Exclusion Criteria:1. Under 18 years old. 2. PET/CT performed with a radioisotope other then FDG.
- -
Key exclusion criteria (0 listed)
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
Production of diagnostic PET images using deep neural networks algorithms
Time frame: 2 years
To produce PET images form very short bed positions equivalent in quality to the standard PET images
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Oct 2019.
- Individual participant dataSharing planned: No. PET image quality of patients scored on a visual basis and with objective parameters.
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. This study is not recruiting at the moment.
Liran Domachevsky, MD principal investigator
Sheba Medical Center
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 PET Images and Deep Neural Networks Algorithms, run by Sheba Medical Center at 1 site in Israel.
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 2 inclusion and 0 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: status not confirmed, as of the sponsor's last update on 30 Oct 2019. 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 Israel. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Liran Domachevsky, MD (972-53-3387635, Liran.Domachevsky@sheba.health.gov.il), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Production of diagnostic PET images using deep neural networks algorithms (time frame: 2 years). 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 30 Oct 2019.