Positron Emission Tomography (PET) Images Using Deep Neural Networks

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

Study Overview

• Status: Unknown
• Conditions: PET Images and Deep Neural Networks 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 Type: Observational
• Enrollment (Anticipated): 200

Contacts and Locations

• Study Contact: Name: Liran Domachevsky, MD; Phone Number: 972-53-3387635; Email: Liran.Domachevsky@sheba.health.gov.il

Study Locations

• Israel
  • Ramat Gan, Israel, 52621
    • Sheba Medical Center Hospital- Tel Hashomer
    • Contact: Liran Domachevsky, MD; Phone Number: 052-53-3387635; Email: Liran.Domachevsky@sheba.health.gov.il

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients who perform FDG PET/CT from vertex to mid thighs.

Description

Inclusion Criteria:Patients who perform FDG PET/CT -

Exclusion Criteria:1. Under 18 years old. 2. PET/CT performed with a radioisotope other then FDG.

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Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	2 years

Collaborators and Investigators

• Sponsor: Sheba Medical Center
• Collaborators: Computational Imaging Lab , Dr. Arnaldo Mayer
• Investigators: Principal Investigator: Liran Domachevsky, MD, Sheba Medical Center

Study record dates

Study Major Dates

• Study Start (ANTICIPATED): November 1, 2019
• Primary Completion (ANTICIPATED): November 1, 2021
• Study Completion (ANTICIPATED): November 1, 2021

Study Registration Dates

• First Submitted: October 24, 2019
• First Submitted That Met QC Criteria: October 24, 2019
• First Posted (ACTUAL): October 28, 2019

Study Record Updates

• Last Update Posted (ACTUAL): October 30, 2019
• Last Update Submitted That Met QC Criteria: October 28, 2019
• Last Verified: October 1, 2019

More Information

Terms related to this study

• Keywords: Machine learning, deep neural networks, PET/CT
• Other Study ID Numbers: SHEBA-19-6267-LD-CTIL

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: PET image quality of patients scored on a visual basis and with objective parameters.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No