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Micro-Networks for Robust MR-Guided Low Count PET Imaging.

Casper O da Costa-Luis1, Andrew J Reader1

  • 1Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging Sciences, St. Thomas' HospitalKing's College LondonLondonSE1 7EHU.K.

IEEE Transactions on Radiation and Plasma Medical Sciences
|March 8, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel convolutional neural network (CNN) for enhanced Positron Emission Tomography (PET) imaging. The micro-net significantly reduces noise and improves resolution in low-count PET scans, outperforming traditional methods.

Keywords:
Convolutional neural network (CNN)deep learning (DL)guided reconstructionimage processingimage reconstructionmachine learningmagnetic resonance (MR)maximum-likelihood expectation maximization (MLEM)positron emission tomography (PET)resolution modeling (RM)resolution recovery

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Processing

Background:

  • Low count Positron Emission Tomography (PET) imaging requires effective noise suppression.
  • Traditional methods like post-smoothing (PS) and regularization reduce noise but also decrease resolution and introduce bias.
  • Anatomical information from modalities like Magnetic Resonance (MR) imaging can enhance PET image quality.

Purpose of the Study:

  • To propose a low-complexity, 3-D convolutional neural network (CNN) for post-reconstruction MR-guided image processing in low count PET scans.
  • To reduce noise and reconstruction artifacts while simultaneously improving resolution.
  • To develop a CNN robust to limited training data and capable of multi-input processing.

Main Methods:

  • A fully 3-D convolutional neural network (CNN) with low complexity (micro-net) was designed for MR-guided PET image post-processing.
  • The CNN was trained using limited data to process low count (30 M) PET scans, aiming for quality comparable to standard (300 M) count reconstructions.
  • The CNN was evaluated against Maximum-Likelihood Expectation Maximization (MLEM) and optimized post-smoothing (PS) methods using simulated and real patient data.

Main Results:

  • The proposed CNN achieved a 36% lower Normalized Root Mean Squared Error (NRMSE) compared to MLEM on simulated low count data.
  • Optimized PS and MR (RM) with optimized PS yielded only 25% and 26% NRMSE reduction, respectively.
  • The CNN demonstrated robustness against overfitting, unlike larger networks (U-net), and produced images with reduced noise, artifacts, and improved resolution on real patient data.

Conclusions:

  • A low-complexity, 3-D CNN (micro-net) effectively improves image quality in low count PET scans by reducing noise and enhancing resolution.
  • The proposed MR-guided CNN approach significantly outperforms conventional MLEM and PS methods, especially with limited training data.
  • This method offers a promising solution for enhancing diagnostic accuracy in low count PET imaging without requiring extensive datasets.