Evaluation of Deep Learning-based Scatter Correction on a Long-axial Field-of-view PET scanner

TL;DR

This study evaluates a deep learning-based scatter correction method for long-axial FOV PET scanners, demonstrating improved accuracy and robustness over traditional methods in both simulated and clinical datasets.

Contribution

The paper introduces a CNN-based deep learning method for scatter correction tailored to LAFOV PET, extending previous work to total-body systems and clinical data.

Findings

DLSE outperforms SSS in accuracy on phantom data

DLSE shows robustness to patient size and dose variations

DLSE improves lesion contrast in clinical PET scans

Abstract

Objective: Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a larger angle of acceptance. However, this extended angle increases the number of multiple scatters and the scatter contribution within oblique planes. As scattering affects both quality and quantification of the reconstructed image, it is crucial to correct this effect with more accurate methods than the state-of-the-art single scatter simulation (SSS) that can reach its limits with such an extended field-of-view (FOV). In this work, which is an extension of our previous assessment of deep learning-based scatter estimation (DLSE) carried out on a conventional PET system, we aim to evaluate the DLSE method performance on LAFOV total-body PET. Approach: The proposed DLSE method based on a convolutional neural network (CNN) U-Net architecture uses emission and attenuation sinograms to estimate scatter sinogram. The network was trained from Monte-Carlo (MC) simulations of XCAT phantoms [18F]-FDG PET acquisitions using a Siemens Biograph Vision Quadra scanner model, with multiple morphologies and dose distributions. We firstly evaluated the method performance on simulated data in both sinogram and image domain by comparing it to the MC ground truth and SSS scatter sinograms. We then tested the method on seven [18F]-FDG and seven [18F]-PSMA clinical datasets, and compare it to SSS estimations. Results: DLSE showed superior accuracy on phantom data, greater robustness to patient size and dose variations compared to SSS, and better lesion contrast recovery. It also yielded promising clinical results, improving lesion contrasts in [18F]-FDG datasets and performing consistently with [18F]-PSMA datasets despite no training with [18F]-PSMA.