Amyloid-beta positron emission tomography (PET) is used for the diagnosis of Alzheimer’s disease (AD). However, the inherent radiation of radioactive tracers used for PET is potentially harmful to the human body. In this study, we present a deep-learning framework for generating high-quality standard-dose PET brain images from scans that have a simulated reduced injected dose of 12.5% of the standard injected dose, thus reducing radiation exposure without compromising image quality. This novel approach achieves remarkable similarity to full-dose images in both visual and quantitative aspects. Our method offers the potential of enabling safer and more accessible PET imaging for early Alzheimer’s disease detection.
The RECIST criteria are used in computed tomography (CT) imaging to assess changes in tumour burden induced by cancer therapeutics throughout treatment. One of its requirements is frequent measurement of lesion diameters , which is often time consuming for clinicians. We aimed to study clinician-interactive AI, defined as deep learning models that use image annotations as input to assist in radiological measurements. Two annotation types are compared in their enhancement of predictive capabilities: mouse clicks in the tumour region, and bounding boxes surrounding lesions. The model architectures compared in this study are the U-Net, V-Net, AH-Net, and SegRes-Net. Models were trained and tested using a non-small cell lung cancer dataset from the cancer imaging archive (TCIA) consisting of CT scans and corresponding gold-standard lesion segmentations inferred from PET/CT scans. Mouse clicks and bounding boxes, representing clinician input, were artificially generated. The absolute percent error between predicted and ground truth diameters was computed for each model architecture. Bounding box annotations yielded mean absolute percent errors of 4.9 ± 2.1 %, 7.8 ± 3.4 %, 5.6 ± 2.4 % and 5.6 ± 2.3 %, respectively, whereas models using clicks annotations yielded 17.0 ± 7.9 %, 19.8 ± 9.3 %, 21.4 ± 10.9 % and 18.1 ± 7.9%. The corresponding mean dice scores across all model architectures were 0.883 ± 0.004 and 0.760 ± 0.012 for bounding box and click annotations respectively. Models were then implemented in an AI pipeline for clinical use at the BC cancer agency using the Ascinta software package; click annotations yielded qualitatively better results than bounding box annotations.
KEYWORDS: Monte Carlo methods, Scattering, Photon transport, Signal attenuation, Positron emission tomography, Modeling, 3D modeling, Databases, Brain, Computer simulations
The many applications of Monte Carlo modeling in PET arouse to increase the accuracy and computational speed of Monte Carlo codes. The accuracy of Monte Carlo simulations strongly depends on cross-section libraries used for photon transport calculations. Furthermore, large amounts of CPU time are required to obtain meaningful simulated data. We present a comparison of different photon cross-section libraries together with the parallel implementation of the 3D PET Monte Carlo simulator, Eidolon on a MIMD parallel architecture. Different photon cross-section libraries and parametrizations show quite large variations as compared to the most recent EPDL97 nuclear data files for energies from 1 keV to 1 MeV. Together with the optimization of the computing time performances of the Monte Carlo software, photon transport in 3D PET could be efficiently modeled to better understand scatter correction techniques. In implementing Eidolon on a parallel platform, a linear speedup factor was achieved with the number of computing nodes.
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