6 research outputs found
Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation
Positron emission tomography (PET) images are widely used in many clinical applications such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both standard-dose and low-dose PET data into a common space and then performing patch based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level Canonical Correlation Analysis (mCCA) scheme to solve this problem. Specifically, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. Additionally, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain datasets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value
Full-dose PET Synthesis from Low-dose PET Using High-efficiency Diffusion Denoising Probabilistic Model
To reduce the risks associated with ionizing radiation, a reduction of
radiation exposure in PET imaging is needed. However, this leads to a
detrimental effect on image contrast and quantification. High-quality PET
images synthesized from low-dose data offer a solution to reduce radiation
exposure. We introduce a diffusion-model-based approach for estimating
full-dose PET images from low-dose ones: the PET Consistency Model (PET-CM)
yielding synthetic quality comparable to state-of-the-art diffusion-based
synthesis models, but with greater efficiency. There are two steps: a forward
process that adds Gaussian noise to a full dose PET image at multiple
timesteps, and a reverse diffusion process that employs a PET Shifted-window
Vision Transformer (PET-VIT) network to learn the denoising procedure
conditioned on the corresponding low-dose PETs. In PET-CM, the reverse process
learns a consistency function for direct denoising of Gaussian noise to a clean
full-dose PET. We evaluated the PET-CM in generating full-dose images using
only 1/8 and 1/4 of the standard PET dose. Comparing 1/8 dose to full-dose
images, PET-CM demonstrated impressive performance with normalized mean
absolute error (NMAE) of 1.233+/-0.131%, peak signal-to-noise ratio (PSNR) of
33.915+/-0.933dB, structural similarity index (SSIM) of 0.964+/-0.009, and
normalized cross-correlation (NCC) of 0.968+/-0.011, with an average generation
time of 62 seconds per patient. This is a significant improvement compared to
the state-of-the-art diffusion-based model with PET-CM reaching this result 12x
faster. In the 1/4 dose to full-dose image experiments, PET-CM is also
competitive, achieving an NMAE 1.058+/-0.092%, PSNR of 35.548+/-0.805dB, SSIM
of 0.978+/-0.005, and NCC 0.981+/-0.007 The results indicate promising low-dose
PET image quality improvements for clinical applications
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning
with amyloidosis, followed by neuronal loss and deterioration in structure,
function, and cognition. The accumulation of amyloid-beta in the brain,
measured through 18F-florbetapir (AV45) positron emission tomography (PET)
imaging, has been widely used for early diagnosis of AD. However, the
relationship between amyloid-beta accumulation and AD pathophysiology remains
unclear, and causal inference approaches are needed to uncover how amyloid-beta
levels can impact AD development. In this paper, we propose a graph varying
coefficient neural network (GVCNet) for estimating the individual treatment
effect with continuous treatment levels using a graph convolutional neural
network. We highlight the potential of causal inference approaches, including
GVCNet, for measuring the regional causal connections between amyloid-beta
accumulation and AD pathophysiology, which may serve as a robust tool for early
diagnosis and tailored care
A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET
PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers. METHODS: Brain [(18)F]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [(18)F]FDG PET images of 45 patients scanned with three different scanners, [(18)F]FET PET images of 18 patients scanned with two different scanners, as well as [(18)F]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting. RESULTS: The improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = −0.71, p < 0.05) and normalized dose acquisition (r = −0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05). CONCLUSION: The deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-021-05644-1