Dynamic PET image reconstruction utilizing intrinsic data-driven HYPR4D denoising kernel

Novel voxelwise residual analysis of [11C]raclopride PET data improves detection of low-amplitude dopamine release

Existing methods for voxelwise transient dopamine (DA) release detection rely on explicit kinetic modeling of the [11C]raclopride PET time activity curve, which at the voxel level is typically confounded by noise, leading to poor performance for detection of low-amplitude DA release-induced signals. Here we present a novel data-driven, task-informed method-referred to as Residual Space Detection (RSD)-that transforms PET time activity curves to a residual space where DA release-induced perturbations can be isolated and processed. Using simulations, we demonstrate that this method significantly increases detection performance compared to existing kinetic model-based methods for low-magnitude DA release (simulated +100% peak increase in basal DA concentration). In addition, results from nine healthy controls injected with a single bolus of [11C]raclopride performing a finger tapping motor task are shown as proof-of-concept. The ability to detect relatively low magnitudes of dopamine release in the human brain using a single bolus injection, while achieving higher statistical power than previous methods, may additionally enable more complex analyses of neurotransmitter systems. Moreover, RSD is readily generalizable to multiple tasks performed during a single PET scan, further extending the capabilities of task-based single-bolus protocols.

HYPR4D kernel method on TOF PET data with validations including image-derived input function

Background

Positron emission tomography (PET) images are typically noisy especially in dynamic imaging where the PET data are divided into a number of short temporal frames often with a low number of counts. As a result, image features such as contrast and time–activity curves are highly variable. Noise reduction in PET is thus essential. Typical noise reduction methods tend to not preserve image features/patterns (e.g. contrast and size dependent) accurately. In this work, we report the first application of our HYPR4D kernel method on time-of-flight (TOF) PET data (i.e. PSF-HYPR4D-K-TOFOSEM). The proposed HYPR4D kernel method makes use of the mean 4D high frequency features and inconsistent noise patterns over OSEM subsets as well as the low noise property of the early reconstruction updates to achieve prior-free de-noising. The method was implemented and tested on the GE SIGNA PET/MR and was compared to the TOF reconstructions with PSF resolution modeling available on the system, namely PSF-TOFOSEM with and without standard post filter and PSF-TOFBSREM (TOF Q.Clear) with various beta values (regularization strengths).

Results

Results from experimental contrast phantom and human subject data with various PET tracers showed that the proposed method provides more robust and accurate image features compared to other regularization methods. The preservation of contrast for the PSF-HYPR4D-K-TOFOSEM was observed to be better and less dependent on the contrast and size of the target structures as compared to TOF Q.Clear and PSF-TOFOSEM with filter. At the same contrast level, PSF-HYPR4D-K-TOFOSEM achieved better 4D noise suppression than other methods (e.g. >2 times lower noise than TOF Q.Clear at the highest contrast). We also present a novel voxel search method to obtain an image-derived input function (IDIF) and demonstrate that the obtained IDIF is the most quantitative w.r.t. the measured blood samples when the acquired data are reconstructed with PSF-HYPR4D-K-TOFOSEM.

Conclusions

The overall results support superior performance of the PSF-HYPR4D-K-TOFOSEM for TOF PET data and demonstrate that the proposed method is likely suitable for all imaging tasks including the generation of IDIF without requiring any prior information as well as further improving the effective sensitivity of the imaging system.

A personalized deep learning denoising strategy for low-count PET images

Objective. Deep learning denoising networks are typically trained with images that are representative of the testing data. Due to the large variability of the noise levels in positron emission tomography (PET) images, it is challenging to develop a proper training set for general clinical use. Our work aims to develop a personalized denoising strategy for the low-count PET images at various noise levels.Approach.We first investigated the impact of the noise level in the training images on the model performance. Five 3D U-Net models were trained on five groups of images at different noise levels, and a one-size-fits-all model was trained on images covering a wider range of noise levels. We then developed a personalized weighting method by linearly blending the results from two models trained on 20%-count level images and 60%-count level images to balance the trade-off between noise reduction and spatial blurring. By adjusting the weighting factor, denoising can be conducted in a personalized and task-dependent way.Main results.The evaluation results of the six models showed that models trained on noisier images had better performance in denoising but introduced more spatial blurriness, and the one-size-fits-all model did not generalize well when deployed for testing images with a wide range of noise levels. The personalized denoising results showed that noisier images require higher weights on noise reduction to maximize the structural similarity and mean squared error. And model trained on 20%-count level images can produce the best liver lesion detectability.Significance.Our study demonstrated that in deep learning-based low dose PET denoising, noise levels in the training input images have a substantial impact on the model performance. The proposed personalized denoising strategy utilized two training sets to overcome the drawbacks introduced by each individual network and provided a series of denoised results for clinical reading.

Spatiotemporal Kernel Reconstruction for Linear Parametric Neurotransmitter PET Kinetic Modeling in Motion Correction Brain PET of Awake Rats

The linear parametric neurotransmitter positron emission tomography (lp-ntPET) kinetic model can be used to detect transient changes (activation) in endogenous neurotransmitter levels. Preclinical PET scans in awake animals can be performed to investigate neurotransmitter transient changes. Here we use the spatiotemporal kernel reconstruction (Kernel) for noise reduction in dynamic PET, and lp-ntPET kinetic modeling. Kernel is adapted for motion correction reconstruction, applied in awake rat PET scans. We performed 2D rat brain phantom simulation using the ntPET model at 3 different noise levels. Data was reconstructed with independent frame reconstruction (IFR), IFR with HYPR denoising, and Kernel, and lp-ntPET kinetic parameters (k2a: efflux rate, γ: activation magnitude, td: activation onset time, and tp: activation peak time) were calculated. Additionally, significant activation magnitude (γ) difference with respect to a region with no activation (rest) was calculated. Finally, [11C]raclopride experiments were performed in anesthetized and awake rats, injecting cold raclopride at 20 min after scan start to simulate endogenous neurotransmitter release. For simulated data at the regional level, IFR coefficient of variation (COV) of k2a, γ, td and tp was reduced with HYPR denoising, but Kernel showed the lowest COV (2 fold reduction compared with IFR). At the pixel level the same trend is observed for k2a, γ, td and tp COV, but reduction is larger with Kernel compared with IFR (10–14 fold). Bias in γ with respect with noise-free values was additionally reduced using Kernel (difference of 292, 72.4, and −6.92% for IFR, IFR+KYPR, and Kernel, respectively). Significant difference in activation between the rest and active region could be detected at a simulated activation of 160% for IFR and IFR+HYPR, and of 120% for Kernel. In rat experiments, lp-ntPET parameters have better confidence intervals using Kernel. In the γ, and td parametric maps, the striatum structure can be identified with Kernel but not with IFR. Striatum voxel-wise γ, td and tp values have lower variability using Kernel compared with IFR and IFR+HYPR. The spatiotemporal kernel reconstruction adapted for motion correction reconstruction allows to improve lp-ntPET kinetic modeling noise in awake rat studies, as well as detection of subtle neurotransmitter activations.

Noise2Noise Improved by Trainable Wavelet Coefficients for PET Denoising

The significant statistical noise and limited spatial resolution of positron emission tomography (PET) data in sinogram space results in the degradation of the quality and accuracy of reconstructed images. Although high-dose radiotracers and long acquisition times improve the PET image quality, the patients’ radiation exposure increases and the patient is more likely to move during the PET scan. Recently, various data-driven techniques based on supervised deep neural network learning have made remarkable progress in reducing noise in images. However, these conventional techniques require clean target images that are of limited availability for PET denoising. Therefore, in this study, we utilized the Noise2Noise framework, which requires only noisy image pairs for network training, to reduce the noise in the PET images. A trainable wavelet transform was proposed to improve the performance of the network. The proposed network was fed wavelet-decomposed images consisting of low- and high-pass components. The inverse wavelet transforms of the network output produced denoised images. The proposed Noise2Noise filter with wavelet transforms outperforms the original Noise2Noise method in the suppression of artefacts and preservation of abnormal uptakes. The quantitative analysis of the simulated PET uptake confirms the improved performance of the proposed method compared with the original Noise2Noise technique. In the clinical data, 10 s images filtered with Noise2Noise are virtually equivalent to 300 s images filtered with a 6 mm Gaussian filter. The incorporation of wavelet transforms in Noise2Noise network training results in the improvement of the image contrast. In conclusion, the performance of Noise2Noise filtering for PET images was improved by incorporating the trainable wavelet transform in the self-supervised deep learning framework.