arXiv subjects

Alan Miranda

Publications and source records attributed to Alan Miranda.

2 recordsLinked to original sources

arXiv2025

Iterative projected gradient descent for dynamic PET kernel reconstruction

Dynamic positron emission tomography (PET) reconstruction often presents high noise due to the use of short duration frames to describe the kinetics of the radiotracer. Here we introduce a new method to calculate a kernel matrix to be used in the kernel reconstruction for noise reduction in dynamic PET. We first show that the kernel matrix originally calculated using a U-net neural network (DeepKernel) can be calculated more efficiently using projected gradient descent (PGDK), with several orders of magnitude faster calculation time for 3D images. Then, using the PGDK formulation, we developed an iterative method (itePGDK) to calculate the kernel matrix without the need of high quality composite priors, instead using the noisy dynamic PET image for calculation of the kernel matrix. In itePGDK, both the kernel matrix and the high quality reference image are iteratively calculated using PGDK. We performed 2D simulations and real 3D mouse whole body scans to compare itePGDK with DeepKernel and PGDK. Brain parametric maps of cerebral blood flow and non-displaceable binding potential were also calculated in 3D images. Performance in terms of bias-variance tradeoff, mean squared error, and parametric maps standard error, was similar between PGDK and DeepKernel, while itePGDK outperformed these methods in these metrics. Particularly in short duration frames, itePGDK presents less bias and less artifacts in fast kinetics organs uptake compared with DeepKernel. itePGDK eliminates the need to define composite frames in the kernel method, producing images and parametric maps with improved quality compared with deep learning methods.

physics.med-ph↗

arXiv2025

Whole body dynamic PET kernel reconstruction using nonnegative matrix factorization features

The kernel reconstruction is a method that reduces noise in dynamic positron emission tomography (PET) by exploiting spatial correlations in the PET image. Although this method works well for large anatomical regions with relatively slow kinetics, whole body PET reconstruction with the kernel method can produce suboptimal results in regions with fast kinetics and high contrast. In this work we propose a new design of the spatial kernel matrix to improve reconstruction in fast and slow kinetics body regions. We calculate voxels features using nonnegative matrix factorization (NMF) with optimal rank selection. These features are then used to calculate similarities between voxels considering relative differences between features to adapt to a wide range of activity levels. Simulations and whole body mouse scans of high temporal resolution [18F]SynVesT-1, low dose [11C]raclopride, and [18F]Fallypride were performed to assess the performance of the method in different settings. In simulations, bias vs variance tradeoff and contrast was improved using the NMF kernel matrix, compared with the original kernel method. In real data, fast kinetic regions such as the heart, veins and kidneys presented oversmoothing or artifacts with the original kernel method. Our proposed method did not present these effects, while reducing noise. Brain kinetic modeling parametric maps with image derived input function ([18F]SynVesT-1) and with reference region ([11C]raclopride and [18F]Fallypride) also had lower standard error using the proposed kernel matrix compared with other methods. The NMF kernel reconstruction reduces noise and maintains high contrast in whole body PET imaging, outperforming the traditional kernel method.

physics.med-ph↗