We propose a method using total variation (TV) regularization in deconvolution for partial volume correction in PET imaging. In the degraded image model, we used TV regularization procedure in Van Cittert (VC) and Richardson-Lucy (RL) deconvolution algorithms. These methods were tested in simulated NCAT images and images of NEMA NU4-2008 IQ phantom and tumor-bearing mouse scanned by Simens Invoen microPET. The simulated experiment and tumor-bearing mouse experiment showed that the algorithms using TV regularization provided superior qualitative and quantitative appearance compared with traditional VC and RL algorithms. When the mean intensity of the tumor increased by (10±1.8)%, the SD increase percentage was decreased from 49.98% to 14.26% and from 42.76% to 4.70%, suggesting the efficiency of the proposed algorithms for reducing PVEs in PET.
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Brief Bioinform
November 2024
College of Computing & Data Science, Nanyang Technological University, 639798, Singapore.
Motivation: Spatial transcriptomics (ST) technologies have revolutionized our ability to map gene expression patterns within native tissue context, providing unprecedented insights into tissue architecture and cellular heterogeneity. However, accurately deconvolving cell-type compositions from ST spots remains challenging due to the sparse and averaged nature of ST data, which is essential for accurately depicting tissue architecture. While numerous computational methods have been developed for cell-type deconvolution and spatial distribution reconstruction, most fail to capture tissue complexity at the single-cell level, thereby limiting their applicability in practical scenarios.
View Article and Find Full Text PDFThe acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions.
View Article and Find Full Text PDFEJNMMI Rep
October 2024
GIGA Research - CRC Human Imaging Unit, University of Liège, Liège, Belgium.
Purpose: Our objective was to assess a deconvolution and denoising technique based on Legendre polynomials compared to matrix deconvolution on dynamic F-FDG renography of healthy patients.
Method: The study was carried out and compared to the data of 24 healthy patients from a published study who underwent examinations with Tc-MAG3 planar scintigraphy and F-FDG PET/MRI. Due to corruption issues in some data used in the published article, post-publication measurements were provided.
IEEE Trans Pattern Anal Mach Intell
December 2024
Node representation learning on attributed graphs-whose nodes are associated with rich attributes (e.g., texts and protein sequences)-plays a crucial role in many important downstream tasks.
View Article and Find Full Text PDFMed Phys
November 2024
Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA.
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