Objectives: The purpose of this study was to determine the ability of dynamic 64 slice multidetector computed tomography (d-MDCT) to provide an accurate measurement of myocardial blood flow (MBF) during first-pass d-MDCT using semiquantitative and quantitative analysis methods.
Materials And Methods: Six dogs with a moderate to severe left-anterior descending artery stenosis underwent adenosine (0.14 mL . kg-1 . min-1) stress d-MDCT imaging according to the following imaging protocol: iopamidol 10 mL/s for 3 seconds, 8 mm x 4 collimation, 400 milliseconds gantry rotation time, 120 kV, and 60 mAs. Images were reconstructed at 1-second intervals. Regions of interest were drawn in the LAD and remote territories, and time-attenuation curves were constructed. Myocardial perfusion was analyzed using a model-based deconvolution method and 2 upslope methods and compared with the microsphere MBF measurements.
Results: The myocardial upslope-to-LV-upslope and myocardial upslope-to-LV-max ratio strongly correlated with MBF (R2 = 0.92, P < 0.0001 and R2 = 0.87, P < 0.0001, respectively). Absolute MBF derived by model-based deconvolution analysis modestly overestimated MBF compared with microsphere MBF (3.0 +/- 2.5 mL . g-1 . min-1 vs. 2.6 +/- 2.7 mL . g-1 . min-1, respectively). Overall, MDCT-derived MBF strongly correlated with microspheres (R = 0.91, P < 0.0001, mean difference: 0.45 mL . g-1 . min-1, P = NS).
Conclusions: d-MDCT MBF measurements using upslope and model-based deconvolution methods correlate well with microsphere MBF. These methods may become clinically applicable in conjunction with coronary angiography and next generation MDCT scanners with larger detector arrays and full cardiac coverage.
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http://dx.doi.org/10.1097/RLI.0b013e318124a884 | DOI Listing |
Transl Oncol
December 2024
Department of General Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address:
Background: Glucose metabolism in breast cancer has a potential effect on tumor progression and is related to the immune microenvironment. Thus, this study aimed to develop a glucose metabolism-tumor microenvironment score to provide new perspectives on breast cancer treatment.
Method: Data were acquired from the Gene Expression Omnibus and UCSC Xena databases, and glucose-metabolism-related genes were acquired from the Gene Set Enrichment Analysis database.
J Genet Genomics
November 2024
Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China. Electronic address:
KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data. ST technologies provide insights into gene expression within tissue context, revealing cellular interactions and microenvironments. To fully leverage this potential, effective computational models are crucial.
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 PDFFringe projection profilometry (FPP) is a widely adopted technique for three-dimensional (3D) reconstruction. However, its depth-of-field (DOF) is constrained when reconstructing defocused scenes, mainly due to limitations in the camera model and image blur. This study introduces a camera model based on the ideal optical system, which effectively reduces the systematic errors associated with the conventional pinhole camera model.
View Article and Find Full Text PDFResearch (Wash D C)
November 2024
School of Mathematics, South China University of Technology, Guangzhou 510640, China.
Current integration methods for single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data are typically designed for specific tasks, such as deconvolution of cell types or spatial distribution prediction of RNA transcripts. These methods usually only offer a partial analysis of ST data, neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk. Here, we present eMCI, an explainable multimodal correlation integration model based on deep neural network framework.
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