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- W4313491803 abstract "Abstract Purpose We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, K trans , plasma volume fraction, v p , and extravascular, extracellular space, v e , directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). Methods In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Results Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, v p , K trans , and v e , respectively. Conclusions This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches." @default.
- W4313491803 created "2023-01-06" @default.
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- W4313491803 date "2023-01-04" @default.
- W4313491803 modified "2023-10-14" @default.
- W4313491803 title "Dynamic Contrast Enhanced (DCE) MRI Estimation of Vascular Parameters using Knowledge-Based Adaptive Models" @default.
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- W4313491803 doi "https://doi.org/10.21203/rs.3.rs-2416613/v1" @default.
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