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There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety’s tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>≤</mo></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10</mn><mo>%</mo></mrow></semantics></math></inline-formula>) and a high-risk group (LSF <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>></mo></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10</mn><mo>%</mo></mrow></semantics></math></inline-formula>). Results: In this patient cohort, LSF was positively correlated with QTM velocity <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>|</mo><mi mathvariant="bold-italic">u</mi><mo>|</mo></mrow></mrow></semantics></math></inline-formula> (r = 0.61, F = 14.0363, <i>p</i> = 0.0021), and no significant correlation was observed with Kety’s parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>|</mo><mi mathvariant="bold-italic">u</mi><mo>|</mo></mrow></mrow></semantics></math></inline-formula> (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, <i>p</i> = 0.0011), and Kety’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>K</mi><mrow><mi>t</mi><mi>r</mi><mi>a</mi><mi>n</mi><mi>s</mi></mrow></msup></mrow></semantics></math></inline-formula> (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, <i>p</i> = 0.0471) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>V</mi><mi>e</mi></msub><mo> </mo></mrow></semantics></math></inline-formula>(0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, <i>p</i> = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>|</mo><mi mathvariant="bold-italic">u</mi><mo>|</mo></mrow></mrow></semantics></math></inline-formula>, 0.80 for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>V</mi><mi>e</mi></msub></mrow></semantics></math></inline-formula> and 0.74 for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>K</mi><mrow><mi>t</mi><mi>r</mi><mi>a</mi><mi>n</mi><mi>s</mi></mrow></msup></mrow></semantics></math></inline-formula>. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing.