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Abstract #0011

Evaluation of automatic methods for arterial input function extraction for perfusion quantification in the lung.

Marta Tibiletti1, Josephine H Naish1,2, Paul JC Hughes3, Helen Marshall3, Colm Leonard4, Sarah Skeoch4,5, Nazia Chaudhuri4,6, Ian Bruce4,7, James A Eaden3,8, Stephen Bianchi8, Jim Wild3, and Geoff JM Parker1,9

1Bioxydyn Limited, Manchester, United Kingdom, 2Division of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom, 3POLARIS, Academic Radiology, University of Sheffield, Sheffield, United Kingdom, 4The University of Manchester, Manchester, United Kingdom, 5Royal United Hospitals Bath NHS Foundation Trust, Bath, United Kingdom, 6The University of Manchester NHS Foundation Trust, Manchester, United Kingdom, 7Central Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom, 8Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom, 9Quantitative biomedical Imaging Lab, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, United Kingdom

We developed automatic methods to extract the AIF for lung first pass perfusion DCE and compared their performance with manual selection of an ROI in the pulmonary artery (PA). Dynamic enhancement was calculated by simple subtraction and with a shuffle subtraction. Early enhancing voxels with the highest enhancing values were selected. We demonstrated that the shuffle subtraction is a more robust method, since it avoids including the early enhancing subclavian vein. Our results suggest obtaining the AIF automatically from pixels within the RV may be more robust against partial volume effects and removes inter-reader variability.

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