Abstract #1648

AI-based reconstruction of T2-weighted sequences in prostate MRI: clinical evaluation and impact on diagnostic confidence

Leon Bischoff^1,2, Alexander Isaak^1,2, Christoph Katemann³, Dmitrij Kravchenko^1,2, Narine Mesropyan^1,2, Christoph Endler^1,2, Barbara Wichtmann^1,2, Oliver Weber³, Johannes Peeters⁴, Claus Christian Pieper¹, Daniel Kütting^1,2, Alois Martin Sprinkart^1,2, Ulrike Attenberger¹, and Julian Luetkens^1,2

¹Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany, ²Quantitative Imaging Lab, University Hospital Bonn, Bonn, Germany, ³Philips GmbH Market DACH, Hamburg, Germany, ⁴Philips MR Clinical Science, Best, Netherlands

Synopsis

Keywords: Prostate, Machine Learning/Artificial Intelligence, Prostate cancerIn this prospective study, 56 male patients with suspected prostate cancer were included to evaluate an artificial intelligence (AI) based reconstruction method for T2-weighted sequences in multiparametric MRI (mpMRI). After comparison with conventionally acquired and reconstructed sequences we found the new AI-based method to produce images with higher image sharpness and delineation of lesions, confirmed by both qualitative and quantitative analysis. This is accompanied by a reduction of scan time by 29-37%. Confidence in the assessed PI-RADS scores was significantly higher for the AI-reconstruction. This new technique could therefore potentially increase diagnostic accuracy of mpMRI of the prostate.

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