PURPOSE: To provide a fully automated algorithm for obtaining stiffness measurements from hepatic magnetic resonance elastography (MRE) images that are consistent with measurements performed by expert readers. MATERIALS AND METHODS: An initial liver contour was found using an adaptive threshold and expanded using an active contour to select a homogeneous area of the liver. The confidence map generated during the stiffness calculation was used to select a region of reliable wave propagation. The average stiffness within the automatically generated region of interest (ROI) was compared to measurements by two trained readers in a set of 88 clinical test cases ranging from healthy to severely fibrotic. RESULTS: The stiffness measurements reported by the readers differed by -6.76% ± 22.8% (95% confidence) and had an intraclass correlation coefficient (ICC) of 0.972 (P < 0.05). The algorithm and the more experienced reader differed by 4.32% ± 14.9 with an ICC of 0.987. CONCLUSION: The automated algorithm performed reliably, even though MRE acquisitions often have motion artifacts present. The correlation between the automated measurements and those from the trained readers was superior to the correlation between the readers.
PURPOSE: To provide a fully automated algorithm for obtaining stiffness measurements from hepatic magnetic resonance elastography (MRE) images that are consistent with measurements performed by expert readers. MATERIALS AND METHODS: An initial liver contour was found using an adaptive threshold and expanded using an active contour to select a homogeneous area of the liver. The confidence map generated during the stiffness calculation was used to select a region of reliable wave propagation. The average stiffness within the automatically generated region of interest (ROI) was compared to measurements by two trained readers in a set of 88 clinical test cases ranging from healthy to severely fibrotic. RESULTS: The stiffness measurements reported by the readers differed by -6.76% ± 22.8% (95% confidence) and had an intraclass correlation coefficient (ICC) of 0.972 (P < 0.05). The algorithm and the more experienced reader differed by 4.32% ± 14.9 with an ICC of 0.987. CONCLUSION: The automated algorithm performed reliably, even though MRE acquisitions often have motion artifacts present. The correlation between the automated measurements and those from the trained readers was superior to the correlation between the readers.
Authors: Laurent Huwart; Christine Sempoux; Eric Vicaut; Najat Salameh; Laurence Annet; Etienne Danse; Frank Peeters; Leon C ter Beek; Jacques Rahier; Ralph Sinkus; Yves Horsmans; Bernard E Van Beers Journal: Gastroenterology Date: 2008-04-04 Impact factor: 22.682
Authors: A Manduca; T E Oliphant; M A Dresner; J L Mahowald; S A Kruse; E Amromin; J P Felmlee; J F Greenleaf; R L Ehman Journal: Med Image Anal Date: 2001-12 Impact factor: 8.545
Authors: John E Eaton; Bogdan Dzyubak; Sudhakar K Venkatesh; Thomas C Smyrk; Gregory J Gores; Richard L Ehman; Nicholas F LaRusso; Andrea A Gossard; Konstantinos N Lazaridis Journal: J Gastroenterol Hepatol Date: 2016-06 Impact factor: 4.029
Authors: Kang Wang; Paul Manning; Nikolaus Szeverenyi; Tanya Wolfson; Gavin Hamilton; Michael S Middleton; Florin Vaida; Meng Yin; Kevin Glaser; Richard L Ehman; Claude B Sirlin Journal: Abdom Radiol (NY) Date: 2017-12
Authors: Suraj D Serai; Nancy A Obuchowski; Sudhakar K Venkatesh; Claude B Sirlin; Frank H Miller; Edward Ashton; Patricia E Cole; Richard L Ehman Journal: Radiology Date: 2017-05-22 Impact factor: 11.105
Authors: Eric R Fenstad; Bogdan Dzyubak; Jae K Oh; Eric E Williamson; James F Glockner; Phillip M Young; Nandan S Anavekar; Michael D Leise; Richard L Ehman; Philip A Araoz; Sudhakar K Venkatesh Journal: J Magn Reson Imaging Date: 2015-12-21 Impact factor: 4.813
Authors: Yogesh K Mariappan; Bogdan Dzyubak; Kevin J Glaser; Sudhakar K Venkatesh; Claude B Sirlin; Jonathan Hooker; Kiaran P McGee; Richard L Ehman Journal: Radiology Date: 2016-08-10 Impact factor: 11.105