Chapter

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011

Volume 6893 of the series Lecture Notes in Computer Science pp 479-486

Automatic View Planning for Cardiac MRI Acquisition

  • Xiaoguang LuAffiliated withLancaster UniversityImage Analytics and Informatics, Siemens Corporate Research
  • , Marie-Pierre JollyAffiliated withLancaster UniversityImage Analytics and Informatics, Siemens Corporate Research
  • , Bogdan GeorgescuAffiliated withLancaster UniversityImage Analytics and Informatics, Siemens Corporate Research
  • , Carmel HayesAffiliated withCarnegie Mellon UniversityHealthcare Sector, H IM MR PLM-AW CARD, Siemens AG
  • , Peter SpeierAffiliated withCarnegie Mellon UniversityHealthcare Sector, H IM MR PLM-AW CARD, Siemens AG
  • , Michaela SchmidtAffiliated withCarnegie Mellon UniversityHealthcare Sector, H IM MR PLM-AW CARD, Siemens AG
  • , Xiaoming BiAffiliated withCarnegie Mellon UniversitySiemens Medical Solutions USA
  • , Randall KroekerAffiliated withCarnegie Mellon UniversitySiemens Medical Solutions Canada
  • , Dorin ComaniciuAffiliated withLancaster UniversityImage Analytics and Informatics, Siemens Corporate Research
    • , Peter KellmanAffiliated withCarnegie Mellon UniversityNational Institutes of Health
    • , Edgar MuellerAffiliated withCarnegie Mellon UniversityHealthcare Sector, H IM MR PLM-AW CARD, Siemens AG
    • , Jens GuehringAffiliated withCarnegie Mellon UniversityHealthcare Sector, H IM MR PLM-AW CARD, Siemens AG

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Abstract

Conventional cardiac MRI acquisition involves a multi-step approach, requiring a few double-oblique localizers in order to locate the heart and prescribe long- and short-axis views of the heart. This approach is operator-dependent and time-consuming. We propose a new approach to automating and accelerating the acquisition process to improve the clinical workflow. We capture a highly accelerated static 3D full-chest volume through parallel imaging within one breath-hold. The left ventricle is localized and segmented, including left ventricle outflow tract. A number of cardiac landmarks are then detected to anchor the cardiac chambers and calculate standard 2-, 3-, and 4-chamber long-axis views along with a short-axis stack. Learning-based algorithms are applied to anatomy segmentation and anchor detection. The proposed algorithm is evaluated on 173 localizer acquisitions. The entire view planning is fully automatic and takes less than 10 seconds in our experiments.