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Deformable mapping using biomechanical models to relate corresponding lesions in digital breast tomosynthesis and automated breast ultrasound images
Institution:1. Department of Nuclear Engineering and Radiological Sciences, University of Michigan, 2355 Bonisteel Blvd. Ann Arbor, MI 48109, USA;2. Department of Radiology, University of Michigan Health System, 1500 E Medical Center Dr. Ann Arbor, MI 48109, USA;3. Department of Imaging Physics, University of Texas MD Anderson Cancer Center, 1400 Pressler St. Houston, TX 77030, USA;4. Biology and Applied Physics, General Electric Research, Research Circle Niskayuna, NY 12309, USA
Abstract:This work investigates the application of a deformable localization/mapping method to register lesions between the digital breast tomosynthesis (DBT) craniocaudal (CC) and mediolateral oblique (MLO) views and automated breast ultrasound (ABUS) images. This method was initially validated using compressible breast phantoms. This methodology was applied to 7 patient data sets containing 9 lesions. The automated deformable mapping algorithm uses finite element modeling and analysis to determine corresponding lesions based on the distance between their centers of mass (dCOM) in the deformed DBT model and the reference ABUS model. This technique shows that location information based on external fiducial markers is helpful in the improvement of registration results. However, use of external markers are not required for deformable registration results described by this methodology. For DBT (CC view) mapped to ABUS, the mean dCOM was 14.9 ± 6.8 mm based on 9 lesions using 6 markers in deformable analysis. For DBT (MLO view) mapped to ABUS, the mean dCOM was 13.7 ± 6.8 mm based on 8 lesions using 6 markers in analysis. Both DBT views registered to ABUS lesions showed statistically significant improvements (p ≤ 0.05) in registration using the deformable technique in comparison to a rigid registration. Application of this methodology could help improve a radiologist's characterization and accuracy in relating corresponding lesions between DBT and ABUS image datasets, especially for cases of high breast densities and multiple masses.
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